Javascript All Over with MongoDB and Node.js

Francesca Krihely
May 30, 2013 | Updated: May 22, 2015
#mongodb#Node.js

Developers have often called MongoDB and Node.js a “match made in heaven,”. Node.js’s powerful event-based concurrency framework matches well to MongoDB’s real-time capabilities, and has made it simple to run new projects with end-to-end JavaScript. Over the past few weeks, there’s been a lot of activity in the MongoDB and Node.js communities.

Industry Validation

  • The MongoDB and Node stack entered the enterprise: eBay’s engineering team announced their first Node.js project, called “Talk”, powered by MongoDB. This particular project had to be “as real-time as possible,” and handle heavy I/O operations, pushing eBay to switch from their typical Java workflow to Node.js.
  • 10gen, Joyent, Clock and Nodejitsu all gave an update on the state of the Nodestack on May 21.
  • Strongloop, a startup that employs some of Node.js’s core contributors, just launched their first enterprise distribution of Node.js. This is appealing to growing enterprise need for an out-of-the-box distribution with pre-installed modules and advanced features like private NPM repos.

Community Enthusiasm

On a period of 2 weeks, MongoDB User Groups in New York City, Atlanta, San Francisco, Silicon Valley and Boston hosted presentations on Node.js. The New York City MongoDB User Group hosted a talk on the “MEAN” stack (MongoDB, ExpressJS, AngularJS and Node.js), and Val Karpov started with an overview of why MongoDB and Node.js are winning in the developer community.

  • MongoDB’s flexible data model makes it simpler to write applications, leaving minimal mental translation from tables to objects. With dynamic schemas, there is no need to keep data consistent with schemas, as is the case with RDBMS. Thinking about objects instead of tables eases development for the long term.
  • Thousands of APIs are readily available and with Node.js you can perform a lot of I/O requests and start many connections at the same time without having a large memory overhead. It’s all done in one single-threaded event loop.
  • If you’re handling most of your data on the front end in JSON, writing a query in SQL isn’t so valuable. With MongoDB and Node.js, you can handle JSON on both the front-end and at the server level.

Javascript Everywhere

The Node.js and MongoDB stack is more than just hype: the stack is meeting requirements for real-time applications in the enterprise and drumming up global community adoption. PaaS providers such as MongoLab, Windows Azure and Nodejitsu among others are betting big on wide Node.js adoption, and gaining traction. We expect to see more JavaScript everywhere.

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Go Agent, Go

Discuss on Hacker News 10gen introduced MongoDB Backup Service in early May. Creating a backup service for MongoDB was a new challenge, and we used the opportunity to explore new technologies for our stack. The final implementation of the MongoDB Backup Service agent is written in Go , an open-source, natively executable language initiated and maintained by Google. Why did we Go with Go? The Backup Service started as a Java project, but as the project matured, the team wanted to move to a language that compiled natively on the machine. After considering a few options, the team decided that Go was the best fit for its C-like syntax, strong standard library, the resolution of concurrency problems via goroutines, and painless multi-platform distribution. mgo As an open-source company, 10gen is fortunate to work with MongoDB developers around the world who build open-source tools for new and emerging languages to provide users with a breadth of options to access MongoDB. One of the MongoDB Masters , Gustavo Niemeyer , has spent over two years building mgo , the MongoDB driver for Go. In that time he’s developed a great framework for accessing MongoDB through Go and Gustavo has been a valuable resource as we’ve built out the Backup Service. In his own words : “It’s great to see not only 10gen making good use of the Go language for first-class services, but contributing to that community of developers by providing its support for the development of the Go driver in multiple ways.” Programming the backup agent in Go and the mgo driver has been extremely satisfying. Between the lightweight syntax, the first-class concurrency and the well documented, idiomatic libraries such as mgo, Go is a great choice for writing anything from small scripts to large distributed applications. Starting a Java project often begins with a group debate: “Maven or Ant? JUnit or TestNG? Spring or Guice?” Go has a number of conventions through which Go team has created a sensible, uniform development experience with the holy trinity of tools: go build, test and fmt. The organization of source code and libraries is standardized to allow using the go build tool. See details here Name test files as XXX_test.go with functions named TestXXX can be run automatically with go test Braces are required on if statements and the first brace goes along with the if condition. E.g. if x { doSomething() } instead of: if x { doSomething() } Methods that end with an f (e.g. Printf, Fatalf) means a string formatted method will be validated in go vet that the number of substitutions (e.g. %v) matches the number of inputs to the function. mgo is a real pleasure to use with high-quality code, thorough documentation and an API that is a thoughtful, natural blend of idiomatic Go and MongoDB.  Our team owes a lot of thanks to Gustavo for his hard work on this project. There are other Go projects being explored at the moment and we hope to see more people using mgo in production going forward. By the 10gen Backup Team

May 29, 2013
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

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