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Over the past year, Kubernetes––also known as K8s––has become a dominant topic of conversation in the infrastructure world. Given its pedigree of literally working at Google-scale, it makes sense that people want to bring that kind of power to their DevOps stories; container orchestration turns many tedious and complex tasks into something as simple as a declarative config file.

The three design principles of CockroachDB are correctness, stability, and performance. Having achieved our correctness and stability goals with CockroachDB 1.0 and 1.1, we focused heavily on performance with CockroachDB 2.0. For more information on which benchmarks matter, or to see a comparison between CockroachDB 1.1 and 2.0, you can read CockroachDB 2.0 Makes Significant Strides. Today we are releasing a comprehensive whitepaper that demonstrates how CockroachDB achieves high OLTP performance of over 128,000 tpmC on a TPC-C dataset over 2 terabytes in size. This OLTP performance is over 10x more TPC-C throughput than Amazon Aurora, in a 3x replicated deployment with single-digit seconds recovery time and zero-downtime migrations and upgrades. This far surpasses a typical active-passive database deployment with manual failure recovery. CockroachDB achieves this in serializable isolation, unlike competing databases that sacrifice isolation for performance. We’re very excited to talk about performance, and have taken care to make this more than just an announcement with vague numbers. In keeping with our open source philosophy, our whitepaper contains a step-by-step reproduction of instructions to verify all our performance claims, as well as context on our benchmarking philosophy and practices. We don’t just want you to take our word for our performance numbers, we’d like to arm you with all the tools you need to check it out for yourself! In this post, we’d like to cover some brief highlights, but do check out the whitepaper for more details and a fully reproducible test script.

As we’ve written about previously, geographically distributed databases like CockroachDB offer a number of benefits including reliability, security, and cost-effective deployments. We believe you shouldn’t have to sacrifice these upsides to realize impressive throughput and low latencies. That’s why we created geo-partitioning. This blog post defines two new features, geo-partitioning and archival-partitioning, as well as explains when you might want to leverage these features. We previously provided a sneak-peak walkthrough of geo-partitioning that can be found here.

Andy Woods

April 12, 2018

CockroachDB debuted as the open source database that made it possible to build massive, reliable cloud applications without giving up SQL. Forward-thinking companies adopted it to hide the complexity of dealing with distributed scale, resilience, and consistency problems in the database layer. The promise was simple: keep your apps simple and your pagers silent. Over the last six months, we’ve welcomed Mesosphere as a customer and helped companies like Kindred and Baidu continue to migrate internet-scale workloads onto CockroachDB. We’ve also watched our distributed SQL database enable exciting new use cases, from a blockchain solution for certifying document authenticity to a system of record for tracking simulations that help optimize oil and gas exploration.

Nate Stewart

April 4, 2018

Correctness, stability, and performance are the foundations of CockroachDB. We've invested tremendous resources into correctness and stability. Today, performance takes the spotlight as we will be publishing benchmarked metrics that demonstrate that you can achieve correctness, stability, and excellent performance within the same database.

Andy Woods

March 29, 2018

We are excited to announce support for JSON in our 2.0 release (coming in April) and available now via our most recent 2.0 Beta release. Now you can use both structured and semi-structured data within the same database. No longer will you need to sacrifice ACID guarantees, accuracy, or the ability to scale in order to use multiple data models within the same database. This post will explain how we implemented JSON and give you a few examples of how JSON can be used to model your data.

\* *This blog from 2018 does not represent the most recent strategies for pinning data to locations at the row level in CockroachDB. And certain capabilities referenced, like interleaving, are no longer supported. This documentation about our multi-region capabilities is the best place to begin learning about the current best practices for scaling your database across multiple regions: https://www.cockroachlabs.com/docs/stable/multiregion-overview* ```

Andy Woods

March 15, 2018

In tech, we hear the importance of “scale” all the time. People plan for it, try to work around not having it, and build companies to help others achieve it. But when it comes time to scale something yourself or integrate a scalable solution with your app, it’s difficult to find practical guides to help you understand what it takes. Why’s that? Well, it’s kind of hard. Actually scaling a database beyond a single availability zone takes considerable planning and engineering investment––but that being said, it’s an incredibly powerful tool to delight your users with low latencies and high availability.

The Baidu DBA team has been an active part of our community for quite some time. Starting in 2015 as open source contributors, they became internal evangelists of CockroachDB, ultimately building several production applications. Late last year, the team approached us with an exciting proposition. They wanted to take part in leading, building, and growing the CockroachDB open source community. Given the rapid growth and excitement surrounding CockroachDB in China, Baidu and Cockroach Labs jointly decided to hold a conference that would bring top-tier DBAs and developers together to discuss CockroachDB.