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Last year on the blog, we shared our point of view on product design and research, two of the three main responsibilities of the Design team at Cockroach Labs. The one that we haven’t discussed yet is brand. Despite having a distinct and memorable name like Cockroach Labs, nailing our brand hasn’t been simple and is still a work-in-progress. From day one, our mission has been to make data easy. But what does a brand that makes data easy look like? That’s the question that keeps the design team up at night. Eight months ago, we attempted to answer this question in a true startup fashion: pick one aspect of the visual system that’s representative of the brand, launch experiments, and iterate until we find what sticks.

May 25, 2018 has loomed over businesses for two years as the day the General Data Protection Regulation (GDPR) takes effect––and that day has finally arrived. If the number of "Updates to Our Privacy Policy" emails I've received in the last week are any indication, people are still hard at work trying to make GDPR compliance happen. However, unlike the cleanly cut deadline of filing your taxes, meeting GDPR's regulations isn't something most companies will complete today. According to Crowd Search Partners' GDPR report, as many as 60% of companies, as of today, fail to comply.

CockroachDB makes it possible to support a global customer base while remaining compliant with data privacy regulations. Operators interact with a single logical control plane that they can use to define how they want CockroachDB to store their row-level data. Meanwhile, developers continue to interact with our PostgreSQL-compatible API that transparently handles distributing queries across a global cluster. With our 2.0 release, we introduced a new cluster visualization in our web UI to help operators monitor a global cluster.

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.