CockroachDB empowers developers to build fast, scalable applications, and one of the ways it does this is by providing rich, Postgres-compatible SQL. And while CockroachDB follows the Postgres wire protocol, the database also has a custom SQL implementation designed for a distributed database. Over the years, we’ve expanded our distributed SQL implementation to include a cost-based optimizer (CBO) and vectorized execution engine - all built to tackle the complexity of distributed data for developers. In CockroachDB 20.2, we’re excited to provide developers with an increasingly rich SQL feature set that includes support for spatial data, materialized views, Enums, ALTER TABLE, and user-defined schema changes. Let’s dive into the new capabilities.

One of the main reasons our customers choose CockroachDB is the easy horizontal scalability it offers, while maintaining data consistency with serializable isolation. This combination lets customers run critical OLTP workloads, like financial ledgers and e-commerce shopping carts, at large scale without the hassle of legacy sharding. With every release, we make significant investments in improving CockroachDB’s performance and scale. We measure CockroachDB’s performance through many diverse tests, including the industry-standard TPC-C benchmark to track our progress across releases. Our latest version, CockroachDB 20.2, passed 140K warehouses with a maximum throughput of 1.7M transactions per minute (tpmC) on TPC-C. This represents a 40% improvement with the same resources as compared to the results previously reported with CockroachDB 19.2 in this post. Additionally, CockroachDB 20.2 was able to load TPC-C 140K in less than 3 hours compared to the ~20 hours it took to load TPC-C 100K in 19.2. This improvement was the result of faster bulk-data loading, which built upon work in Pebble, CockroachDB’s new storage engine.

Even before the stay-at-home orders spiked the demand for digital learning platforms, MyMahi was architecting for scale. MyMahi, a New Zealand-based digital education company, built their student platform, which helps 8,000+ monthly active students track their learning journeys, with CockroachDB Core embedded in a technology stack that includes spot instances (e.g., AWS Fargate), serverless functions (e.g., AWS Lambda), and GraphQL (e.g., GraphQL.js, GraphQL Tools). Starting in 2018, MyMahi designed their new application to take advantage of technologies that allow them to scale out seamlessly in response to student activity. In this blog post, we'll highlight MyMahi’s application architecture and discuss how different technologies like AWS Lambda interact with CockroachDB to provide a scalable, serverless backend for their application.