/PostgreSQL

The Unexpected Find That Freed 20GB Of Unused Index Space

- Haki Benita tl;dr: Haki’s team managed to free up more than 70GB of database storage without dropping any indexes or deleting data. They initially used conventional techniques like rebuilding indexes and tables to clear up space. However, a surprising discovery allowed them to free an additional ~20GB. They realized that PostgreSQL indexes NULL values, which led them to create a partial index that excludes these NULL values, thereby significantly reducing the index size. The article also delves into the concept of "bloat" in PostgreSQL tables and indexes, offering solutions like using the REINDEX command and the pg\_repack extension to manage it. Haki suggests that partial indexes are particularly useful for fields with a high percentage of NULL values.

featured in #443


Create An Advanced Search Engine With PostgreSQL

- Tudor Golubenco tl;dr: “The Postgres approach to full-text search offers building blocks that you can combine to create your own search engine. This is quite flexible but it also means it generally feels lower-level compared to search engines like Elasticsearch, Typesense, or Mellisearch.” The article covers: (1) The tsvector and tsquery data types. (2) The match operator @@ to check if a tsquery matches a tsvector. (3) Functions to rank each match (ts\_rank, ts\_rank\_cd). (4) The GIN index type, an inverted index to efficiently query tsvector.

featured in #430


Vectors Are In The New JSON In PostgresQL

- Jonathon Katz tl;dr: “This in itself is an interesting statement, given vectors are a well-studied mathematical structure, and JSON is a data interchange format. And yet in the world of data storage and retrieval, both of these data representations have become the lingua franca of their domains and are either essential, or soon-to-be-essential, ingredients in modern application development. And if current trends continue, vectors will be as crucial as JSON is for building applications.”

featured in #427


Understanding Database Indexes In PostgreSQL

- Pawel Dąbrowski tl;dr: “This article will help you organize your knowledge and remind you about good practices. SQL is a declarative language meaning it tells the database what we want to do but not how to achieve it. The database engine decides how to pull data. We can help the query planner by using indexes.”

featured in #416


Nine Ways To Shoot Yourself In The Foot With PostgreSQL

- Phil Booth tl;dr: (1) Keep the default value for work\_mem. (2) Push application logic into Postgres functions and procedures. (3) Use lots of triggers. (4) Use NOTIFY heavily. And more.

featured in #409


Postgres: The Graph Database You Didn't Know You Had

- Dylan Paulus tl;dr: Dylan shows us how we can store and query graph data structures in Postgres, something he did at his previous job to dynamically generate work instructions on a manufacturing line. “Based on parameters given, and rules defined on each edge, we could generate the correct document by traversing a graph stored entirely in Postgres.”

featured in #402


PSQL Tips

tl;dr: 43 tips starting with: (1) If you want to simply send a single line command to psql and exit, try using the -c or --command=command flag. (2) You can combine several -c or --command=command flags to execute several commands or queries. (3) With the --csv flag, psql will display the result as a csv file. And more.

featured in #392


Just Use Postgres For Everything

- Stephan Schmidt tl;dr: "One way to simplify your stack and reduce the moving parts, speed up development, lower the risk and deliver more features in your startup is “Use Postgres for everything”. Postgres can replace - up to millions of users - many backend technologies, Kafka, RabbitMQ, Mongo and Redis among them." Stephan gives explicit examples how.

featured in #375


Six Findings We Rely On When Managing PostgreSQL Indexes

- Billy Ceskavich tl;dr: "A rough guide on how to think through indexing strategy in most Postgres databases: (1) Every index has a read and write cost. Postgres considers these costs for each query to determine which indexes to use. (2) The more selective your index, the more efficient it becomes to read data from the table itself. But, more selective indexes (multicolumn indexes, sorted indexes, partial indexes) require specific query patterns to maximize this efficiency." And more.

featured in #374


Scaling PostgresML To 1 Million Requests Per Second

- Lev Kokotov tl;dr: "In this post, we'll discuss how we horizontally scale PostgresML to achieve more than 1 million XGBoost predictions per second on commodity hardware.

featured in #367