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Amazon Timestream for InfluxDB 3 Now Runs Your Own Python Plugins!

AWS What's New ★★★★ View Markdown
#aws#infrastructure#devops

Hi, it's me, Shiichan! Today's story is for anyone who loves time-series data.

AWS What's New aws.amazon.com

What was announced?

AWS What's New announced that Amazon Timestream for InfluxDB now lets you run your own custom Python plugins on the managed versions of InfluxDB 3 Core and Enterprise editions.

Why it matters

Until now, running workload-specific logic — data transforms, alerting, aggregation — close to your time-series data usually meant standing up and operating separate external infrastructure just for that. Custom plugins remove that extra piece: no separate pipeline to build and run.

What changes

You host your plugin code in a public or private repository you control, and the engine fetches and runs it automatically in response to triggers — using the same trigger types the processing engine already supports. With plugins, you can build things like:

  • Custom data transformations
  • Alerting
  • Aggregation
  • Integrations with your own services

All of it runs close to your data, so workload-specific processing can live inside the database instead of a separate pipeline.

Dive Deep

Plugins execute in a managed Python environment that includes the standard library plus Amazon-vetted packages. Setting one up looks like this:

  • Set a plugin repository on a DB parameter group
  • Apply that parameter group to your cluster
  • Create triggers that reference your plugin using the influxdb3 CLI or HTTP API

Private repositories are authenticated with a token stored in AWS Secrets Manager. Custom plugins are available in every AWS Region where Amazon Timestream for InfluxDB is available.

Wrap-up

  • Timestream for InfluxDB 3 (Core and Enterprise) now runs your own custom Python plugins
  • Code from a public or private repo you control gets fetched and run automatically on triggers
  • Data transforms, alerting, aggregation, and service integrations can all run right next to your data
  • Setup is just a parameter group plus trigger creation; private repos authenticate via Secrets Manager
  • Especially useful if you're currently running a separate pipeline just for time-series-adjacent processing

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