Developing and Testing a MongoDB and Node.js REST API
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The document discusses the development of a REST API using MongoDB and Node.js, emphasizing MongoDB's advantages as a document database for web and mobile app data storage. It covers essential topics like test-driven development, schema design principles, and the use of the Mongoose ODM for managing objects in an asynchronous environment. Key features of the Express framework for building REST APIs and the importance of testing with tools like Mocha and Superagent are also highlighted.
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Category Schema Takeaways
•Queries in MongoDB should be simple
•Strive for minimal data transformation by server
•“Store what you query for”
•“If you need [the aggregation framework in a heavily
used API endpoint], you're screwed anyway, and should
fix your program.” - Linus Torvalds
•Good for performance and developer sanity](https://image.slidesharecdn.com/ato2015-tddarestapiwithmongodbnode-151020211442-lva1-app6892-151120143012-lva1-app6891/85/Developing-and-Testing-a-MongoDB-and-Node-js-REST-API-24-320.jpg)
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Leveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Appl...
Leveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Appl...All Things Open Presented at All Things Open AI 2025
Presented by David vonThenen - DigitalOcean
Title: Leveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Applications
Abstract: In the ever-evolving field of AI, retrieval-augmented generation (RAG) systems have become critical for delivering high-quality, contextually relevant answers in applications powered by large language models (LLMs). While vector databases have traditionally dominated RAG applications, graph databases, specifically knowledge graphs, offer a transformative approach to contextual AI that’s often overlooked. This approach provides unique advantages for applications requiring deep insights, intelligent search, and reasoning over both structured and unstructured sources, making it ideal for complex business scenarios.
Attendees will leave with an understanding of how to build a RAG system using a graph database and practical skills for data querying and insights retrieval. By comparing graph and vector database approaches, we’ll highlight when and why graph databases may offer superior benefits for managing complex data relationships. The session will provide concrete examples and advanced techniques, empowering participants to incorporate knowledge graphs into their AI systems for better data-driven outcomes and improved LLM performance. This discussion will conclude with a live demo showcasing key techniques and insights covered in this talk.
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Artificial Intelligence Needs Community Intelligence - Sriram Raghavan, IBM R...
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Presented by Sriram Raghavan - IBM Research AI
Title: Artificial Intelligence Needs Community Intelligence
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Don't just talk to AI, do more with AI: how to improve productivity with AI a...
Don't just talk to AI, do more with AI: how to improve productivity with AI a...All Things Open Presented at All Things Open AI 2025
Presented by Sheng Liang - Acorn Labs
Title: Don't just talk to AI, do more with AI: how to improve productivity with AI agents
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Open-Source GenAI vs. Enterprise GenAI: Navigating the Future of AI Innovatio...
Open-Source GenAI vs. Enterprise GenAI: Navigating the Future of AI Innovatio...All Things Open Presented at All Things Open AI 2025
Presented by Dr. Ruth Akintunde - SAS Institute Inc.
Title: Open-Source GenAI vs. Enterprise GenAI: Navigating the Future of AI Innovation
Abstract: This talk explores the critical differences between Open-Source Generative AI and Enterprise Generative AI, highlighting their respective strengths and challenges. Open-Source GenAI fosters innovation through community collaboration, accessibility, and adaptability, while Enterprise GenAI prioritizes security, scalability, and reliability. Key aspects such as cost, ethical considerations, and long-term sustainability are examined to understand their impact on AI development and deployment. Ultimately, the talk advocates for a hybrid approach, leveraging the best of both worlds to drive AI innovation forward.
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The Death of the Browser - Rachel-Lee Nabors, AgentQL
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Presented by Rachel-Lee Nabors - AgentQL
Title: The Death of the Browser
Abstract: In ten years, Internet Browsers may be a nostalgic memory. As enterprises face mounting API costs and integration headaches, a new paradigm is emerging. The internet's evolution from an open highway into a maze of walled gardens and monetized APIs has created significant challenges for businesses—but it has also set the stage for accessing and organizing the world’s information.
This lightning talk traces our journey from the invention of the browser to the arms race of scraping for data and access to it to the dawn of AI agents, showing how the challenges of today opened the door to tomorrow. See how technologies refined by the web scraping community are combining with large language models to create practical alternatives to costly API integrations.
From the rise of platform monopolies to the emergence of AI agents, this timeline-based exploration will help you understand where we've been, where we are, and where we're heading. Join us for a glimpse of how AI agents are enabling a return to the era of free information with the web as the API.
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The Open Source Ecosystem for eBPF in Kubernetes
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Developing and Testing a MongoDB and Node.js REST API
- 1. Developing and Testing a MongoDB and Node.js REST API Valeri Karpov Node.js Engineer, MongoDB www.thecodebarbarian.com github.com/vkarpov15 @code_barbarian
- 2. * What is this talk about? •Briefly introduce MongoDB and Node.js •MongoDB is great for storing web/mobile app data •So let’s build a REST API using Node.js! •+ learn a bit about test-driven dev with Node.js •+ learn two MongoDB schema design principles •Server-side only •Upcoming EdX course
- 3. * What is MongoDB? •Document database - store objects, not columns •Often better* performance • Better data locality than RDBMS for 1 query vs 5 JOINs
- 4. * What is MongoDB? •Easier ops • No need to set up databases, tables, schemas, etc. • Run one executable, you now have a working db • MongoDB Cloud Manager and Ops Manager •Developer sanity • In my experience, easiest database to get working • Minimal data transformation between DB and server • Company has dedicated “Developer Experience” team
- 5. * Overview •Part 1: Shopping Cart Application • Search for products • Add them to your cart • Check out with Stripe •Part 2: Using Node.js and the Mongoose ODM •Part 3: Schema Design •Part 4: Building an API with the Express framework •Part 5: Testing with Mocha + Superagent
- 6. * Part 1: What Does the App Do?
- 7. * What Does the App Do?
- 8. * What Does the App Do?
- 9. * App Structure •"Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds •3 schemas for 3 collections: •Products •Categories •Users
- 10. * Schema Relationships •Product belongs to one or more categories •Users can have multiple products in their cart •Representing relationships in MongoDB is tricky • MongoDB doesn’t have a JOIN equivalent •But that’s what mongoose is for • Mongoose provides pseudo-JOINs (separate queries)
- 11. * Part 2: Using the Mongoose ODM •“Object document mapper” (like ORM, but for MongoDB) •“MongoDB object modeling designed to work in an asynchronous environment” •Written for Node.js •Provides schema validation, pseudo-JOINs, etc.
- 12. * Brief Overview of Node.js •require() ● Async I/O
- 13. * What Does Async Mean? Register event handler In node.js, you don’t execute I/O imperatively. You register a callback to execute when the I/O is done Single thread - handler can’t be interrupted Prints before “done reading”
- 14. * Why Async? •Most web app/mobile app backends are I/O bound • Multi-threading doesn’t help if CPU is just waiting for I/O • Mostly just waiting for database to respond •Nowadays there’s a REST API for everything • Analytics and tracking: Segment • Transactional email: Vero • Mailing lists: MailChimp •Threads are cumbersome for I/O bound programs
- 15. * Your First Mongoose Schema matches [email protected]
- 16. * Using Your Schema Create User Save user to MongoDB Load user from MongoDB Print user to stdout
- 17. * Part 2 Takeaways •Mongoose provides several neat features • Model part of MVC • Default values • Schema validation and declarative schema design
- 18. * Part 3: Schema Design •3 schemas: • Product • Category • User •Going to use mongoose to define schemas •Will use a couple key schema design principles
- 19. * Product Schema
- 20. * Product Schema in Action
- 22. * Category Schema Queries •What categories are descendants of “Electronics”? • • •What categories are children of “Non-Fiction”? • •What categories are ancestors of “Phones”?
- 23. * Product + Category Schemas
- 24. * Category Schema Takeaways •Queries in MongoDB should be simple •Strive for minimal data transformation by server •“Store what you query for” •“If you need [the aggregation framework in a heavily used API endpoint], you're screwed anyway, and should fix your program.” - Linus Torvalds •Good for performance and developer sanity
- 25. * User Schema User’s cart as an array of ObjectIds...
- 26. * Principle of Least Cardinality •Product and user = many-to-many relationship •Don’t necessarily need a mapping table •User won’t have 1000s of products in cart •Can represent relationship as array in user since one side is small •If one side of many-to-many is bounded and/or small, it is a good candidate for embedding •Arrays that grow without bound are an antipattern! • 16mb document size limit • network overhead
- 27. * Part 4: The Express Framework •Most popular Node.js web framework •Simple, pluggable, and fast •Great tool for building REST APIs
- 28. * Your First Express App Route Parameter
- 29. * What is REST? •Representational State Transfer •HTTP request -> JSON HTTP response •Business logic on top of MongoDB schemas • Access control, emails, analytics, etc.
- 30. * Structuring Your REST API
- 31. * GET /category/id/:id Find Category Error handling Output JSON
- 34. * Adding Products to User’s Cart •Recall cart is an array of products
- 35. * Adding Products to User’s Cart Get cart from HTTP request Overwrite user’s cart Let mongoose handle casting and validating data
- 36. * PUT /me/cart Takeaways •Mongoose lets you be lazy •Access control using subdocs
- 37. * Bonus: Stripe Checkout Create a Stripe charge with the npm ‘stripe’ module
- 38. * Bonus: Stripe Checkout Error handling Empty user cart on success
- 39. * Part 4 Takeaways •Express REST API on top of mongoose • Access control • Business logic • Define what operations user can take on database •Mongoose casting and validation for APIs
- 40. * Part 5: Test-Driven Development •Building an API is tricky •Lots of different error conditions •Express has a lot of magic under the hood
- 41. * NodeJS Concurrency and Testing •Node.js runs in an event loop •Single threaded •Can run client and server on same thread! • Client sends HTTP request • Client registers a callback awaiting the result • Server’s “on HTTP request” event handler is triggered • Server sends response, continues waiting for events • Client’s callback gets fired •Test server end-to-end
- 42. * Superagent •NodeJS HTTP client •Isomorphic: runs in both browser and NodeJS •Same author as Express
- 43. * Mocha •Testing Framework for NodeJS •Same author as Express •BDD-style syntax • describe() -> test suite • it() -> individual test
- 44. * Setting Up Category API Tests
- 46. * Part 5 Takeaways •NodeJS concurrency makes testing easy •Not just unit tests - full E2E for your REST API •Can manipulate database and make arbitrary HTTP requests
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