Resampler plugin

Note: This plugin requires InfluxDB 3 Core.8.2 or later (uses the synchronous write API).

The Resampler Plugin interpolates time series with non-uniform timestamps (sensor timestamp jitter) onto a uniform time grid and writes the result to a separate measurement. Uniform sampling is required by most signal-processing and forecasting algorithms (FIR/IIR filters, FFT, ML models).

Numeric fields are interpolated; all other fields (strings, booleans) are carried onto the same grid points by last known value, so the full data set is preserved. A separate snap mode skips interpolation entirely: each point’s timestamp is rounded to the nearest grid node and all values and types stay unchanged (on node collisions the latest point wins).

Each run processes a sliding window of history. Output timestamps are exact multiples of the grid interval, so overlapping runs overwrite the same points idempotently and late-arriving data is picked up automatically. Every unique tag combination is resampled as an independent series, with all tags preserved.

The plugin does not fill large data gaps: when two neighboring source points are more than max_gap apart, grid points between them are not written, so sensor outages stay visible in the output.

Configuration

Plugin parameters may be specified as key-value pairs in the --trigger-arguments flag (CLI) or in the trigger_arguments field (API) when creating a trigger. This plugin supports TOML configuration files, which can be specified using the config_file_path parameter.

Plugin metadata

This plugin includes a JSON metadata schema in its docstring that defines supported trigger types and configuration parameters. This metadata enables the InfluxDB 3 Explorer UI to display and configure the plugin.

Required parameters

Grid parameters

Processing parameters

Output parameters

TOML configuration

To use a TOML configuration file, set the PLUGIN_DIR environment variable and specify the config_file_path in the trigger arguments. This is in addition to the --plugin-dir flag when starting InfluxDB 3 Core.

Example TOML configuration

resampler_config_scheduler.toml

For more information on using TOML configuration files, see the Using TOML Configuration Files section in the influxdb3_plugins/README.md.

Data requirements

Interpolation needs at least 2 source points per series inside the window; a grid node is written only when it has a source point on each side at most max_gap apart (or an exact source match). Snap mode has no such requirement.

Software Requirements

• InfluxDB 3 Core: with the Processing Engine enabled
• Python packages (declared in manifest.toml):
  • influxdata-plugin-utils>=0.3.0
  • scipy (installs numpy)

Installation steps

1. Start InfluxDB 3 Core with the Processing Engine enabled (--plugin-dir /path/to/plugins):

   influxdb3 serve \
     --node-id node0 \
     --object-store file \
     --data-dir ~/.influxdb3 \
     --plugin-dir ~/.plugins

2. Install required Python packages:

   influxdb3 install package "influxdata-plugin-utils>=0.3.0"
   influxdb3 install package scipy

Trigger setup

Scheduled resampling

influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/resampler/resampler.py \
  --trigger-spec "every:10s" \
  --trigger-arguments 'measurement=signal,target_measurement=signal_resampled,interval=1s,window=1min' \
  resampler_trigger

Scheduled resampling with TOML configuration

# Copy and edit the configuration file
cp resampler_config_scheduler.toml $PLUGIN_DIR/resampler_config.toml

influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/resampler/resampler.py \
  --trigger-spec "every:10s" \
  --trigger-arguments config_file_path=resampler_config.toml \
  resampler_trigger

Example usage

Example 1: Linear interpolation onto a 1-second grid

# Create the trigger
influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/resampler/resampler.py \
  --trigger-spec "every:10s" \
  --trigger-arguments 'measurement=signal,target_measurement=signal_resampled,interval=1s,window=10min' \
  resampler_trigger

# Query resampled data (after trigger runs)
influxdb3 query --database mydb "SELECT * FROM signal_resampled"

Source data with timestamp jitter:

signal value=39.1 1750000000083000000   # 00.083
signal value=40.2 1750000000947000000   # 00.947
signal value=41.0 1750000002114000000   # 02.114

Expected output

signal_resampled value=40.24 1750000001000000000  # 01.000
signal_resampled value=40.92 1750000002000000000  # 02.000

Grid points inside gaps larger than max_gap are not written and can be filled by a downstream fill plugin. Non-numeric fields follow the same gap rules but are carried by last known value instead of being interpolated.

Example 2: Snap mode

influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/resampler/resampler.py \
  --trigger-spec "every:10s" \
  --trigger-arguments 'measurement=signal,target_measurement=signal_resampled,interval=1s,mode=snap' \
  resampler_snap

Expected output

Timestamps are aligned to the grid; values and types are unchanged:

signal_resampled value=39.1 1750000000000000000  # 00.083 -> 00.000
signal_resampled value=40.2 1750000001000000000  # 00.947 -> 01.000
signal_resampled value=41.0 1750000002000000000  # 02.114 -> 02.000

Code overview

Files

• resampler.py: The main plugin code containing the handler for scheduled resampling
• resampler_config_scheduler.toml: Example TOML configuration file

Logging

Logs are stored in the _internal database in the system.processing_engine_logs table. To view logs:

influxdb3 query --database _internal \
  "SELECT * FROM system.processing_engine_logs WHERE trigger_name = 'resampler_trigger' ORDER BY event_time DESC LIMIT 20"

Main functions

process_scheduled_call(influxdb3_local, call_time, args)

Entry point for the scheduled trigger. Parses and validates the configuration, queries the source window, groups rows by tag set, resamples or snaps each series, and writes the result with retries.

resample_series(...)

Resamples all fields of one series: numeric fields selected for interpolation are recalculated at grid nodes (resample_field), all other carried fields use last known value (carry_field_previous), with the same max_gap rules.

snap_series(...)

Rounds each source point’s timestamp to the nearest grid node, keeping values and types unchanged; on node collisions the latest point wins per field.

Troubleshooting

Common issues

Issue: No output data

Solution: Check that the window contains at least 2 source points per series and that interval is not larger than window.

Issue: Missing points near “now”

Solution: The freshest grid points need a source neighbor on the right; they are written by the next run once that neighbor arrives. Increase offset to delay processing instead.

Issue: Holes in the output

Solution: Source gaps larger than max_gap are preserved by design. Increase max_gap to interpolate across larger gaps.

Issue: cubic falls back to linear

Solution: Cubic interpolation needs at least 4 points per series in the window; add more data or use linear.

Issue: A numeric field is missing from the output

Solution: fields is set and does not list it — numeric fields not listed there are dropped by design.

Issue: Duplicate-looking values in snap mode

Solution: Two source points rounded to the same grid node — the later one wins per field. Use a smaller interval to keep them apart.

Report an issue

For plugin issues, see the Plugins repository issues page.

Find support for InfluxDB 3 Core

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