Psycopg 3.0 Released

- Daniele Varrazzo tl;dr: Leverages more modern PostgreSQL and Python features, such as asynchronous support, server-side parameters binding, prepared statements, and more.

featured in #260

Herding Elephants: Lessons Learned From Sharding Postgres At Notion

- Garrett Fidalgo tl;dr: The inflection point was when the Postgres VACUUM process began to stall consistently, preventing the database from reclaiming disk space. Garrett discusses 3 key decisions the team made and lessons learned: (1) shard earlier. (2) Aim for a zero-downtime migration. (3) Introduce a combined primary key instead of a separate partition key.

featured in #258

PostgreSQL 14 Released!

tl;dr: "This latest release adds to PostgreSQL's trend on improving high performance and distributed data workloads, with advances in connection concurrency, high-write workloads, query parallelism and logical replication."

featured in #256

Postgres 14: It's The Little Things

- Craig Kerstiens tl;dr: "A lot of years Postgres will have some big pillar or theme to the release." However, Craig wants to highlight how the little things are improving too highlighting improvements in JSON syntax, read only roles, psgl and query pipelining.

featured in #254

In Praise Of PostgreSQL

- Drew DeVault tl;dr: "Postgres stands today as one of the most significant pillars of profound achievement in free software, alongside the likes of Linux and Firefox. PostgreSQL has taken a complex problem and solved it to such an effective degree that all of its competitors are essentially obsolete, perhaps with the exception of SQLite."

featured in #243

Better JSON In Postgres With PostgreSQL 14

- Craig Kerstiens tl;dr: "Postgres 14 makes JSON even more user friendly than before. While I wouldn't recommend simply using the subscript format everywhere in your application due to it not always leveraging indexes, for casual querying it proves to be a big win."

featured in #233

PostgreSQL 13 Released!

tl;dr: "Includes significant improvements to its indexing and lookup system that benefit large databases, including space savings and performance gains for indexes, faster response times for queries that use aggregates or partitions, better query planning when using enhanced statistics, and more."

featured in #207

Building A Recommendation Engine Inside Postgres With Python And Pandas

- Craig Kerstiens tl;dr: Craig guides us through his experimental recommendation engine - with "SciPy, NumPy and Pandas there is a lot of interesting potential here."

featured in #200

How To Implement Search By Color When All You Have Is A Good Coffee

- Mike Alche tl;dr: How search was implemented on a Next.JS app using PostgreSQL and third party library called "color thief."

featured in #188

10 Things I Hate About PostgreSQL

- Rick Branson tl;dr: A dive into PostgreSQL’s imperfections (1) Disastrous XID wraparound (2) Failover will probably lose data (3) Inefficient replication that spreads corruption. 

featured in #180