Friday, May 22, 2020

Native (PostgreSQL only) streaming data tables

If you want to see (and analyze) only a window of data over some continuous data stream in PostgreSQL, one way is to use a specialized tool like the PipelineDB extension. But if you can't do that, e.g. because you are stuck with AWS RDS or for some other reason, streaming data tables, or continuous views, can be implemented with pretty much PostgreSQL alone.

The basic idea is to have a table that allows for fast INSERT operations, is aggressively VACUUMed, and has some key that can be used to prune outdated entries. This table is fed with the events from the data stream and regularly pruned. VoilĂ : a streaming data table.

We have done some testing with two approaches on an UNLOGGED table, prune on every INSERT, and pruning at reqular intervals. UNLOGGED is not a problem here, since a view on a data stream can be considered pretty much as ephemeral.

The timed variant is about 5x - 8x faster on INSERTs. And if you balance the timing and the pruning interval right, the window size is almost as stable.

The examples are implemented in Python3 with psycopg2. Putting an index on the table can help or hurt performance, INSERT might get slower but pruning with DELETE faster, depending on the size and structure of the data. Feel free to experiment. In our case, a vanilla BRIN index did just fine.

Instead of using an external scheduler for pruning, like the Python daemon thread in the example, other scheduling mechanisms can be of course used, e.g. pg_cron, or a scheduled Lambda on AWS, or similar.

Feel free to experiment and improve...

Tuesday, May 19, 2020

MQTT as transport for PostgreSQL events

MQTT has become a de-facto standard for the transport of messages between IoT devices. As a result, a plethora of libraries and MQTT message brokers have become available. Can we use this to transport messages originating from PostgreSQL?

Aa message broker we use Eclipse Mosquitto which is dead simple to set up if you don't have to change the default settings. Such a default installation is neither secure nor highly available, but for our demo it will do just fine. The event generators are written in Python3 with Eclipse paho mqtt for Python.

There are at least two ways to generate events from a PostgreSQL database, pg_recvlogical and NOTIFY / LISTEN. Both have their advantages and shortcomings.


  • Configured on server and database level
  • Generates comprehensive information about everything that happens in the database
  • No additional programming neccessary
  • Needs plugins to decode messages, e.g. into JSON
  • Filtering has to be done later, e.g. by the decoder plugin
  • Configured on DDL and table level
  • Generates exactly the information and format you program into the triggers
  • Filtering can be done before sending the message
  • Needs trigger programming
  • The message size is limited to 8000 bytes
Examples for both approaches can be found here. The NOTIFY / LISTEN example lacks a proper decoder but this makes be a good excercise to start with. The pg_recvlogical example needs the wal2json plugin, which can be found here and the proper setup, which is also explained in the Readme. Please note, that the slot used in the example is mqtt_slot, not test_slot:

pg_recvlogical -d postgres --slot mqtt_slot --create-slot -P wal2json

Otherwise, setup.sql should generate all objects to run both examples.