Processing engine and Python plugins

Use the Processing Engine in InfluxDB 3 Core to extend your database with custom Python code. Trigger your code on write, on a schedule, or on demand to automate workflows, transform data, and create API endpoints.

What is the Processing Engine?

The Processing Engine is an embedded Python virtual machine that runs inside your InfluxDB 3 Core database. You configure triggers to run your Python plugin code in response to:

• Data writes - Process and transform data as it enters the database
• Scheduled events - Run code at defined intervals or specific times
• HTTP requests - Expose custom API endpoints that execute your code

You can use the Processing Engine’s in-memory cache to manage state between executions and build stateful applications directly in your database.

This guide walks you through setting up the Processing Engine, creating your first plugin, and configuring triggers that execute your code on specific events.

Before you begin

Ensure you have:

• A working InfluxDB 3 Core instance
• Access to command line
• Python installed if you’re writing your own plugin
• Basic knowledge of the InfluxDB CLI

Once you have all the prerequisites in place, follow these steps to implement the Processing Engine for your data automation needs.

• Set up the Processing Engine
• Add a Processing Engine plugin
  • Upload plugins from local machine
  • Update existing plugins
  • View loaded plugins
• Set up a trigger
• Manage plugin dependencies
• Plugin security

Set up the Processing Engine

To activate the Processing Engine, start your InfluxDB 3 Core server with the --plugin-dir flag. This flag tells InfluxDB where to load your plugin files.

your-install-location/
├── influxdb3
└── python/

influxdb3 serve \
  --
NODE_ID
 \
  --object-store 
OBJECT_STORE_TYPE
 \
  --plugin-dir 
PLUGIN_DIR

In the example above, replace the following:

• NODE_ID: Unique identifier for your instance
• OBJECT_STORE_TYPE: Type of object store (for example, file or s3)
• PLUGIN_DIR: Absolute path to the directory where plugin files are stored. Store all plugin files in this directory or its subdirectories.

Configure distributed environments

When running InfluxDB 3 Core in a distributed setup, follow these steps to configure the Processing Engine:

1. Decide where each plugin should run
   • Data processing plugins, such as WAL plugins, run on ingester nodes
   • HTTP-triggered plugins run on nodes handling API requests
   • Scheduled plugins can run on any configured node
2. Enable plugins on the correct instance
3. Maintain identical plugin files across all instances where plugins run
   • Use shared storage or file synchronization tools to keep plugins consistent

Add a Processing Engine plugin

A plugin is a Python script that defines a specific function signature for a trigger (trigger spec). When the specified event occurs, InfluxDB runs the plugin.

Choose a plugin strategy

You have two main options for adding plugins to your InfluxDB instance:

• Use example plugins - Get started with prebuilt plugins
• Create a custom plugin - Build your own for specialized use cases

Use example plugins

InfluxData maintains a repository of official and community plugins that you can use immediately in your Processing Engine setup.

Browse the plugin library to find examples and InfluxData official plugins for:

• Data transformation: Process and transform incoming data
• Alerting: Send notifications based on data thresholds
• Aggregation: Calculate statistics on time series data
• Integration: Connect to external services and APIs
• System monitoring: Track resource usage and health metrics

For community contributions, see the influxdb3_plugins repository on GitHub.

Add example plugins

You have two options for using plugins from the repository:

Option 1: Copy plugins locally

Clone the influxdata/influxdb3_plugins repository and copy plugins to your configured plugin directory:

# Clone the repository
git clone https://github.com/influxdata/influxdb3_plugins.git
   
# Copy a plugin to your configured plugin directory
cp influxdb3_plugins/influxdata/system_metrics/system_metrics.py /path/to/plugins/

Option 2: Reference plugins directly from GitHub

Skip downloading plugins by referencing them directly from GitHub using the gh: prefix:

# Create a trigger using a plugin from GitHub
influxdb3 create trigger \
  --trigger-spec "every:1m" \
  --plugin-filename "gh:influxdata/system_metrics/system_metrics.py" \
  --database my_database \
  system_metrics

This approach:

• Ensures you’re using the latest version
• Simplifies updates and maintenance
• Reduces local storage requirements

Option 3: Use a custom plugin repository

For organizations that maintain their own plugin repositories or need to use private/internal plugins, configure a custom plugin repository URL:

# Start the server with a custom plugin repository
influxdb3 serve \
  --node-id node0 \
  --object-store file \
  --data-dir ~/.influxdb3 \
  --plugin-dir ~/.plugins \
  --plugin-repo "https://internal.company.com/influxdb-plugins/"

Then reference plugins from your custom repository using the gh: prefix:

# Fetches from: https://internal.company.com/influxdb-plugins/myorg/custom_plugin.py
influxdb3 create trigger \
  --trigger-spec "every:5m" \
  --plugin-filename "gh:myorg/custom_plugin.py" \
  --database my_database \
  custom_trigger

Use cases for custom repositories:

• Private plugins: Host proprietary plugins not suitable for public repositories
• Air-gapped environments: Use internal mirrors when external internet access is restricted
• Development and staging: Test plugins from development branches before production deployment
• Compliance requirements: Meet data governance policies requiring internal hosting

The --plugin-repo option accepts any HTTP/HTTPS URL that serves raw plugin files. See the plugin-repo configuration option for more details.

Plugins have various functions such as:

• Receive plugin-specific arguments (such as written data, call time, or an HTTP request)
• Access keyword arguments (as args) passed from trigger arguments configurations
• Access the influxdb3_local shared API to write data, query data, and managing state between executions

For more information about available functions, arguments, and how plugins interact with InfluxDB, see how to Extend plugins.

Create a custom plugin

To build custom functionality, you can create your own Processing Engine plugin.

Prerequisites

Before you begin, make sure:

• The Processing Engine is enabled on your InfluxDB 3 Core instance.
• You’ve configured the --plugin-dir where plugin files are stored.
• You have access to that plugin directory.

Steps to create a plugin:

• Choose your plugin type
• Create your plugin file
• Next Steps

Choose your plugin type

Choose a plugin type based on your automation goals:

Create your plugin file

Plugins now support both single-file and multifile architectures:

Single-file plugins:

• Create a .py file in your plugins directory
• Add the appropriate function signature based on your chosen plugin type
• Write your processing logic inside the function

Multifile plugins:

• Create a directory in your plugins directory
• Add an __init__.py file as the entry point (required)
• Organize supporting modules in additional .py files
• Import and use modules within your plugin code

Example multifile plugin structure

my_plugin/
├── __init__.py       # Required - entry point with trigger function
├── utils.py          # Supporting module
├── processors.py     # Data processing functions
└── config.py         # Configuration helpers

The __init__.py file must contain your trigger function:

# my_plugin/__init__.py
from .processors import process_data
from .config import get_settings

def process_writes(influxdb3_local, table_batches, args=None):
    settings = get_settings()
    for table_batch in table_batches:
        process_data(influxdb3_local, table_batch, settings)

Supporting modules can contain helper functions:

# my_plugin/processors.py
def process_data(influxdb3_local, table_batch, settings):
    # Processing logic here
    pass

After writing your plugin, create a trigger to connect it to a database event and define when it runs.

Create a data write plugin

Use a data write plugin to process data as it’s written to the database. These plugins use table: or all_tables: trigger specifications. Ideal use cases include:

• Data transformation and enrichment
• Alerting on incoming values
• Creating derived metrics

def process_writes(influxdb3_local, table_batches, args=None):
    # Process data as it's written to the database
    for table_batch in table_batches:
        table_name = table_batch["table_name"]
        rows = table_batch["rows"]
        
        # Log information about the write
        influxdb3_local.info(f"Processing {len(rows)} rows from {table_name}")
        
        # Write derived data back to the database
        line = LineBuilder("processed_data")
        line.tag("source_table", table_name)
        line.int64_field("row_count", len(rows))
        influxdb3_local.write(line)

Create a scheduled plugin

Scheduled plugins run at defined intervals using every: or cron: trigger specifications. Use them for:

• Periodic data aggregation
• Report generation
• System health checks

def process_scheduled_call(influxdb3_local, call_time, args=None):
    # Run code on a schedule
    
    # Query recent data
    results = influxdb3_local.query("SELECT * FROM metrics WHERE time > now() - INTERVAL '1 hour'")
    
    # Process the results
    if results:
        influxdb3_local.info(f"Found {len(results)} recent metrics")
    else:
        influxdb3_local.warn("No recent metrics found")

Create an HTTP request plugin

HTTP request plugins respond to API calls using request: trigger specifications. Use them for:

• Creating custom API endpoints
• Webhooks for external integrations
• User interfaces for data interaction

def process_request(influxdb3_local, query_parameters, request_headers, request_body, args=None):
    # Handle HTTP requests to a custom endpoint
    
    # Log the request parameters
    influxdb3_local.info(f"Received request with parameters: {query_parameters}")
    
    # Process the request body
    if request_body:
        import json
        data = json.loads(request_body)
        influxdb3_local.info(f"Request data: {data}")
    
    # Return a response (automatically converted to JSON)
    return {"status": "success", "message": "Request processed"}

Next steps

After writing your plugin:

• Create a trigger to connect your plugin to database events
• Install any Python dependencies your plugin requires
• Learn how to extend plugins with the API

Upload plugins from local machine

For local development and testing, you can upload plugin files directly from your machine when creating triggers. This eliminates the need to manually copy files to the server’s plugin directory.

Use the --upload flag with --path to transfer local files or directories:

# Upload single-file plugin
influxdb3 create trigger \
  --trigger-spec "every:10s" \
  --path "/local/path/to/plugin.py" \
  --upload \
  --database metrics \
  my_trigger

# Upload multifile plugin directory
influxdb3 create trigger \
  --trigger-spec "every:30s" \
  --path "/local/path/to/plugin-dir" \
  --upload \
  --database metrics \
  complex_trigger

When to use plugin upload:

• Local plugin development and testing
• Deploying plugins without SSH access to the server
• Rapid iteration on plugin code
• Automating plugin deployment in CI/CD pipelines

For more information, see the influxdb3 create trigger CLI reference.

Update existing plugins

Modify plugin code for running triggers without recreating them. This allows you to iterate on plugin development while preserving trigger configuration and history.

Use the influxdb3 update trigger command:

# Update single-file plugin
influxdb3 update trigger \
  --database metrics \
  --trigger-name my_trigger \
  --path "/path/to/updated/plugin.py"

# Update multifile plugin
influxdb3 update trigger \
  --database metrics \
  --trigger-name complex_trigger \
  --path "/path/to/updated/plugin-dir"

The update operation:

• Replaces plugin files immediately
• Preserves trigger configuration (spec, schedule, arguments)
• Requires admin token for security
• Works with both local paths and uploaded files

For complete reference, see influxdb3 update trigger.

View loaded plugins

Monitor which plugins are loaded in your system for operational visibility and troubleshooting.

Option 1: Use the CLI command

# List all plugins
influxdb3 show plugins --token $ADMIN_TOKEN

# JSON format for programmatic access
influxdb3 show plugins --format json --token $ADMIN_TOKEN

Option 2: Query the system table

The system.plugin_files table in the _internal database provides detailed plugin file information:

influxdb3 query \
  -d _internal \
  "SELECT * FROM system.plugin_files ORDER BY plugin_name" \
  --token $ADMIN_TOKEN

Available columns:

• plugin_name (String): Trigger name
• file_name (String): Plugin file name
• file_path (String): Full server path
• size_bytes (Int64): File size
• last_modified (Int64): Modification timestamp (milliseconds)

Example queries:

-- Find plugins by name
SELECT * FROM system.plugin_files WHERE plugin_name = 'my_trigger';

-- Find large plugins
SELECT plugin_name, size_bytes
FROM system.plugin_files
WHERE size_bytes > 10000;

-- Check modification times
SELECT plugin_name, file_name, last_modified
FROM system.plugin_files
ORDER BY last_modified DESC;

For more information, see the influxdb3 show plugins reference and Query system data.

Set up a trigger

Understand trigger types

Use the create trigger command

Use the influxdb3 create trigger command with the appropriate trigger specification:

influxdb3 create trigger \
  --trigger-spec 
SPECIFICATION
 \
  --plugin-filename 
PLUGIN_FILE
 \
  --database 
DATABASE_NAME
 \
  
TRIGGER_NAME

In the example above, replace the following:

• SPECIFICATION: Trigger specification
• PLUGIN_FILE: Plugin filename relative to your configured plugin directory
• DATABASE_NAME: Name of the database
• TRIGGER_NAME: Name of the new trigger

Trigger specification examples

Trigger on data writes

# Trigger on writes to a specific table
# The plugin file must be in your configured plugin directory
influxdb3 create trigger \
  --trigger-spec "table:sensor_data" \
  --plugin-filename "process_sensors.py" \
  --database my_database \
  sensor_processor

# Trigger on writes to all tables
influxdb3 create trigger \
  --trigger-spec "all_tables" \
  --plugin-filename "process_all_data.py" \
  --database my_database \
  all_data_processor

The trigger runs when the database flushes ingested data for the specified tables to the Write-Ahead Log (WAL) in the Object store (default is every second).

The plugin receives the written data and table information.

Trigger on data writes with table exclusion

If you want to use a single trigger for all tables but exclude specific tables, you can use trigger arguments and your plugin code to filter out unwanted tables–for example:

influxdb3 create trigger \
  --database 
DATABASE_NAME
 \
  --token 
AUTH_TOKEN
 \
  --plugin-filename processor.py \
  --trigger-spec "all_tables" \
  --trigger-arguments "exclude_tables=temp_data,debug_info,system_logs" \
  data_processor

Replace the following:

• DATABASE_NAME: the name of the database
• AUTH_TOKEN: your token

Then, in your plugin:

# processor.py
def on_write(self, database, table_name, batch):
    # Get excluded tables from trigger arguments
    excluded_tables = set(self.args.get('exclude_tables', '').split(','))

    if table_name in excluded_tables:
        return

    # Process allowed tables
    self.process_data(database, table_name, batch)

Recommendations

• Early return: Check exclusions as early as possible in your plugin.
• Efficient lookups: Use sets for O(1) lookup performance with large exclusion lists.
• Performance: Log skipped tables for debugging but avoid excessive logging in production.
• Multiple triggers: For few tables, consider creating separate table-specific triggers instead of filtering within plugin code. See HTTP API Processing engine endpoints for managing triggers.

Trigger on a schedule

# Run every 5 minutes
influxdb3 create trigger \
  --trigger-spec "every:5m" \
  --plugin-filename "periodic_check.py" \
  --database my_database \
  regular_check

# Run on a cron schedule (8am daily)
# Supports extended cron format with seconds
influxdb3 create trigger \
  --trigger-spec "cron:0 0 8 * * *" \
  --plugin-filename "daily_report.py" \
  --database my_database \
  daily_report

The plugin receives the scheduled call time.

Trigger on HTTP requests

# Create an endpoint at /api/v3/engine/webhook
influxdb3 create trigger \
  --trigger-spec "request:webhook" \
  --plugin-filename "webhook_handler.py" \
  --database my_database \
  webhook_processor

Access your endpoint at /api/v3/engine/{REQUEST_PATH} (in this example, /api/v3/engine/webhook). The trigger is enabled by default and runs when an HTTP request is received at the specified path.

To run the plugin, send a GET or POST request to the endpoint–for example:

curl http://localhost:8181/api/v3/engine/webhook

The plugin receives the HTTP request object with methods, headers, and body.

To view triggers associated with a database, use the influxdb3 show summary command:

influxdb3 show summary --database my_database --token AUTH_TOKEN

Pass arguments to plugins

Use trigger arguments to pass configuration from a trigger to the plugin it runs. You can use this for:

• Threshold values for monitoring
• Connection properties for external services
• Configuration settings for plugin behavior

influxdb3 create trigger \
  --trigger-spec "every:1h" \
  --plugin-filename "threshold_check.py" \
  --trigger-arguments threshold=90,notify_email=admin@example.com \
  --database my_database \
  threshold_monitor

The arguments are passed to the plugin as a Dict[str, str] where the key is the argument name and the value is the argument value:

def process_scheduled_call(influxdb3_local, call_time, args=None):
    if args and "threshold" in args:
        threshold = float(args["threshold"])
        email = args.get("notify_email", "default@example.com")
        
        # Use the arguments in your logic
        influxdb3_local.info(f"Checking threshold {threshold}, will notify {email}")

Control trigger execution

By default, triggers run synchronously—each instance waits for previous instances to complete before executing.

To allow multiple instances of the same trigger to run simultaneously, configure triggers to run asynchronously:

# Allow multiple trigger instances to run simultaneously
influxdb3 create trigger \
  --trigger-spec "table:metrics" \
  --plugin-filename "heavy_process.py" \
  --run-asynchronous \
  --database my_database \
  async_processor

Configure error handling for a trigger

To configure error handling behavior for a trigger, use the --error-behavior <ERROR_BEHAVIOR> CLI option with one of the following values:

• log (default): Log all plugin errors to stdout and the system.processing_engine_logs system table.
• retry: Attempt to run the plugin again immediately after an error.
• disable: Automatically disable the plugin when an error occurs (can be re-enabled later via CLI).

# Automatically retry on error
influxdb3 create trigger \
  --trigger-spec "table:important_data" \
  --plugin-filename "critical_process.py" \
  --error-behavior retry \
  --database my_database \
  critical_processor

# Disable the trigger on error
influxdb3 create trigger \
  --trigger-spec "request:webhook" \
  --plugin-filename "webhook_handler.py" \
  --error-behavior disable \
  --database my_database \
  auto_disable_processor

Manage plugin dependencies

Use the influxdb3 install package command to add third-party libraries (like pandas, requests, or influxdb3-python) to your plugin environment.
This installs packages into the Processing Engine’s embedded Python environment to ensure compatibility with your InfluxDB instance.

# Use the CLI to install a Python package
influxdb3 install package pandas

# Use the CLI to install a Python package in a Docker container
docker exec -it 
CONTAINER_NAME
 influxdb3 install package pandas

These examples install the specified Python package (for example, pandas) into the Processing Engine’s embedded virtual environment.

• Use the CLI command when running InfluxDB directly on your system.
• Use the Docker variant if you’re running InfluxDB in a containerized environment.

InfluxDB creates a Python virtual environment in your plugins directory with the specified packages installed.

Disable package installation for secure environments

For air-gapped deployments or environments with strict security requirements, you can disable Python package installation while maintaining Processing Engine functionality.

Start the server with --package-manager disabled:

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

When package installation is disabled:

• The Processing Engine continues to function normally for triggers
• Plugin code executes without restrictions
• Package installation commands are blocked
• Pre-installed dependencies in the virtual environment remain available

Pre-install required dependencies:

Before disabling the package manager, install all required Python packages:

# Install packages first
influxdb3 install package pandas requests numpy

# Then start with disabled package manager
influxdb3 serve \
  --plugin-dir ~/.plugins \
  --package-manager disabled

Use cases for disabled package management:

• Air-gapped environments without internet access
• Compliance requirements prohibiting runtime package installation
• Centrally managed dependency environments
• Security policies requiring pre-approved packages only

For more configuration options, see –package-manager.

Plugin security

The Processing Engine includes security features to protect your InfluxDB 3 Core instance from unauthorized code execution and file system attacks.

Plugin path validation

All plugin file paths are validated to prevent directory traversal attacks. The system blocks:

• Relative paths with parent directory references (../, ../../)
• Absolute paths (/etc/passwd, /usr/bin/script.py)
• Symlinks that escape the plugin directory

When creating or updating triggers, plugin paths must resolve within the configured --plugin-dir.

Example of blocked paths:

# These will be rejected
influxdb3 create trigger \
  --path "../../../etc/passwd" \  # Blocked: parent directory traversal
  ...

influxdb3 create trigger \
  --path "/tmp/malicious.py" \    # Blocked: absolute path
  ...

Valid plugin paths:

# These are allowed
influxdb3 create trigger \
  --path "myapp/plugin.py" \      # Relative to plugin-dir
  ...

influxdb3 create trigger \
  --path "transforms/data.py" \    # Subdirectory in plugin-dir
  ...

Upload and update permissions

Plugin upload and update operations require admin tokens to prevent unauthorized code deployment:

• --upload flag requires admin privileges
• update trigger command requires admin token
• Standard resource tokens cannot upload or modify plugin code

This security model ensures only administrators can introduce or modify executable code in your database.

Best practices

For development:

• Use the --upload flag to deploy plugins during development
• Test plugins in non-production environments first
• Review plugin code before deployment

For production:

• Pre-deploy plugins to the server’s plugin directory via secure file transfer
• Use custom plugin repositories for vetted, approved plugins
• Disable package installation (--package-manager disabled) in locked-down environments
• Audit plugin files using the system.plugin_files table
• Implement change control processes for plugin updates

For more security configuration options, see Configuration options.