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.

1. Set up the Processing Engine
2. Add a Processing Engine plugin
   • Use example plugins
   • Create a custom plugin
3. Set up a trigger
   • Understand trigger types
   • Use the create trigger command
   • Trigger specification examples
4. Advanced trigger configuration
   • Access community plugins from GitHub
   • Pass arguments to plugins
   • Control trigger execution
   • Configure error handling for a trigger
   • Install Python dependencies

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.

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 - Quickly get started with prebuilt plugins
• Create a custom plugin - Build your own for specialized use cases

Use example plugins

InfluxData provides a public repository of example plugins that you can use immediately.

Browse plugin examples

Visit the influxdb3_plugins repository to find examples 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

Add example plugins

You can either copy a plugin or retrieve it directly from the repository:

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

# To retrieve and use a plugin directly from GitHub,
#  use the `gh:` prefix in the plugin filename:
influxdb3 create trigger \
    --trigger-spec "every:1m" \
    --plugin-filename "gh:examples/schedule/system_metrics/system_metrics.py" \
    --database my_database \
    system_metrics

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

• 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

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. 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. 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. 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

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

Data write example

# 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.

Scheduled events example

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

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

The plugin receives the scheduled call time.

HTTP requests example

# 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 available at /api/v3/engine/<ENDPOINT_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.

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

Advanced trigger configuration

After creating basic triggers, you can enhance your plugins with these advanced features:

Access community plugins from GitHub

Skip downloading plugins by referencing them directly from GitHub:

# Create a trigger using a plugin from GitHub
influxdb3 create trigger \
  --trigger-spec "every:1m" \
  --plugin-filename "gh:examples/schedule/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

Configure your triggers

Pass configuration arguments

Provide runtine configuration to your plugins:

# Pass threshold and email settings to a plugin
Provide runtime configuration to your plugins:
  --trigger-spec "every:1h" \
  --plugin-filename "threshold_check.py" \
  --trigger-arguments threshold=90,notify_email=admin@example.com \
  --database my_database \
  threshold_monitor

Your plugin accesses these values through the args parameter:

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}")

Set execution mode

Choose between synchronous (default) or asynchronous execution:

# 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

Use asynchronous execution when:

• Processing might take longer than the trigger interval
• Multiple events need to be handled simultaneously
• Performance is more important than sequential execution

Configure error handling

Control how your trigger responds to errors:

# 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

Install Python dependencies

If your plugin needs additional Python packages, use the influxdb3 install command:

# Install a package directly
influxdb3 install package pandas

# With Docker
docker exec -it CONTAINER_NAME influxdb3 install package pandas

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