tl;dr:Haki’s article delves into the intricacies of text field sizes and their impact on PostgreSQL query performance. He classifies text fields into "small", "medium", and "large", highlighting the unexpected performance implications of medium-sized texts. Through the lens of PostgreSQL's TOAST mechanism, which compresses and/or breaks up large field values, Haki demonstrates that medium texts can sometimes lead to slower queries than even larger texts. "The main problem with medium-size texts is that they make the rows very wide," affecting performance due to increased IO.
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.
tl;dr:20 features including: (1) Get the number of updated and inserted rows in an Upsert. (2) Grant permissions on specific columns. (3) Match against multiple patterns, and more.
tl;dr:Haki shares "non-trivial tips" around database development with explanations, such as (1) update only what needs updating (2) disable constraints and indexes during bulk loads, and more