{"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/atlas-search-cene-1", "action": "created", "body": "# The Atlas Search 'cene: Season 1\n\n# The Atlas Search 'cene: Season 1\n\nWelcome to the first season of a video series dedicated to Atlas Search! This series of videos is designed to guide you through the journey from getting started and understanding the concepts, to advanced techniques.\n\n## What is Atlas Search?\n\n[Atlas Search][1] is an embedded full-text search in MongoDB Atlas that gives you a seamless, scalable experience for building relevance-based app features. Built on Apache Lucene, Atlas Search eliminates the need to run a separate search system alongside your database.\n\nBy integrating the database, search engine, and sync mechanism into a single, unified, and fully managed platform, Atlas Search is the fastest and easiest way to build relevance-based search capabilities directly into applications.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "The Atlas Search 'cene: Season 1", "contentType": "Video"}, "title": "The Atlas Search 'cene: Season 1", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04333272948861122, 0.008474143221974373, 0.02093709260225296, -0.013369143009185791, 0.021301070228219032, 0.00247253873385489, 0.021123070269823074, 0.01927790604531765, 0.024801388382911682, -0.014641942456364632, 0.008706260472536087, -0.01648327149450779, 0.04427856579422951, 0.006863763555884361, 0.027722662314772606, -0.020478136837482452, 0.0037219722289592028, 0.062365900725126266, -0.025491032749414444, 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Or it's a short form of \"scene\". \n\n## Episode Guide\n\n### **[Episode 1: What is Atlas Search & Quick Start][2]**\n\nIn this first episode of the Atlas Search 'cene, learn what Atlas Search is, and get a quick start introduction to setting up Atlas Search on your data. Within a few clicks, you can set up a powerful, full-text search index on your Atlas collection data, and leverage the fast, relevant results to your users queries.\n\n### **[Episode 2: Configuration / Development Environment][3]**\n\nIn order to best leverage Atlas Search, configuring it for your querying needs leads to success. In this episode, learn how Atlas Search maps your documents to its index, and discover the configuration control you have.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "The Atlas Search 'cene: Season 1", "contentType": "Video"}, "title": "The Atlas Search 'cene: Season 1", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05399884656071663, -0.0029614008963108063, 0.018389998003840446, -0.02181326411664486, 0.02867722511291504, 0.01909821666777134, 0.014562747441232204, 0.025323189795017242, 0.004523050971329212, -0.0012164791114628315, 0.0032280415762215853, -0.015654992312192917, 0.02656267024576664, 0.020170288160443306, 0.028149627149105072, 0.0045599183067679405, -0.00027926231268793344, 0.05402538925409317, -0.027845222502946854, 0.014043186791241169, 0.03822312131524086, -0.023143496364355087, 0.007098224479705095, -0.11790483444929123, -0.010376131162047386, 0.030233420431613922, 0.0009822832653298974, -0.027784768491983414, -0.0718483030796051, 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"created", "body": "### **[Episode 3: Indexing][4]**\n\nWhile Atlas Search automatically indexes your collections content, it does demand attention to the indexing configuration details in order to match users queries appropriately. This episode covers how Atlas Search builds an inverted index, and the options one must consider.\n\n### **[Episode 4: Searching][5]**\n\nAtlas Search provides a rich set of query operators and relevancy controls. This episode covers the common query operators, their relevancy controls, and ends with coverage of the must-have Query Analytics feature.\n\n### **[Episode 5: Faceting][6]**\n\nFacets produce additional context for search results, providing a list of subsets and counts within. This episode details the faceting options available in Atlas Search.\n\n### **[Episode 6: Advanced Search Topics][7]**\n\nIn this episode, we go through some more advanced search topics including embedded documents, fuzzy search, autocomplete, highlighting, and geospatial.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "The Atlas Search 'cene: Season 1", "contentType": "Video"}, "title": "The Atlas Search 'cene: Season 1", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.051897041499614716, -0.005160131957381964, 0.02444884181022644, 0.020137501880526543, 0.04741182178258896, 0.007022537756711245, 0.025010867044329643, 0.010959460400044918, -0.02913862094283104, -0.0008940236293710768, 0.025229420512914658, -0.026444939896464348, -0.004209971986711025, 0.019271085038781166, 0.0322609506547451, 0.0009836186654865742, -0.03307507187128067, 0.047115106135606766, -0.04549193009734154, 0.018683448433876038, 0.057220399379730225, -0.023716222494840622, 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Are your top queries returning the best results? This episode covers the important topic of query analytics. If you're using search, you need this!\n\n### **[Episode 8: Tips & Tricks][9]**\n\nIn this final episode of The Atlas Search 'cene Season 1, useful techniques to introspect query details and see the relevancy scoring computation details. Also shown is how to get facets and search results back in one API call.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "The Atlas Search 'cene: Season 1", "contentType": "Video"}, "title": "The Atlas Search 'cene: Season 1", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06676021963357925, -0.004111364483833313, 0.004736207891255617, -0.013263901695609093, 0.030556093901395798, 0.01958758383989334, 0.03644321858882904, 0.0224448349326849, 0.006258488167077303, -0.0028273065108805895, -0.005389469675719738, -0.024699581786990166, 0.014987430535256863, 0.0380328968167305, 0.04337989166378975, -0.0011837705969810486, -0.0032263752073049545, 0.053115591406822205, -0.04344338923692703, 0.005567796062678099, 0.026066476479172707, -0.012734340503811836, -0.01339627429842949, -0.0965215265750885, -0.005380951799452305, 0.006771787069737911, -0.011258012615144253, -0.06593237072229385, -0.050652820616960526, -0.25136610865592957, -0.016041914001107216, 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MongoDB Atlas triggers, combined with the power of OpenAI's models, can help summarize and analyze these reviews in real-time. In this article, we'll explore how to utilize MongoDB Atlas triggers to process Airbnb reviews, yielding concise summaries and relevant tags.\n\nThis article is an additional feature added to the hotels and apartment sentiment search application developed in Leveraging OpenAI and MongoDB Atlas for Improved Search Functionality.\n\n## Introduction\n\nMongoDB Atlas triggers allow users to define functions that execute in real-time in response to database operations. These triggers can be harnessed to enhance data processing and analysis capabilities. In this example, we aim to generate summarized reviews and tags for a sample Airbnb dataset.\n\nOur original data model has each review embedded in the listing document as an array:", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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\"633940\", \n\"reviewer_name\": \"Patricia\", \n\"comments\": \"I booked the room at Marinete's apartment for my husband. He was staying in Rio for a week because he was studying Portuguese. He loved the place. Marinete was very helpfull, the room was nice and clean. \\r\\nThe location is perfect. He loved the time there. \\r\\n\\r\\n\" },\n { \"_id\": \"2741592\", \n\"date\": { \"$date\": \"2012-10-28T04:00:00.000Z\" }, \n\"listing_id\": \"664017\",", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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encuentres c\u00f3moda y te sugiere que actividades puedes realizar. Disfrut\u00e9 mucho la estancia durante esos d\u00edas, el sector es central y seguro.\" }, ... ]\n```", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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Ensure linkage to the cluster with the Airbnb data.\n- OpenAI account with API access. \n\n![Open AI Key\n\n### Secrets and Values\n\n1. Navigate to your App Services application.\n2. Under \"Values,\" create a secret named `openAIKey` with your OPEN AI API key.\n\n3. Create a linked value named OpenAIKey and link to the secret.\n\n## The trigger code\n\nThe provided trigger listens for changes in the sample_airbnb.listingsAndReviews collection. Upon detecting a new review, it samples up to 50 reviews, sends them to OpenAI's API for summarization, and updates the original document with the summarized content and tags.\n\nPlease notice that the trigger reacts to updates that were marked with `\"process\" : false` flag. This field indicates that there were no summary created for this batch of reviews yet.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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-0.019824732095003128, 0.07084361463785172, 0.028852714225649834, -0.033244144171476364, 0.04214802011847496, 0.06253758817911148, 0.012557042762637138, -0.027478070929646492, -0.004871553275734186, -0.008676417171955109, 0.046776797622442245, 0.05892984941601753, -0.040813226252794266, -0.06404493004083633, -0.036694787442684174, 0.009991960600018501, 0.006920890416949987, -0.03159690275788307, -0.06883613765239716, -0.02473052591085434, 0.005888038780540228, 0.0308793094009161, 0.062120676040649414, -0.05504105985164642, -0.016723817214369774, -0.05487603694200516, -0.008041457273066044, -0.007853987626731396, -0.08127798140048981, 0.03255319967865944, 0.002323440508916974, 0.01737217605113983]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/atlas-open-ai-review-summary", "action": "created", "body": "Example of a review update operation that will fire this trigger:\n```javascript\nlistingsAndReviews.updateOne({\"_id\" : \"1129303\"}, { $push : { \"reviews\" : new_review } , $set : { \"process\" : false\" }});\n```\n\n### Sample reviews function\nTo prevent overloading the API with a large number of reviews, a function sampleReviews is defined to randomly sample up to 50 reviews:\n\n```javscript\nfunction sampleReviews(reviews) {\n if (reviews.length <= 50) {\n return reviews;\n }\n\n const sampledReviews = ];\n const seenIndices = new Set();\n\n while (sampledReviews.length < 50) {\n const randomIndex = Math.floor(Math.random() * reviews.length);\n if (!seenIndices.has(randomIndex)) {\n seenIndices.add(randomIndex);\n sampledReviews.push(reviews[randomIndex]);\n }\n }\n\n return sampledReviews;\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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`\"process\" : false` field.\n```javascript\nexports = async function(changeEvent) {\n // A Database Trigger will always call a function with a changeEvent.\n // Documentation on ChangeEvents: https://www.mongodb.com/docs/manual/reference/change-events\n\n // This sample function will listen for events and replicate them to a collection in a different Database\nfunction sampleReviews(reviews) {\n// Logic above...\n if (reviews.length <= 50) {\n return reviews;\n }\n const sampledReviews = [];\n const seenIndices = new Set();\n\n while (sampledReviews.length < 50) {\n const randomIndex = Math.floor(Math.random() * reviews.length);\n if (!seenIndices.has(randomIndex)) {\n seenIndices.add(randomIndex);\n sampledReviews.push(reviews[randomIndex]);\n }\n }", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. ", "contentType": "Tutorial"}, "title": "Using MongoDB Atlas Triggers to Summarize Airbnb Reviews with OpenAI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07496263831853867, -0.0022931282874196768, 0.015449314378201962, -0.032833877950906754, 0.0274550449103117, 0.023118797689676285, 0.040175266563892365, 0.06100309640169144, 0.019930480048060417, -0.014482250437140465, -0.013135907240211964, -0.04342693090438843, 0.0036264185328036547, 0.047674015164375305, -0.02095816656947136, 0.002703540027141571, -0.01807858981192112, -0.029787622392177582, -0.054402049630880356, -0.018537817522883415, 0.025597235187888145, -0.01623210869729519, -0.007547144778072834, -0.04344843327999115, 0.024264786392450333, 0.04574121907353401, -0.02343820221722126, -0.018024537712335587, -0.04892485588788986, -0.201643705368042, 0.004162245895713568, -0.07264216989278793, 0.052506789565086365, -0.040060415863990784, 0.028708696365356445, -0.03929087147116661, -0.026130525395274162, 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changeEvent.fullDocument;\n \n\n // Get the MongoDB service you want to use (see \"Linked Data Sources\" tab)\n const serviceName = \"mongodb-atlas\";\n const databaseName = \"sample_airbnb\";\n const collection = context.services.get(serviceName).db(databaseName).collection(changeEvent.ns.coll);\n\n // This function is the endpoint's request handler. \n // URL to make the request to the OpenAI API.\n const url = 'https://api.openai.com/v1/chat/completions';\n\n // Fetch the OpenAI key stored in the context values.\n const openai_key = context.values.get(\"openAIKey\");", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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doc.reviews.map((review) => {return {\"comments\" : review.comments}});\n \n const sampledReviews= sampleReviews(reviews);\n\n // Prepare the request string for the OpenAI API.\n const reqString = `Summerize the reviews provided here: ${JSON.stringify(sampledReviews)} | instructions example:\\n\\n [{\"comment\" : \"Very Good bed\"} ,{\"comment\" : \"Very bad smell\"} ] \\nOutput: {\"overall_review\": \"Overall good beds and bad smell\" , \"neg_tags\" : [\"bad smell\"], pos_tags : [\"good bed\"]}. No explanation. No 'Output:' string in response. Valid JSON. `;\n console.log(`reqString: ${reqString}`);", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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'Content-Type': ['application/json']\n },\n body: JSON.stringify({\n model: \"gpt-4\",\n temperature: 0,\n messages: [\n {\n \"role\": \"system\",\n \"content\": \"Output json generator follow only provided example on the current reviews\"\n },\n {\n \"role\": \"user\",\n \"content\": reqString\n }\n ]\n })\n });\n\n // Parse the JSON response\n let responseData = JSON.parse(resp.body.text());\n\n // Check the response status.\n if(resp.statusCode === 200) {\n console.log(\"Successfully received code.\");\n console.log(JSON.stringify(responseData));", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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document does not need further re-processing \n updateDoc.process = true\n await collection.updateOne({_id : docId}, {$set : updateDoc});\n \n\n } else {\n console.error(\"Failed to generate filter JSON.\");\n console.log(JSON.stringify(responseData));\n return {};\n }\n};\n```\n\nKey steps include:", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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The format and instructions are tailored to ensure the API returns a valid JSON with summarized content and tags.\n- API interaction: Using the context.http.post method, the trigger sends the prepared data to the OpenAI API.\n- Updating the original document: Upon a successful response from the API, the trigger updates the original document with the summarized content, negative tags (neg_tags), positive tags (pos_tags), and a process flag set to true.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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0.011808876879513264, 0.05543488264083862, 0.003983714617788792, -0.06788983196020126, 0.03243669867515564, 0.007018649950623512, 0.013100042007863522, -0.051379624754190445, -0.0008577078115195036, -0.012391326017677784, -0.02466396428644657, 0.04445702210068703, -0.025990063324570656, -0.10638641566038132, -0.04741152748465538, -0.012900975532829762, 0.016406551003456116, -0.03365130349993706, -0.04512076824903488, -0.02106362022459507, 0.012543195858597755, 0.019798140972852707, 0.04594813287258148, -0.027035346254706383, -0.040822792798280716, -0.06573566794395447, -0.008962317369878292, -0.0009495624690316617, -0.06619101017713547, 0.011487587355077267, 0.026358217000961304, -0.009724405594170094]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/atlas-open-ai-review-summary", "action": "created", "body": "Here is a sample result that is added to the processed listing document:\n```\n\"process\": true, \n\"overall_review\": \"Overall, guests had a positive experience at Marinete's apartment. They praised the location, cleanliness, and hospitality. However, some guests mentioned issues with the dog and language barrier.\",\n\"neg_tags\": [ \"language barrier\", \"dog issues\" ], \n\"pos_tags\": [ \"great location\", \"cleanliness\", \"hospitality\" ]\n```\n\nOnce the data is added to our documents, providing this information in our VUE application is as simple as adding this HTML template:\n\n```html\n\n Overall Review (ai based) : {{ listing.overall_review }}\n \n {{tag}}\n \n \n {{tag}}\n \n \n```", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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triggers with OpenAI's powerful models, we can efficiently process and analyze large volumes of reviews in real-time. This setup not only provides concise summaries of reviews but also categorizes them into positive and negative tags, offering valuable insights to property hosts and potential renters.\n\nQuestions? Comments? Let\u2019s continue the conversation over in our [community forums.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "AI", "Node.js"], "pageDescription": "Uncover the synergy of MongoDB Atlas triggers and OpenAI models in real-time analysis and summarization of Airbnb reviews. 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trained on billions of lines of code and can generate code suggestions \u2014 ranging from snippets to full functions \u2014 in real-time, based on your comments and existing code. AI code assistants have revolutionized developers\u2019 coding experience, but what sets Amazon CodeWhisperer apart is that MongoDB has collaborated with the AWS Data Science team, enhancing its capabilities!\n\nAt MongoDB, we are always looking to enhance the developer experience, and we've fine-tuned the CodeWhisperer Foundational Models to deliver top-notch code suggestions \u2014 trained on, and tailored for, MongoDB. This gives developers of all levels the best possible experience when using CodeWhisperer for MongoDB functions.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06456639617681503, -0.02141782082617283, 0.006294905208051205, -0.021012524142861366, 0.04662040248513222, -0.0047924211248755455, -0.004582950379699469, 0.02320190891623497, -0.01318175345659256, 0.016072852537035942, 0.006855227518826723, -0.038180913776159286, 0.020347001031041145, 0.05467842519283295, 0.010857654735445976, 0.025391986593604088, 0.004596427083015442, 0.013453453779220581, -0.05720871686935425, -0.013090621680021286, 0.061904337257146835, -0.02909773401916027, -0.02180362120270729, -0.07464475184679031, -0.04748569801449776, 0.06908779591321945, -0.056018631905317307, -0.044964488595724106, 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"https://www.mongodb.com/developer/products/mongodb/getting-started-with-mongodb-and-codewhisperer", "action": "created", "body": "This tutorial will help you get CodeWhisperer up and running in VS Code, but CodeWhisperer also works with a number of other IDEs, including IntelliJ IDEA, AWS Cloud9, AWS Lambda console, JupyterLab, and Amazon SageMaker Studio. On the [Amazon CodeWhisperer site][1], you can find tutorials that demonstrate how to set up CodeWhisperer on different IDEs, as well as other documentation.\n\n*Note:* CodeWhisperer allows users to start without an AWS account because usually, creating an AWS account requires a credit card. Currently, CodeWhisperer is free for individual users. So it\u2019s super easy to get up and running.\n\n**Installing CodeWhisperer for VS Code** \n\nCodeWhisperer doesn\u2019t have its own VS Code extension. It is part of a larger extension for AWS services called AWS Toolkit. AWS Toolkit is available in the VS Code extensions store.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05912330374121666, -0.04956725239753723, -0.010123714804649353, -0.0076818810775876045, 0.027913479134440422, 0.0193786658346653, -0.010468638502061367, 0.02033318765461445, -0.01157626323401928, -0.004519777838140726, 0.02005293220281601, -0.03447508439421654, 0.04998226463794708, 0.028343981131911278, 0.014512008056044579, 0.01335840206593275, -0.01684892177581787, 0.026301970705389977, -0.0733121708035469, 0.0018834564834833145, 0.034816935658454895, -0.03135986998677254, -0.018258119001984596, -0.07494761794805527, -0.04543324187397957, 0.06402965635061264, -0.023332593962550163, -0.040348295122385025, -0.03521714359521866, -0.1633090227842331, 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"https://www.mongodb.com/developer/products/mongodb/getting-started-with-mongodb-and-codewhisperer", "action": "created", "body": "1. Open VS Code and navigate to the extensions store (bottom icon on the left panel).\n 2. Search for CodeWhisperer and it will show up as part of the AWS Toolkit.\n![Searching for the AWS ToolKit Extension][2]\n 3. Once found, hit Install. Next, you\u2019ll see the full AWS Toolkit\n Listing\n![The AWS Toolkit full listing][3]\n 4. Once installed, you\u2019ll need to authorize CodeWhisperer via a Builder\n ID to connect to your AWS developer account (or set up a new account\n if you don\u2019t already have one).\n![Authorise CodeWhisperer][4]\n\n**Using CodeWhisperer**\n-----------------------\n\nNavigating code suggestions \n\n![CodeWhisperer Running][5]", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05871956795454025, -0.01820199191570282, 0.02271755412220955, 0.0016068365657702088, 0.03996641933917999, 0.03970911726355553, -0.014900146052241325, 0.007821847684681416, -0.019832387566566467, 0.009616336785256863, 0.01070330385118723, -0.04804794862866402, 0.03952047973871231, 0.0033300723880529404, 0.05069538205862045, 0.006339354906231165, -0.005725797265768051, 0.03981662914156914, 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If you want to keep the suggestion, use **TAB** to accept it. CodeWhisperer may provide multiple suggestions to choose from depending on your use case. To navigate between suggestions, use the left and right arrow keys to view them, and **TAB** to accept.\n\nIf you don\u2019t like the suggestions you see, keep typing (or hit **ESC**). The suggestions will disappear, and CodeWhisperer will generate new ones at a later point based on the additional context.\n\n**Requesting suggestions manually**", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07219529896974564, -0.007200576364994049, -0.0023679619189351797, -0.04683566093444824, -0.007351596374064684, 0.04573453217744827, 0.0234117042273283, 0.047622475773096085, 0.004880059976130724, -0.01350026112049818, 0.0017455840716138482, -0.01147446222603321, 0.003620678326115012, 0.06456026434898376, 0.04201526194810867, 0.027978448197245598, -0.005891167558729649, 0.03633394464850426, -0.056057725101709366, -0.006549903191626072, 0.07699013501405716, -0.024495506659150124, 0.01454988494515419, -0.04487595707178116, -0.0369144044816494, 0.058137234300374985, 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"https://www.mongodb.com/developer/products/mongodb/getting-started-with-mongodb-and-codewhisperer", "action": "created", "body": "**Requesting suggestions manually**\n\nYou can request suggestions at any time. Use **Option-C** on Mac or **ALT-C** on Windows. After you receive suggestions, use **TAB** to accept and arrow keys to navigate.\n\n**Getting the best recommendations**\n\nFor best results, follow these practices.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.037793900817632675, 0.009321185760200024, 0.018807388842105865, -0.022428542375564575, -0.023728396743535995, 0.04572127014398575, 0.054113537073135376, 0.027041301131248474, 0.012802472338080406, -0.03669269382953644, 0.012890598736703396, -0.03439677134156227, -0.017111748456954956, 0.061397772282361984, 0.045659832656383514, 0.030186286196112633, 0.007265117019414902, 0.017791898921132088, -0.03217662125825882, -0.008247212506830692, 0.04321622475981712, -0.026222215965390205, -0.029573161154985428, -0.04485222324728966, 0.0008112976211123168, 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"url": "https://www.mongodb.com/developer/products/mongodb/getting-started-with-mongodb-and-codewhisperer", "action": "created", "body": "**Getting the best recommendations**\n\nFor best results, follow these practices.\n\n - Give CodeWhisperer something to work with. The more code your file contains, the more context CodeWhisperer has for generating recommendations.\n - Write descriptive comments in natural language \u2014 for example\n```\n// Take a JSON document as a String and store it in MongoDB returning the _id\n```\nOr\n```\n//Insert a document in a collection with a given _id and a discountLevel\n```\n - Specify the libraries you prefer at the start of your file by using import statements.\n```\n// This Java class works with MongoDB sync driver.\n// This class implements Connection to MongoDB and CRUD methods.\n```\n - Use descriptive names for variables and functions\n - Break down complex tasks into simpler tasks\n\n**Provide feedback**\n----------------", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.055695611983537674, 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If you are using Amazon CodeWhisperer in your MongoDB development, we\u2019d love to hear from you. \n\nWe\u2019ve created a special \u201ccodewhisperer\u201d tag on our [Developer Forums][6], and if you tag any post with this, it will be visible to our CodeWhisperer project team and we will get right on it to help and provide feedback. If you want to see what others are doing with CodeWhisperer on our forums, the [tag search link][7] will jump you straight into all the action. \n\nWe can\u2019t wait to see your thoughts and impressions of MongoDB and Amazon CodeWhisperer together.", "format": "md", "metadata": {"tags": ["MongoDB", "JavaScript", "Java", "Python", "AWS", "AI"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Getting Started with MongoDB and AWS Codewhisperer", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0754004493355751, -0.030515208840370178, 0.0367886982858181, -0.012632420286536217, 0.01949852705001831, -0.024691054597496986, -0.026394423097372055, 0.0338144414126873, -0.04351126775145531, 0.01708023063838482, 0.016154516488313675, -0.04983565956354141, 0.021673787385225296, 0.06961667537689209, 0.015173956751823425, 0.02188725769519806, 0.0063801114447414875, -0.00046674476470798254, -0.05445189028978348, -0.022309133782982826, 0.05680975317955017, -0.041075706481933594, 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I wrote this template to get you started. I have tried to solve as many problems as possible in it.\n\nSo if you want to start writing REST APIs in Java, clone this project, and you will be up to speed in no time.\n\n```shell\ngit clone https://github.com/mongodb-developer/java-spring-boot-mongodb-starter\n```\n\nThat\u2019s all folks! All you need is in this repository. Below I will explain a few of the features and details about this template, but feel free to skip what is not necessary for your understanding.\n\n## README\n\nAll the extra information and commands you need to get this project going are in the `README.md` file which you can read in GitHub.\n\n## Spring and MongoDB configuration", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.056110650300979614, 0.020723596215248108, -0.0050460901111364365, -0.04011356085538864, 0.03915746137499809, -0.02171265333890915, -0.023249952122569084, 0.043229542672634125, -0.022295985370874405, -0.02515980415046215, -0.016533667221665382, -0.05829558148980141, 0.04635586589574814, 0.03355865925550461, 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fromRegistries(MongoClientSettings.getDefaultCodecRegistry(), pojoCodecRegistry);\n return MongoClients.create(MongoClientSettings.builder()\n .applyConnectionString(new ConnectionString(connectionString))\n .codecRegistry(codecRegistry)\n .build());\n }\n\n}\n```", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.022041186690330505, 0.012386796064674854, 0.04889116808772087, -0.04572535306215286, 0.022289298474788666, -0.004703840706497431, 0.03612551838159561, 0.06614290177822113, -0.007171238772571087, -0.02755134366452694, -0.01405954547226429, -0.058601442724466324, 0.0952560231089592, 0.04907258599996567, 0.007889390923082829, -0.019541222602128983, -0.03721519559621811, 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-0.068546824157238, -0.014366376213729382, 0.017758946865797043, -0.06594141572713852, -0.0004583374538924545, 0.03399384394288063, 0.021157441660761833]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/code-examples/java/rest-apis-java-spring-boot", "action": "created", "body": "}\n```\n\nThe important section here is the MongoDB configuration, of course. Firstly, you will notice the connection string is automatically retrieved from the `application.properties` file, and secondly, you will notice the configuration of the `MongoClient` bean.\n\nA `Codec` is the interface that abstracts the processes of decoding a BSON value into a Java object and encoding a Java object into a BSON value.\n\nA `CodecRegistry` contains a set of `Codec` instances that are accessed according to the Java classes that they encode from and decode to.\n\nThe MongoDB driver is capable of encoding and decoding BSON for us, so we do not have to take care of this anymore. All the configuration we need for this project to run is here and nowhere else.\n\nYou can read [the driver documentation if you want to know more about this topic.\n\n## Multi-document ACID transactions", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03185790032148361, -0.0073826974257826805, 0.01151951216161251, -0.036193642765283585, 0.03182685747742653, -0.014662986621260643, 0.03447699174284935, 0.05345042794942856, -0.01536285225301981, -0.04201623424887657, -0.018850605934858322, -0.09083906561136246, 0.028942318633198738, 0.03361458703875542, -0.0012896183179691434, -0.002771188272163272, 0.007655917201191187, 0.01593383215367794, -0.011712565086781979, 0.02023034729063511, 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0.031989824026823044, -0.09582625329494476, -0.004361026454716921, 0.04191102832555771, -0.01728322170674801]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/code-examples/java/rest-apis-java-spring-boot", "action": "created", "body": "You can read [the driver documentation if you want to know more about this topic.\n\n## Multi-document ACID transactions\n\nJust for the sake of it, I also used multi-document ACID transactions in a few methods where it could potentially make sense to use ACID transactions. You can check all the code in the `MongoDBPersonRepository` class.\n\nHere is an example:\n\n```java\nprivate static final TransactionOptions txnOptions = TransactionOptions.builder()\n .readPreference(ReadPreference.primary())\n .readConcern(ReadConcern.MAJORITY)\n .writeConcern(WriteConcern.MAJORITY)\n .build();\n\n@Override\npublic List saveAll(List personEntities) {\n try (ClientSession clientSession = client.startSession()) {\n return clientSession.withTransaction(() -> {\n personEntities.forEach(p -> p.setId(new ObjectId()));\n personCollection.insertMany(clientSession, personEntities);\n return personEntities;\n }, txnOptions);\n }\n}\n```", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", 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This helps me to keep the code clean and simple.\n\nSome of you may argue that it is actually too simple because transactions (and write operations, in general) can throw exceptions, and I\u2019m not handling any of them here\u2026 You are absolutely right and this is an excellent transition to the next part of this article.\n\n## Exception management\n\nTransactions in MongoDB can raise exceptions for various reasons, and I don\u2019t want to go into the details too much here, but since MongoDB 3.6, any write operation that fails can be automatically retried once. And the transactions are no different. See the documentation for retryWrites.\n\nIf retryable writes are disabled or if a write operation fails twice, then MongoDB will send a MongoException (extends RuntimeException) which should be handled properly.", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06570691615343094, -0.00028689877944998443, 0.05140234902501106, -0.04029842093586922, 0.01408341247588396, -0.039414696395397186, 0.020902082324028015, 0.04618111252784729, -0.016687754541635513, -0.05932946503162384, -0.021208424121141434, -0.03566288203001022, -0.007504936773329973, 0.0984702780842781, -0.029951199889183044, 0.0061104572378098965, -0.017786506563425064, 0.03574834018945694, -0.04935130104422569, 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0.06568127870559692, -0.034379784017801285, -0.006480319891124964, 0.03771016001701355, 0.011624054051935673]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/code-examples/java/rest-apis-java-spring-boot", "action": "created", "body": "Luckily, Spring provides the annotation `ExceptionHandler` to help us do that. See the code in my controller `PersonController`. Of course, you will need to adapt and enhance this in your real project, but you have the main idea here.\n\n```java\n@ExceptionHandler(RuntimeException.class)\npublic final ResponseEntity handleAllExceptions(RuntimeException e) {\n logger.error(\"Internal server error.\", e);\n return new ResponseEntity<>(e, HttpStatus.INTERNAL_SERVER_ERROR);\n}\n```\n\n## Aggregation pipeline\n\nMongoDB's aggregation pipeline is a very powerful and efficient way to run your complex queries as close as possible to your data for maximum efficiency. Using it can ease the computational load on your application.\n\nJust to give you a small example, I implemented the `/api/persons/averageAge` route to show you how I can retrieve the average age of the persons in my collection.", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06039218604564667, 0.0036323911044746637, 0.03805512934923172, -0.040028877556324005, 0.011331590823829174, -0.0352645181119442, 0.05585698410868645, 0.054624419659376144, -0.028550488874316216, -0.017760273069143295, 0.002833765931427479, -0.03987063094973564, 0.04272453114390373, 0.05995500087738037, -0.02279675006866455, 0.01856701634824276, -0.007778359577059746, 0.07441327720880508, -0.028045443817973137, 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0.00567997433245182, -0.027620980516076088, -0.007906299084424973, 0.020357990637421608, -0.011486854404211044]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/code-examples/java/rest-apis-java-spring-boot", "action": "created", "body": "```java\n@Override\npublic double getAverageAge() {\n List pipeline = List.of(group(new BsonNull(), avg(\"averageAge\", \"$age\")), project(excludeId()));\n return personCollection.aggregate(pipeline, AverageAgeDTO.class).first().averageAge();\n}\n```\n\nAlso, you can note here that I\u2019m using the `personCollection` which was initially instantiated like this:\n\n```java\nprivate MongoCollection personCollection;\n\n@PostConstruct\nvoid init() {\n personCollection = client.getDatabase(\"test\").getCollection(\"persons\", PersonEntity.class);\n}\n```\n\nNormally, my personCollection should encode and decode `PersonEntity` object only, but you can overwrite the type of object your collection is manipulating to return something different \u2014 in my case, `AverageAgeDTO.class` as I\u2019m not expecting a `PersonEntity` class here but a POJO that contains only the average age of my \"persons\".\n\n## Swagger", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03430325910449028, 0.004300291184335947, -0.0027935784310102463, -0.038299523293972015, 0.009678411297500134, -0.027354616671800613, 0.037777598947286606, 0.026876822113990784, -0.01637119986116886, 0.011425045318901539, 0.015342768281698227, -0.0679003968834877, 0.03717166185379028, 0.05920882523059845, -0.025988196954131126, 0.009677802212536335, 0.01794459857046604, 0.0672374814748764, -0.07171178609132767, -0.0019018951570615172, 0.0843391939997673, 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-0.030897215008735657, 0.05023577809333801, -0.018302002921700478]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/code-examples/java/rest-apis-java-spring-boot", "action": "created", "body": "## Swagger\n\nSwagger is the tool you need to document your REST APIs. You have nothing to do \u2014 the configuration is completely automated. Just run the server and navigate to http://localhost:8080/swagger-ui.html. the interface will be waiting for you.\n\n for more information.\n\n## Nyan Cat\n\nYes, there is a Nyan Cat section in this post. Nyan Cat is love, and you need some Nyan Cat in your projects. :-)\n\nDid you know that you can replace the Spring Boot logo in the logs with pretty much anything you want?\n\n and the \"Epic\" font for each project name. It's easier to identify which log file I am currently reading.\n\n## Conclusion\n\nI hope you like my template, and I hope I will help you be more productive with MongoDB and the Java stack.", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.051025472581386566, 0.0020606087055057287, 0.008362272754311562, -0.01653616689145565, 0.04463132098317146, 0.00867606233805418, -0.00596108753234148, 0.03738109767436981, -0.02173328772187233, -0.016612080857157707, -0.005427626892924309, -0.05795499309897423, 0.04639716446399689, 0.05937735363841057, 0.03109944984316826, -0.026716219261288643, 0.010451672598719597, 0.027732087299227715, -0.03541772812604904, -0.010855968110263348, 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They are very welcome. :-)\n\nIf you are new to MongoDB Atlas, give our Quick Start post a try to get up to speed with MongoDB Atlas in no time.\n\n [1]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt876f3404c57aa244/65388189377588ba166497b0/swaggerui.png\n [2]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/bltf2f06ba5af19464d/65388188d31953242b0dbc6f/nyancat.png", "format": "md", "metadata": {"tags": ["Java", "Spring"], "pageDescription": "Take a shortcut to REST APIs with this Java/Spring Boot and MongoDB example application that embeds all you'll need to get going.", "contentType": "Code Example"}, "title": "REST APIs with Java, Spring Boot, and MongoDB", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0710957869887352, 0.011432668194174767, -0.004561296198517084, -0.005724127404391766, 0.02573459781706333, -0.0029782771598547697, 0.010772356763482094, 0.0374654084444046, -0.01542743295431137, -0.02575158327817917, -0.0037470050156116486, 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We remain excited about Swift and will continue our development of our mobile Swift SDK.\n\nWe released our server-side Swift driver in 2020 as an open source project and are incredibly proud of the work that our engineering team has contributed to the Swift community over the last four years. Unfortunately, today we are announcing our decision to stop development of the MongoDB server-side Swift driver. We understand that this news may come as a disappointment to the community of current users.\n\nThere are still ways to use MongoDB with Swift:\n\n - Use the MongoDB driver with server-side Swift applications as is \n - Use the MongoDB C Driver directly in your server-side Swift projects\n - Usage of another community Swift driver, mongokitten", "format": "md", "metadata": {"tags": ["Swift", "MongoDB"], "pageDescription": "The latest news regarding the MongoDB driver for Swift.", "contentType": "News & Announcements"}, "title": "Halting Development on MongoDB Swift Driver", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.02048601023852825, -0.010558489710092545, 0.035269901156425476, -0.029257163405418396, 0.0026281876489520073, -0.0030582291074097157, -0.007431581616401672, 0.01594264805316925, -0.02352409064769745, -0.014450808987021446, 0.010975237935781479, -0.026762347668409348, 0.023372916504740715, 0.017757641151547432, -0.0018557376461103559, 0.03157683089375496, 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For those developing client/mobile applications, MongoDB offers the Realm Swift SDK with real time sync to MongoDB Atlas.\n\nWe would like to take this opportunity to express our heartfelt appreciation for the enthusiastic support that the Swift community has shown for MongoDB. Your loyalty and feedback have been invaluable to us throughout our journey, and we hope to resume development on the server-side Swift driver in the future.", "format": "md", "metadata": {"tags": ["Swift", "MongoDB"], "pageDescription": "The latest news regarding the MongoDB driver for Swift.", "contentType": "News & Announcements"}, "title": "Halting Development on MongoDB Swift Driver", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04201998934149742, -0.036628011614084244, 0.021529875695705414, -0.02369876578450203, 0.014935408718883991, 0.003615425433963537, 0.012501300312578678, 0.03791311755776405, -0.027779564261436462, -0.04076854884624481, 0.02252265438437462, -0.03456484153866768, 0.0034513145219534636, 0.020197466015815735, 0.0023855012841522694, 0.03150872141122818, 0.003886809339746833, -0.02235211245715618, -0.02498810738325119, 0.0039032818749547005, 0.013486853800714016, -0.022320445626974106, -0.04440910369157791, -0.0654623880982399, 0.027000555768609047, 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"https://www.mongodb.com/developer/products/atlas/online-archive-query-performance", "action": "created", "body": "# Optimizing your Online Archive for Query Performance\n\n## Contributed By\nThis article was contributed by Prem Krishna, a Senior Product Manager for Analytics at MongoDB.\n\n## Introduction\nWith Atlas Online Archive, you can tier off cold data or infrequently accessed data from your MongoDB cluster to a MongoDB-managed cloud object storage - Amazon S3 or Microsoft Azure Blob Storage. This can lower the cost via archival cloud storage for old data, while active data that is more often accessed and queried remains in the primary database. \n\n> FYI: If using Online Archive and also using MongoDB's Atlas Data Federation, users can also see a unified view of production data, and *archived data* side by side through a read-only, federated database instance.\n\nIn this blog, we are going to be discussing how to improve the performance of your online archive by choosing the correct partitioning fields.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.031625352799892426, -0.008480050601065159, 0.031467147171497345, 0.012029411271214485, 0.07238798588514328, -0.012630869634449482, -0.008400843478739262, 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Therefore, you'll want to think critically about your partitioning strategy beforehand.\n\nAlso, archival query performance is determined by how the data is structured in object storage, so it is important to not only choose the correct partitions but also choose the correct order of partitions.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03244185820221901, 0.00013426669465843588, 0.04931750148534775, -0.036095451563596725, 0.07204870134592056, 0.008243436925113201, -0.015980297699570656, -0.004394145682454109, -0.004748839419335127, -0.0018426604801788926, 0.008875750936567783, -0.009179570712149143, 0.035492319613695145, 0.030345750972628593, 0.018028754740953445, -0.001121240435168147, -0.002778533147647977, 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Ensure that the most frequently queried fields for the archive are chosen. Note that we are talking about how you are going to query the archive and not the custom query criteria provided at the time of archiving!\n\n**Check the order of partitioned fields.** While selecting the partitions is important, it is equally critical to choose the correct *order* of partitions. The most frequently queried field should be the first chosen partition field, followed by the second and third. That's simple enough.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.048373546451330185, 0.010478157550096512, 0.02949291653931141, -0.03198837488889694, 0.041628655046224594, -0.017276842147111893, -0.03300536051392555, -0.01103147678077221, -0.00147473462857306, -0.008745572529733181, 0.006687208078801632, -0.016997799277305603, 0.02684960886836052, 0.02441471442580223, 0.024158306419849396, 0.0029990242328494787, -0.012909255921840668, 0.029689190909266472, -0.039469894021749496, 0.03828827664256096, 0.052166055887937546, -0.027832617983222008, 0.0020251937676221132, -0.058536674827337265, 0.002027087612077594, 0.028240540996193886, -0.05250430107116699, -0.03326007351279259, -0.053111813962459564, -0.21409837901592255, 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this\n**Don't add irrelevant fields as partitions.** If you are not querying a specific field from the archive, then that field should not be added as a partition field. Remember that you can add a maximum of 2 or 3 partition fields, so it is important to choose these fields carefully based on how you query your archive.\n\n**Don't ignore the \u201cMove down\u201d option.** The \u201cMove down\u201d option is applicable to an archive with a data-based rule. For example, if you want to query on Field_A the most, then Field_B, and then on exampleDate, ensure you are selecting the \u201cMove Down\u201d option next to the \u201cArchive date field\u201d on top.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.049739446491003036, 0.006466949824243784, 0.04906553030014038, -0.024883650243282318, 0.04166816547513008, -0.011757899075746536, -0.02664254605770111, -0.0012480935547500849, -0.0026517603546380997, 0.028028201311826706, 0.03241767734289169, -0.039367277175188065, 0.05200689658522606, 0.015279162675142288, -0.0019333354430273175, -0.014354315586388111, -0.013654335401952267, 0.07386275380849838, -0.06262682378292084, 0.01194003876298666, 0.039201412349939346, -0.005831572227180004, 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0.03724202141165733]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/online-archive-query-performance", "action": "created", "body": "**Don't choose high cardinality partition(s).** Choosing a high cardinality field such as `_id` will create a large number of partitions in the object storage. Then querying the archive for any aggregate based queries will cause increased latency. The same is applicable if multiple partitions are selected such that the collective fields when grouped together can be termed as high cardinality. For example, if you are selecting Field_A, Field_B and Field_C as your partitions and if a combination of these fields are creating unique values, then it will result in high cardinality partitions. \n> Please note that this is **not applicable** for new Online Archives. \n\n## Additional guidance\nIn addition to the partitioning guidelines, there are a couple of additional considerations that are relevant for the optimal configuration of your data archival strategy.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.01170691940933466, -0.0012132225092500448, 0.00652391416952014, -0.010388919152319431, 0.06165760010480881, -0.02153152972459793, -0.019311871379613876, 0.015235300175845623, 0.00196845643222332, -0.005715647712349892, 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You can run an explain plan on the archival query to verify whether the archival rule will use an index. \n\n## Conclusion\nIt is important to follow these do\u2019s and don\u2019ts before hitting \u201cBegin Archiving\u201d to archive your data so that the partitions are correctly configured thereby optimizing the performance of your online archives.\n\nFor more information on configuration or Online Archive, please see the documentation for setting up an Online Archive and our blog post on how to create an Online Archive. \n\nDig deeper into this topic with this tutorial.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04666883870959282, 0.00422804756090045, 0.059722308069467545, -0.009661548770964146, 0.06599495559930801, 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Atlas supports paying for usage via the AWS Marketplace (AWS MP) without any upfront commitment \u2014 simply sign up for MongoDB Atlas via AWS Marketplace.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Get all the do's and don'ts around optimization of your data archival strategy.", "contentType": "Article"}, "title": "Optimizing your Online Archive for Query Performance", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05158604681491852, -0.052005890756845474, 0.0029884346295148134, 0.015366647392511368, 0.0006438301061280072, -0.01108179334551096, 0.01832607574760914, -0.007439817767590284, -0.014531333930790424, -0.009240147657692432, 0.03066110610961914, -0.0543217658996582, 0.003617677139118314, -0.002209739526733756, 0.02513059787452221, -0.004738209769129753, 0.014720902778208256, 0.0023951134644448757, -0.03657875210046768, 0.03372890502214432, -0.00407671183347702, -0.04266868159174919, -0.01622687466442585, -0.053787555545568466, 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{"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/using-confluent-cloud-atlas-stream-processing", "action": "created", "body": "# Using the Confluent Cloud with Atlas Stream Processing\n\n> Atlas Stream Processing is now available. Learn more about it here.\n\nApache Kafka is a massively popular streaming platform today. It is available in the open-source community and also as software (e.g., Confluent Platform) for self-managing. Plus, you can get a hosted Kafka (or Kafka-compatible) service from a number of providers, including AWS Managed Streaming for Apache Kafka (MSK), RedPanda Cloud, and Confluent Cloud, to name a few.\n\nIn this tutorial, we will configure network connectivity between MongoDB Atlas Stream Processing instances and a topic within the Confluent Cloud. By the end of this tutorial, you will be able to process stream events from Confluent Cloud topics and emit the results back into a Confluent Cloud topic.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to configure network connectivity between Confluent Cloud and MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Using the Confluent Cloud with Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.015456649474799633, -0.05480249971151352, -0.003914199769496918, -0.020000917837023735, 0.03494960442185402, -0.012897667475044727, -0.0019430441316217184, 0.03312560170888901, -0.028532303869724274, -0.02998463623225689, 0.01608048379421234, -0.04674335941672325, 0.012475202791392803, 0.00478158937767148, 0.052446361631155014, 0.015091661363840103, 0.014087640680372715, 0.043786901980638504, -0.046102333813905716, 0.028902577236294746, 0.027459848672151566, -0.03110751137137413, -0.046358946710824966, 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-0.012120521627366543]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/using-confluent-cloud-atlas-stream-processing", "action": "created", "body": "Confluent Cloud dedicated clusters support connectivity through secure public internet endpoints with their Basic and Standard clusters. Private network connectivity options such as Private Link connections, VPC/VNet peering, and AWS Transit Gateway are available in the Enterprise and Dedicated cluster tiers. \n\n**Note:** At the time of this writing, Atlas Stream Processing only supports internet-facing Basic and Standard Confluent Cloud clusters. This post will be updated to accommodate Enterprise and Dedicated clusters when support is provided for private networks.\n\nThe easiest way to get started with connectivity between Confluent Cloud and MongoDB Atlas is by using public internet endpoints. Public internet connectivity is the only option for Basic and Standard Confluent clusters. Rest assured that Confluent Cloud clusters with internet endpoints are protected by a proxy layer that prevents types of DoS, DDoS, SYN flooding, and other network-level attacks. We will also use authentication API keys with the SASL_SSL authentication method for secure credential exchange.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to configure network connectivity between Confluent Cloud and MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Using the Confluent Cloud with Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.01947430521249771, -0.06790168583393097, -0.01760980300605297, -0.01615196280181408, 0.016279540956020355, 0.0002706421946641058, 0.042080555111169815, 0.03153383359313011, -0.031810909509658813, -0.03453424200415611, 0.04952060431241989, -0.05975239351391792, 0.045030705630779266, -0.003600464668124914, 0.005991579033434391, 0.04405566304922104, 0.005415818654000759, 0.014996395446360111, -0.030659107491374016, 0.061891183257102966, 0.04007013142108917, -0.04894223064184189, -0.03016357310116291, -0.08233402669429779, 0.0036092721857130527, 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"devcenter", "url": "https://www.mongodb.com/developer/products/atlas/using-confluent-cloud-atlas-stream-processing", "action": "created", "body": "In this tutorial, we will set up and configure Confluent Cloud and MongoDB Atlas for network connectivity and then work through a simple example that uses a sample data generator to stream data between MongoDB Atlas and Confluent Cloud.\n\n## Tutorial prerequisites\n\nThis is what you\u2019ll need to follow along:\n\n- An Atlas project (free or paid tier)\n- An Atlas database user with atlasAdmin permission \n - For the purposes of this tutorial, we\u2019ll have the user \u201ctutorialuser.\u201d\n- MongoDB shell (Mongosh) version 2.0+\n- Confluent Cloud cluster (any configuration)\n\n## Configure Confluent Cloud\n\nFor this tutorial, you need a Confluent Cloud cluster created with a topic, \u201csolardata,\u201d and an API access key created. If you already have this, you may skip to Step 2.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to configure network connectivity between Confluent Cloud and MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Using the Confluent Cloud with Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.017411526292562485, -0.017036197707057, -0.012765389867126942, -0.04100590571761131, 0.029164718464016914, -0.012195363640785217, 0.011741956695914268, 0.012938239611685276, -0.03342513367533684, -0.033147845417261124, 0.00614933529868722, -0.05562334507703781, 0.04299475625157356, 0.00835435464978218, 0.021976929157972336, 0.039617590606212616, -0.014268787577748299, 0.005703575909137726, -0.04406564682722092, 0.044792428612709045, 0.030648697167634964, -0.03731927275657654, -0.0480000302195549, -0.07540251314640045, 0.001155502861365676, 0.08076269179582596, -0.0013818759471178055, -0.051452700048685074, 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"https://www.mongodb.com/developer/products/atlas/using-confluent-cloud-atlas-stream-processing", "action": "created", "body": "To create a Confluent Cloud cluster, log into the Confluent Cloud portal, select or create an environment for your cluster, and then click the \u201cAdd Cluster\u201d button. \n\nIn this tutorial, we can use a **Basic** cluster type.\n\n, click on \u201cStream Processing\u201d from the Services menu. Next, click on the \u201cCreate Instance\u201d button. Provide a name, cloud provider, and region. Note: For a lower network cost, choose the cloud provider and region that matches your Confluent Cloud cluster. In this tutorial, we will use AWS us-east-1 for both Confluent Cloud and MongoDB Atlas.\n\n before continuing this tutorial.\n\nConnection information can be found by clicking on the \u201cConnect\u201d button on your SPI. The connect dialog is similar to the connect dialog when connecting to an Atlas cluster. To connect to the SPI, you will need to use the **mongosh** command line tool.\n\n.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to configure network connectivity between Confluent Cloud and MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Using the Confluent Cloud with Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.032883692532777786, -0.06877793371677399, -0.018512757495045662, -0.018576322123408318, 0.004193569533526897, 0.002793220104649663, 0.01884966716170311, 0.02706879936158657, -0.039367493242025375, -0.012674644589424133, 0.022237371653318405, -0.10904789716005325, 0.01999366655945778, -0.024591993540525436, 0.025927947834134102, 0.03380803391337395, -0.012176988646388054, 0.014964012429118156, -0.07325104624032974, 0.04840235412120819, 0.03824949637055397, -0.042955487966537476, -0.04192914068698883, -0.07063509523868561, -0.006889146752655506, 0.05795075744390488, 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"https://www.mongodb.com/developer/products/atlas/using-confluent-cloud-atlas-stream-processing", "action": "created", "body": ". \n\n> Log in today to get started. Atlas Stream Processing is now available to all developers in Atlas. Give it a try today!", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to configure network connectivity between Confluent Cloud and MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Using the Confluent Cloud with Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.020578589290380478, -0.022939030081033707, 0.02774185687303543, -0.026139581575989723, -0.015020504593849182, 0.0011803517118096352, 0.012284724041819572, 0.00012355610670056194, -0.02621406316757202, -0.029760517179965973, 0.02622395195066929, -0.0471661351621151, -0.028404980897903442, -0.00016820048040244728, -0.0009071786189451814, 0.0058584390208125114, -0.007354362867772579, 0.022840183228254318, -0.047424767166376114, 0.01370314136147499, 0.0020228943321853876, -0.04545466601848602, -0.020581241697072983, -0.07919920235872269, 0.00656104227527976, 0.016686387360095978, -0.05688904970884323, -0.06646823137998581, -0.06456461548805237, 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Discover how to effortlessly send notifications to your desired Slack channels, effectively connecting the operations happening within your collections and relaying them in real-time updates. \n\nThe overall flow will be: \n\n.\n\nOnce this has been completed, we are ready to start creating our first database trigger that will react every time there is an operation in a certain collection. \n\n## Atlas trigger\n\nFor this tutorial, we will create a trigger that monitors all changes in a `test` collection for `insert`, `update`, and `delete` operations.\n\nTo create a new database trigger, you will need to:", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04861075058579445, -0.005022979341447353, 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Click the **Data Services** tab in the top navigation of your screen if you haven't already navigated to Atlas.\n2. Click **Triggers** in the left-hand navigation.\n3. On the **Overview** tab of the **Triggers** page, click **Add Trigger** to open the trigger configuration page.\n4. Enter the configuration values for the trigger and click **Save** at the bottom of the page.\n\nPlease note that this trigger will make use of the *event ordering* as we want the operations to be processed according to when they were performed. \n\nThe trigger configuration values will look like this: \n\n using the UI, we need to: \n\n1. Click the **Data Services** tab in the top navigation of your screen if you haven't already navigated to Atlas.\n\n2. Click **Functions** in the left navigation menu.", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06363121420145035, -0.011451954022049904, -0.004165022633969784, -0.04976668953895569, -0.020153731107711792, 0.040829598903656006, 0.022447839379310608, 0.023276960477232933, 0.02600770816206932, -0.05873127654194832, 0.0033851326443254948, -0.017807122319936752, 0.031925491988658905, 0.036146972328424454, 0.008659526705741882, 0.003985736519098282, -0.033163536339998245, 0.009837460704147816, -0.04157084971666336, 0.04975997656583786, 0.014914117753505707, -0.04868052154779434, -0.01435988862067461, -0.023582424968481064, 0.00110141234472394, 0.05776244029402733, -0.05501389130949974, 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"https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "2. Click **Functions** in the left navigation menu.\n\n3. Click **New Function** in the top right of the **Functions** page.\n\n4. Enter a unique, identifying name for the function in the **Name** field.\n\n5. Configure **User Authentication**. Functions in App Services always execute in the context of a specific application user or as a system user that bypasses rules. For this tutorial, we are going to use **System user**.\n\n### \"processEvent\" function\n\nThe processEvent function will process the change events every time an operation we are monitoring in the given collection is processed. In this way, we are going to create an object that we will then send to the function in charge of sending this message in Slack. \n\nThe code of the function is the following:\n\n```javascript\nexports = function(changeEvent) {\n\n const docId = changeEvent.documentKey._id;", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04692066088318825, -0.0004975453484803438, 0.03182193636894226, -0.07418668270111084, -0.006427539046853781, 0.007713283412158489, 0.08849595487117767, 0.05095505341887474, 0.014411067590117455, -0.007796722464263439, -0.033935464918613434, -0.011143178679049015, 0.013655604794621468, 0.025667842477560043, 0.002632334129884839, -0.008077342063188553, -0.021785994991660118, 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-0.052956122905015945, -0.0028913463465869427, 0.03406389057636261, -0.0527292899787426, 0.027103442698717117, 0.045141976326704025, 0.007255996577441692]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "```javascript\nexports = function(changeEvent) {\n\n const docId = changeEvent.documentKey._id;\n\n const { updateDescription, operationType } = changeEvent;\n\n var object = {\n operationType,\n docId,\n };\n\n if (updateDescription) {\n const updatedFields = updateDescription.updatedFields; // A document containing updated fields\n const removedFields = updateDescription.removedFields; // An array of removed fields\n object = {\n ...object,\n updatedFields,\n removedFields\n };\n }\n\n const result = context.functions.execute(\"sendToSlack\", object);\n\n return true;\n};\n```\n\nIn this function, we will create an object that we will then send as a parameter to another function that will be in charge of sending to our Slack channel. \n\nHere we will use change event and its properties to capture the:", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.034497227519750595, -0.013948632404208183, 0.04669148847460747, -0.043825455009937286, 0.03716447949409485, -0.002747032791376114, 0.07384465634822845, 0.0626535639166832, 0.004230445716530085, 0.0033294830936938524, -0.046797655522823334, -0.027347128838300705, 0.0022223719861358404, 0.02311544492840767, -0.007984912022948265, -0.006065954454243183, -0.03895506635308266, 0.041876260191202164, -0.1110982596874237, 0.019256290048360825, 0.034600164741277695, -0.012748697772622108, -0.03425220400094986, 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-0.0007510721334256232]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "Here we will use change event and its properties to capture the: \n\n1. `_id` of the object that has been modified/inserted.\n2. Operation that has been performed.\n3. Fields of the object that have been modified or deleted when the operation has been an `update`.\n\nWith all this, we create an object and make use of the internal function calls to execute our `sendToSlack` function.\n\n### \"sendToSlack\" function\n\nThis function will make use of the \"chat.postMessage\" method of the Slack API to send a message to a specific channel.\n\nTo use the Slack library, you must add it as a dependency in your Atlas function. Therefore, in the **Functions** section, we must go to the **Dependencies** tab and install `@slack/web-api`.\n\nYou will need to have a Slack token that will be used for creating the `WebClient` object as well as a Slack application. Therefore:", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05097302049398422, 0.0019437941955402493, 0.03132161125540733, -0.040236834436655045, 0.01810695044696331, -0.002948417793959379, 0.038074202835559845, 0.04767387732863426, -0.014090952463448048, 0.001797234290279448, 0.010150925256311893, -0.0475931279361248, 0.002250481629744172, -0.004234065301716328, -0.012923148460686207, 0.0026206800248473883, -0.023749537765979767, 0.015202711336314678, -0.10027981549501419, 0.00514796981588006, -0.0023260037414729595, -0.009678584523499012, -0.009784797206521034, -0.011381808668375015, 0.016925323754549026, 0.04441208764910698, -0.051233869045972824, 0.003105215262621641, -0.060901496559381485, 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"https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "1. Create or use an existing Slack app: This is necessary as the subsequent token we will need will be linked to a Slack App. For this step, you can navigate to the Slack application and use your credentials to authenticate and create or use an existing app you are a member of. \n\n2. Within this app, we will need to create a bot token that will hold the authentication API key to send messages to the corresponding channel in the Slack app created. Please note that you will need to add as many authorization scopes on your token as you need, but the bare minimum is to add the `chat:write` scope to allow your app to post messages.\n\nA full guide on how to get these two can be found in the Slack official documentation.\n\nFirst, we will perform the logic with the received object to create a message adapted to the event that occurred.", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05435250699520111, -0.036910705268383026, -0.0005872239707969129, -0.0655151978135109, -0.009174820967018604, 0.013583007268607616, 0.028298940509557724, 0.04419535771012306, -0.030170777812600136, 0.017300833016633987, -0.01671484299004078, -0.06589360535144806, 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arg.removedFields.length > 0) {\n message += ` The fileds ${JSON.stringify(arg.removedFields)} has been removed.`;\n }\n} else {\n message += `An unexpected operation affecting document \\`${arg.docId}\\` ocurred`;\n}\n```", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08079223334789276, 0.0107524199411273, 0.02410375513136387, -0.038039304316043854, 0.024088529869914055, -0.01552523672580719, 0.059473130851984024, 0.047304026782512665, 0.005416723433881998, 0.010476653464138508, -0.02539830096065998, -0.0082736536860466, -0.02138364501297474, 0.0015987413935363293, 0.015565197914838791, -0.00010951335571007803, -0.03417541831731796, -0.00031203898834064603, -0.09651162475347519, 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-0.049646563827991486, 0.023456217721104622, 0.05145557224750519, -0.018462901934981346]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "Once we have the library, we must use it to create a `WebClient` client that we will use later to make use of the methods we need. \n\n```javascript\n const { WebClient } = require('@slack/web-api');\n // Read a token from the environment variables\n const token = context.values.get('SLACK_TOKEN');\n // Initialize\n const app = new WebClient(token);\n```\n\nFinally, we can send our message with: \n\n```javascript\ntry {\n // Call the chat.postMessage method using the WebClient\n const result = await app.chat.postMessage({\n channel: channelId,\n text: `New Event: ${message}`\n });\n\n console.log(result);\n}\ncatch (error) {\n console.error(error);\n}\n```\n\nThe full function code will be as:\n\n```javascript\nexports = async 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"https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "The full function code will be as:\n\n```javascript\nexports = async function(arg){\n\n const { WebClient } = require('@slack/web-api');\n // Read a token from the environment variables\n const token = context.values.get('SLACK_TOKEN');\n const channelId = context.values.get('CHANNEL_ID');\n // Initialize\n const app = new WebClient(token);", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06603668630123138, -0.03279050067067146, 0.012644114904105663, -0.0491916760802269, 0.04329785332083702, 0.0019121739314869046, 0.03035837598145008, 0.056558966636657715, 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${JSON.stringify(arg.removedFields)} has been removed.`;\n }\n } else {\n message += `An unexpected operation affecting document \\`${arg.docId}\\` ocurred`;\n }", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08266864717006683, -0.019241712987422943, 0.02353842556476593, -0.02521379105746746, 0.027662349864840508, -0.026150666177272797, 0.03329278901219368, 0.03873886168003082, -0.00866732094436884, -0.017852121964097023, -0.015996860340237617, -0.008567520417273045, -0.009459811262786388, 0.008727648295462132, -0.002101360121741891, -0.0025883610360324383, -0.03882452845573425, -0.015273385681211948, -0.09161218255758286, 0.047304365783929825, 0.056767698377370834, 0.009678095579147339, 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-0.0141184376552701]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "try {\n // Call the chat.postMessage method using the WebClient\n const result = await app.chat.postMessage({\n channel: channelId,\n text: `New Event: ${message}`\n });\n console.log(result);\n }\n catch (error) {\n console.error(error);\n }\n\n};\n```\n\nNote: The bot token we use must have the minimum permissions to send messages to a certain channel. We must also have the application created in Slack added to the channel where we want to receive the messages.\n\nIf everything is properly configured, every change in the collection and monitored operations will be received in the Slack channel:\n\n to only detect certain changes and then adapt the change event to only receive certain fields with a \"$project\".\n\n## Conclusion", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.038253337144851685, 0.00445207953453064, 0.008817768655717373, -0.06323850154876709, 0.024260761216282845, -0.004889675881713629, 0.02167149819433689, 0.04973961040377617, -0.040696412324905396, -0.0053010256960988045, -0.01145285926759243, -0.04720069468021393, 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This integration allows you to send real-time notifications to your Slack channels, keeping your team informed about important operations within your database collections.\n\nWe started by creating a new application in Atlas and then set up a database trigger that reacts to specific collection operations. We explored the `processEvent` function, which processes change events and prepares the data for Slack notifications. Through a step-by-step process, we demonstrated how to create a message and use the Slack API to post it to a specific channel.\n\nNow that you've grasped the basics, it's time to take your integration skills to the next level. Here are some steps you can follow:", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04670480266213417, -0.008130618371069431, 0.02248843014240265, -0.03379162400960922, 0.04564126580953598, -0.024753117933869362, 0.017661316320300102, 0.028665034100413322, -0.005296927411109209, -0.022395316511392593, 0.018970653414726257, 0.014520364813506603, -0.001965910429134965, 0.019226333126425743, -0.010632704943418503, -0.012642518617212772, -0.018924547359347343, 0.029404710978269577, -0.08787618577480316, -0.006237317807972431, 0.0014935373328626156, 0.013637294061481953, 0.005585606209933758, 0.002943699713796377, -0.0014394638128578663, 0.08201222866773605, -0.05658303573727608, -0.03440228849649429, 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"https://www.mongodb.com/developer/products/atlas/how-send-mongodb-document-changes-slack-channel", "action": "created", "body": "- **Explore advanced use cases**: Consider how you can adapt the principles you've learned to more complex scenarios within your organization. Whether it's custom notifications or handling specific database events, there are countless possibilities.\n- **Dive into the Slack API documentation**: For a deeper understanding of what's possible with Slack's API, explore their official documentation. This will help you harness the full potential of Slack's features.\n\nBy taking these steps, you'll be well on your way to creating powerful, customized integrations that can streamline your workflow and keep your team in the loop with real-time updates. Good luck with your integration journey!", "format": "md", "metadata": {"tags": ["Atlas", "JavaScript"], "pageDescription": "Learn how to use triggers in MongoDB Atlas to send information about changes to a document to Slack.", "contentType": "Tutorial"}, "title": "How to Send MongoDB Document Changes to a Slack Channel", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06648917496204376, -0.03259650990366936, 0.04062449932098389, -0.032923996448516846, 0.04692851006984711, -0.013970009982585907, 0.04763020947575569, 0.015161968767642975, -0.017257830128073692, -0.0023698739241808653, 0.01546394545584917, 0.0043011815287172794, 0.006748454179614782, -0.007997393608093262, 0.021818049252033234, -0.01197674311697483, 0.01562045980244875, 0.023345958441495895, -0.10882646590471268, -0.02107604406774044, -0.004928265698254108, -0.006501466501504183, -0.009019279852509499, 0.01906806230545044, -0.02809542417526245, 0.07636311650276184, -0.05299493670463562, -0.01635485328733921, 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incorporate vector embeddings. \n\nData modeling in MongoDB revolves around organizing your data into documents within various collections. Varied projects or organizations will require different ways of structuring data models due to the fact that successful data modeling depends on the specific requirements of each application, and for the most part, no one document design can be applied for every situation. There are some commonalities, though, that can guide the user. These are:", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05040832981467247, 0.0035094867926090956, 0.02393709309399128, -0.029764365404844284, 0.05191889777779579, -0.006890544667840004, -0.004012200981378555, 0.045998383313417435, -0.021268324926495552, -0.007306348532438278, 0.019453100860118866, -0.03377308323979378, 0.010670991614460945, 0.030146095901727676, 0.03869108110666275, -0.014482393860816956, -0.008190643042325974, 0.04822875186800957, -0.00908602960407734, 0.014945761300623417, 0.02855316549539566, -0.024538356810808182, -0.04720211401581764, -0.06446068733930588, 0.027065172791481018, 0.07646237313747406, -0.05196554213762283, -0.034105416387319565, -0.02715664729475975, 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"https://www.mongodb.com/developer/products/mongodb/doc-modeling-vector-search", "action": "created", "body": "1. Choosing whether to embed or reference your related data. \n 2. Using arrays in a document.\n 3. Indexing your documents (finding fields that are frequently used and applying the appropriate indexing, etc.).\n\nFor a more in-depth explanation and a comprehensive guide of data modeling with MongoDB, please check out our data modeling article.\n\n## Setting up an example data model\n\nWe are going to be building our vector embedding example using a MongoDB document for our MongoDB TV series. Here, we have a single MongoDB document representing our MongoDB TV show, without any embeddings in place. We have a nested array featuring our array of seasons, and within that, our array of different episodes. This way, in our document, we are capable of seeing exactly which season each episode is a part of, along with the episode number, the title, the description, and the date:", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.02317127026617527, -0.0021741336677223444, 0.029314830899238586, -0.05016374960541725, 0.07130555808544159, -0.008104021660983562, 0.003823608625680208, 0.044665563851594925, 0.0007575505296699703, 0.010353545658290386, 0.0147004509344697, -0.0447501540184021, 0.01832723245024681, 0.01814274862408638, 0.00821580458432436, -0.00586828263476491, -0.03840725123882294, 0.05477845296263695, -0.029100781306624413, 0.016857480630278587, 0.0500655397772789, -0.014249741099774837, 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for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.02710265852510929, -0.011942173354327679, 0.03338083624839783, -0.08127890527248383, 0.07385067641735077, -0.009587489068508148, 0.004387813154608011, 0.01023946888744831, -0.00046368289622478187, 0.031469572335481644, -0.016225295141339302, -0.0647592544555664, 0.03261403739452362, 0.034690141677856445, 0.005603570956736803, -0.021877136081457138, -0.020833376795053482, 0.023456208407878876, -0.05947495996952057, 0.02772299200296402, 0.05066042020916939, -0.011268621310591698, -0.022385261952877045, -0.07108607888221741, 0.03413992375135422, 0.06180543452501297, 0.010650722309947014, -0.013350475579500198, -0.05754261463880539, -0.21845053136348724, -0.03417021036148071, -0.051279548555612564, 0.07131647318601608, -0.01793834939599037, 0.031290218234062195, -0.01780608668923378, -0.019301239401102066, 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\"episodeNumber\": 1,\n \"title\": \"Cloud Connect - Harness the Power of AI/ML and Generative AI on AWS with MongoDB Atlas\",\n \"description\": \"Join Igor Alekseev\u2026.\",\n \"date\": ISODate(\"Oct32023\")\n },\n {\n \"episodeNumber\": 2,\n \"title\": \"The Index: Here\u2019s what you missed last week\u2026\",\n \"description\": \"Join Megan Grant\u2026\",\n \"date\": ISODate(\"Oct22023\")\n }\n ]\n }\n ]\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06628566235303879, -0.03930354490876198, 0.011942955665290356, -0.043195690959692, 0.07272123545408249, 0.02715747244656086, -0.004824345000088215, 0.02833166904747486, 0.0046721044927835464, 0.006590971257537603, 0.03282574191689491, 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This comes in handy when querying using similarities rather than searching based on keywords. When using vector search, you can query using a question or a phrase rather than just a word. In a nutshell, vector search is great for when you can\u2019t think of *exactly* that book or movie, but you remember the plot or the climax.", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06387308239936829, -0.01285748090595007, 0.00989945326000452, -0.011655112728476524, 0.04453819990158081, -0.0001472049771109596, 0.014776562340557575, 0.038629353046417236, 0.012935629114508629, -0.0017955395160242915, 0.035523075610399246, -0.031217288225889206, 0.04906724765896797, 0.03525857627391815, 0.06443703174591064, -0.028667692095041275, 0.013120077550411224, 0.07429749518632889, -0.026599129661917686, -0.02110394649207592, 0.03420095145702362, -0.020224833860993385, -0.00966064352542162, 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0.0032183353323489428]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/doc-modeling-vector-search", "action": "created", "body": "This process happens when text, video, or audio is transformed via an encoder into vectors. With MongoDB, we can do this using OpenAI, Hugging Face, or other natural language processing models. Once we have our vectors, we can upload them in the base of our document and conduct vector search using them. Please keep in mind the [current limitations of vector search and how to properly embed your vectors. \n\nYou can store your vector embeddings alongside other data in your document, or you can store them in a new collection. It is really up to the user and the project goals. Let\u2019s go over what a document with vector embeddings can look like when you incorporate them into your data model, using the same example from above:", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.048436153680086136, -0.021079035475850105, 0.003924472723156214, -0.022311773151159286, 0.05012430250644684, -0.004514416214078665, -0.003736359067261219, 0.021510127931833267, -0.0028633545152843, 0.014669610187411308, 0.014674393460154533, -0.034258510917425156, 0.029364895075559616, 0.030789274722337723, 0.04357849061489105, 0.008410358801484108, -0.011204810813069344, 0.027950134128332138, -0.03945349529385567, 0.0016620258102193475, 0.025633104145526886, -0.0361076258122921, -0.011544735170900822, 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-0.012864546850323677]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/doc-modeling-vector-search", "action": "created", "body": "```\n{\n \"_id\": ObjectId(\"238478293\"),\n \"title\": \"MongoDB TV\",\n \"description\": \"All your MongoDB updates, news, videos, and podcast episodes, straight to you!\",\n \"genre\": \"Programming\", \"Database\", \"MongoDB\"],\n \u201cvectorEmbeddings\u201d: [ 0.25, 0.5, 0.75, 0.1, 0.1, 0.8, 0.2, 0.6, 0.6, 0.4, 0.9, 0.3, 0.2, 0.7, 0.5, 0.8, 0.1, 0.8, 0.2, 0.6 ],\n \"seasons\": [\n {\n \"seasonNumber\": 1,\n \"episodes\": [", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03143370524048805, -0.01130755990743637, 0.03120499663054943, 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0.007708286866545677]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/doc-modeling-vector-search", "action": "created", "body": "\"seasonNumber\": 2,\n \"episodes\": [\n {\n \"episodeNumber\": 1,\n \"title\": \"Cloud Connect - Harness the Power of AI/ML and Generative AI on AWS with MongoDB Atlas\",\n \"description\": \"Join Igor Alekseev\u2026.\",\n \"date\": ISODate(\"Oct 3, 2023\")\n },\n {\n \"episodeNumber\": 2,\n \"title\": \"The Index: Here\u2019s what you missed last week\u2026\",\n \"description\": \"Join Megan Grant\u2026\",\n \"date\": ISODate(\"Oct 2, 2023\")\n }\n ]\n }\n ]\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05909251049160957, 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Currently, there is a limitation where vector embeddings cannot be nested in an array in your document. Please ensure your document has your embeddings at the base. There are various tutorials on our [Developer Center, alongside our YouTube account and our documentation, that can help you figure out how to embed these vectors into your document and how to acquire the necessary vectors in the first place.", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.020648935809731483, -0.049511369317770004, 0.012658742256462574, -0.04835701361298561, 0.04308772832155228, 0.023603098466992378, -0.007924037985503674, 0.06322376430034637, -0.007988965138792992, -0.02596079558134079, 0.019380705431103706, -0.051357775926589966, 0.031482573598623276, 0.03253173828125, 0.04449724406003952, -0.007148863282054663, -0.01585005410015583, 0.06746862083673477, -0.08552459627389908, 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To do this, please view our Vector Search documentation. Here is the skeleton code provided by our documentation:\n\n```\n{\n \"fields\":\n {\n \"type\": \"vector\",\n \"path\": \"\",\n \"numDimensions\": ,\n \"similarity\": \"euclidean | cosine | dotProduct\"\n },\n {\n \"type\": \"filter\",\n \"path\": \"\"\n },\n ...\n ]\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07858610898256302, -0.03655095398426056, 0.024139143526554108, -0.011957501992583275, 0.05302802473306656, -0.026552822440862656, 0.011656902730464935, 0.04356744512915611, -0.0245579332113266, 0.012442640028893948, 0.04635108634829521, -0.04989326745271683, 0.008488439954817295, 0.016740603372454643, 0.0406145416200161, -0.012993239797651768, 0.008054032921791077, 0.05515563115477562, 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In our case, it would be \u201cvectorEmbeddings\u201d. \u201ctype\u201d can stay the way it is. For \u201cnumDimensions\u201d, please match the dimensions of the model you\u2019ve chosen. This is just the number of vector dimensions, and the value cannot be greater than 4096. This limitation comes from the base embedding model that is being used, so please ensure you\u2019re using a supported LLM (large language model) such as OpenAI or Hugging Face. When using one of these, there won\u2019t be any issues running into vector dimensions. For \u201csimilarity\u201d, please pick which vector function you want to use to search for the top K-nearest neighbors. \n\n## Extras: Querying with vector search", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05278448015451431, -0.04473148658871651, 0.03215004503726959, 0.016879158094525337, 0.06253910064697266, 0.006722410675138235, 0.003707692725583911, 0.007163537200540304, -0.008732017129659653, -0.010686831548810005, 0.048368312418460846, -0.05051687732338905, 0.03182551637291908, 0.043730687350034714, 0.05084003135561943, 0.004824239294975996, 0.002103665377944708, 0.04966598004102707, -0.00526935700327158, 0.020030058920383453, 0.05499477684497833, -0.0318509042263031, 0.032291483134031296, 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0.01711348257958889]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/doc-modeling-vector-search", "action": "created", "body": "## Extras: Querying with vector search\n\nWhen you\u2019re ready to query and find results from your embedded documents, it\u2019s time to create an aggregation pipeline on your embedded vector data. To do this, you can use the\u201c$vectorSearch\u201d operator, which is a new aggregation stage in Atlas. It helps execute an Approximate Nearest Neighbor query. \n\nFor more information on this step, please check out the tutorial on Developer Center about [building generative AI applications, and our YouTube video on vector search.", "format": "md", "metadata": {"tags": ["MongoDB", "AI"], "pageDescription": "Follow along with this comprehensive tutorial on how to properly model your documents for MongoDB Vector Search.", "contentType": "Tutorial"}, "title": "How to Model Your Documents for Vector Search", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08270145207643509, -0.02409922517836094, 0.04185096547007561, -0.0005988531047478318, 0.011469149962067604, -0.005833218805491924, 0.017487570643424988, 0.027966871857643127, -0.01327602006494999, 0.0018545862985774875, 0.040629949420690536, -0.01056613028049469, 0.02905406802892685, -0.027371421456336975, 0.018223697319626808, 0.005903297103941441, -0.006655000150203705, 0.07579542696475983, -0.04551229625940323, 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This project aimed to develop a database for dog care providers and demonstrate how this data can be manipulated in MongoDB. The Dog Welfare Federation (DWF) is concerned that some providers who provide short/medium term care for dogs when the owner is unable to \u2013 e.g., when away on holidays, may not be delivering the service they promise. Up to now, the DWF has managed the data using a SQL database. As the scale of its operations expanded, the organization needed to invest in a cloud database application. As an alternative to the relational SQL database, the Dog Welfare Federation decided to look at the database development using MongoDB services.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0673043504357338, -0.024489762261509895, 0.021226804703474045, -0.056178152561187744, 0.014945073053240776, -0.022923143580555916, 0.007646982558071613, 0.01873447746038437, -0.015842663124203682, -0.03195275366306305, 0.012720059603452682, -0.045217279344797134, 0.03697912022471428, 0.01763303205370903, -0.020710797980427742, -0.0026506097055971622, 0.006373800802975893, 0.02810048498213291, -0.04309139400720596, 0.048842038959264755, 0.003943501506000757, -0.03134962171316147, -0.060697343200445175, -0.04667700082063675, -0.004658414516597986, 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"https://www.mongodb.com/developer/code-examples/python/dog-care-example-app", "action": "created", "body": "The Dog database uses fictitious data that I have created myself. The different practical stages of the project have been documented in my project report and may guide the beginners taking their first steps into MongoDB. \n\n## Inspiration\n\nThe assignment was given to me by my lecturer. And when he was deciding on the topics for the project, he knew that I love dogs. And that's why my project was all about the dogs. Even though the lecturer gave me the assignment, it was my idea to prepare this project in a way that does not only benefit me.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03518880531191826, -0.0029791968408972025, 0.028004679828882217, -0.05337212234735489, 0.03973725810647011, -0.026476705446839333, 0.04751341789960861, 0.039526376873254776, 0.017678383737802505, -0.0029798562172800303, 0.010395782999694347, -0.08637396991252899, 0.04233550280332565, 0.0473819226026535, -0.0393768772482872, 0.014566276222467422, -0.0065844436176121235, 0.011716443113982677, -0.061024826020002365, 0.033376723527908325, 0.016004586592316628, -0.03840858116745949, -0.03887207806110382, -0.06202035769820213, 0.02885352447628975, 0.10035798698663712, 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"https://www.mongodb.com/developer/code-examples/python/dog-care-example-app", "action": "created", "body": "When I followed courses via MongoDB University, I noticed that these courses gave me a flavor of MongoDB, but not the basic concepts. I wanted to turn a database development project into a kind of a guide for somebody who never used MongoDB and who actually can take the project say: \"Okay, these are the basic concepts, this is what happens when you run the query, this is the result of what you get, and this is how you can validate that your result and your query is correct.\" So that's how the whole MongoDB project for beginners was born. \n\nMy guide tells you how to use MongoDB, what steps you need to follow to create an application, upload data, use the data, etc. It's one thing to know what those operators are doing, but it's an entirely different thing to understand how they connect and what impact they make.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": 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He gave them instructions on the project and what kind of outcome he would like to see. I was asked to use MongoDB, and I decided to dive deeper into everything the platform offers. Besides that, as I mentioned briefly in the introduction: the organization DWF was planning on scaling and expanding their business, and they wanted to look into database development with MongoDB. This was a good chance for me to learn everything about NoSQL.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06260152906179428, -0.0006223433883860707, 0.0036406510043889284, -0.04687513783574104, 0.045890241861343384, -0.01253566425293684, 0.0499887578189373, 0.033365845680236816, -0.021059362217783928, -0.023712217807769775, -0.026301216334104538, -0.048075538128614426, 0.016038881614804268, 0.05390596389770508, 0.011864579282701015, 0.002829626901075244, -0.02733536995947361, -0.021936984732747078, -0.029860688373446465, 0.016739869490265846, 0.02649511583149433, -0.01900225132703781, -0.059155456721782684, -0.05935574322938919, 0.010922449640929699, 0.07352419942617416, -0.02676556631922722, -0.05719360336661339, -0.02195855602622032, 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"https://www.mongodb.com/developer/code-examples/python/dog-care-example-app", "action": "created", "body": "## How it works\n \nThe project teaches you how to set up a MongoDB database for dog care providers. It includes three main sections, including MongoDB Shell, MongoDB Atlas, and MongoDB Compass. The MongoDB Shell section demonstrates how the data can be manipulated using simple queries and the aggregation method. I'm discussing how to import data into a local cluster, create queries, and retrieve & update queries. The other two areas include an overview of MongoDB Atlas and MongoDB Compass; I also discuss querying and the aggregation framework per topic. Each section shows step-by-step instructions on how to set up the application and how also to include some data manipulation examples. As mentioned above, I created all the sample data myself, which was a ton of work! I made a spreadsheet with 2000 different lines of sample data. To do that, I had to Google dog breeds, dog names, and their temperaments. I wanted it to be close to reality.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03803024813532829, -0.002202673116698861, 0.027613164857029915, -0.03669954091310501, 0.021218925714492798, -0.026672158390283585, 0.003918041475117207, 0.016388019546866417, -0.008963847532868385, 0.009202348068356514, 0.021327244117856026, -0.0764559730887413, 0.047654666006565094, 0.019952287897467613, -0.01432684063911438, 0.02531006559729576, 0.003862708806991577, -0.0041437107138335705, -0.04307540878653526, 0.050186462700366974, -0.006087084766477346, -0.028818611055612564, -0.05423814803361893, -0.04030546173453331, 0.025685366243124008, 0.11380332708358765, -0.013313807547092438, -0.04668610170483589, -0.015548297204077244, -0.22259730100631714, 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"created", "body": "## Challenges and learning\n\nWhen I started working with MongoDB, the first big thing that I had to get over was the braces everywhere. So it was quite challenging for me to understand where the query finishes. But I\u2019ve been reading a lot of documentation, and creating this guide gave me quite a good understanding of the basics of MongoDB. I learned a lot about the technical side of databases because I was never familiar with them; I even had no idea how it works. Using MongoDB and learning about MongoDB, and using MongoDB was a great experience. When I had everything set up: the MongoDB shell, Compass, and Atlas, I could see how that information is moving between all these different environments, and that was awesome. I think it worked quite well. I hope that my guide will be valuable for new learners. It demonstrates that users like me, who had no prior skills in using MongoDB, can quickly become MongoDB developers.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08073952794075012, 0.016111226752400398, 0.022742534056305885, -0.02772919088602066, 0.031608451157808304, -0.007856469601392746, 0.018731724470853806, 0.02807675674557686, -0.024212004616856575, -0.018364524468779564, -0.0006483024917542934, -0.03308921679854393, 0.033242009580135345, 0.03138059005141258, 0.0013567273272201419, 0.02243141643702984, -0.029374361038208008, 0.013754121959209442, -0.04989510402083397, 0.01278665754944086, 0.018881838768720627, -0.004133838228881359, -0.03910408914089203, -0.06363652646541595, 0.017894301563501358, 0.09859832376241684, 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"https://www.mongodb.com/developer/code-examples/python/dog-care-example-app", "action": "created", "body": "Access the complete report, which includes the queries you need - here.", "format": "md", "metadata": {"tags": ["Python", "MongoDB"], "pageDescription": " Learn MongoDB by creating a database for dog care providers!", "contentType": "Code Example"}, "title": "Example Application for Dog Care Providers (DCP)", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.011670069769024849, 0.001684882096014917, 0.02273530513048172, -0.0020611488725990057, 0.0173810962587595, 0.04146561026573181, 0.03775247931480408, 0.05070570856332779, -0.012176265008747578, 0.031127506867051125, -0.007994591258466244, -0.043679725378751755, 0.05251575633883476, 0.015666281804442406, 0.017403772100806236, 0.0030851862393319607, -0.01070321910083294, 0.004768695216625929, -0.010985714383423328, 0.04045230895280838, 0.009144539944827557, -0.018209116533398628, -0.005296766292303801, 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0.05791277065873146]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/leafsteroidsresources", "action": "created", "body": "# Leafsteroid Resources\n\n \nLeafsteroids is a MongoDB Demo showing the following services and integrations\n------------------------------------------------------------------------\n\n**Atlas App Services** \nAll in one backend. Atlas App Services offers a full-blown REST service using Atlas Functions and HTTPS endpoints. \n\n**Atlas Search** \nUsed to find the player nickname in the Web UI. \n\n**Atlas Charts** \nEvent & personalized player dashboards accessible over the web. Built-in visualization right with your data. No additional tools required. \n\n**Document Model** \nEvery game run is a single document demonstrating rich documents and \u201cdata that works together lives together\u201d, while other data entities are simple collections (configuration). \n\n**AWS Beanstalk** Hosts the Blazor Server Application (website). \n\n**AWS EC2** \nUsed internally by AWS Beanstalk. Used to host our Python game server. \n\n**AWS S3** \nUsed internally by AWS Beanstalk.", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Leafsteroid Resources", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.026094362139701843, -0.04921271279454231, 0.037323854863643646, -0.0299040786921978, 0.0705348402261734, -0.04460279271006584, -0.02254570834338665, 0.027888087555766106, -0.01923629269003868, -0.0016965757822617888, 0.0184225644916296, -0.071848064661026, 0.059057142585515976, 0.0400724895298481, 0.00437071593478322, -0.04342217370867729, -0.026989039033651352, -0.009313307702541351, -0.04375355318188667, 2.5546767574269325e-05, 0.011182322166860104, -0.0269267950206995, -0.022833041846752167, -0.06285865604877472, 0.003122264752164483, 0.04382132738828659, -0.07430806756019592, -0.03947918862104416, -0.013412544503808022, -0.18486498296260834, -0.0001120356100727804, -0.03657965362071991, 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Beanstalk. \n\n**AWS Private Cloud** \nPrivate VPN connection between AWS and MongoDB. \n\n \n\n**At a MongoDB .local Event and want to register to play Leafsteroids? Register Here**\n\nYou can build & play Leafsteroids yourself with the following links\n\n## Development Resources \n|Resource| Link|\n|---|---|\n|Github Repo |Here|\n|MongoDB TV Livestream\n|Here|\n|MongoDB & AWS |Here|\n|MongoDB on the AWS Marketplace\n|Here|", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "", "contentType": "Tutorial"}, "title": "Leafsteroid Resources", "updated": "2024-05-20T17:32:23.500Z", "embedding": [0.01239380706101656, -0.07263623923063278, 0.0013125673867762089, -0.026878532022237778, 0.07820390909910202, -0.011997164227068424, 0.02472584694623947, 0.020452357828617096, -0.005557980854064226, 0.03173254057765007, 0.034861087799072266, -0.06892214715480804, 0.08139581233263016, -0.009213442914187908, 0.005969208665192127, -0.03469688817858696, -0.022742832079529762, -0.0077144065871834755, -0.061135344207286835, 0.03537717089056969, 0.02232908271253109, -0.01999819464981556, -0.03638417646288872, -0.056565914303064346, 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"devcenter", "url": "https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "# Get Started with Atlas Stream Processing: Creating Your First Stream Processor\n\n>Atlas Stream Processing is now available. Learn more about it here.\n\nIf you're not already familiar, Atlas Stream Processing enables processing high-velocity streams of complex data using the same data model and Query API that's used in MongoDB Atlas databases. Streaming data is increasingly critical to building responsive, event-driven experiences for your customers. Stream processing is a fundamental building block powering these applications, by helping to tame the firehouse of data coming from many sources, by finding important events in a stream, or by combining data in motion with data in rest. \n\nIn this tutorial, we will create a stream processor that uses sample data included in Atlas Stream Processing. By the end of the tutorial, you will have an operational Stream Processing Instance (SPI) configured with a stream processor. This environment can be used for further experimentation and Atlas Stream Processing tutorials in the future.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03181539103388786, -0.00400774460285902, 0.012517690658569336, -0.03853747621178627, 0.010155958123505116, -0.02912072464823723, 0.01552774291485548, 0.016902806237339973, -0.011794387362897396, -0.00721775321289897, -0.0028351363725960255, -0.04551303759217262, -0.01961543597280979, -0.010232603177428246, -0.011652976274490356, 0.011292673647403717, 0.021659187972545624, 0.033568304032087326, -0.042417142540216446, 0.012643293477594852, 0.02595498412847519, -0.02844896912574768, -0.03222966194152832, -0.07410971075296402, 0.01068089623004198, 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-0.031843654811382294]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "### Tutorial Prerequisites \nThis is what you'll need to follow along:\n* An Atlas user with atlasAdmin permission. For the purposes of this tutorial, we'll have the user \"tutorialuser\". \n* MongoDB shell (Mongosh) version 2.0+\n\n## Create the Stream Processing Instance \n\nLet's first create a Stream Processing Instance (SPI). Think of an SPI as a logical grouping of one or more stream processors. When created, the SPI has a connection string similar to a typical MongoDB Atlas cluster. \n\nUnder the Services tab in the Atlas Project click, \"Stream Processing\". Then click the \"Create Instance\" button. \n\nThis will launch the Create Instance dialog. \n\nEnter your desired cloud provider and region, and then click \"Create\". You will receive a confirmation dialog upon successful creation. \n\n## Configure the connection registry", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03332658112049103, -0.025232858955860138, 0.0059943185187876225, -0.030978763476014137, -0.0009012561058625579, -0.03817266225814819, 0.009256967343389988, 0.00630947994068265, -0.04340631887316704, -0.017612531781196594, -0.006332408636808395, -0.08398216217756271, -0.009786981157958508, -0.0147026889026165, -0.015016620978713036, 0.02962903305888176, -0.0011716355802491307, 0.006613539066165686, -0.05817507952451706, 0.029341844841837883, 0.0209086611866951, -0.033521562814712524, -0.05621888488531113, -0.06497829407453537, -0.00039901694981381297, 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"https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "## Configure the connection registry \n\nThe connection registry stores connection information to the external data sources you wish to use within a stream processor. In this example, we will use a sample data generator that is available without any extra configuration, but typically you would connect to either Kafka or an Atlas database as a source. \n\nTo manage the connection registry, click on \"Configure\" to navigate to the configuration screen. \n\nOnce on the configuration screen, click on the \"Connection Registry\" tab. \n\nNext, click on the \"Add Connection\" button. This will launch the Add Connection dialog. \n\nFrom here, you can add connections to Kafka, other Atlas clusters within the project, or a sample stream. In this tutorial, we will use the Sample Stream connection. Click on \"Sample Stream\" and select \"sample_stream_solar\" from the list of available sample streams. Then, click \"Add Connection\".", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0217453446239233, -0.023016583174467087, 0.024743005633354187, -0.021385468542575836, 0.010694996453821659, 0.0028331701178103685, 0.006185399368405342, 0.029014887288212776, -0.030587879940867424, -0.024031855165958405, 0.0004896759055554867, -0.055913813412189484, 0.01234001200646162, -0.012739966623485088, 0.03743487969040871, 0.019005313515663147, -0.014269016683101654, 0.045909471809864044, -0.017969414591789246, -0.009455106221139431, 0.014028930105268955, -0.05505020543932915, -0.04382612556219101, -0.0538625530898571, 0.01233952958136797, 0.03403843939304352, -0.007181111257523298, -0.0626760944724083, 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"https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "The new \"sample_stream_solar\" will show up in the list of connections. \n\n## Connect to the Stream Processing Instance (SPI)\n\nNow that we have both created the SPI and configured the connection in the connection registry, we can create a stream processor. First, we need to connect to the SPI that we created previously. This can be done using the MongoDB Shell (mongosh). \n\nTo obtain the connection string to the SPI, return to the main Stream Processing page by clicking on the \"Stream Processing\" menu under the Services tab. \n\nNext, locate the \"Tutorial\" SPI we just created and click on the \"Connect\" button. This will present a connection dialog similar to what is found when connecting to MongoDB Atlas clusters. \n\nFor connecting, we'll need to add a connection IP address and create a database user, if we haven't already.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05499737337231636, -0.01048199087381363, 0.008497720584273338, 0.0005277139716781676, 0.014967719092965126, -0.03215320035815239, 0.04762919247150421, 0.0029842632357031107, -0.0082230344414711, -0.013334925286471844, 0.01503572054207325, -0.06465968489646912, -0.00460809888318181, 0.02405514009296894, 0.009181705303490162, 0.015406550839543343, 0.02431238815188408, 0.009915262460708618, -0.029174594208598137, 0.017767326906323433, 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0.03446437045931816, -0.006761484779417515, -0.0030480544082820415]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "For connecting, we'll need to add a connection IP address and create a database user, if we haven't already. \n\nThen we'll choose our connection method. If you do not already have mongosh installed, install it using the instructions provided in the dialog. \n\nOnce mongosh is installed, copy the connection string from the \"I have the MongoDB Shell installed\" view and run it in your terminal. \n\n```\nCommand Terminal > mongosh <> --tls --authenticationDatabase admin --username tutorialuser\n\nEnter password: *******************\n\nCurrent Mongosh Log ID: 64e9e3bf025581952de31587\nConnecting to: mongodb://*****\nUsing MongoDB: 6.2.0\nUsing Mongosh: 2.0.0\n\nFor mongosh info see: https://docs.mongodb.com/mongodb-shell/\n\nAtlasStreamProcessing>", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [0.0005484123830683529, -0.02924477495253086, 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Our connection to sample_stream_solar is shown as expected.\n\n```\nAtlasStreamProcessing> sp.listConnections()\n{\n ok: 1,\n connections: \n {\n name: 'sample_stream_solar',\n type: 'inmemory',\n createdAt: ISODate(\"2023-08-26T18:42:48.357Z\")\n } \n ]\n}\n```\n\n## Create a stream processor\nIf you are reading through this post as a prerequisite to another tutorial, you can return to that tutorial now to continue.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04493936151266098, -0.015181382186710835, 0.007576096337288618, -0.003372494364157319, 0.0029510222375392914, -0.04490417242050171, 0.03645902872085571, -0.0029803963843733072, -0.005037997383624315, -0.012822633609175682, 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This sample_stream_solar source represents the observed energy production of different devices (unique solar panels). Stream processing could be helpful in measuring characteristics such as panel efficiency or when replacement is required for a device that is no longer producing energy at all.\n\nFirst, let's define a [$source stage to describe where Atlas Stream Processing will read the stream data from. \n\n```\nvar solarstream={$source:{\"connectionName\": \"sample_stream_solar\"}}\n```\nNow we will issue .process to view the contents of the stream in the console. \n`sp.process(solarstream])`\n\n.process lets us sample our source data and quickly test the stages of a stream processor to ensure that it is set up as intended. A sample of this data is as follows:", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05555489659309387, 0.02191448211669922, 0.03463173285126686, -0.011256235651671886, 0.01715617999434471, -0.009063491597771645, 0.046450525522232056, 0.005418573971837759, -0.024784475564956665, 0.0008564062300138175, 0.022811485454440117, -0.0411125086247921, -0.02638285979628563, 0.007888407446444035, 0.01814872771501541, 0.007982154376804829, 0.009729336947202682, 0.028259044513106346, -0.03216644749045372, 0.014941132627427578, 0.050318289548158646, -0.06118948012590408, -0.06329368054866791, -0.06971721351146698, 0.03900527209043503, 0.03204283490777016, -0.016196204349398613, -0.029770074412226677, 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"https://www.mongodb.com/developer/products/atlas/create-first-stream-processor", "action": "created", "body": "```\n{\n device_id: 'device_2',\n group_id: 3,\n timestamp: '2023-08-27T13:51:53.375+00:00',\n max_watts: 250,\n event_type: 0,\n obs: {\n watts: 168,\n temp: 15\n },\n _ts: ISODate(\"2023-08-27T13:51:53.375Z\"),\n _stream_meta: {\n sourceType: 'sampleData',\n timestamp: ISODate(\"2023-08-27T13:51:53.375Z\")\n }\n}\n```\n## Wrapping up", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.008765537291765213, 0.010638762265443802, 0.04561035707592964, -0.03054041974246502, 0.07004795223474503, -0.05146593227982521, -0.0014320466434583068, -0.02007678709924221, -0.027997657656669617, 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We then walked through the basics of creating a stream processor \u2013 we created a Stream Processing Instance, configured a source in our connection registry using sample solar data (included in Atlas Stream Processing), connected to a Stream Processing Instance, and finally tested our first stream processor using .process. You are now ready to explore Atlas Stream Processing and create your own stream processors, adding advanced functionality like windowing and validation.\n\nIf you enjoyed this tutorial and would like to learn more check out the [MongoDB Atlas Stream Processing announcement blog post. For more on stream processors in Atlas Stream Processing, visit our documentation. \n\n### Learn more about MongoDB Atlas Stream Processing\n\nFor more on managing stream processors in Atlas Stream Processing, visit our documentation. \n\n>Log in today to get started. Atlas Stream Processing is now available to all developers in Atlas. Give it a try today!", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to create a stream processor end-to-end using MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Get Started with Atlas Stream Processing: Creating Your First Stream Processor", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05537842586636543, -0.00036551570519804955, 0.018568983301520348, -0.0229959636926651, 0.014584510587155819, -0.023160990327596664, 0.02517032064497471, 0.007532201241701841, -0.015971040353178978, -0.021215766668319702, 0.011296478100121021, -0.05792250484228134, -0.00696675106883049, 0.010578185319900513, -0.004903759341686964, 0.006714197341352701, 0.0005305887898430228, 0.017466016113758087, -0.041783638298511505, 0.01567656733095646, 0.027986058965325356, -0.0460767038166523, -0.056989122182130814, -0.07224854081869125, 0.030975017696619034, 0.054668985307216644, -0.03995835781097412, -0.044328201562166214, -0.03804906830191612, 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"https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "# Instant GraphQL APIs for MongoDB with Grafbase\n\n# Instant GraphQL APIs for MongoDB with Grafbase\n\nIn the ever-evolving landscape of web development, efficient data management and retrieval are paramount for creating dynamic and responsive applications. MongoDB, a versatile NoSQL database, and GraphQL, a powerful query language for APIs, have emerged as a dynamic duo that empowers developers to build robust, flexible, and high-performance applications.\n\nWhen combined, MongoDB and GraphQL offer a powerful solution for front-end developers, especially when used at the edge.\n\nYou may be curious about the synergy between an unstructured database and a structured query language. Fortunately, Grafbase offers a solution that seamlessly combines both by leveraging its distinctive connector schema transformations.\n\n## Prerequisites\n\nIn this tutorial, you\u2019ll see how easy it is to get set up with MongoDB and Grafbase, simplifying the introduction of GraphQL into your applications. \n\nYou will need the following to get started:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.060496795922517776, -0.013936665840446949, -0.02468806877732277, -0.03338273987174034, 0.024320261552929878, -0.011687575839459896, -0.023852085694670677, 0.03201303258538246, -0.027110237628221512, 0.012231258675456047, 0.00724195409566164, -0.04257605969905853, 0.045680586248636246, 0.03559825196862221, 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We\u2019ll refer to this instance as your \u201cData Source\u201d later.\n\n through the `g.datasource(mongodb)` call.\n\n## Create models for data\n\nThe MongoDB connector empowers developers to organize their MongoDB collections in a manner that allows Grafbase to autonomously generate the essential queries and mutations for document creation, retrieval, update, and deletion within these collections.\n\nWithin Grafbase, each configuration for a collection is referred to as a \"model,\" and you have the flexibility to employ the supported GraphQL Scalars to represent data within the collection(s).", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03375741466879845, -0.033944129943847656, 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Using the Grafbase TypeScript SDK, we can write the schema for each user model. It looks something like this:\n\n```ts\nconst address = g.type('Address', {\n street: g.string().mapped('street_name')\n})\n\nmongodb\n .model('User', {\n name: g.string(),\n email: g.string().optional(),\n address: g.ref(address)\n })\n .collection('users')\n```\n\nThis schema will generate a fully working GraphQL API with queries and mutations as well as all input types for pagination, ordering, and filtering:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05429348349571228, 0.013072926551103592, 0.0013313607778400183, -0.04195773974061012, 0.04100897163152695, 0.010005906224250793, -0.006218921858817339, 0.012553784996271133, -0.01156857330352068, 0.013234520331025124, 0.011654912494122982, 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collection\n\nMongoDB automatically generates collections when you first store data, so there\u2019s no need to manually create a collection for users at this step.\n\nWe\u2019re now ready to start the Grafbase development server using the CLI:\n\n```bash\nnpx grafbase dev\n```", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04429103806614876, 0.0030695730820298195, -0.0010024664225056767, -0.047095879912376404, 0.020874353125691414, 0.016409657895565033, 0.0023989833425730467, 0.02798275463283062, -0.019348371773958206, 0.014236866496503353, 0.01740475744009018, -0.06229391321539879, 0.0627003014087677, 0.013415040448307991, -0.008063498884439468, 0.034517403692007065, -0.053409211337566376, 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-0.027461346238851547, -0.029999300837516785, 0.03480610251426697, -0.04130844399333, -0.026451069861650467, 0.00956677459180355, -0.021192729473114014]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "We\u2019re now ready to start the Grafbase development server using the CLI:\n\n```bash\nnpx grafbase dev\n```\n\nThis command runs the entire Grafbase GraphQL API locally that you can use when developing your front end. The Grafbase API communicates directly with your Atlas Data API.\n\nOnce the command is running, you\u2019ll be able to visit http://127.0.0.1:4000 and explore the GraphQL API.\n\n## Insert users with GraphQL to MongoDB instance\n\nLet\u2019s test out creating users inside our MongoDB collection using the generated `userCreate` mutation that was provided to us by Grafbase.\n\nUsing Pathfinder at http://127.0.0.1:4000, execute the following mutation:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06417394429445267, -0.02632194757461548, -0.004139021970331669, -0.032131195068359375, 0.01252369862049818, -0.013693425804376602, 0.002537115477025509, 0.02528579719364643, -0.03510201722383499, 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0.013195095583796501, 0.001455717021599412, -0.057876985520124435, -0.04408032074570656, -0.021290278062224388, -0.053531572222709656, -0.00961373746395111, 0.05536005273461342, -0.013178414665162563, -0.0023809701669961214, -0.037391070276498795, -0.031775519251823425, 0.040417373180389404, -0.09457488358020782, 0.006392831914126873, 0.02274804562330246, -0.022277167066931725]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "Using Pathfinder at http://127.0.0.1:4000, execute the following mutation:\n\n```\nmutation {\n mongo {\n userCreate(input: {\n name: \"Jamie Barton\",\n email: \"jamie@grafbase.com\",\n age: 40\n }) {\n insertedId\n }\n }\n}\n```\n\nIf everything is hooked up correctly, you should see a response that looks something like this:\n\n```json\n{\n \"data\": {\n \"mongo\": {\n \"userCreate\": {\n \"insertedId\": \"65154a3d4ddec953105be188\"\n }\n }\n }\n}\n```\n\nYou should repeat this step a few times to create multiple users.\n\n## Update user by ID\n\nNow we\u2019ve created some users in our MongoDB collection, let\u2019s try updating a user by `insertedId`:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06117311865091324, -0.0031077698804438114, 0.018838027492165565, -0.026751015335321426, 0.0010560760274529457, -0.010990395210683346, 0.03595113381743431, 8.211717795347795e-05, -0.027257390320301056, 0.006800724193453789, 0.007833351381123066, -0.06216532364487648, 0.02361544407904148, -0.007560629863291979, 0.0006799221155233681, 0.012981433421373367, -0.03905041888356209, -0.0027573611587285995, -0.05120895057916641, 0.06296812742948532, 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-0.00022175462800078094, -0.029296178370714188, -0.011553495191037655, -0.004004312213510275, -0.026123305782675743]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "## Update user by ID\n\nNow we\u2019ve created some users in our MongoDB collection, let\u2019s try updating a user by `insertedId`:\n\n```\nmutation {\n mongo {\n userUpdate(by: {\n id: \"65154a3d4ddec953105be188\"\n }, input: {\n age: {\n set: 35\n }\n }) {\n modifiedCount\n }\n }\n}\n```\n\nUsing the `userUpdate` mutation above, we `set` a new `age` value for the user where the `id` matches that of the ObjectID we passed in.\n\nIf everything was successful, you should see something like this:\n\n```json\n{\n \"data\": {\n \"mongo\": {\n \"userUpdate\": {\n \"modifiedCount\": 1\n }\n }\n }\n}\n```\n\n## Delete user by ID", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": 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update mutations above, but we don\u2019t need to provide any additional `input` data since we\u2019re deleting only:\n\n```\nmutation {\n mongo {\n userDelete(by: {\n id: \"65154a3d4ddec953105be188\"\n }) {\n deletedCount\n }\n }\n}\n```\n\nIf everything was successful, you should see something like this:\n\n```json\n{\n \"data\": {\n \"mongo\": {\n \"userDelete\": {\n \"deletedCount\": 1\n }\n }\n }\n}\n```\n\n## Fetch all users\n\nGrafbase generates the query `userCollection` that you can use to fetch all users. Grafbase requires a `first` or `last` pagination value with a max value of `100`:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05340757966041565, 0.022377613931894302, 0.027686472982168198, -0.02726184017956257, 0.047844938933849335, -0.02716010808944702, 0.04417137801647186, 0.04182431846857071, -0.015378356911242008, 0.019935769960284233, 0.013906395994126797, -0.051592398434877396, 0.047898877412080765, 0.035340383648872375, -0.03046814724802971, 0.011831899173557758, -0.04147226735949516, 0.008972374722361565, -0.060253292322158813, 0.04009062424302101, 0.04820552095770836, -0.03551562875509262, -0.038842007517814636, -0.06352554261684418, 0.0346246138215065, 0.054756224155426025, 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"https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "```\nquery {\n mongo {\n userCollection(first: 100) {\n edges {\n node {\n id\n name\n email\n age\n }\n }\n }\n }\n}\n```\n\nHere we are fetching the `first` 100 users from the collection. You can also pass a filter and order argument to tune the results:\n\n```\nquery {\n mongo {\n userCollection(first: 100, filter: {\n age: {\n gt: 30\n }\n }, orderBy: {\n age: ASC\n }) {\n edges {\n node {\n id\n name\n email\n age\n }\n }\n }\n }\n}\n```\n\n## Fetch user by ID\n\nUsing the same GraphQL API, we can fetch a user by the object ID. Grafbase automatically generates the query `user` where we can pass the `id` to the `by` input type:", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.043748993426561356, 0.030563896521925926, -0.003987419418990612, -0.01940014772117138, 0.01786317117512226, -0.005038009956479073, 0.028533639386296272, 0.025421829894185066, -0.008042658679187298, 0.01552485954016447, -0.004550616722553968, -0.04388279467821121, 0.049167294055223465, 0.03163282573223114, -0.032693177461624146, 0.055880531668663025, -0.020412174984812737, 0.018008816987276077, -0.05512841045856476, 0.010892760008573532, 0.04467720165848732, -0.0020794374868273735, -0.053736183792352676, -0.06891556084156036, 0.03383046016097069, 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"url": "https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "```\nquery {\n mongo {\n user(\n by: {\n id: \"64ee1cfbb315482287acea78\"\n }\n ) {\n id\n name\n email\n age\n }\n }\n}\n```\n\n## Enable faster responses with GraphQL Edge Caching\n\nEvery request we make so far to our GraphQL API makes a round trip to the MongoDB database. This is fine, but we can improve response times even further by enabling GraphQL Edge Caching for GraphQL queries.\n\nTo enable GraphQL Edge Caching, inside `grafbase/grafbase.config.ts`, add the following to the `config` export:\n\n```ts\nexport default config({\n schema: g,\n cache: {\n rules: \n {\n types: 'Query',\n maxAge: 60\n }\n ]\n }\n})\n```", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04972122609615326, 0.02118612453341484, 0.00963200069963932, 0.0016703347209841013, 0.029073672369122505, -0.023298271000385284, 0.011379036121070385, 0.029985908418893814, -0.018492111936211586, -0.009112842381000519, -0.00013112025044392794, -0.04699208214879036, 0.04552290216088295, 0.04094374552369118, 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If you only want to disable caching on some collections, you can do that too. [Learn more about GraphQL Edge Caching.\n\n## Deploy to the edge\n\nSo far, we\u2019ve been working with Grafbase locally using the CLI, but now it\u2019s time to deploy this around the world to the edge with GitHub.\n\nIf you already have an existing GitHub repository, go ahead and commit the changes we\u2019ve made so far. If you don\u2019t already have a GitHub repository, you will need to create one, commit this code, and push it to GitHub.\n\nNow, create a new project with Grafbase and connect your GitHub account. You\u2019ll need to permit Grafbase to read your repository contents, so make sure you select the correct repository and allow that.", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.02148193120956421, -0.010524870827794075, 0.0006254873587749898, 0.02242985926568508, -0.0030463174916803837, -0.030734268948435783, 0.016447149217128754, 0.00230612326413393, -0.013110250234603882, -0.013083008117973804, 0.010736558586359024, -0.020929548889398575, 0.0785050094127655, 0.03869276121258736, 0.017762985080480576, 0.03663960471749306, -0.04330509155988693, 0.028661400079727173, -0.03352659195661545, -0.025980144739151, 0.04130053520202637, -0.029769254848361015, -0.013331171125173569, -0.03951195254921913, 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-0.017705896869301796]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/instant-graphql-apis-mongodb-grafbase", "action": "created", "body": "Before you click **Deploy**, make sure to insert the environment variables obtained previously in the tutorial. Grafbase also supports environment variables for preview environments, so if you want to use a different MongoDB database for any Grafbase preview deployment, you can configure that later.\n\n, URQL, and Houdini.\n\nIf you have questions or comments, continue the conversation over in the MongoDB Developer Community.", "format": "md", "metadata": {"tags": ["Atlas", "TypeScript", "GraphQL"], "pageDescription": "Learn how to quickly and easily create a GraphQL API from your MongoDB data with Grafbase.", "contentType": "Tutorial"}, "title": "Instant GraphQL APIs for MongoDB with Grafbase", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.032048068940639496, -0.015040350146591663, 0.02186408080160618, -0.014235897921025753, 0.046102169901132584, -0.014689587987959385, -0.03554341569542885, 0.01145886816084385, -0.015134288929402828, 0.016420645639300346, 0.022054368630051613, -0.05332082137465477, 0.04446163401007652, 0.03673122078180313, 0.006024898961186409, 0.03775956481695175, 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"https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "# Exploring Window Operators in Atlas Stream Processing\n\n> Atlas Stream Processing is now available. Learn more about it here.\n\nIn our previous post on windowing, we introduced window operators available in Atlas Stream Processing. Window operators are one of the most commonly used operations to effectively process streaming data. Atlas Stream Processing provides two window operators: $tumblingWindow and $hoppingWindow. In this tutorial, we will explore both of these operators using the sample solar data generator provided within Atlas Stream Processing.\n\n## Getting started\n\nBefore we begin creating stream processors, make sure you have a database user who has \u201catlasAdmin\u201d access to the Atlas Project. Also, if you do not already have a Stream Processing Instance created with a connection to the sample_stream_solar data generator, please follow the instructions in Get Started with Atlas Stream Processing: Creating Your First Stream Processor and then continue on.\n\n## View the solar stream sample data", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.048927512019872665, -0.003921780269593, 0.04130646586418152, -0.004971460439264774, 0.017873959615826607, 0.0039832135662436485, 0.04579886049032211, -0.015007194131612778, -0.0012598464963957667, -0.02444789744913578, -0.009361923672258854, -0.04687841609120369, -0.019811274483799934, 0.009019111283123493, 0.017321893945336342, 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0.02582123689353466, -0.08965470641851425, -0.04921524226665497, -0.02329842559993267, 0.02514078840613365, 0.05069950968027115, -0.09783326834440231, 0.007191018667072058, 0.013228412717580795, 0.00767116341739893]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "## View the solar stream sample data\n\nFor this tutorial, we will be using the MongoDB shell. \n\nFirst, confirm sample_stream_solar is added as a connection by issuing `sp.listConnections()`.\n\n```\nAtlasStreamProcessing> sp.listConnections()\n{\n ok: 1,\n connections: \n {\n name: 'sample_stream_solar',\n type: 'inmemory',\n createdAt: ISODate(\"2023-08-26T18:42:48.357Z\")\n } \n ]\n}\n```\n\nNext, let\u2019s define a **$source** stage to describe where Atlas Stream Processing will read the stream data from.\n\n```\nvar solarstream={ $source: { \"connectionName\": \"sample_stream_solar\" } }\n```\n\nThen, issue a **.process** command to view the contents of the stream on the console.\n\n```\nsp.process([solarstream])\n```", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05985584482550621, 0.011241280473768711, 0.01399822998791933, -0.02559373527765274, 0.024506209418177605, -0.024106847122311592, 0.04378458485007286, -0.013441276736557484, -0.025579752400517464, -0.01239199098199606, 0.0058982293121516705, -0.040709663182497025, -0.017621947452425957, 0.02196154184639454, -0.004951581358909607, 0.003982188180088997, -0.0010406646179035306, 0.026952659711241722, -0.04225614666938782, 0.012566500343382359, 0.041538409888744354, -0.03646957129240036, -0.05340170860290527, 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-0.005928804166615009]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "```\nsp.process([solarstream])\n```\n\nYou will see the stream of solar data printed on the console. A sample of this data is as follows:\n\n```json\n{\n device_id: 'device_2',\n group_id: 3,\n timestamp: '2023-08-27T13:51:53.375+00:00',\n max_watts: 250,\n event_type: 0,\n obs: {\n watts: 168,\n temp: 15\n },\n _ts: ISODate(\"2023-08-27T13:51:53.375Z\"),\n _stream_meta: {\n sourceType: 'sampleData',\n timestamp: ISODate(\"2023-08-27T13:51:53.375Z\")\n }\n}\n```\n\n## Create a tumbling window query", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04297640919685364, 0.012074882164597511, 0.027006974443793297, -0.02037661150097847, 0.0616239532828331, -0.04768279194831848, 0.05545692890882492, -0.02141115628182888, 0.037646740674972534, 0.00899985060095787, -0.006340178195387125, 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In Atlas Stream Processing, you use the [$tumblingWindow operator. In this example, let\u2019s use the operator to compute the average watts over one-minute intervals.\n\nRefer back to the schema from the sample stream solar data. To create a tumbling window, let\u2019s create a variable and define our tumbling window stage. \n\n```javascript\nvar Twindow= { \n $tumblingWindow: { \n interval: { size: NumberInt(1), unit: \"minute\" },\n pipeline: \n { \n $group: { \n _id: \"$device_id\", \n max: { $max: \"$obs.watts\" }, \n avg: { $avg: \"$obs.watts\" } \n }\n }\n ]\n } \n}\n```", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0490262396633625, -0.0163712240755558, 0.03372541069984436, 0.010498758405447006, 0.01721784844994545, -0.0047952551394701, 0.023109519854187965, -0.020730765536427498, 0.03215262293815613, -0.023619646206498146, -0.02630598656833172, -0.046467944979667664, -0.0013564852997660637, 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Let\u2019s use the `.process` command to run the streaming query in the foreground and view our results in the console.\n\n```\nsp.process([solarstream,Twindow])\n```\n\nHere is an example output of the statement:", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08582590520381927, -0.017711130902171135, 0.02370045706629753, -0.038152217864990234, 0.02320169284939766, -0.031578607857227325, 0.04338984936475754, -0.008935372345149517, 0.05619140341877937, 0.002810732927173376, 0.029374748468399048, -0.0434701107442379, -0.013165724463760853, 0.022194497287273407, -0.022225651890039444, 0.0053441813215613365, -0.015731502324342728, 0.0448758639395237, -0.04676254093647003, 0.02967538684606552, 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0.01588255725800991, -0.024324271827936172, 0.040318939834833145, -0.04416482523083687, 0.09089240431785583, 0.05516933649778366, 0.18589262664318085, -0.030870286747813225, 0.03478667140007019, 0.06544450670480728, -0.052726004272699356, 0.019908979535102844, 0.04966204985976219, -0.023128068074584007, 0.006802170537412167, 0.04284582659602165, 0.08894528448581696, -0.016668103635311127, 0.04635096341371536, 0.04596151411533356, -0.018090028315782547, 0.04039498791098595, 0.03937670961022377, -0.03400669991970062, -0.010323762893676758, -0.0326630137860775, 0.0035676960833370686, -0.04715907573699951, 0.094629667699337, -0.018107742071151733, -0.03625389561057091, -0.08675460517406464, 0.015225937590003014, -0.006821095012128353, -0.11806949973106384, 0.007046933751553297, 0.0019910146947950125, 0.0005936318775638938, 0.020661408081650734, 0.01702226512134075, -0.016251858323812485, -0.021733662113547325, -0.043964240700006485, -0.014882223680615425, 0.012298918329179287, -0.07308682799339294, 0.04343295097351074, 0.020508283749222755, -0.011415209621191025]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "Here is an example output of the statement:\n\n```json\n{\n _id: 'device_4',\n max: 236,\n avg: 95,\n _stream_meta: {\n sourceType: 'sampleData',\n windowStartTimestamp: ISODate(\"2023-08-27T13:59:00.000Z\"),\n windowEndTimestamp: ISODate(\"2023-08-27T14:00:00.000Z\")\n }\n}\n{\n _id: 'device_2',\n max: 211,\n avg: 117.25,\n _stream_meta: {\n sourceType: 'sampleData',\n windowStartTimestamp: ISODate(\"2023-08-27T13:59:00.000Z\"),\n windowEndTimestamp: ISODate(\"2023-08-27T14:00:00.000Z\")\n }\n}\n```\n\n## Exploring the window operator pipeline", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06474524736404419, 0.018204856663942337, 0.04410383105278015, -0.023478558287024498, 0.05389182269573212, -0.017676012590527534, 0.035181980580091476, 0.004596846178174019, 0.0043223244138062, 0.0188174769282341, -0.03164099529385567, -0.05980885401368141, -0.0036178804002702236, 0.011883306317031384, 0.009274542331695557, -0.008710700087249279, -0.007656954228878021, 0.001476413570344448, -0.07559259235858917, -0.004502665717154741, 0.06233580410480499, -0.019501904025673866, -0.04272213578224182, -0.09584194421768188, 0.05348818749189377, 0.07749461382627487, -0.01055697351694107, 0.009390957653522491, -0.049874380230903625, -0.2132711410522461, -0.029242679476737976, -0.06633546948432922, 0.035737503319978714, -0.0368313193321228, -0.01278630644083023, -0.037103526294231415, -0.028753288090229034, 0.06372366100549698, 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stages. \n\n[Accumulator operators such as `$avg`, `$count`, `$sort`, and `$limit` can be used within blocking stages. Meaningful data returned from these operators are obtained when run over a series of data versus a single data point. This is why they are considered blocking.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0676313042640686, -0.021568244323134422, 0.032407741993665695, -0.030507676303386688, -0.008578830398619175, 0.0313553623855114, 0.04627580568194389, 0.010022683069109917, 0.0404842309653759, -0.0068512349389493465, -0.03195171430706978, -0.02525431290268898, 0.018437296152114868, 0.005771163385361433, -0.047799039632081985, -0.0020865295082330704, -0.005729084834456444, 0.05589750409126282, -0.07099448889493942, -0.03846060484647751, 0.030238820239901543, -0.013687588274478912, -0.06360165774822235, -0.08695156127214432, 0.02244965173304081, 0.04327637329697609, -0.07982304692268372, 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"https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "Non-blocking stages do not require multiple data points to be meaningful, and they include operators such as `$addFields`, `$match`, `$project`, `$set`, `$unset`, and `$unwind`, to name a few. You can use non-blocking before, after, or within the blocking stages. To illustrate this, let\u2019s create a query that shows the average, maximum, and delta (the difference between the maximum and average). We will use a non-blocking **$match** to show only the results from device_1, calculate the tumblingWindow showing maximum and average, and then include another non-blocking `$addFields`. \n\n```\nvar m= { '$match': { device_id: 'device_1' } }\n```", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08450532704591751, -0.012346778996288776, 0.03441380709409714, -0.03814255818724632, 0.022254062816500664, 0.021236564964056015, 0.0389101505279541, -0.0034526961389929056, 0.03986368700861931, 0.002080135280266404, 0.0027994918636977673, 0.0005163490423001349, 0.008730844594538212, 0.03468247875571251, -0.0571993924677372, 0.024769505485892296, -0.015336457639932632, 0.05606229230761528, 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on the minute][1]\n\nAdditionally, notice that the output includes the difference between the calculated values of maximum and average for each window.\n\n## Create a hopping window\n\nA hopping window, sometimes referred to as a sliding window, is a fixed-size window that moves forward in time at overlapping intervals. In Atlas Stream Processing, you use the `$hoppingWindow` operator. In this example, let\u2019s use the operator to see the average.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07166609913110733, -0.021013671532273293, 0.04947468638420105, -0.006552341394126415, 0.019174879416823387, 0.03226522356271744, 0.04914402216672897, -0.018224550411105156, 0.03693750500679016, -0.01090160571038723, -0.0012216155882924795, -0.02683074213564396, 0.001475909142754972, 0.006982921157032251, -0.018654746934771538, 0.005372846499085426, -0.03656015172600746, 0.024107839912176132, -0.049724776297807693, 0.03221945837140083, 0.02747119404375553, -0.0417235791683197, -0.03030836395919323, -0.04915885254740715, 0.03593071922659874, 0.04660484939813614, 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"https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": "```javascript\nvar Hwindow = {\n '$hoppingWindow': {\n interval: { size: 1, unit: 'minute' },\n hopSize: { size: 30, unit: 'second' },\n pipeline: [\n {\n '$group': {\n _id: '$device_id',\n max: { '$max': '$obs.watts' },\n avg: { '$avg': '$obs.watts' }\n }\n }\n ]\n }\n}\n```\n\nTo help illustrate the start and end time segments, let's create a filter to only return device_1.\n\n```\nvar m = { '$match': { device_id: 'device_1' } }\n```\n\nNow let\u2019s issue the `.process` command to view the results in the console.\n\n```\nsp.process([solarstream,m,Hwindow])\n```\n\nAn example result is as follows:", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window 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}\n}\n{\n _id: 'device_1',\n max: 238,\n avg: 82.71428571428571,\n _stream_meta: {\n sourceType: 'sampleData',\n windowStartTimestamp: ISODate(\"2023-08-27T19:38:00.000Z\"),", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04295774921774864, 0.0006684493855573237, 0.05419166386127472, -0.038589153438806534, 0.07302572578191757, -0.022559432312846184, 0.011814754456281662, 0.017255695536732674, -0.009724478237330914, 0.029542602598667145, -0.006891238037496805, -0.08086171746253967, -0.005541868042200804, 0.013804072514176369, 0.001022070413455367, -0.01374768652021885, 0.031096672639250755, -0.009888418018817902, -0.07124043256044388, 0.028897326439619064, 0.0589677132666111, 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Hopping windows are useful to capture short-term patterns in data. \n\n## Summary\n\nBy continuously processing data within time windows, you can generate real-time insights and metrics, which can be crucial for applications like monitoring, fraud detection, and operational analytics. Atlas Stream Processing provides both tumbling and hopping window operators. Together these operators enable you to perform various aggregation operations such as sum, average, min, and max over a specific window of data. In this tutorial, you learned how to use both of these operators with solar sample data. \n\n### Learn more about MongoDB Atlas Stream Processing\n\nCheck out the [MongoDB Atlas Stream Processing announcement blog post. For more on window operators in Atlas Stream Processing, learn more in our documentation.", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05368148908019066, -0.005898487754166126, 0.042008090764284134, -0.018526144325733185, 0.03428162634372711, 0.00844896025955677, 0.05682070180773735, -0.02843351848423481, 0.022768769413232803, -0.016144035384058952, -0.0005389516591094434, -0.034369248896837234, 0.0018051794031634927, 0.022084517404437065, -0.005454127676784992, 0.0070350924506783485, -0.006777559872716665, 0.017755558714270592, -0.03767511621117592, 0.023188436403870583, 0.018573852255940437, -0.046199776232242584, -0.044362884014844894, -0.04440516605973244, 0.02743902988731861, 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"devcenter", "url": "https://www.mongodb.com/developer/products/atlas/exploring-window-operators-atlas-stream-processing", "action": "created", "body": ">Log in today to get started. Atlas Stream Processing is available to all developers in Atlas. Give it a try today!\n\n [1]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt73ff54f0367cad3b/650da3ef69060a5678fc1242/image1.jpg\n [2]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt833bc1a824472d14/650da41aa5f15dea3afc5b55/image3.jpg", "format": "md", "metadata": {"tags": ["Atlas"], "pageDescription": "Learn how to use the various window operators such as tumbling window and hopping window with MongoDB Atlas Stream Processing.", "contentType": "Tutorial"}, "title": "Exploring Window Operators in Atlas Stream Processing", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.011378276161849499, -0.01672467216849327, 0.020003018900752068, -0.040189094841480255, -0.008565499447286129, 0.010022525675594807, -0.00259906193241477, -0.0034882749896496534, -0.019957955926656723, -0.04163186252117157, 0.029949022457003593, -0.08186767995357513, -0.024198634549975395, -0.0025164252147078514, 0.002207045676186681, -0.006065156776458025, 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While it might not be as established as some other Python frameworks such as Django, it is already in production at companies such as Uber, Netflix, and Microsoft.\n\nFastAPI is async, and as its name implies, it is super fast; so, MongoDB is the perfect accompaniment. In this quick start, we will create a CRUD (Create, Read, Update, Delete) app showing how you can integrate MongoDB with your FastAPI projects.\n\n## Prerequisites\n\n- Python 3.9.0\n- A MongoDB Atlas cluster. Follow the \"Get Started with Atlas\" guide to create your account and MongoDB cluster. Keep a note of your username, password, and connection string as you will need those later.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08094657212495804, -0.01399584487080574, -0.01831529662013054, -0.00742050725966692, 0.025800786912441254, -0.028907081112265587, 0.0032158594112843275, -0.021332303062081337, -0.0046873935498297215, -0.0030043884180486202, 0.03144683688879013, -0.0724397674202919, 0.037368737161159515, 0.03018716536462307, -0.020412353798747063, -0.008385245688259602, 0.00337994541041553, -0.025762390345335007, -0.03272180259227753, 0.029673809185624123, 0.017490483820438385, -0.01025362964719534, -0.041525665670633316, -0.07380195707082748, 0.013911193236708641, 0.07987357676029205, -0.032139088958501816, -0.04426749423146248, 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"https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "## Running the Example\n\nTo begin, you should clone the example code from GitHub.\n\n``` shell\ngit clone git@github.com:mongodb-developer/mongodb-with-fastapi.git\n```\n\nYou will need to install a few dependencies: FastAPI, Motor, etc. I always recommend that you install all Python dependencies in a virtualenv for the project. Before running pip, ensure your virtualenv is active.\n\n``` shell\ncd mongodb-with-fastapi\npip install -r requirements.txt\n```\n\nIt may take a few moments to download and install your dependencies. This is normal, especially if you have not installed a particular package before.\n\nOnce you have installed the dependencies, you need to create an environment variable for your MongoDB connection string.\n\n``` shell\nexport MONGODB_URL=\"mongodb+srv://:@/?retryWrites=true&w=majority\"\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06842947006225586, -0.01364817377179861, 0.013708416372537613, -0.04137702286243439, 0.032189372926950455, -0.06256020069122314, -0.013447193428874016, -0.007026028353720903, -0.014803295955061913, -0.010356332175433636, 0.007446484174579382, -0.07129909843206406, 0.040027979761362076, 0.013055621646344662, -0.011305258609354496, 0.012081543914973736, 0.0035907661076635122, -0.002443899167701602, -0.02729286067187786, 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-0.02184637077152729, 0.023220429196953773, -0.0838332548737526, 0.03095460683107376, 0.030993366613984108, 0.014236404560506344]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "``` shell\nexport MONGODB_URL=\"mongodb+srv://:@/?retryWrites=true&w=majority\"\n```\n\nRemember, anytime you start a new terminal session, you will need to set this environment variable again. I use direnv to make this process easier.\n\nThe final step is to start your FastAPI server.\n\n``` shell\nuvicorn app:app --reload\n```\n\nOnce the application has started, you can view it in your browser at .\n\nOnce you have had a chance to try the example, come back and we will walk through the code.\n\n## Creating the Application\n\nAll the code for the example application is within `app.py`. I'll break it down into sections and walk through what each is doing.\n\n### Connecting to MongoDB\n\nOne of the very first things we do is connect to our MongoDB database.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07526013255119324, -0.03035750240087509, -0.004864227958023548, -0.05822206661105156, 0.03205810859799385, -0.04861610382795334, 0.004985445644706488, -0.02333456091582775, -0.004641909617930651, -0.015153534710407257, 0.008255629800260067, -0.05333547294139862, 0.049077633768320084, 0.042664047330617905, -0.04892650246620178, 0.009037360548973083, 0.004362848587334156, -0.014875303953886032, -0.035877883434295654, 0.029178665950894356, -0.007522947620600462, -0.01590767316520214, -0.024493902921676636, -0.06092322617769241, 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"devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "### Connecting to MongoDB\n\nOne of the very first things we do is connect to our MongoDB database.\n\n``` python\nclient = motor.motor_asyncio.AsyncIOMotorClient(os.environ\"MONGODB_URL\"])\ndb = client.get_database(\"college\")\nstudent_collection = db.get_collection(\"students\")\n```\n\nWe're using the async [motor driver to create our MongoDB client, and then we specify our database name `college`.\n\n### The \\_id Attribute and ObjectIds\n\n``` python\n# Represents an ObjectId field in the database.\n# It will be represented as a `str` on the model so that it can be serialized to JSON.\nPyObjectId = Annotatedstr, BeforeValidator(str)]\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB 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FastAPI encodes and decodes data as JSON strings. BSON has support for additional non-JSON-native data types, including `ObjectId` which can't be directly encoded as JSON. Because of this, we convert `ObjectId`s to strings before storing them as the `id` field.\n\n### Database Models\n\nMany people think of MongoDB as being schema-less, which is wrong. MongoDB has a flexible schema. That is to say that collections do not enforce document structure by default, so you have the flexibility to make whatever data-modelling choices best match your application and its performance requirements. So, it's not unusual to create models when working with a MongoDB database. Our application has three models, the `StudentModel`, the `UpdateStudentModel`, and the `StudentCollection`.\n\n``` python\nclass StudentModel(BaseModel):\n \"\"\"\n Container for a single student record.\n \"\"\"", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.036785420030355453, -0.0014439199585467577, 0.01233137957751751, -0.033406928181648254, 0.03540828078985214, -0.039402998983860016, -0.0024045882746577263, 0.010649129748344421, -0.014845358207821846, 0.0029708321671932936, -0.01446025725454092, -0.042816996574401855, 0.04882654920220375, 0.041095633059740067, -0.043592315167188644, -0.02380426786839962, -0.0001443002256564796, -0.0232172142714262, -0.03828990086913109, 0.07200098037719727, 0.046451035887002945, -0.004998530261218548, 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0.06231014057993889, -0.015745369717478752]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "# The primary key for the StudentModel, stored as a `str` on the instance.\n # This will be aliased to `_id` when sent to MongoDB,\n # but provided as `id` in the API requests and responses.\n id: OptionalPyObjectId] = Field(alias=\"_id\", default=None)\n name: str = Field(...)\n email: EmailStr = Field(...)\n course: str = Field(...)\n gpa: float = Field(..., le=4.0)\n model_config = ConfigDict(\n populate_by_name=True,\n arbitrary_types_allowed=True,\n json_schema_extra={\n \"example\": {\n \"name\": \"Jane Doe\",\n \"email\": \"jdoe@example.com\",\n \"course\": \"Experiments, Science, and Fashion in Nanophotonics\",\n \"gpa\": 3.0,\n }\n },\n )\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", 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MongoDB uses `_id`, but in Python, underscores at the start of attributes have special meaning. If you have an attribute on your model that starts with an underscore, pydantic\u2014the data validation framework used by FastAPI\u2014will assume that it is a private variable, meaning you will not be able to assign it a value! To get around this, we name the field `id` but give it an alias of `_id`. You also need to set `populate_by_name` to `True` in the model's `model_config`\n\nWe set this `id` value automatically to `None`, so you do not need to supply it when creating a new student.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04247277230024338, 0.002603664994239807, 0.01781989261507988, -0.011398901231586933, 0.02709372155368328, -0.015862761065363884, 0.06356722861528397, 0.012980454601347446, 0.012753420509397984, -0.016897467896342278, 0.02297328971326351, -0.1002793237566948, 0.034461721777915955, 0.02104627899825573, -0.04114419221878052, -0.0063802762888371944, -0.022471027448773384, 0.05602903664112091, -0.029694540426135063, 0.029110969975590706, 0.0443931445479393, 0.011228920891880989, -0.04384505748748779, -0.027190782129764557, 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-0.024998920038342476]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "We set this `id` value automatically to `None`, so you do not need to supply it when creating a new student.\n\n``` python\nclass UpdateStudentModel(BaseModel):\n \"\"\"\n A set of optional updates to be made to a document in the database.\n \"\"\"\n\n name: Optionalstr] = None\n email: Optional[EmailStr] = None\n course: Optional[str] = None\n gpa: Optional[float] = None\n model_config = ConfigDict(\n arbitrary_types_allowed=True,\n json_encoders={ObjectId: str},\n json_schema_extra={\n \"example\": {\n \"name\": \"Jane Doe\",\n \"email\": \"jdoe@example.com\",\n \"course\": \"Experiments, Science, and Fashion in Nanophotonics\",\n \"gpa\": 3.0,\n }\n },\n )\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", 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wish to update.\n\nFinally, `StudentCollection` is defined to encapsulate a list of `StudentModel` instances. In theory, the endpoint could return a top-level list of StudentModels, but there are some vulnerabilities associated with returning JSON responses with top-level lists.\n\n```python\nclass StudentCollection(BaseModel):\n \"\"\"\n A container holding a list of `StudentModel` instances.\n\n This exists because providing a top-level array in a JSON response can be a [vulnerability\n \"\"\"\n\n students: ListStudentModel]\n```\n\n### Application Routes\n\nOur application has five routes:", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06129203364253044, -0.031690631061792374, 0.07336078584194183, -0.02322508953511715, 0.01382946502417326, -0.01076928898692131, -0.014039653353393078, 0.0550353042781353, -0.00840585958212614, 0.003970395773649216, 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-0.06775279343128204, -0.050439395010471344, -0.006700241006910801, 0.0557074174284935, 0.04739706963300705, -0.03611087054014206, -0.02435307204723358, -0.05105428397655487, -0.001582522876560688, 0.04434347525238991, 0.007710004225373268, 0.017011582851409912, 0.02897261269390583, -0.014897767454385757]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "students: ListStudentModel]\n```\n\n### Application Routes\n\nOur application has five routes:\n\n- POST /students/ - creates a new student.\n- GET /students/ - view a list of all students.\n- GET /students/{id} - view a single student.\n- PUT /students/{id} - update a student.\n- DELETE /students/{id} - delete a student.\n\n#### Create Student Route\n\n``` python\n@app.post(\n \"/students/\",\n response_description=\"Add new student\",\n response_model=StudentModel,\n status_code=status.HTTP_201_CREATED,\n response_model_by_alias=False,\n)\nasync def create_student(student: StudentModel = Body(...)):\n \"\"\"\n Insert a new student record.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05035531148314476, -0.023272689431905746, 0.05021994933485985, -0.03733350709080696, -0.012861769646406174, -0.002017427235841751, 0.0109634380787611, 0.030082829296588898, 0.028843844309449196, 0.02689547836780548, 0.021831469610333443, -0.06060954928398132, 0.02382783591747284, 0.04222917929291725, 0.00036502646980807185, -0.03901904821395874, -0.04942666366696358, 0.0021190904080867767, -0.016708802431821823, 0.012853387743234634, 0.03971957787871361, -0.004568071570247412, -0.01202387921512127, -0.024074837565422058, 0.031500764191150665, 0.05857529118657112, 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"https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "A unique `id` will be created and provided in the response.\n \"\"\"\n new_student = await student_collection.insert_one(\n student.model_dump(by_alias=True, exclude=[\"id\"])\n )\n created_student = await student_collection.find_one(\n {\"_id\": new_student.inserted_id}\n )\n return created_student\n```\n\nThe `create_student` route receives the new student data as a JSON string in a `POST` request. We have to decode this JSON request body into a Python dictionary before passing it to our MongoDB client.\n\nThe `insert_one` method response includes the `_id` of the newly created student (provided as `id` because this endpoint specifies `response_model_by_alias=False` in the `post` decorator call. After we insert the student into our collection, we use the `inserted_id` to find the correct document and return this in our `JSONResponse`.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04479850083589554, 0.010139931924641132, 0.03931158781051636, -0.008711033500730991, -0.02367403544485569, -0.008504027500748634, 0.04167981445789337, 0.038248512893915176, 0.007574561517685652, 0.017758511006832123, 0.025114968419075012, -0.055452559143304825, 0.004346201196312904, 0.013213389553129673, -0.024546731263399124, -0.02843734435737133, -0.03024139069020748, -0.003227192908525467, -0.056498460471630096, 0.03529490903019905, 0.03641814738512039, -0.006497804541140795, -0.039294078946113586, -0.03946014866232872, 0.022190753370523453, 0.04901155084371567, 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"https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "FastAPI returns an HTTP `200` status code by default; but in this instance, a `201` created is more appropriate.\n\n##### Read Routes\n\nThe application has two read routes: one for viewing all students and the other for viewing an individual student.\n\n``` python\n@app.get(\n \"/students/\",\n response_description=\"List all students\",\n response_model=StudentCollection,\n response_model_by_alias=False,\n)\nasync def list_students():\n \"\"\"\n List all of the student data in the database.\n\n The response is unpaginated and limited to 1000 results.\n \"\"\"\n return StudentCollection(students=await student_collection.find().to_list(1000))\n```\n\nMotor's `to_list` method requires a max document count argument. For this example, I have hardcoded it to `1000`; but in a real application, you would use the [skip and limit parameters in `find` to paginate your results.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08843803405761719, -0.012546289712190628, 0.006548120640218258, -0.013807214796543121, 0.006571369711309671, -0.0006667625275440514, 0.0026781607884913683, 0.023186706006526947, 0.016790403053164482, 0.035735711455345154, 0.06728202849626541, -0.04253337159752846, 0.04577231779694557, 0.09549236297607422, -0.022397179156541824, 0.00789462961256504, 0.0014448196161538363, -0.01851237379014492, -0.021323006600141525, 0.04771631583571434, 0.07997287809848785, 0.006717071868479252, 0.013930367305874825, -0.04354235529899597, 0.022544246166944504, 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"url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "``` python\n@app.get(\n \"/students/{id}\",\n response_description=\"Get a single student\",\n response_model=StudentModel,\n response_model_by_alias=False,\n)\nasync def show_student(id: str):\n \"\"\"\n Get the record for a specific student, looked up by `id`.\n \"\"\"\n if (\n student := await student_collection.find_one({\"_id\": ObjectId(id)})\n ) is not None:\n return student\n\n raise HTTPException(status_code=404, detail=f\"Student {id} not found\")\n```\n\nThe student detail route has a path parameter of `id`, which FastAPI passes as an argument to the `show_student` function. We use the `id` to attempt to find the corresponding student in the database. The conditional in this section is using an assignment expression, an addition to Python 3.8 and often referred to by the cute sobriquet \"walrus operator.\"", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05271805450320244, -0.020202862098813057, 0.027271287515759468, 0.0033538551069796085, 0.00918623898178339, -0.009227155707776546, 0.05800343304872513, 0.023984309285879135, 0.021717699244618416, 0.014004060067236423, 0.019657155498862267, -0.06508216261863708, 0.04738813266158104, 0.04852741211652756, -0.029210178181529045, -0.031261153519153595, -0.015386389568448067, 0.02224734053015709, -0.015062082558870316, 0.03153470531105995, 0.03669341653585434, -0.010682264342904091, -0.038714393973350525, -0.06350352615118027, 0.05407849699258804, 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"devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "If a document with the specified `_id` does not exist, we raise an `HTTPException` with a status of `404`.\n\n##### Update Route\n\n``` python\n@app.put(\n \"/students/{id}\",\n response_description=\"Update a student\",\n response_model=StudentModel,\n response_model_by_alias=False,\n)\nasync def update_student(id: str, student: UpdateStudentModel = Body(...)):\n \"\"\"\n Update individual fields of an existing student record.\n\n Only the provided fields will be updated.\n Any missing or `null` fields will be ignored.\n \"\"\"\n student = {\n k: v for k, v in student.model_dump(by_alias=True).items() if v is not None\n }", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": 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-0.04351532459259033, -0.05909799039363861, 0.0085917878895998, -0.029695795848965645, 0.000596399127971381, 0.08128159493207932, -0.00959184393286705, -0.047361232340335846, -0.05727926269173622, -0.04937838390469551, 0.016456715762615204, -0.04076013341546059, -0.03161746636033058, -0.056900691241025925, 0.04640856757760048, 0.05392053350806236, 0.053374033421278, -0.030930770561099052, -0.05466625094413757, -0.06287215650081635, -0.043424393981695175, 0.04082091152667999, -0.029062384739518166, -0.016643714159727097, 0.023197315633296967, -0.007292138412594795]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "if len(student) >= 1:\n update_result = await student_collection.find_one_and_update(\n {\"_id\": ObjectId(id)},\n {\"$set\": student},\n return_document=ReturnDocument.AFTER,\n )\n if update_result is not None:\n return update_result\n else:\n raise HTTPException(status_code=404, detail=f\"Student {id} not found\")\n\n # The update is empty, but we should still return the matching document:\n if (existing_student := await student_collection.find_one({\"_id\": id})) is not None:\n return existing_student\n\n raise HTTPException(status_code=404, detail=f\"Student {id} not found\")\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07646340131759644, -0.003919348120689392, 0.053970757871866226, -0.0025844797492027283, 0.027997590601444244, 0.02127731963992119, 0.01200995221734047, 0.03355879709124565, 0.024608004838228226, -0.00607854500412941, 0.018509235233068466, -0.06335566192865372, 0.0245219673961401, 0.04015521705150604, -0.06019510328769684, -0.008745122700929642, -0.029991280287504196, 0.009406100027263165, 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-0.048771824687719345, 0.029715266078710556, -0.004274691920727491, -0.013354538939893246, 0.02825949899852276, 8.581415750086308e-05]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "raise HTTPException(status_code=404, detail=f\"Student {id} not found\")\n```\n\nThe `update_student` route is like a combination of the `create_student` and the `show_student` routes. It receives the `id` of the document to update as well as the new data in the JSON body. We don't want to update any fields with empty values; so, first of all, we iterate over all the items in the received dictionary and only add the items that have a value to our new document.\n\nIf, after we remove the empty values, there are no fields left to update, we instead look for an existing record that matches the `id` and return that unaltered. However, if there are values to update, we use find_one_and_update to $set the new values, and then return the updated document.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08804577589035034, -0.01666226051747799, 0.07870104908943176, -0.000939752149861306, 0.030516302213072777, 0.020044341683387756, -0.011871187016367912, 0.04733797162771225, 0.0004857280873693526, 0.02439991384744644, 0.03992502763867378, -0.03405192494392395, 0.03859996050596237, 0.05143630504608154, -0.030859695747494698, -0.0021622232161462307, -0.039278723299503326, 0.0011860211379826069, -0.054394688457250595, 0.023423895239830017, 0.040222056210041046, 0.004707372281700373, -0.009461737237870693, -0.028661688789725304, 0.02288256399333477, 0.07393302768468857, 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"url": "https://www.mongodb.com/developer/languages/python/python-quickstart-fastapi", "action": "created", "body": "If we get to the end of the function and we have not been able to find a matching document to update or return, then we raise a `404` error again.\n\n##### Delete Route\n\n``` python\n@app.delete(\"/students/{id}\", response_description=\"Delete a student\")\nasync def delete_student(id: str):\n \"\"\"\n Remove a single student record from the database.\n \"\"\"\n delete_result = await student_collection.delete_one({\"_id\": ObjectId(id)})\n\n if delete_result.deleted_count == 1:\n return Response(status_code=status.HTTP_204_NO_CONTENT)\n\n raise HTTPException(status_code=404, detail=f\"Student {id} not found\")\n```", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": 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Again, because this is acting upon a single document, we have to supply an `id` in the URL. If we find a matching document and successfully delete it, then we return an HTTP status of `204` or \"No Content.\" In this case, we do not return a document as we've already deleted it! However, if we cannot find a student with the specified `id`, then instead we return a `404`.\n\n## Our New FastAPI App Generator\n\nIf you're excited to build something more production-ready with FastAPI, React & MongoDB, head over to the Github repository for our new FastAPI app generator and start transforming your web development experience.\n\n## Wrapping Up", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.062126949429512024, -0.013828318566083908, 0.040117502212524414, 0.0020532135386019945, 0.02998032420873642, 0.0006066916394047439, -0.011185038834810257, 0.055585168302059174, -0.010046875104308128, 0.007376098074018955, 0.053599026054143906, -0.031491201370954514, 0.07071076333522797, 0.0747600719332695, -0.02376635931432247, 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If you would like to learn more, check out my post introducing the FARM stack (FastAPI, React and MongoDB) as well as the FastAPI documentation and this awesome list.\n\n>If you have questions, please head to our developer community website where MongoDB engineers and the MongoDB community will help you build your next big idea with MongoDB.", "format": "md", "metadata": {"tags": ["Python", "MongoDB", "Django", "FastApi"], "pageDescription": "Getting started with MongoDB and FastAPI", "contentType": "Quickstart"}, "title": "Getting Started with MongoDB and FastAPI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07101202756166458, 0.017700325697660446, -0.008951045572757721, -0.002964386250823736, 0.046981945633888245, -0.041667379438877106, 0.016372691839933395, 0.004191009793430567, -0.006941914092749357, -0.006653723306953907, 0.01128230057656765, -0.0520976297557354, 0.06283412128686905, 0.05564560368657112, -0.013533560559153557, 0.008690097369253635, 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We help to accelerate and simplify how you build resilient and performant global applications on the cloud provider of your choice.\n\nAWS CloudFormation lets you model, provision, and manage AWS and third-party resources like MongoDB Atlas by treating infrastructure as code (IaC). CloudFormation templates are written in either JSON or YAML. \n\nWhile there are multiple ways to use CloudFormation to provision and manage your Atlas clusters, such as with Partner Solution Deployments or the AWS CDK, today we\u2019re going to go over how to create your first YAML CloudFormation templates to deploy Atlas clusters with CloudFormation.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Learn how to quickly and easily deploy MongoDB Atlas instances with Amazon Web Services (AWS) CloudFormation.", "contentType": "Tutorial"}, "title": "How to Deploy MongoDB Atlas with AWS CloudFormation", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.050000738352537155, -0.06382527947425842, 0.009798458777368069, -0.006723190192133188, 0.04475439339876175, 0.011174911633133888, -0.016620002686977386, -0.0016239485703408718, -0.03084622137248516, -0.011253193952143192, 0.004472709260880947, -0.057009682059288025, 0.02297678217291832, 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Using these is best for users who seek to be tightly integrated into AWS with fine-grained access controls. \n\nLet\u2019s get started! \n\n*Prerequisites:* \n\n- Install and configure an AWS Account and the AWS CLI.\n- Install and configure the MongoDB Atlas CLI (optional but recommended). \n\n## Step 1: Create a MongoDB Atlas account\n\nSign up for a free MongoDB Atlas account, verify your email address, and log into your new account.\n\nAlready have an AWS account? Atlas supports paying for usage via the AWS Marketplace (AWS MP) without any upfront commitment \u2014 simply sign up for MongoDB Atlas via AWS Marketplace.\n\nand contact AWS support directly, who can help confirm the CIDR range to be used in your Atlas PAK IP Whitelist. \n\n on MongoDB Atlas.", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Learn how to quickly and easily deploy MongoDB Atlas instances with Amazon Web Services (AWS) CloudFormation.", "contentType": "Tutorial"}, "title": "How to Deploy MongoDB Atlas with AWS CloudFormation", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04774578660726547, -0.05048114061355591, -0.0001565893180668354, 0.009149537421762943, 4.828570308745839e-05, 0.008926029317080975, 0.0212631206959486, -0.0011259728344157338, 0.0017904330743476748, -0.015232358127832413, 0.018078532069921494, -0.05467306077480316, 0.036294467747211456, -0.016227124258875847, 0.01826229691505432, -0.0022802595049142838, 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You can set this up with AWS IAM (Identity and Access Management). You can find that in the navigation bar of your AWS. You can find the ARN in the user information in the \u201cRoles\u201d button. Once there, find the role whose ARN you want to use and add it to the Extension Details in CloudFormation. Learn how to create user roles/permissions in the IAM. \n\n required from our GitHub repo. It\u2019s important that you use an ARN with sufficient permissions each time it\u2019s asked for.\n\n. \n\n## Step 7: Deploy the CloudFormation template\n\nIn the AWS management console, go to the CloudFormation tab. Then, in the left-hand navigation, click on \u201cStacks.\u201d In the window that appears, hit the \u201cCreate Stack\u201d drop-down. 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"https://www.mongodb.com/developer/products/atlas/deploy-mongodb-atlas-aws-cloudformation", "action": "created", "body": "Next, select \u201ctemplate is ready\u201d in the \u201cPrerequisites\u201d section and \u201cUpload a template\u201d in the \u201cSpecify templates\u201d section. From here, you will choose the YAML (or JSON) file containing the MongoDB Atlas deployment that you created in the prior step.\n\n. \n\nThe fastest way to get started is to create a MongoDB Atlas account from the AWS Marketplace. \n\nAdditionally, you can watch our demo to learn about the other ways to get started with MongoDB Atlas and CloudFormation\n\nGo build with MongoDB Atlas and AWS CloudFormation today!", "format": "md", "metadata": {"tags": ["Atlas", "AWS"], "pageDescription": "Learn how to quickly and easily deploy MongoDB Atlas instances with Amazon Web Services (AWS) CloudFormation.", "contentType": "Tutorial"}, "title": "How to Deploy MongoDB Atlas with AWS CloudFormation", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05122458562254906, -0.02530042827129364, 0.007350148633122444, -0.010524928569793701, 0.027842823415994644, 0.021863631904125214, -0.04583825543522835, 0.003944881726056337, -0.02962489239871502, -0.018052484840154648, 0.0024154798593372107, 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The Pages Router is still supported and recommended for production environments.\n\nAre you building your next amazing application with Next.js? Do you wish you could integrate MongoDB into your Next.js app effortlessly? Do you need this done before your coffee has finished brewing? If you answered yes to these three questions, I have some good news for you. We have created a Next.js<>MongoDB integration that will have you up and running in minutes, and you can consider this tutorial your official guide on how to use it.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05091717094182968, -0.0035527863074094057, 0.02439594268798828, -0.05126512795686722, 0.025426870211958885, -0.02679506316781044, -0.0391407236456871, 0.01078982837498188, 0.0029375809244811535, -0.003531744470819831, 4.127466309000738e-05, -0.03999471664428711, 0.024899380281567574, 0.03224119171500206, 0.001976798288524151, 0.021433405578136444, -0.011468429118394852, -0.000938544690143317, -0.049989424645900726, 0.007756524719297886, 0.024946529418230057, -0.002246780088171363, 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0.003953581675887108, 0.007669810205698013]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "In this tutorial, we'll take a look at how we can use the **with-mongodb** example to create a new Next.js application that follows MongoDB best practices for connectivity, connection pool monitoring, and querying. We'll also take a look at how to use MongoDB in our Next.js app with things like serverSideProps and APIs. Finally, we'll take a look at how we can easily deploy and host our application on Vercel, the official hosting platform for Next.js applications. If you already have an existing Next.js app, not to worry. Simply drop the MongoDB utility file into your existing project and you are good to go. We have a lot of exciting stuff to cover, so let's dive right in!", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06978491693735123, 0.015135692432522774, 0.016253117471933365, -0.07611209154129028, 0.03712327033281326, -0.00919279269874096, -0.021473359316587448, 0.003786151995882392, -0.0037624710239470005, -0.030912041664123535, -0.0008145315223373473, -0.048215605318546295, 0.022445082664489746, 0.04416751116514206, -0.019974790513515472, 0.03414493799209595, -0.017782699316740036, 0.009398085065186024, -0.05839480087161064, 0.012871540151536465, 0.012701951898634434, 0.005495065823197365, -0.027143780142068863, -0.0775163397192955, -0.01419161632657051, 0.08252586424350739, -0.03579434007406235, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "## Next.js and MongoDB with one click\nOur app is now deployed and running in production. If you weren't following along with the tutorial and just want to quickly start your Next.js application with MongoDB, you could always use the `with-mongodb` starter found on GitHub, but I\u2019ve got an even better one for you.\n\nVisit Vercel and you'll be off to the races in creating and deploying the official Next.js with the MongoDB integration, and all you'll need to provide is your connection string.\n\n## Prerequisites\nFor this tutorial, you'll need:\n\n - MongoDB Atlas (sign up for free).\n - A Vercel account (sign up for free).\n - NodeJS 18+.\n - npm and npx.\n\nTo get the most out of this tutorial, you need to be familiar with React and Next.js. I will cover unique Next.js features with enough details to still be valuable to a newcomer.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07331588119268417, -0.007387739606201649, 0.03933708369731903, -0.0619853138923645, 0.03491160646080971, 0.010563663206994534, -0.03459610417485237, -0.0009352266788482666, -0.0017970841145142913, -0.04273605719208717, -0.004332574084401131, -0.07404487580060959, 0.025871461257338524, 0.03922523185610771, -0.01671157032251358, 0.03944030776619911, -0.028572209179401398, -0.015947306528687477, -0.06463560461997986, 0.01479289960116148, 0.015530850738286972, -0.008364940993487835, -0.02152569778263569, -0.06451401114463806, 0.011999586597084999, 0.06756478548049927, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "## What is Next.js?\nIf you're not already familiar with it, Next.js is a React-based framework for building modern web applications. The framework adds a lot of powerful features \u2014 such as server-side rendering, automatic code splitting, and incremental static regeneration \u2014 that make it easy to build, scalable, and production-ready apps.\n\n. You can use a local MongoDB installation if you have one, but if you're just getting started, MongoDB Atlas is a great way to get up and running without having to install or manage your MongoDB instance. MongoDB Atlas has a forever free tier that you can sign up for as well as get the sample data that we'll be using for the rest of this tutorial. \n\nTo get our MongoDB URI, in our MongoDB Atlas dashboard:", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06650304794311523, -0.010857834480702877, 0.013117867521941662, -0.0421123281121254, 0.04913495481014252, 0.01354681421071291, -0.03373359516263008, 0.008091864176094532, 0.01712179370224476, -0.029484054073691368, 0.00486372783780098, -0.03683509677648544, 0.0025807477068156004, 0.04448055475950241, 0.007962300442159176, 0.017713306471705437, -0.02271953970193863, 0.011999054811894894, -0.05152759701013565, 0.013889109715819359, 0.03058030642569065, 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0.016365736722946167, 0.0025773965753614902]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "To get our MongoDB URI, in our MongoDB Atlas dashboard: \n\n 1. Hit the **Connect** button. \n 2. Then, click the **Connect to your application** button, and here you'll see a string that contains your **URI** that will look like this:\n\n```\nmongodb+srv://:@cluster0..mongodb.net/?retryWrites=true&w=majority\n```\nIf you are new to MongoDB Atlas, you'll need to go to the **Database Access** section and create a username and password, as well as the **Network Access** tab to ensure your IP is allowed to connect to the database. However, if you already have a database user and network access enabled, you'll just need to replace the `` and `` fields with your information.\n\nFor the ``, we'll load the MongoDB Atlas sample datasets and use one of those databases.\n\n, and we'll help troubleshoot.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.061701688915491104, -0.04253983497619629, 0.014697193168103695, -0.012526524253189564, 0.0203979704529047, 0.00273298192769289, 0.03841505944728851, 0.01597476191818714, -0.0033922733273357153, -0.02007523737847805, 0.004304900765419006, -0.04132925346493721, 0.045536816120147705, 0.034793589264154434, -0.02026432193815708, 0.016391806304454803, -0.004111089278012514, 0.01745268516242504, 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Next.js supports multiple ways to get data. We can create API endpoints, get data by running server-side rendered functions for a particular page, and even generate static pages by getting our data at build time. We'll look at all three examples.\n\n## Example 1: Next.js API endpoint with MongoDB\nThe first example we'll look at is building and exposing an API endpoint in our Next.js application. To create a new API endpoint route, we will first need to create an `api` directory in our `pages` directory, and then every file we create in this `api` directory will be treated as an individual API endpoint.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05682733282446861, -0.002558856038376689, 0.026809358969330788, -0.054368164390325546, 0.03055047243833542, -0.002027659211307764, -0.00936425756663084, -0.0025921454653143883, -0.013027796521782875, -0.04335874319076538, 0.007278938312083483, -0.033978935331106186, 0.0004032631404697895, 0.034531835466623306, -0.004568788222968578, 0.010617527179419994, -0.017081184312701225, -0.011356576345860958, -0.05890212580561638, 0.026502028107643127, 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This endpoint will return a list of 20 movies from our MongoDB database. The implementation for this route is as follows:\n\n```\nimport clientPromise from \"../../lib/mongodb\";\nimport { NextApiRequest, NextApiResponse } from 'next';\n\nexport default async (req: NextApiRequest, res: NextApiResponse) => {\n try {\n const client = await clientPromise;\n const db = client.db(\"sample_mflix\");\n const movies = await db\n .collection(\"movies\")\n .find({})\n .sort({ metacritic: -1 })\n .limit(10)\n .toArray();\n res.json(movies);\n } catch (e) {\n console.error(e);\n }\n}\n```", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08413121849298477, -0.013491722755134106, 0.03753519058227539, -0.04501663148403168, 0.03524985536932945, -0.005136992782354355, 0.010879690758883953, 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We are importing our `clientPromise` method from the `lib/mongodb` file. This file contains all the instructions on how to connect to our MongoDB Atlas cluster. Additionally, within this file, we cache the instance of our connection so that subsequent requests do not have to reconnect to the cluster. They can use the existing connection. All of this is handled for you!\n\nNext, our API route handler has the signature of `export default async (req, res)`. If you're familiar with Express.js, this should look very familiar. This is the function that gets run when the `localhost:3000/api/movies` route is called. We capture the request via `req` and return the response via the `res` object.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04080770164728165, -0.014187704771757126, 0.007827481254935265, -0.005622378084808588, 0.030692078173160553, -0.02683299221098423, 0.0438162125647068, -0.008875356987118721, 0.006549777928739786, -0.01643410325050354, 0.03642597794532776, -0.058663949370384216, -0.021883724257349968, 0.02552318386733532, -0.018908601254224777, 0.03326756879687309, -0.0015534850535914302, -0.06942133605480194, -0.03607608750462532, 0.04501299560070038, 0.013623839244246483, -0.018397143110632896, -0.035987403243780136, -0.04392740875482559, 0.017704704776406288, 0.04212244227528572, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "Our handler function implementation calls the `clientPromise` function to get the instance of our MongoDB database. Next, we run a MongoDB query using the MongoDB Node.js driver to get the top 20 movies out of our **movies** collection based on their **metacritic** rating sorted in descending order.\n\nFinally, we call the `res.json` method and pass in our array of movies. This serves our movies in JSON format to our browser. If we navigate to `localhost:3000/api/movies`, we'll see a result that looks like this:", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03949460759758949, -0.005377274937927723, 0.00629689684137702, -0.02052428387105465, 0.0445094034075737, -0.00857863761484623, 0.03903236985206604, -0.0029697122517973185, 0.03043171763420105, -0.008273706771433353, 0.028260083869099617, -0.02497355453670025, 0.005518490914255381, 0.034866705536842346, -0.008512506261467934, -0.0020497385412454605, 0.01238667219877243, -0.016903312876820564, -0.047465164214372635, 0.027830414474010468, 0.029882173985242844, -0.001725057139992714, -0.02939775213599205, -0.06646186858415604, 0.008993548341095448, 0.05255510285496712, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "to capture the `id`. So, if a user calls `http://localhost:3000/api/movies/573a1394f29313caabcdfa3e`, the movie that should be returned is Seven Samurai. **Another tip**: The `_id` property for the `sample_mflix` database in MongoDB is stored as an ObjectID, so you'll have to convert the string to an ObjectID. If you get stuck, create a thread on the MongoDB Community forums and we'll solve it together! Next, we'll take a look at how to access our MongoDB data within our Next.js pages.\n\n## Example 2: Next.js pages with MongoDB\nIn the last section, we saw how we can create an API endpoint and connect to MongoDB with it. In this section, we'll get our data directly into our Next.js pages. We'll do this using the getServerSideProps() method that is available to Next.js pages.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05308668315410614, -0.0027448690962046385, 0.006311097647994757, -0.033480629324913025, 0.023331422358751297, -0.005645269528031349, 0.03145787492394447, 0.014674024656414986, 0.003098683897405863, -0.03459465876221657, -0.01884458027780056, -0.04600854963064194, 0.045073483139276505, 0.043796949088573456, -0.036722660064697266, 0.014887489378452301, 0.01142855640500784, 0.025769025087356567, -0.07357170432806015, 0.017684727907180786, 0.022659024223685265, -0.010181146673858166, -0.025320298969745636, -0.061095040291547775, -0.008664212189614773, 0.06380971521139145, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "The `getServerSideProps()` method forces a Next.js page to load with server-side rendering. What this means is that every time this page is loaded, the `getServerSideProps()` method runs on the back end, gets data, and sends it into the React component via props. The code within `getServerSideProps()` is never sent to the client. This makes it a great place to implement our MongoDB queries.\n\nLet's see how this works in practice. Let's create a new file in the `pages` directory, and we'll call it `movies.tsx`. In this file, we'll add the following code:\n\n```\nimport clientPromise from \"../lib/mongodb\";\nimport { GetServerSideProps } from 'next';\n\ninterface Movie {\n _id: string;\n title: string;\n metacritic: number;\n plot: string;\n}\n\ninterface MoviesProps {\n movies: Movie];\n}", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06791716814041138, 0.00970955565571785, 0.015756264328956604, -0.04637715220451355, 0.01597617007791996, -0.00572946947067976, 0.039456479251384735, 0.03633836656808853, 0.022398153319954872, -0.017584942281246185, 0.013807207345962524, -0.013827488757669926, 0.03139973431825638, 0.03909500688314438, -0.018199922516942024, 0.013388398103415966, -0.002314335200935602, 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-0.02053370326757431, -0.06936100870370865, 0.0049886503256857395, 0.052811361849308014, -0.08799216896295547, 0.017897486686706543, 0.03498253598809242, 0.04379960894584656]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "interface MoviesProps {\n movies: Movie];\n}\n\nconst Movies: React.FC = ({ movies }) => {\n return (\n \n\n \n\nTOP 20 MOVIES OF ALL TIME\n\n \n\n (According to Metacritic)\n \n\n \n\n {movies.map((movie) => (\n \n\n \n\n{MOVIE.TITLE}\n\n \n\n{MOVIE.METACRITIC}\n\n \n\n{movie.plot}\n\n \n ))}\n \n\n \n\n );\n};\n\nexport default Movies;", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": 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JSON.parse(JSON.stringify(movies)) },\n };\n } catch (e) {\n console.error(e);\n return { props: { movies: [] } };\n }\n};\n```\nAs you can see from the example above, we are importing the same `clientPromise` utility class, and our MongoDB query is exactly the same within the `getServerSideProps()` method. The only thing we really needed to change in our implementation is how we parse the response. We need to stringify and then manually parse the data, as Next.js is strict.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0726381167769432, 0.002975661540403962, -0.012631257064640522, -0.05644145607948303, 0.036750029772520065, -0.03653828054666519, 0.03789034113287926, 0.01676994003355503, 0.03038822114467621, -0.010426053777337074, 0.0019497150788083673, -0.04342467337846756, 0.020575568079948425, 0.02527722902595997, -0.02604559063911438, 0.0087038679048419, 0.021046990528702736, 0.01830269582569599, -0.05059955641627312, 0.033944789320230484, 0.024707114323973656, 0.010185864754021168, -0.039440643042325974, -0.0712587758898735, 0.02408234030008316, 0.05782490596175194, -0.011166717857122421, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "Our page component called `Movies` gets the props from our `getServerSideProps()` method, and we use that data to render the page showing the top movie title, metacritic rating, and plot. Your result should look something like this:\n\n![Top 20 movies][6]\n\nThis is great. We can directly query our MongoDB database and get all the data we need for a particular page. The contents of the `getServerSideProps()` method are never sent to the client, but the one downside to this is that this method runs every time we call the page. Our data is pretty static and unlikely to change all that often. What if we pre-rendered this page and didn't have to call MongoDB on every refresh? We'll take a look at that next!", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07934286445379257, 0.024927552789449692, 0.008632437326014042, -0.03727265074849129, 0.02639603801071644, 0.0029331708792597055, 0.022144939750432968, 0.036172036081552505, 0.018635347485542297, 0.0023246079217642546, 0.010047949850559235, 0.008073222823441029, 0.047162171453237534, 0.07017906755208969, -0.029662642627954483, 0.0027278857305645943, 0.03586852177977562, 0.02983892895281315, -0.025294989347457886, -0.001955720130354166, 0.05630237236618996, 0.00039925880264490843, -0.012250684201717377, -0.0527881495654583, 0.028997695073485374, 0.027651935815811157, -0.05266151577234268, -0.020194219425320625, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "## Example 3: Next.js static generation with MongoDB\nFor our final example, we'll take a look at how static page generation can work with MongoDB. Let's create a new file in the `pages` directory and call it `top.tsx`. For this page, what we'll want to do is render the top 1,000 movies from our MongoDB database.\n\nTop 1,000 movies? Are you out of your mind? That'll take a while, and the database round trip is not worth it. Well, what if we only called this method once when we built the application so that even if that call takes a few seconds, it'll only ever happen once and our users won't be affected? They'll get the top 1,000 movies delivered as quickly as or even faster than the 20 using `serverSideProps()`. The magic lies in the `getStaticProps()` method, and our implementation looks like this:", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.08200717717409134, 0.019842373207211494, 0.006574586965143681, -0.048410262912511826, 0.04935380443930626, 0.0016335244290530682, -0.026443805545568466, 0.003986811731010675, 0.002904495457187295, -0.023392029106616974, 0.02663506753742695, -0.013289735652506351, 0.008659806102514267, 0.0324106328189373, -0.004542049486190081, 0.01807013712823391, -0.00038546198629774153, 0.006240572780370712, -0.06668401509523392, -0.00936805084347725, 0.047456637024879456, -0.004310563672333956, -0.027788540348410606, -0.038916654884815216, -0.008584653958678246, 0.012252378277480602, 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"https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "```\nimport { ObjectId } from \"mongodb\";\nimport clientPromise from \"../lib/mongodb\";\nimport { GetStaticProps } from \"next\";\n\ninterface Movie {\n _id: ObjectId;\n title: string;\n metacritic: number;\n plot: string;\n}\n\ninterface TopProps {\n movies: Movie[];\n}\n\nexport default function Top({ movies }: TopProps) {\n return (\n \n\n \n\nTOP 1000 MOVIES OF ALL TIME\n\n \n\n (According to Metacritic)\n \n\n \n\n {movies.map((movie) => (\n \n\n \n\n{MOVIE.TITLE}\n\n \n\n{MOVIE.METACRITIC}\n\n \n\n{movie.plot}\n\n \n ))}\n \n\n \n\n );\n}\n\nexport const getStaticProps: GetStaticProps = async () => {\n try {\n const client = await clientPromise;\n\n const db = client.db(\"sample_mflix\");", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07732196897268295, -0.0028191395103931427, 0.017954330891370773, -0.07057406008243561, 0.03374779224395752, -0.016224265098571777, 0.032442402094602585, 0.01407907996326685, 0.04335147887468338, -0.0023040513042360544, -0.0017834517639130354, -0.047438833862543106, 0.030973918735980988, 0.05178685113787651, -0.04407494142651558, 0.016430649906396866, -0.006794694811105728, 0.01441500149667263, -0.071825310587883, 0.005986860021948814, 0.056084223091602325, -0.010873050428926945, -0.046053871512413025, -0.07572352886199951, 0.02689109370112419, 0.05457790195941925, -0.017284376546740532, -0.014626519754529, -0.022354833781719208, -0.1866675168275833, -0.03388216346502304, -0.08007225394248962, 0.04057248681783676, -0.03484172374010086, -0.02543904446065426, -0.03711016848683357, 0.019549762830138206, 0.02364606410264969, 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return {\n props: { movies: JSON.parse(JSON.stringify(movies)) },\n };\n } catch (e) {\n console.error(e);\n return {\n props: { movies: [] },\n };\n }\n};\n```\nAt a glance, this looks very similar to the `movies.tsx` file we created earlier. The only significant changes we made were changing our `limit` from `20` to `1000` and our `getServerSideProps()` method to `getStaticProps()`. If we navigate to `localhost:3000/top` in our browser, we'll see a long list of movies.\n\n![Top 1000 movies][7]", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07689962536096573, -0.01311954390257597, 0.05014989897608757, -0.049845971167087555, 0.031692661345005035, 0.0019217567751184106, 0.020809579640626907, 0.013268438167870045, 0.05017213895916939, -0.013291768729686737, 0.00988257396966219, -0.010821443051099777, 0.0353815071284771, 0.059674378484487534, -0.017720943316817284, 0.0027455068193376064, 0.017617976292967796, -0.01398245058953762, -0.0522945411503315, 0.0021760950330644846, 0.03073311410844326, 0.009791875258088112, -0.029407808557152748, -0.08399999141693115, 0.03691866621375084, 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"url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "![Top 1000 movies][7]\n\nLook at how tiny that scrollbar is. Loading this page took about 3.79 seconds on my machine, as opposed to the 981-millisecond response time for the `/movies` page. The reason it takes this long is that in development mode, the `getStaticProps()` method is called every single time (just like the `getServerSideProps()` method). But if we switch from development mode to production mode, we'll see the opposite. The `/top` page will be pre-rendered and will load almost immediately, while the `/movies` and `/api/movies` routes will run the server-side code each time.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.10511308908462524, -0.021257376298308372, 0.02515305019915104, -0.024123365059494972, 0.032037440687417984, 0.02732013165950775, 8.039743261178955e-05, 0.007103505544364452, 0.040098752826452255, -0.010298719629645348, 0.012645035982131958, 0.001660182373598218, 0.042859453707933426, 0.06203105300664902, -0.0070670777931809425, 0.022087838500738144, 0.04739584028720856, -0.06480194628238678, -0.05133950710296631, 0.025567272678017616, 0.053357578814029694, -0.012328566052019596, -0.0202239491045475, 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0.058005400002002716]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "Let's switch to production mode. In your terminal window, stop the current app from running. To run our Next.js app in production mode, we'll first need to build it. Then, we can run the `start` command, which will serve our built application. In your terminal window, run the following commands:\n\n```\nnpm run build\nnpm run start\n```\nWhen you run the `npm run start` command, your Next.js app is served in production mode. The `getStaticProps()` method will not be run every time you hit the `/top` route as this page will now be served statically. We can even see the pre-rendered static page by navigating to the `.next/server/pages/top.html` file and seeing the 1,000 movies listed in plain HTML.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04567830637097359, 0.014786011539399624, 0.04421338811516762, -0.057106759399175644, 0.042399123311042786, 0.03348507732152939, 0.0035972867626696825, 0.0016018872847780585, -0.00277165905572474, -0.03651996701955795, 0.02198774740099907, -0.023785030469298363, 0.014511496759951115, 0.05336891859769821, -0.006267094984650612, 0.028321249410510063, -0.01107482798397541, 0.0026188562624156475, -0.036953166127204895, 0.02198973298072815, 0.0186839047819376, 0.005637870170176029, -0.0039765252731740475, 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0.04420369490981102]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/languages/javascript/nextjs-with-mongodb", "action": "created", "body": "Next.js can even update this static content without requiring a rebuild with a feature called [Incremental Static Regeneration, but that's outside of the scope of this tutorial. Next, we'll take a look at deploying our application on Vercel.\n\n## Deploying your Next.js app on Vercel\nThe final step in our tutorial today is deploying our application. We'll deploy our Next.js with MongoDB app to Vercel. I have created a GitHub repo that contains all of the code we have written today. Feel free to clone it, or create your own.\n\nNavigate to Vercel and log in. Once you are on your dashboard, click the **Import Project** button, and then **Import Git Repository**.\n\n, https://nextjs-with-mongodb-mauve.vercel.app/api/movies, and https://nextjs-with-mongodb-mauve.vercel.app/top routes.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.083499014377594, -0.023502593860030174, 0.05679013207554817, -0.06440140306949615, 0.036152176558971405, 0.02100345492362976, -0.07245520502328873, 0.00984513945877552, -0.01820792257785797, -0.045378223061561584, 0.024564048275351524, -0.052754998207092285, 0.02976970002055168, 0.005671914666891098, 0.004769859369844198, 0.03944084420800209, -0.025368478149175644, 0.030479663982987404, -0.08385515958070755, -0.016191476956009865, 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I showed you how to connect your MongoDB database to your Next.js application and run queries in multiple ways. Then, we deployed our application using Vercel.\n\nIf you have any questions or feedback, reach out through the MongoDB Community forums and let me know what you build with Next.js and MongoDB.", "format": "md", "metadata": {"tags": ["JavaScript", "Next.js"], "pageDescription": "Learn how to easily integrate MongoDB into your Next.js application with the official MongoDB package.", "contentType": "Tutorial"}, "title": "How to Integrate MongoDB Into Your Next.js App", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06619331985712051, 0.014232930727303028, 0.02264905348420143, -0.0738404169678688, 0.03399281948804855, 0.0009515137644484639, -0.042180582880973816, -0.000898331927601248, 3.01612035400467e-05, -0.033645447343587875, -0.020732002332806587, -0.06568428874015808, 0.03513399511575699, 0.03699658811092377, -0.007938207127153873, 0.039909932762384415, -0.02353457547724247, 0.009049121290445328, -0.07483483105897903, 0.018704421818256378, 0.0117753054946661, 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The language is fast, simple, and lightweight while supporting powerful features like concurrency, strong typing, and a robust standard library. In this tutorial, we\u2019ll use the popular Gin web framework along with MongoDB to build a Go-based web application.\n\nGin is a minimalist web framework for Golang that provides an easy way to build web servers and APIs. It is fast, lightweight, and modular, making it ideal for building microservices and APIs, but can be easily extended to build full-blown applications.\n\nWe'll use Gin to build a web application with three endpoints that connect to a MongoDB database. MongoDB is a popular document-oriented NoSQL database that stores data in JSON-like documents. MongoDB is a great fit for building modern applications.", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06787771731615067, -0.012960485182702541, 0.0006823365692980587, -0.031336840242147446, 0.0005002072430215776, -0.0019981965888291597, -0.011269290000200272, 0.0245248693972826, -0.025746358558535576, 0.00892161950469017, -0.002255581319332123, -0.02585158310830593, 0.05319559946656227, 0.0691743716597557, -0.010784477926790714, -0.018017390742897987, -0.01940760761499405, 0.005281358025968075, -0.04634047672152519, 0.023820839822292328, 0.06896346807479858, -0.031531818211078644, -0.026165083050727844, -0.055685076862573624, 0.004313794430345297, 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"https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "Rather than building the entire application by hand, we\u2019ll leverage a coding AI assistant by Sourcegraph called Cody to help us build our Go application. Cody is the only AI assistant that knows your entire codebase and can help you write, debug, test, and document your code. We\u2019ll use many of these features as we build our application today.\n\n## Prerequisites\n\nBefore you begin, you\u2019ll need:\n\n- Go installed on your development machine. Download it on their website.\n- A MongoDB Atlas account. Sign up for free.\n- Basic familiarity with Go and MongoDB syntax.\n- Sourcegraph Cody installed in your favorite IDE. (For this tutorial, we'll be using VS Code). Get it for free.\n\nOnce you meet the prerequisites, you\u2019re ready to build. Let\u2019s go.\n\n## Getting started", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04911436140537262, -0.02002384327352047, 0.011819763109087944, -0.026090355589985847, 0.0070401933044195175, -0.002453114604577422, 0.01636004075407982, 0.03129266947507858, -0.049594223499298096, 0.006113497540354729, -0.007689142599701881, -0.0656534805893898, 0.056570589542388916, 0.04403359442949295, -0.0038349695969372988, -0.006364644505083561, -0.029622958973050117, 0.008998741395771503, -0.047587718814611435, 0.01846502162516117, 0.025093069300055504, -0.012292495928704739, -0.019960295408964157, -0.09457770735025406, -0.02541275881230831, 0.09035160392522812, 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"https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "Once you meet the prerequisites, you\u2019re ready to build. Let\u2019s go.\n\n## Getting started\n\nWe'll start by creating a new Go project for our application. For this example, we\u2019ll name the project **mflix**, so let\u2019s go ahead and create the project directory and navigate into it:\n\n```bash\nmkdir mflix\ncd mflix\n```\n\nNext, initialize a new Go module, which will manage dependencies for our project:\n\n```bash\ngo mod init mflix\n```\n\nNow that we have our Go module created, let\u2019s install the dependencies for our project. We\u2019ll keep it really simple and just install the `gin` and `mongodb` libraries.\n\n```bash\ngo get github.com/gin-gonic/gin\ngo get go.mongodb.org/mongo-driver/mongo\n```\n\nWith our dependencies fetched and installed, we\u2019re ready to start building our application.\n\n## Gin application setup with Cody", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0393575094640255, -0.01464158296585083, 0.035994503647089005, -0.05541827902197838, -0.006708794739097357, -0.00030420927214436233, -0.020268550142645836, 0.024914070963859558, -0.04246092215180397, -0.014285317622125149, 0.010057779960334301, -0.0577499121427536, 0.06342416256666183, 0.01116478256881237, 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Next, while we can set up our application manually, we\u2019ll instead leverage Cody to build out our starting point. In the Cody chat window, we can ask Cody to create a basic Go Gin application.\n\n guide. The database that we will work with is called `sample_mflix` and the collection in that database we\u2019ll use is called `movies`. This dataset contains a list of movies with various information like the plot, genre, year of release, and much more.\n\n on the movies collection. Aggregation operations process multiple documents and return computed results. So with this endpoint, the end user could pass in any valid MongoDB aggregation pipeline to run various analyses on the `movies` collection.", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.055039599537849426, -0.022808924317359924, 0.012010747566819191, -0.05039064213633537, -0.00813840702176094, -0.0073469108901917934, 0.007991711609065533, 0.008952494710683823, -0.04051196575164795, -0.006907064933329821, 0.001612439751625061, -0.04975254461169243, 0.07331635802984238, 0.03943450376391411, -0.02135615423321724, -0.00694167148321867, 0.0006604283698834479, -0.019162502139806747, -0.042433660477399826, 0.019732123240828514, 0.05275159701704979, 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But for the sake of the tutorial, we opted to keep it in. As a homework assignment for further learning, try using Cody to limit the number of stages or the types of operations that the end user can perform on this endpoint.", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03626853972673416, -0.05535136163234711, 0.004521859344094992, -0.032165057957172394, -0.01100823562592268, 0.011267037130892277, 0.026952212676405907, 0.005513760726898909, 0.004569754935801029, -0.013932806439697742, 0.012227154336869717, -0.047977834939956665, 0.0666903704404831, 0.04802536964416504, -0.010988319292664528, -0.017791766673326492, 0.007131694816052914, 0.02545074187219143, -0.05761486664414406, 0.05382977053523064, 0.04255848005414009, 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-0.02640765905380249, 0.04972822591662407, 0.002978924661874771]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "```go\n// POST /movies/aggregations - Run aggregations on movies\nfunc aggregateMovies(c *gin.Context) {\n // Get aggregation pipeline from request body\n var pipeline interface{}\n if err := c.ShouldBindJSON(&pipeline); err != nil {\n c.JSON(http.StatusBadRequest, gin.H{\"error\": err.Error()})\n return\n }\n \n // Run aggregations\n cursor, err := mongoClient.Database(\"sample_mflix\").Collection(\"movies\").Aggregate(context.TODO(), pipeline)\n if err != nil {\n c.JSON(http.StatusInternalServerError, gin.H{\"error\": err.Error()})\n return\n }\n\n // Map results\n var result ]bson.M\n if err = cursor.All(context.TODO(), &result); err != nil {\n c.JSON(http.StatusInternalServerError, gin.H{\"error\": err.Error()})\n return\n }\n\n // Return result\n c.JSON(http.StatusOK, result)\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07706353068351746, -0.04038127139210701, 0.026832031086087227, -0.015367158688604832, 0.02524922974407673, -0.005921077914535999, 0.008182321675121784, -0.013385878875851631, -0.020976919680833817, -0.01635492965579033, 0.02561192587018013, -0.11304902285337448, 0.035426292568445206, 0.03596234694123268, -0.008178485557436943, -0.023584425449371338, 0.028909742832183838, 0.021354777738451958, -0.05983690544962883, 0.028027962893247604, 0.04395870119333267, -0.031170332804322243, -0.02238243818283081, -0.0592956580221653, 0.022161150351166725, 0.015294951386749744, 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"https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "// Return result\n c.JSON(http.StatusOK, result)\n}\n```\n\nNow that we have our endpoints implemented, let\u2019s add them to our router so that we can call them. Here again, we can use another feature of Cody, called autocomplete, to intelligently give us statement completions so that we don\u2019t have to write all the code ourselves. \n\n![Cody AI Autocomplete with Go][6]\n\nOur `main` function should now look like:\n\n```go\nfunc main() {\nr := gin.Default()\nr.GET(\"/\", func(c *gin.Context) {\nc.JSON(200, gin.H{\n\"message\": \"Hello World\",\n})\n})\nr.GET(\"/movies\", getMovies)\nr.GET(\"/movies/:id\", getMovieByID)\nr.POST(\"/movies/aggregations\", aggregateMovies)\n\nr.Run()\n}\n```", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05938602238893509, -0.014004160650074482, 0.03543040528893471, -0.002516123466193676, 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Restart the server and navigate to **localhost:8080/movies**. If all goes well, you should see a large list of movies returned in JSON format in your browser window. If you do not see this, check your IDE console to see what errors are shown.\n\n![Sample Output for the Movies Endpoint][7]\n\nLet\u2019s test the second endpoint. Pick any `id` from the movies collection and navigate to **localhost:8080/movies/{id}** \u2014 so for example, **localhost:8080/movies/573a1390f29313caabcd42e8**. If everything goes well, you should see that single movie listed. But if you\u2019ve been following this tutorial, you actually won\u2019t see the movie.\n\n![String to Object ID Results Error][8]", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06483202427625656, -0.025853043422102928, 0.03659021481871605, 0.012926132418215275, 0.029615670442581177, 0.008374655619263649, 0.0007005480001680553, 0.020831769332289696, 0.010043771006166935, -0.017459869384765625, 0.043202485889196396, -0.023029286414384842, 0.016834169626235962, 0.03556615486741066, 0.008600511588156223, -0.001440647873096168, 0.005664145108312368, 0.025761930271983147, -0.0634264349937439, 0.01696193404495716, 0.022424688562750816, 0.01191020105034113, 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0.02887662872672081, -0.013064999133348465]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "![String to Object ID Results Error][8]\n\nThe issue is that in our `getMovie` function implementation, we are accepting the `id` value as a `string`, while the data type in our MongoDB database is an `ObjectID`. So when we run the `FindOne` method and try to match the string value of `id` to the `ObjectID` value, we don\u2019t get a match. \n\nLet\u2019s ask Cody to help us fix this by converting the string input we get to an `ObjectID`.\n\n![Cody AI MongoDB String to ObjectID][9]\n\nOur updated `getMovieByID` function is as follows:\n\n```go\nfunc getMovieByID(c *gin.Context) {\n\n// Get movie ID from URL\nidStr := c.Param(\"id\")", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06964364647865295, -0.009724597446620464, 0.0018036237452179193, 0.0012720117811113596, 0.009976607747375965, -0.04142439365386963, 0.03506843000650406, 0.01502987090498209, 0.0007704202434979379, 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{\nc.JSON(http.StatusInternalServerError, gin.H{\"error\": err.Error()})\nreturn\n}\n\n// Return movie\nc.JSON(http.StatusOK, movie)\n}\n```\n\nDepending on your IDE, you may need to add the `primitive` dependency in your import statement. The final import statement looks like:\n\n```go\nimport (\n\"context\"\n\"log\"\n\"net/http\"", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0685332864522934, -0.0014796762261539698, 0.03180394321680069, -0.025883419439196587, 0.02782430127263069, -0.01769174449145794, 0.05349667742848396, 0.018662355840206146, -0.038612257689237595, 0.0008505504811182618, 7.356749847531319e-05, -0.07971331477165222, 0.06278591603040695, 0.049463722854852676, -0.023050954565405846, -0.026241833344101906, 0.03124186396598816, -0.0019397991709411144, -0.0777350515127182, 0.025087906047701836, 0.05699149891734123, -0.05658027529716492, -0.00832131877541542, -0.06761758774518967, 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-0.0034827927593141794]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "```go\nimport (\n\"context\"\n\"log\"\n\"net/http\"\n\n\"github.com/gin-gonic/gin\"\n\"go.mongodb.org/mongo-driver/bson\"\n\"go.mongodb.org/mongo-driver/bson/primitive\"\n\"go.mongodb.org/mongo-driver/mongo\"\n\"go.mongodb.org/mongo-driver/mongo/options\"\n)\n```\n\nIf we examine the new code that Cody provided, we can see that we are now getting the value from our `id` parameter and storing it into a variable named `idStr`. We then use the primitive package to try and convert the string to an `ObjectID`. If the `idStr` is a valid string that can be converted to an `ObjectID`, then we are good to go and we use the new `id` variable when doing our `FindOne` operation. If not, then we get an error message back.", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.023004325106739998, -0.015790536999702454, 0.016936462372541428, -0.01364209782332182, -0.0016608020523563027, -0.019356945529580116, 0.05201926454901695, 0.04129093140363693, -0.022341130301356316, -0.02693437598645687, 0.009278630837798119, -0.054840851575136185, 0.03636576235294342, 0.02565125748515129, -0.06642255932092667, -0.001124271540902555, -0.007701466325670481, -0.0037875822745263577, -0.07605806738138199, 0.04380262270569801, 0.03550812974572182, -0.03862615302205086, -0.029519569128751755, -0.05567336827516556, 0.03906462714076042, 0.10410543531179428, 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"https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "Restart your server and now try to get a single movie result by navigating to **localhost:8080/movies/{id}**.\n\n![Single Movie Response Endpoint][10]\n\nFor our final endpoint, we are allowing the end user to provide an aggregation pipeline that we will execute on the `mflix` collection. The user can provide any aggregation they want. To test this endpoint, we\u2019ll make a POST request to **localhost:8080/movies/aggregations**. In the body of the request, we\u2019ll include our aggregation pipeline.\n\n![Postman Aggregation Endpoint in MongoDB][11]", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05939396843314171, -0.022953838109970093, 0.028358299285173416, -0.015371912159025669, 0.0005927639431320131, -0.003748072776943445, 0.00951032992452383, 0.008553973399102688, -0.02210836112499237, -0.034420326352119446, 0.03477118909358978, -0.07884728908538818, 0.032459378242492676, 0.02413351647555828, 0.001778675359673798, 0.00656379759311676, 0.029483847320079803, -0.0062894271686673164, -0.04731651395559311, 0.009408491663634777, 0.02934245951473713, -0.006890406832098961, 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0.03472074121236801, 0.007901576347649097]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "![Postman Aggregation Endpoint in MongoDB][11]\n\nLet\u2019s run an aggregation to return a count of comedy movies, grouped by year, in descending order. Again, remember aggregations are very powerful and can be abused. You normally would not want to give direct access to the end user to write and run their own aggregations ad hoc within an HTTP request, unless it was for something like an internal tool. Our aggregation pipeline will look like the following:\n\n```json\n[\n {\"$match\": {\"genres\": \"Comedy\"}},\n {\"$group\": {\n \"_id\": \"$year\", \n \"count\": {\"$sum\": 1}\n }},\n {\"$sort\": {\"count\": -1}}\n]\n```\n\nRunning this aggregation, we\u2019ll get a result set that looks like this:", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.07401272654533386, -0.0269711185246706, 0.01877683959901333, -0.019535332918167114, 0.03007514216005802, -0.021243993192911148, 0.026631206274032593, -0.004758930765092373, 0.013068437576293945, -0.01688944548368454, 0.03419562056660652, -0.04383700713515282, 0.03483673185110092, 0.03955230489373207, -0.032808568328619, 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-0.02502165362238884, 0.002462766133248806, -0.03253668546676636, -0.021992193534970284, -0.01798657700419426, -0.04977209493517876, -0.02962602861225605, 0.06249190494418144, -0.005035455338656902]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/build-go-web-application-gin-mongodb-help-ai", "action": "created", "body": "Running this aggregation, we\u2019ll get a result set that looks like this:\n\n```json\n[\n {\n \"_id\": 2014,\n \"count\": 287\n },\n {\n \"_id\": 2013,\n \"count\": 286\n },\n {\n \"_id\": 2009,\n \"count\": 268\n },\n {\n \"_id\": 2011,\n \"count\": 263\n },\n {\n \"_id\": 2006,\n \"count\": 260\n },\n ...\n]\n```\n\nIt seems 2014 was a big year for comedy. If you are not familiar with how aggregations work, you can check out the following resources:\n\n- [Introduction to the MongoDB Aggregation Framework\n- MongoDB Aggregation Pipeline Queries vs SQL Queries\n- A Better MongoDB Aggregation Experience via Compass", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.06820350140333176, -0.029377540573477745, 0.023366276174783707, -0.018552958965301514, 0.02511957287788391, -0.012584185227751732, 0.04258553683757782, 0.023017894476652145, 0.031415607780218124, -0.00668705627322197, 0.023607298731803894, -0.034718867391347885, 0.04244041070342064, 0.024339986965060234, -0.0863889753818512, 0.009063449688255787, 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Happy coding.", "format": "md", "metadata": {"tags": ["MongoDB", "Go"], "pageDescription": "Learn how to build a web application with the Gin framework for Go and MongoDB using the help of Cody AI from Sourcegraph.", "contentType": "Tutorial"}, "title": "How to Build a Go Web Application with Gin, MongoDB, and with the Help of AI", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.05043232440948486, -0.040659449994564056, -0.0008779990021139383, -0.01649775356054306, 0.028525717556476593, -0.02412003092467785, 0.011591386049985886, 0.015299403108656406, -0.015202571637928486, -0.011851189658045769, -0.009630112908780575, -0.06817132234573364, 0.05715791881084442, 0.024973860010504723, -0.030025769025087357, -0.002727566985413432, 0.012146005406975746, 0.006302025634795427, -0.0666676014661789, 0.03516162186861038, 0.023096203804016113, -0.007734506856650114, -0.06494233757257462, -0.06711213290691376, 0.04092875495553017, 0.06914912909269333, -0.04733073711395264, 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time-series data has become indispensable for driving key organizational decisions, trend analyses, and forecasts. This kind of data is everywhere \u2014 from stock markets and IoT sensors to user behavior analytics. But as these datasets grow in volume and complexity, so does the challenge of efficiently storing and analyzing them. Whether you\u2019re an IoT developer or a data analyst dealing with time-sensitive information, MongoDB offers a robust ecosystem tailored to meet both your storage and analytics needs for complex time-series data.", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.02833707258105278, 0.0024371319450438023, 0.02177046239376068, -0.04160935431718826, 0.07728856801986694, -0.02501668781042099, 0.011450113728642464, 0.015000634826719761, 0.019216835498809814, 0.022285381332039833, -0.007820398546755314, -0.06186875328421593, 0.019022731110453606, 0.02063073217868805, -0.0029469747096300125, -0.006700383499264717, -0.01784657873213291, 0.011010394431650639, -0.05583612248301506, 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Time-series collections are different from the normal collections. Time-series collections use an underlying columnar storage format and store data in time-order with an automatically created clustered index. The columnar storage format provides the following benefits:\n* Reduced complexity: The columnar format is tailored for time-series data, making it easier to manage and query.\n* Query efficiency: MongoDB automatically creates an internal clustered index on the time field which improves query performance.\n* Disk usage: This storage approach uses disk space more efficiently compared to traditional collections.\n* I/O optimization: The read operations require fewer input/output operations, improving the overall system performance.\n* Cache usage: The design allows for better utilization of the WiredTiger cache, further enhancing query performance.", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": 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We will see how you can query it in MongoDB as well as how you can read that data into pandas DataFrame, run some analytics on it, and write the modified data back to MongoDB. This tutorial is meant to be a complete deep dive into working with time-series data in MongoDB.\n\n### Tutorial Prerequisites \nWe will be using the following tools/frameworks:\n* MongoDB Atlas database, to store our time-series data. If you don\u2019t already have an Atlas cluster created, go ahead and create one, set up a user, and add your connection IP address to your IP access list. \n* PyMongo driver(to connect to your MongoDB Atlas database, see the installation instructions).\n* Jupyter Notebook (to run the code, see the installation instructions).", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.032573454082012177, 0.00856885127723217, 0.016349075362086296, -0.019809279590845108, 0.058270130306482315, -0.014143265783786774, 0.01614360511302948, 0.011343705467879772, 0.038788989186286926, 0.031106671318411827, 0.0025901116896420717, -0.06290453672409058, 0.02396705374121666, 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This helps to isolate dependencies, manage packages, and avoid conflicts that may arise from different package versions. Creating an environment is an optional but highly recommended step.\n\nAt this point, we are assuming that you have an Atlas cluster created and ready to be used, and PyMongo and Jupyter Notebook installed. Let\u2019s go ahead and launch Jupyter Notebook by running the following command in the terminal:\n```\nJupyter Notebook\n```\n\nOnce you have the Jupyter Notebook up and running, let\u2019s go ahead and fetch the connection string of your MongoDB Atlas cluster and store that as an environment variable, which we will use later to connect to our database. After you have done that, let\u2019s go ahead and connect to our Atlas cluster by running the following commands:\n\n```\nimport pymongo\nimport os\n\nfrom pymongo import MongoClient", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04386018589138985, -0.003356406930834055, 0.018787220120429993, -0.031067360192537308, -0.007703299168497324, -0.03215634450316429, -0.00855077151209116, 0.012959943152964115, -0.002509333658963442, -0.005472216755151749, 0.021563204005360603, -0.08182912319898605, 0.02342536486685276, 0.00016862548363860697, -0.024346034973859787, 0.005457338877022266, -0.026895374059677124, -0.022675802931189537, -0.0513794869184494, 0.019168106839060783, 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-0.1744406819343567, 0.04396974667906761, 0.03653866797685623, -0.002223973162472248]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "```\nimport pymongo\nimport os\n\nfrom pymongo import MongoClient\n\nMONGO_CONN_STRING = os.environ.get(\"MONGODB_CONNECTION_STRING\")\n\nclient = MongoClient(MONGO_CONN_STRING)\n```\n\n## Creating a time-series collection\n\nNext, we are going to create a new database and a collection in our cluster to store the time-series data. We will call this database \u201cstock_data\u201d and the collection \u201cstocks\u201d. \n\n```\n# Let's create a new database called \"stock data\"\ndb = client.stock_data\n\n# Let's create a new time-series collection in the \"stock data\" database called \"stocks\"\n\ncollection = db.create_collection('stocks', timeseries={\n\n timeField: \"timestamp\",\n metaField: \"metadata\",\n granularity: \"hours\"", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.03470398485660553, 0.02037637121975422, 0.020632611587643623, -0.02892887033522129, 0.04226795956492424, -0.013874098658561707, 0.0049956198781728745, -0.0023642193991690874, 0.028369830921292305, 0.006251920945942402, 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-0.015913579612970352, -0.0039154402911663055, 0.014182512648403645, 0.03264838829636574, 0.0009793585631996393, 0.02630319632589817, 0.005018799565732479, -0.02734312415122986, 0.021122777834534645, -0.11706556379795074, 0.0012032410595566034, 0.0005363746895454824, 0.01337939128279686]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "timeField: \"timestamp\",\n metaField: \"metadata\",\n granularity: \"hours\"\n\n})\n```\nHere, we used the db.create_collection() method to create a time-series collection called \u201cstock\u201d. In the example above, \u201ctimeField\u201d, \u201cmetaField\u201d, and \u201cgranularity\u201d are reserved fields (for more information on what these are, visit our documentation). The \u201ctimeField\u201d option specifies the name of the field in your collection that will contain the date in each time-series document. \n\nThe \u201cmetaField\u201d option specifies the name of the field in your collection that will contain the metadata in each time-series document. \n\nFinally, the \u201cgranularity\u201d option specifies how frequently data will be ingested in your time-series collection. \n\nNow, let\u2019s insert some stock-related information into our collection. We are interested in storing and analyzing the stock of a specific company called \u201cXYZ\u201d which trades its stock on \u201cNASDAQ\u201d.", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.007283143233507872, -0.0360238179564476, 0.018067574128508568, -0.0043113501742482185, 0.053505368530750275, -0.00604756036773324, 0.04005758464336395, -0.019382478669285774, 0.050368763506412506, -0.00763551564887166, -0.002951798029243946, -0.03941888362169266, 0.01129188109189272, 0.026950741186738014, -0.022877225652337074, 0.0026891049928963184, -0.0378134623169899, -0.023407207801938057, -0.05040810629725456, 0.013422095216810703, 0.07452978938817978, -0.027570614591240883, 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-0.009845019318163395, 0.028317566961050034]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "We are storing some price metrics of this stock at an hourly interval and for each time interval, we are storing the following information:\n\n* **open:** the opening price at which the stock traded when the market opened\n* **close:** the final price at which the stock traded when the trading period ended\n* **high:** the highest price at which the stock traded during the trading period\n* **low:** the lowest price at which the stock traded during the trading period\n* **volume:** the total number of shares traded during the trading period\n\nNow that we have become an expert on stock trading and terminology (sarcasm), we will now insert some documents into our time-series collection. Here we have four sample documents. The data points are captured at an interval of one hour. \n\n```\n# Create some sample data", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.0289372019469738, -0.005756106227636337, 0.028133099898695946, -0.006204315926879644, 0.047459352761507034, 0.020132794976234436, 0.03826991841197014, 0.02221592143177986, 0.05162712186574936, -0.011748210527002811, -0.00811641663312912, -0.05436116084456444, 0.014850490726530552, 0.05203000456094742, -0.009462469257414341, 0.007415982894599438, 0.005199449602514505, -0.0366935171186924, -0.06303047388792038, 0.05899021029472351, 0.054862480610609055, -0.054261524230241776, -0.0017459142254665494, -0.02634465880692005, 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0.02969297207891941]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "```\n# Create some sample data\n\ndata = \n{\n \"metadata\": {\n \"stockSymbol\": \"ABC\",\n \"exchange\": \"NASDAQ\"\n },\n \"timestamp\": datetime(2023, 9, 12, 15, 19, 48),\n \"open\": 54.80,\n \"high\": 59.20,\n \"low\": 52.60,\n \"close\": 53.50,\n \"volume\": 18000\n},\n\n{\n \"metadata\": {\n \"stockSymbol\": \"ABC\",\n \"exchange\": \"NASDAQ\"\n },\n \"timestamp\": datetime(2023, 9, 12, 16, 19, 48),\n \"open\": 51.00,\n \"high\": 54.30,\n \"low\": 50.50,\n \"close\": 51.80,\n \"volume\": 12000\n},", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", 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-0.04482158273458481, -0.013205744326114655, -0.010341272689402103, -0.0630510002374649, -0.040543608367443085, 0.01386722456663847, 0.029976777732372284]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "# insert the data into our collection\n\ncollection.insert_many(data)\n\n```\n\nNow, let\u2019s run a find query on our collection to retrieve data at a specific timestamp. Run this query in the Jupyter Notebook after the previous script. \n\n```\ncollection.find_one({'timestamp': datetime(2023, 9, 12, 15, 19, 48)})\n```\n\n//OUTPUT\n![Output of find_one() command\n\nAs you can see from the output, we were able to query our time-series collection and retrieve data points at a specific timestamp. \n\nSimilarly, you can run more powerful queries on your time-series collection by using the aggregation pipeline. For the scope of this tutorial, we won\u2019t be covering that. But, if you want to learn more about it, here is where you can go:", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.025767987594008446, -0.013252289965748787, 0.029701393097639084, -0.05221915617585182, 0.0028829695656895638, -0.00946586299687624, 0.009364370256662369, -0.023838480934500694, 0.05999794602394104, 0.016339341178536415, 0.022351693361997604, -0.08539100736379623, 0.02368733659386635, 0.02574850618839264, -0.022210460156202316, -0.011330022476613522, -0.05863852798938751, -0.016624804586172104, -0.07031133770942688, 0.026663856580853462, 0.01167814526706934, -0.024315522983670235, -0.03799235820770264, -0.06316853314638138, 0.04014581814408302, 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"https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "1. MongoDB Aggregation Learning Byte\n 2. MongoDB Aggregation in Python Learning Byte\n 3. MongoDB Aggregation Documentation\n 4. Practical MongoDB Aggregation Book\n\n## Analyzing the data with a pandas DataFrame\n\nNow, let\u2019s see how you can move your time-series data into pandas DataFrame to run some analytics operations.\n\nMongoDB has built a tool just for this purpose called PyMongoArrow. PyMongoArrow is a Python library that lets you move data in and out of MongoDB into other data formats such as pandas DataFrame, Numpy array, and Arrow Table. \n\nLet\u2019s quickly install PyMongoArrow using the pip command in your terminal. We are assuming that you already have pandas installed on your system. If not, you can use the pip command to install it too.\n\n```\npip install pymongoarrow\n```", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.04088534787297249, -0.05206771939992905, 0.0020554903894662857, -0.017960892990231514, 0.058068353682756424, -0.021622950211167336, 0.0023880628868937492, 0.006660361308604479, 0.0019726513419300318, 0.034535232931375504, 0.017797740176320076, -0.1180969774723053, 0.04280159994959831, -0.0009599205222912133, -0.023985302075743675, -0.005183670204132795, -0.034808285534381866, 0.0124112693592906, -0.05763411521911621, 0.01964283362030983, -0.008490155450999737, -0.01935860887169838, -0.056721050292253494, -0.0326627716422081, 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0.015766283497214317]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "```\npip install pymongoarrow\n```\n\nNow, let\u2019s import all the necessary libraries. We are going to be using the same file or notebook (Jupyter Notebook) to run the codes below. \n\n```\nimport pymongoarrow\nimport pandas as pd\n\n# pymongoarrow.monkey module provided an interface to patch pymongo, in place, and add pymongoarrow's functionality directly to collection instance. \n\nfrom pymongoarrow.monkey import patch_all\npatch_all()\n\n# Let's use the pymongoarrow's find_pandas_all() function to read MongoDB query result sets into \n\ndf = collection.find_pandas_all({})\n```\n\nNow, we have read all of our stock data stored in the \u201cstocks\u201d collection into a pandas DataFrame \u2018df\u2019.\n\nLet\u2019s quickly print the value stored in the \u2018df\u2019 variable to verify it.\n\n```\nprint(df)\n\nprint(type(df))\n```\n\n//OUTPUT", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": 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As you can see, we have successfully read our MongoDB data into pandas DataFrame. \n\nNow, if you are a stock market trader, you would be interested in doing a lot of analysis on this data to get meaningful insights. But for this tutorial, we are just going to calculate the hourly percentage change in the closing prices of the stock. This will help us understand the daily price movements in terms of percentage gains or losses. \n\nWe will add a new column in our \u2018df\u2019 DataFrame called \u201cdaily_pct_change\u201d. \n\n```\ndf = df.sort_values('timestamp')\n\ndf'daily_pct_change'] = df['close'].pct_change() * 100\n\n# print the dataframe to see the modified data\nprint(df)\n```\n\n//OUTPUT\n![Output of modified DataFrame", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.035437993705272675, -0.030191706493496895, 0.025402601808309555, 0.00815054401755333, 0.06877458840608597, -0.014922619797289371, 0.02516753226518631, 0.02115354686975479, 0.03495802357792854, 0.0010031373240053654, 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0.008635352365672588, -0.0074972109869122505, 0.018035942688584328, 0.004402873106300831, -0.0019998853094875813, 0.018363265320658684, 0.013377171941101551, -0.05532329902052879, 0.017388779670000076, -0.0881514772772789, 0.00201659114100039, 0.00893574021756649, 0.02012445405125618]} {"sourceName": "devcenter", "url": "https://www.mongodb.com/developer/products/mongodb/time-series-data-pymongoarrow", "action": "created", "body": "# print the dataframe to see the modified data\nprint(df)\n```\n\n//OUTPUT\n![Output of modified DataFrame\n\nAs you can see, we have successfully added a new column to our DataFrame. \n\nNow, we would like to persist the modified DataFrame data into a database so that we can run more analytics on it later. So, let\u2019s write this data back to MongoDB using PyMongoArrow\u2019s write function. \n\nWe will just create a new collection called \u201cmy_new_collection\u201d in our database to write the modified DataFrame back into MongoDB, ensuring data persistence. \n\n```\nfrom pymongoarrow.api import write\n\ncoll = db.my_new_collection\n\n# write data from pandas into MongoDB collection called 'coll'\nwrite(coll, df)\n\n# Now, let's verify that the modified data has been written into our collection\n\nprint(coll.find_one({}))\n```\n\nCongratulations on successfully completing this tutorial. \n\n## Conclusion", "format": "md", "metadata": {"tags": ["MongoDB"], "pageDescription": "Learn how to create and query a time-series collection in MongoDB, and analyze the data using PyMongoArrow and pandas.", "contentType": "Tutorial"}, "title": "Analyze Time-Series Data with Python and MongoDB Using PyMongoArrow and Pandas", "updated": "2024-05-20T17:32:23.500Z", "embedding": [-0.078060083091259, 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We learned how to store stock market data in a MongoDB time-series collection, and then how to perform simple analytics using a pandas DataFrame. We also explored how PyMongoArrow makes it easy to move data between MongoDB and pandas. Finally, we saved our analyzed data back into MongoDB. This guide provides a straightforward way to manage, analyze, and store time-series data. Great job if you\u2019ve followed along \u2014 you\u2019re now ready to handle time-series data in your own projects.\n\nIf you want to learn more about PyMongoArrow, check out some of these additional resources:\n\n 1. Video tutorial on PyMongoArrow\n 2. 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