Citus Blog

Many of you rely on databases to return correct results for your SQL queries, however complex your queries might be. And you probably place your trust with no questions asked—since you know relational databases are built on top of proven mathematical foundations, and since there is no practical way to manually verify your SQL query output anyway.

Since it is possible that a database’s implementation of the SQL logic could have a few errors, database developers apply extensive testing methods to avoid such flaws. For instance, the Citus open source repo on GitHub has more than twice as many lines related to automated testing than lines of database code. However, checking correctness for all possible SQL queries is challenging because of the lack of a "ground truth" to compare their outputs against, and the infinite number of possible SQL queries.

A key part of running a reliable database service is ensuring you have a good plan for disaster recovery. Disaster recovery comes into play when disks or instances fail, and you need to be able to recover your data. In those type of cases logical backups, via pg_dump, may be days old and in such cases not ideal for you to restore from. To remove the risk of data loss, many of us turn to the Postgres WAL to keep safe.

Years ago Daniel Farina, now a principal engineer at Citus Data, authored a continuous archiving utility to make it easy for Postgres users to prepare for and recover from disasters. The tool, WAL-E, has been used to keep millions of Postgres databases safe. Today we're excited to introduce an exciting new version of this tool: WAL-G. WAL-G, the successor to WAL-E, was created by a software engineering intern here at Citus Data, Katie Li, who is an undergraduate at UC Berkeley.

One of the ideas we wanted to explore more has been speeding up in-memory aggregations in PostgreSQL through vectorized execution. The opportunity to do so came up when we had our intern, Can, take this on as a project during his summer internship...