Announcing MongoDB 3.4

Eliot Horowitz


Today we are announcing MongoDB 3.4, another milestone in our march to being the default database for modern applications. 3.4 makes MongoDB more flexible than ever, allowing developers to consolidate even more use cases into their MongoDB deployment, even as we continue to mature the platform and its ecosystem.

MongoDB was created to make it easy for developers to work with their data, beginning with introducing the document model itself. Documents are the best rudimentary unit for a data store, because they let you represent any kind of data, and embody their structure however best suits your use case. Whether that means deep or shallow nesting (or no nesting), documents can handle it. The key is being able to add many types of queries and algorithms to the data.

MongoDB 3.4 adds a stage to the aggregation pipeline that enables faceted search, greatly simplifying the query load for applications that browse and explore that data. It also adds operators to power graph queries. As we continue to add query features, users can consolidate more uses cases, instead of bloating their application footprint with a proliferation of specialized data stores.

Just because it’s easy to work with data in MongoDB, it doesn’t mean we can’t make it easier. In 3.4, the aggregation pipeline continues to mature, with more operators and expressions, enhancing string handling, allowing more sophisticated use of array elements, testing fields for type, and support for branching. Financial calculations are made simple with the addition of a Decimal data type.

I think it was John Donne who said: “No database is an island,” but whoever said it, they were very right. A database has to work as the heart of an ecosystem, and in 3.4, we continue to build that thriving ecosystem. Connecting MongoDB to the outside world is better than ever. MongoDB 3.4 introduces a ground-up rewrite of the BI connector, which improves performance, simplifies installation and configuration, and supports Windows. 3.4 also includes an update for our Apache Spark connector, with support for the Spark 2.0.

We’ve also extended the platforms that MongoDB runs on, including ARM-64, and IBM’s POWER8 and zSeries platforms.

MongoDB Compass is growing up with 3.4. It has new ways to depict data, such as the map view for geographic data, and it has become a data manipulation and performance tuning tool as well. In 3.4, Compass offers visual plan explanations, real-time stats, CRUD operations and index creation, so now you can identify, diagnose, and fix performance and data problems all from within Compass.

Of course, MongoDB 3.4 is supported by our trifecta of enterprise-grade ops management platforms: Ops Manager, Cloud Manager, and MongoDB Atlas, each of which add new features with this release. Ops Manager, for example, has improved its monitoring with built in telemetry gathering tailored to each deployment platform, and now allows ops teams to create server pools to serve up database-as-a-service to internal teams. Atlas introduces Virtual Private Cloud (VPC) Peering, allowing teams to use convenient private IPs to talk to their MongoDB service from within their AWS VPC.

There’s a ton more than I can fit into a blog post. That’s what release notes are for. But I shouldn’t leave out a few highlights, like: tunable consistency control for replica sets, including linearizable reads; collations for queries and indexes; and read-only views, which enable us to bring field level security to apps handling regulated data.

We’re incredibly excited to ship MongoDB 3.4 to you, so it can help your data serve you, not the other way around. Our approach is to build a database that can handle any kind of data, and the capabilities to query that data however you need to.

Learn more about MongoDB 3.4, register for our upcoming webinar:

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About the Author - Eliot Horowitz

Eliot Horowitz is CTO and Co-Founder of MongoDB. Eliot is one of the core MongoDB kernel committers. Previously, he was Co-Founder and CTO of ShopWiki. Eliot developed the crawling and data extraction algorithm that is the core of its innovative technology. He has quickly become one of Silicon Alley's up and coming entrepreneurs and was selected as one of BusinessWeek's Top 25 Entrepreneurs Under Age 25 nationwide in 2006. Earlier, Eliot was a software developer in the R&D group at DoubleClick (acquired by Google for $3.1 billion). Eliot received a BS in Computer Science from Brown University.