MongoDB 3.0.5-rc2 is released

MongoDB
July 23, 2015
#Releases

MongoDB 3.0.5-rc2 is out and is ready for testing. This is a release candidate containing only fixes since 3.0.4. The next stable release 3.0.5 will be a recommended upgrade for all 3.0 users.

Fixed in this release:

  • SERVER-17254 WT: drop collection while concurrent oplog tailing may greatly reduce throughput
  • SERVER-17689 Server crash during initial replication sync
  • SERVER-18875 Oplog performance on WT degrades over time after accumulation of deleted items
  • SERVER-18902 Retrieval of large documents slower on WiredTiger than MMAPv1
  • SERVER-18926 Full text search extremely slow and uses a lot of memory under WiredTiger
  • SERVER-19255 Listener::waitUntilListening may return before listening has started
  • SERVER-19489 Assertion failure and segfault in WorkingSet::free in 3.0.5-rc0
  • SERVER-19513 Truncating a capped collection may not unindex deleted documents in WiredTiger

3.0 Release Notes | Downloads | All Issues

As always, please let us know of any issues.

– The MongoDB Team

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MongoDB Pluggable Storage Engines: State of the Union, Storage Engine Summit

Back at the inaugural MongoDB World user conference in June 2014, our co-founder and CTO, Eliot Horowitz, announced that MongoDB would incorporate a storage engine API into the next major version of the database – MongoDB 3.0 . The goal of the storage engine API was to make it fast and easy for MongoDB and the community to build new pluggable storage engines that allow the database to be extended with new capabilities, and to be configured for specific hardware architectures. 12 months on, following both the MongoDB 3.0 release and the MongoDB World 2015 conference (keynotes and sessions from the event are now online ), we hosted our first Storage Engine Summit, bringing together storage engine developers from across both MongoDB and the community. The summit’s goals were to: Review the status of current MongoDB storage engines Provide developers with visibility into the roadmap for both the storage engine API itself, and the database roadmap Collect storage engine developer’s feature requirements Develop best practices for community collaboration and development of the API In this blog, I’ll provide an overview of the progress happening around MongoDB storage engines, and key outcomes from the summit. Why Pluggable Storage Engines? First, a bit of background. With users building increasingly complex data-driven apps, there is no longer a "one size fits all" database storage technology capable of powering every type of application built by the business. Modern applications need to support a variety of workloads with different access patterns and price/performance profiles – from low latency, in-memory read and write applications, to real-time analytics, to highly compressed "active" archives. The storage engine API allows MongoDB to be configured with a choice of storage engines, each configured for specific workloads. This “pluggable” approach significantly reduces developer and operational complexity compared to running multiple databases. Now users can leverage the same MongoDB query language, data model, scaling, security and operational tooling across different applications, each powered by pluggable MongoDB storage engines that are optimized for specific workloads. So what progress has been made so far? Quite a bit, and in a very short time frame. MongoDB 3.0 shipped with two supported storage engines: The default MMAPv1 engine, an improved version of the engine used in prior MongoDB releases, enhanced with collection level concurrency control. The new WiredTiger storage engine. For many applications, WiredTiger's more granular concurrency control and native compression provide significant benefits in terms of lower storage costs, greater hardware utilization, higher throughput, and more predictable performance. Benchmarks show MongoDB 3.0 configured with WiredTiger delivers 7-10x higher performance than MongoDB 2.6 using the original MMAP storage engine. In addition, storage compression rates of up to 80% are fairly common. To demonstrate how developers could start to innovate, several experimental engines also shipped with MongoDB 3.0, including the in-memory and /dev/null storage engines. Beyond the walls of MongoDB engineering teams, the community also started releasing its own engines. Within one quarter of 3.0’s availability: Facebook released mongo-rocks , a MongoDB storage engine based on its RocksDB embedded database project. Mongo-rocks is already in use for some production workloads at Facebook’s Parse mobile backend-as-a-service division, which today supports over 500,000 applications on MongoDB. Percona published a release candidate of its TokuMXse storage engine. What’s Next? Held in our New York offices on June 4th, the summit drew diverse representation from the community. Some attendees already had storage engines for MongoDB; some were in varying stages of development; while others were just starting to evaluate the opportunities. Attendees included: Facebook, who already has RocksDB Percona, readying TokuMXse SanDisk, developing an engine optimized for its Fusion-io SSD storage devices Deep , who have developed a MySQL storage engine featuring an adaptive database kernel 8k Data , currently building a JSON database on top of Postgres There was also representation from MongoDB’s storage engine teams, who discussed additional engines planned for the MongoDB 3.2 timeframe, including: An encrypted storage engine protecting data at-rest, with keys secured by the industry standard Key Management Interoperability Protocol (KMIP). At-rest encryption is especially critical for regulated industries such as healthcare, financial services, retailers and certain government agencies. And with so many high profile breaches of sensitive data at high profile companies over the past five years, increasingly all data is being encrypted. An in-memory engine designed to serve ultra high-throughput, low-latency apps typical in finance, ad-tech, gaming, real-time analytics, session management and general cache use cases. A new option for insert only workloads (e.g., streaming IoT sensor data, log file analysis, social media feed ingestion), based on the LSM option in the WiredTiger storage engine. Much more than just sharing feeds and speeds, the intent of the storage engine summit was to bring together developers to share best practices in how to collaborate: Mark Callaghan, one of our esteemed MongoDB masters and member of Facebook’s technical staff, gave great insight into his experiences working as part of the MySQL storage engine community. He shared the good, the bad, and the downright ugly. Dr Michael Cahill, co-founder of WiredTiger, and now Director of Storage Engineering at MongoDB, shared his experiences implementing the WiredTiger storage engine, both while outside MongoDB, and within the company. Mathias Stearn & Geert Bosch, senior database kernel engineers, provided a deep dive walkthrough into the storage engine API implementation, and plans for the future. This gave all those present an opportunity to provide feedback on issues they had faced, solutions to move forward, and future requirements to shape the evolution of the API All of the talks gave a great frame-of-reference in how the MongoDB storage engine community should move forward. So what did the summit achieve in one short day? The formation of a focused community committed to building MongoDB storage engines. The establishment of a framework for communications, contributions, collaboration & future summits. It connected engine developers directly with MongoDB engineers designing & implementing API functionality. Not only has feedback already helped evolve the storage engine API in the current 3.0 release, additional enhancements will be reflected in MongoDB 3.2’s API. Wrapping Up Huge progress has been made, and the MongoDB storage engine ecosystem is continuing to grow. This diversity helps MongoDB users solve new classes of use cases with a single database framework. The storage engine API helps developers innovate faster. And it helps storage engine partners get their technology in front of a whole new audience. To learn more of the promise of multiple storage engines, download the What’s New in MongoDB 3.0 guide. What’s New in MongoDB 3.0

July 22, 2015
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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.

April 25, 2024