Use the {{% product-name %}} Processing engine to run code and perform tasks for different database events. {{% product-name %}} provides the InfluxDB 3 Processing engine, an embedded Python VM that can dynamically load and trigger Python plugins in response to events in your database. ## Key Concepts ### Plugins A Processing engine _plugin_ is Python code you provide to run tasks, such as downsampling data, monitoring, creating alerts, or calling external services. > [!Note] > #### Contribute and use community plugins > > [influxdata/influxdb3_plugins](https://github.com/influxdata/influxdb3_plugins) is a public repository on GitHub where you can find > and contribute example plugins. > You can reference plugins from the repository directly within a trigger configuration. ### Triggers A _trigger_ is an InfluxDB 3 resource you create to associate a database event (for example, a WAL flush) with the plugin that should run. When an event occurs, the trigger passes configuration details, optional arguments, and event data to the plugin. The Processing engine provides four types of triggers--each type corresponds to an event type with event-specific configuration to let you handle events with targeted logic. - **WAL Flush**: Triggered when the write-ahead log (WAL) is flushed to the object store (default is every second). - **Scheduled Tasks**: Triggered on a schedule you specify using cron syntax. - **On-request**: Triggered on a GET or POST request to the bound HTTP API endpoint at `/api/v3/engine/`. ### Activate the Processing engine To enable the Processing engine, start the {{% product-name %}} server with the `--plugin-dir` option and a path to your plugins directory. If the directory doesn’t exist, the server creates it. ```bash influxdb3 serve \ --node-id node0 \ --object-store [OBJECT STORE TYPE]\ --plugin-dir /path/to/plugins ``` ## Shared API All plugin types provide the InfluxDB 3 _shared API_ for interacting with the database. The shared API provides access to the following: - `LineBuilder` to create Line Protocol lines for writing to the database - `query` to query data from any database - `info`, `warn`, and `error` to log messages to the database log, which is output in the server logs and captured in system tables queryable by SQL ### LineBuilder The `LineBuilder` is a simple API for building lines of Line Protocol to write into the database. Writes are buffered while the plugin runs and are flushed when the plugin completes. The `LineBuilder` API is available in all plugin types. The following example shows how to use the `LineBuilder` API: ```python # Build line protocol incrementally line = LineBuilder("weather") line.tag("location", "us-midwest") line.float64_field("temperature", 82.5) line.time_ns(1627680000000000000) influxdb3_local.write(line) # Output line protocol as a string ("weather,location=us-midwest temperature=82.5 1627680000000000000") line_str = line.build() ``` Here is the Python implementation of the `LineBuilder` API: ```python from typing import Optional from collections import OrderedDict class InfluxDBError(Exception): """Base exception for InfluxDB-related errors""" pass class InvalidMeasurementError(InfluxDBError): """Raised when measurement name is invalid""" pass class InvalidKeyError(InfluxDBError): """Raised when a tag or field key is invalid""" pass class InvalidLineError(InfluxDBError): """Raised when a line protocol string is invalid""" pass class LineBuilder: def __init__(self, measurement: str): if ' ' in measurement: raise InvalidMeasurementError("Measurement name cannot contain spaces") self.measurement = measurement self.tags: OrderedDict[str, str] = OrderedDict() self.fields: OrderedDict[str, str] = OrderedDict() self._timestamp_ns: Optional[int] = None def _validate_key(self, key: str, key_type: str) -> None: """Validate that a key does not contain spaces, commas, or equals signs.""" if not key: raise InvalidKeyError(f"{key_type} key cannot be empty") if ' ' in key: raise InvalidKeyError(f"{key_type} key '{key}' cannot contain spaces") if ',' in key: raise InvalidKeyError(f"{key_type} key '{key}' cannot contain commas") if '=' in key: raise InvalidKeyError(f"{key_type} key '{key}' cannot contain equals signs") def tag(self, key: str, value: str) -> 'LineBuilder': """Add a tag to the line protocol.""" self._validate_key(key, "tag") self.tags[key] = str(value) return self def uint64_field(self, key: str, value: int) -> 'LineBuilder': """Add an unsigned integer field to the line protocol.""" self._validate_key(key, "field") if value < 0: raise ValueError(f"uint64 field '{key}' cannot be negative") self.fields[key] = f"{value}u" return self def int64_field(self, key: str, value: int) -> 'LineBuilder': """Add an integer field to the line protocol.""" self._validate_key(key, "field") self.fields[key] = f"{value}i" return self def float64_field(self, key: str, value: float) -> 'LineBuilder': """Add a float field to the line protocol.""" self._validate_key(key, "field") # Check if value has no decimal component self.fields[key] = f"{int(value)}.0" if value % 1 == 0 else str(value) return self def string_field(self, key: str, value: str) -> 'LineBuilder': """Add a string field to the line protocol.""" self._validate_key(key, "field") # Escape quotes and backslashes in string values escaped_value = value.replace('"', '\\"').replace('\\', '\\\\') self.fields[key] = f'"{escaped_value}"' return self def bool_field(self, key: str, value: bool) -> 'LineBuilder': """Add a boolean field to the line protocol.""" self._validate_key(key, "field") self.fields[key] = 't' if value else 'f' return self def time_ns(self, timestamp_ns: int) -> 'LineBuilder': """Set the timestamp in nanoseconds.""" self._timestamp_ns = timestamp_ns return self def build(self) -> str: """Build the line protocol string.""" # Start with measurement name (escape commas only) line = self.measurement.replace(',', '\\,') # Add tags if present if self.tags: tags_str = ','.join( f"{k}={v}" for k, v in self.tags.items() ) line += f",{tags_str}" # Add fields (required) if not self.fields: raise InvalidLineError(f"At least one field is required: {line}") fields_str = ','.join( f"{k}={v}" for k, v in self.fields.items() ) line += f" {fields_str}" # Add timestamp if present if self._timestamp_ns is not None: line += f" {self._timestamp_ns}" return line ``` ### Query The shared API `query` function executes an SQL query with optional parameters (a [parameterized query](/influxdb3/version/query-data/sql/parameterized-queries/)) and returns results as a `List` of `Dict[String, Any]` where the key is the column name and the value is the column value. The `query` function is available in all plugin types. The following examples show how to use the `query` function: ```python influxdb3_local.query("SELECT * from foo where bar = 'baz' and time > now() - INTERVAL '1 hour'") # Or using parameterized queries args = {"bar": "baz"} influxdb3_local.query("SELECT * from foo where bar = $bar and time > now() - INTERVAL '1 hour'", args) ``` ### Logging The shared API `info`, `warn`, and `error` functions log messages to the database log, which is output in the server logs and captured in system tables queryable by SQL. The `info`, `warn`, and `error` functions are available in all plugin types. Each function accepts multiple arguments, converts them to strings, and logs them as a single, space-separated message. The following examples show how to use the `info`, `warn`, and `error` logging functions: ```python influxdb3_local.info("This is an info message") influxdb3_local.warn("This is a warning message") influxdb3_local.error("This is an error message") # Log a message that contains a data object obj_to_log = {"hello": "world"} influxdb3_local.info("This is an info message with an object", obj_to_log) ``` ### Trigger Settings #### 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: ```bash # Create an asynchronous trigger influx create trigger --run-asynchronously #### Configure error handling #### Configure error behavior for plugins The Processing engine logs all plugin errors to stdout and the `system.processing_engine_logs` system table. To configure additional error handling for a trigger, use the `--error-behavior` flag: - `--error-behavior retry`: Attempt to run the plugin again immediately after an error - `--error-behavior disable`: Automatically disable the plugin when an error occurs (can be re-enabled later via CLI) ```bash # Create a trigger that retries on error influx create trigger --error-behavior retry # Create a trigger that disables the plugin on error influx create trigger --error-behavior disable This behavior can be changed by specifying the "Error behavior", via the `--error-behavior` flag. Apart from the default `log`, you may set * `--error-behavior retry` will immediately retry the plugin trigger in the event of error. * `--error-behavior disable` will turn off the plugin as soon as an error occurs. You can enable it again using the CLI. ### Trigger arguments A plugin can receive arguments from the trigger that runs it. You can use this to provide runtime configuration and drive behavior of a plugin—for example: - threshold values for monitoring - connection properties for connecting to third-party services To pass arguments to a plugin, specify trigger arguments in a comma-separated list of key-value pairs--for example, using the CLI: ```bash influxdb3 create trigger --trigger-arguments key1=val1,key2=val2 ``` 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--for example: ```python args = { "key1": "value1", "key2": "value2", } ``` The following example shows how to access and use an argument in a WAL plugin: ```python def process_writes(influxdb3_local, table_batches, args=None): if args and "threshold" in args: threshold = int(args["threshold"]) influxdb3_local.info(f"Threshold is {threshold}") else: influxdb3_local.warn("No threshold provided") ``` The `args` parameter is optional. If a plugin doesn’t require arguments, you can omit it from the trigger definition. ## Import plugin dependencies Use the `influxdb3 install` command to download and install Python packages that your plugin depends on. ```bash influxdb3 install package ``` ### Use `influxdb3 install` with Docker 1. Start the server ```bash docker run \ --name CONTAINER_NAME \ -v /path/to/.influxdb3/data:/data \ -v /path/to/.influxdb3/plugins:/plugins \ quay.io/influxdb/influxdb3-{{< product-key >}}:latest \ serve --node-id=node0 \ --object-store=file \ --data-dir=/data \ --http-bind=localhost:8183 \ --plugin-dir=/plugins ``` 2. Use `docker exec` to run the `influxdb3 install` command: ```bash docker exec -it CONTAINER_NAME influxdb3 install package pandas ``` The result is an active Python virtual environment with the package installed in `/.venv`. You can specify additional options to install dependencies from a `requirements.txt` file or a custom virtual environment path. For more information, see the `influxdb3` CLI help: ```bash influxdb3 install package --help ``` ## Configure plugin triggers Triggers define when and how plugins execute in response to database events. Each trigger type corresponds to a specific event, allowing precise control over automation within {{% product-name %}}. ### WAL flush trigger When a WAL flush plugin is triggered, the plugin receives a list of `table_batches` filtered by the trigger configuration (either _all tables_ in the database or a specific table). The following example shows a simple WAL flush plugin: ```python def process_writes(influxdb3_local, table_batches, args=None): for table_batch in table_batches: # Skip the batch if table_name is write_reports if table_batch["table_name"] == "write_reports": continue row_count = len(table_batch["rows"]) # Double the row count if table name matches args table_name if args and "double_count_table" in args and table_batch["table_name"] == args["double_count_table"]: row_count *= 2 # Use the LineBuilder API to write data line = LineBuilder("write_reports")\ .tag("table_name", table_batch["table_name"])\ .int64_field("row_count", row_count) influxdb3_local.write(line) influxdb3_local.info("wal_plugin.py done") ``` #### WAL flush trigger configuration When you create a trigger, you associate it with a database and provide configuration specific to the trigger type. For a WAL flush trigger you specify a `trigger-spec`, which determines when the plugin is triggered (and what table data it receives): - `all-tables`: triggers the plugin on every write to the associated database - `table:` triggers the plugin function only for writes to the specified table. The following example creates a WAL flush trigger for the `gh:examples/wal_plugin/wal_plugin.py` plugin. ```bash influxdb3 create trigger \ --trigger-spec "table:TABLE_NAME" \ --plugin-filename "gh:examples/wal_plugin/wal_plugin.py" \ --database DATABASE_NAME TRIGGER_NAME ``` The `gh:` prefix lets you fetch a plugin file directly from the [influxdata/influxdb3_plugins](https://github.com/influxdata/influxdb3_plugins) repository in GitHub. Without the prefix, the server looks for the file inside of the plugins directory. To provide additional configuration to your plugin, pass a list of key-value pairs in the `--trigger-arguments` option and, in your plugin, use the `args` parameter to receive the arguments. For more information about trigger arguments, see the CLI help: ```bash influxdb3 create trigger help ``` ### Schedule trigger Schedule plugins run on a schedule specified in cron syntax. The plugin receives the local API, the time of the trigger, and any arguments passed in the trigger definition. Here's an example of a simple schedule plugin: ```python # see if a table has been written to in the last 5 minutes def process_scheduled_call(influxdb3_local, time, args=None): if args and "table_name" in args: table_name = args["table_name"] result = influxdb3_local.query(f"SELECT * FROM {table_name} WHERE time > now() - 'interval 5m'") # write an error log if the result is empty if not result: influxdb3_local.error(f"No data in {table_name} in the last 5 minutes") else: influxdb3_local.error("No table_name provided for schedule plugin") ``` #### Schedule trigger configuration Schedule plugins are set with a `trigger-spec` of `schedule:` or `every:`. The `args` parameter can be used to pass configuration to the plugin. For example, if we wanted to use the system-metrics example from the Github repo and have it collect every 10 seconds we could use the following trigger definition: ```bash influxdb3 create trigger \ --trigger-spec "every:10s" \ --plugin-filename "gh:examples/schedule/system_metrics/system_metrics.py" \ --database mydb system-metrics ``` ### On Request trigger On Request plugins are triggered by a request to a custom HTTP API endpoint. The plugin receives the shared API, query parameters `Dict[str, str]`, request headers `Dict[str, str]`, the request body (as bytes), and any arguments passed in the trigger definition. On Request plugin responses follow conventions for [Flask responses](https://flask.palletsprojects.com/en/stable/quickstart/#about-responses). #### Example: On Request plugin ```python import json def process_request(influxdb3_local, query_parameters, request_headers, request_body, args=None): for k, v in query_parameters.items(): influxdb3_local.info(f"query_parameters: {k}={v}") for k, v in request_headers.items(): influxdb3_local.info(f"request_headers: {k}={v}") request_data = json.loads(request_body) influxdb3_local.info("parsed JSON request body:", request_data) # write the data to the database line = LineBuilder("request_data").tag("tag1", "tag1_value").int64_field("field1", 1) # get a string of the line to return as the body line_str = line.build() influxdb3_local.write(line) return {"status": "ok", "line": line_str} ``` #### On Request trigger configuration To create a trigger for an On Request plugin, specify the `request:` trigger-spec. For example, the following command creates an HTTP API `/api/v3/engine/my-plugin` endpoint for the plugin file: ```bash influxdb3 create trigger \ --trigger-spec "request:my-plugin" \ --plugin-filename "examples/my-on-request.py" \ --database mydb my-plugin ``` To run the plugin, you send an HTTP request to `/api/v3/engine/my-plugin`. Because all On Request plugins for a server share the same `/api/v3/engine/` base URL, the trigger-spec you define must be unique across all plugins configured for a server, regardless of which database they are associated with. ## In-memory cache The Processing engine provides a powerful in-memory cache system that enables plugins to persist and retrieve data between executions. This cache system is essential for maintaining state, tracking metrics over time, and optimizing performance when working with external data sources. ### Key Benefits - **State persistence**: Maintain counters, timestamps, and other state variables across plugin executions. - **Performance and cost optimization**: Store frequently used data to avoid expensive recalculations. Minimize external API calls by caching responses and avoiding rate limits. - **Data enrichment**: Cache lookup tables, API responses, or reference data to enrich data efficiently. ### Cache API The cache API is accessible via the `cache` property on the `influxdb3_local` object provided to all plugin types: ```python # Basic usage pattern influxdb3_local.cache.METHOD(PARAMETERS) ``` | Method | Parameters | Returns | Description | |--------|------------|---------|-------------| | `put` | `key` (str): The key to store the value under
`value` (Any): Any Python object to cache
`ttl` (Optional[float], default=None): Time in seconds before expiration
`use_global` (bool, default=False): If True, uses global namespace | None | Stores a value in the cache with an optional time-to-live | | `get` | `key` (str): The key to retrieve
`default` (Any, default=None): Value to return if key not found
`use_global` (bool, default=False): If True, uses global namespace | Any | Retrieves a value from the cache or returns default if not found | | `delete` | `key` (str): The key to delete
`use_global` (bool, default=False): If True, uses global namespace | bool | Deletes a value from the cache. Returns True if deleted, False if not found | ### Cache Namespaces The cache system offers two distinct namespaces, providing flexibility for different use cases: | Namespace | Scope | Best For | | --- | --- | --- | | **Trigger-specific** (default) | Isolated to a single trigger | Plugin state, counters, timestamps specific to one plugin | | **Global** | Shared across all triggers | Configuration, lookup tables, service states that should be available to all plugins | ### Using the In-memory cache The following examples show how to use the cache API in plugins: ```python # Store values in the trigger-specific namespace influxdb3_local.cache.put("last_processed_time", time.time()) influxdb3_local.cache.put("error_count", 0) influxdb3_local.cache.put("processed_records", {"total": 0, "errors": 0}) # Store values with expiration influxdb3_local.cache.put("temp_data", {"value": 42}, ttl=300) # Expires in 5 minutes influxdb3_local.cache.put("auth_token", "t0k3n", ttl=3600) # Expires in 1 hour # Store values in the global namespace influxdb3_local.cache.put("app_config", {"version": "1.0.2"}, use_global=True) influxdb3_local.cache.put("global_counter", 0, use_global=True) # Retrieve values last_time = influxdb3_local.cache.get("last_processed_time") auth = influxdb3_local.cache.get("auth_token") config = influxdb3_local.cache.get("app_config", use_global=True) # Provide defaults for missing keys missing = influxdb3_local.cache.get("missing_key", default="Not found") count = influxdb3_local.cache.get("visit_count", default=0) # Delete cached values influxdb3_local.cache.delete("temp_data") influxdb3_local.cache.delete("app_config", use_global=True) ``` #### Example: maintaining state between executions The following example shows a WAL plugin that uses the cache to maintain a counter across executions: ```python def process_writes(influxdb3_local, table_batches, args=None): # Get the current counter value or default to 0 counter = influxdb3_local.cache.get("execution_counter", default=0) # Increment the counter counter += 1 # Store the updated counter back in the cache influxdb3_local.cache.put("execution_counter", counter) influxdb3_local.info(f"This plugin has been executed {counter} times") # Process writes normally... ``` #### Example: sharing configuration across triggers One benefit of using a global namespace is being more responsive to changing conditions. This example demonstrates using the global namespace to share configuration, so a scheduled call can check thresholds placed by prior trigger calls, without making a query to the DB itself: ```python def process_scheduled_call(influxdb3_local, time, args=None): # Check if we have cached configuration config = influxdb3_local.cache.get("alert_config", use_global=True) if not config: # Load configuration from database results = influxdb3_local.query("SELECT * FROM system.alert_config") # Transform query results into config object config = {row["name"]: row["value"] for row in results} # Cache the configuration with a 5-minute TTL influxdb3_local.cache.put("alert_config", config, ttl=300, use_global=True) influxdb3_local.info("Loaded fresh configuration from database") else: influxdb3_local.info("Using cached configuration") # Use the configuration threshold = float(config.get("cpu_threshold", "90.0")) # ... ``` The cache is designed to support stateful operations while maintaining isolation between different triggers. Use the trigger-specific namespace for most operations and the global namespace only when data sharing across triggers is necessary. ### Best practices - [Use TTL appropriately](#use-ttl-appropriately) - [Cache computation results](#cache-computation-results) - [Warm the cache](#warm-the-cache) - [Consider cache limitations](#consider-cache-limitations) #### Use TTL appropriately Set realistic expiration times based on how frequently data changes. ```python # Cache external API responses for 5 minutes influxdb3_local.cache.put("weather_data", api_response, ttl=300) ``` #### Cache computation results Store the results of expensive calculations that need to be utilized frequently. ```python # Cache aggregated statistics influxdb3_local.cache.put("daily_stats", calculate_statistics(data), ttl=3600) ``` #### Warm the cache For critical data, prime the cache at startup. This can be especially useful for global namespace data where multiple triggers need the data. ```python # Check if cache needs to be initialized if not influxdb3_local.cache.get("lookup_table"): influxdb3_local.cache.put("lookup_table", load_lookup_data()) ``` ## Consider cache limitations - **Memory Usage**: Since cache contents are stored in memory, monitor your memory usage when caching large datasets. - **Server Restarts**: Because the cache is cleared when the server restarts, design your plugins to handle cache initialization (as noted above). - **Concurrency**: Be cautious of accessing inaccurate or out-of-date data when multiple trigger instances might simultaneously update the same cache key.