making apps, making webs.

In the article on his blog Peter asks " How much faster is Redis at storing a blob of JSON compared to PostgreSQL? ". Answer: 14x slower. Seems about right. Usually Redis is about 4x faster for a simple query like that compared to using PostgreSQL as a cache in my experience. It's why so many people use Redis as a cache. But I'd suggest PostgreSQL is good enough to act as a cache for many people. Django is pretty slow at fetching from PostgreSQL compared to other python options, so this could explain part of the 14x VS 4x difference. Note that Django should be adding an index because of ForeignKey. However it's possible it isn't being used, or the table may need to analyze stats again. Also note that Django does not support prepared statements built in, and when you don't use prepared statements you do not use the PostgreSQL query cache. Prepared statements can give a 50% speedup often, because they don't have to do the query parsing and do a n...

tldr; you can do jobs, queues, real time change feeds, time series, object store, document store, full text search with PostgreSQL. How to, pros/cons, rough performance and complexity levels are all discussed. Many sources and relevant documentation is linked to. Your database is first. But can PostgreSQL be second? Web/app projects these days often have many distributed parts. It's not uncommon for groups to use the right tool for the job. The right tools are often something like the choice below. Redis for queuing, and caching. Elastic Search for searching, and log stash. Influxdb or RRD for timeseries. S3 for an object store. PostgreSQL for relational data with constraints, and validation via schemas. Celery for job queues. Kafka for a buffer of queues or stream processing. Exception logging with PostgreSQL (perhaps using Sentry) KDB for low latency analytics on your column oriented data. Mongo/ZODB for storing documents JSON (or mangodb for /dev/null replacemen...