Five Easy to Miss PostgreSQL Query Performance Bottlenecks

1. postgrest

   1 113 25,654 9.6 Haskell

   REST API for any Postgres database

   

   For OLAP-style use-cases, I wouldn't recommend SQLite, but rather something equally (apparently) unfashionable: stored procedures.

   In both cases, the goal is to save the time spent round-tripping queries and responses to the DB. But with OLAP, you more-than-likely have a large dataset that needs its own dedicated beefy DB to process. So rather than moving the data to the code (with e.g. SQLite), move the code to the data (by moving as much of your business layer as possible into the DB. Or possibly all of it, ala https://postgrest.org/.)

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. factory_boy

   2 3 3,677 7.5 Python

   A test fixtures replacement for Python

   

   When releasing a new application (or feature) I've always loaded each table in my development environments database with a few million rows. Tools like Python's Factory Boy [1] or Ruby's Factory Bot [2] make this trivial.

   After the data is loaded up, start navigating through the application and it will become evident where improvements need to be made. Tools like Django Debug Toolbar [3] help expose where the bad ORM calls are or also by tailing Postgres log files.

   [1] https://github.com/FactoryBoy/factory_boy

4. factory_bot

   3 32 8,122 7.7 Ruby

   A library for setting up Ruby objects as test data.

   

   [2] https://github.com/thoughtbot/factory_bot

5. django-debug-toolbar

   4 22 8,277 9.0 Python

   A configurable set of panels that display various debug information about the current request/response.

   

Suggest a related project