Pipeline Settings – Feldera Test

Last Updated: 5/8/2026

Pipelines come with a set of configuration settings to toggle features, tune performance, and help with operations. If you’re on the Enterprise Edition, you will likely need to configure the resources section to tune the CPU, memory, and storage resources used by the Pipeline depending on your infrastructure needs. Other than that, users rarely need to deviate from the supplied defaults.

Editing configuration

. You can edit all pipeline settings when the pipeline is Stopped with storage cleared, and a limited subset when it storage is in use. <Tabs items=web-consolepythonfdaapi> <Tabs.Tab> Press the gear button in the top right corner of the code editor to access the dialog where you can edit the runtime and program configuration JSON.

Configure pipeline in web-console </Tabs.Tab> <Tabs.Tab> You can use RuntimeConfig.from_dict() to set the runtime configuration of a pipeline.

Example: Runtime configuration of a Pipeline </Tabs.Tab> <Tabs.Tab> You can toggle the storage for a pipeline with


  fda set-config {pipeline_name} storage [true|false]

</Tabs.Tab> <Tabs.Tab> Include only runtime_config or program_config fields in the body of a PATCH /v0/pipelines/{pipeline_name}, e.g. :


  curl -X PATCH "http://localhost:8080/v0/pipelines/feature-engineering" -H "Content-Type: application/json" -d '{"runtime_config":{"workers":8,"fault_tolerance":{"model":"none","checkpoint_interval_secs":60},"resources":{"memory_mb_max":16000}},"program_config":{"profile":"dev"}}' -s -o /dev/null

</Tabs.Tab> </Tabs>

Runtime configuration

<Callout type=“info”> Make sure to appropriately size resource limits (memory and storage), the number of worker threads and the storage backend to utilize available cluster resources. </Callout>

See the RuntimeConfig schema in the API reference.

Program configuration

The “optimized” compilation profile (default) should be used when running production pipelines where performance is important.

The “optimized_symbols” profile adds minimal debug information (to display line number and file information in backtraces). It can lead to significantly larger pipeline binary sizes (can be up to 2x in practice). However, it is useful to run with this profile if you want to debug unexpected panics during pipeline execution.

See the ProgramConfig schema in the API reference.