This article was originally posted on the personal blog of the Peewee ORM founder, Charles Leifer. Peewee is a simple and small Python ORM. It has few (but expressive) concepts, making it easy to learn and intuitive to use. And as of Peewee's most recent release (3.13.0), it supports CockroachDB! I'm pleased to announce that Peewee now supports CockroachDB (CRDB), the distributed, horizontally-scalable SQL database. I'm excited about this release, because it's now quite easy to get up-and-running with a robust SQL…

At Cockroach Labs, we write quite a bit about consensus algorithms. They are a critical component of CockroachDB and we rely on them in the lower layers of our transactional, scalable, distributed key-value store. In fact, large clusters can contain tens of thousands of consensus groups because in CockroachDB, every Range (similar to a shard) is an independent consensus group. Under the hood, we run a large number of instances of Raft (a consensus algorithm), which has come with interesting…

Distributed ACID transactions form the beating heart of CockroachDB. They allow users to manipulate any and all of their data transactionally, no matter where it physically resides. Distributed transactions are so important to CockroachDB's goal to "Make Data Easy" that we spend a lot of time thinking about how to make them as fast as possible. Specifically, CockroachDB specializes in globally distributed deployments, so we put a lot of effort into optimizing CockroachDB's transaction protocol for clusters with high inter-node…

CockroachDB is an OLTP database, specialized for serving high-throughput queries that read or write a small number of rows. As we gained more usage, we found that customers weren’t getting the performance they expected from analytic queries that read a lot of rows, like large scans, joins, or aggregations. In April 2018, we started to seriously investigate how to improve the performance of these types of queries in CockroachDB, and began working on a new SQL execution engine. In this…

Even in the 80’s, before Facebook knew everything there was to know about us, we as an industry had vast reams of data we needed to be able to answer questions about. To deal with this, data analysts were starting to flex their JOIN muscles in increasingly creative ways. But back in that day and age, we had neither machine learning nor rooms full of underpaid Excel-proficient interns to save us from problems we didn’t understand; we were on our…