Schema validator plugin

Note: This plugin requires InfluxDB 3 Enterprise.8.2 or later.

An InfluxDB 3 Enterprise Processing Engine plugin that validates incoming line protocol data against a user-defined JSON schema. Only data that conforms to the schema is written to a target database or table, enabling a clean data pipeline pattern.

Use Case

You have data coming into a “raw” database (for example, raw_db) from various sources. You want to ensure only properly-structured, validated data makes it into your “clean” database (for example, clean_db). This plugin sits on the WAL flush trigger and validates every incoming row against your schema definition before writing it to the target.

Common patterns:

• raw_db -> validate -> clean_db (cross-database)
• raw_table -> validate -> validated_table (same database, different table)
• source_table -> validate -> source_table_clean (same database, with suffix)

This is a single-file plugin (schema_validator.py) and can be loaded from GitHub via gh: trigger paths or created in InfluxDB Explorer.

Note: The JSON schema configuration file (schema_validator_config.json) must be manually uploaded to the plugin directory on the server. There is currently no API for uploading non-plugin files, so Explorer cannot upload it for you. You can use scp, rsync, or any other file transfer method to place the schema file in the plugin directory alongside the plugin.

Features

• Measurement validation: Define a whitelist of allowed measurement/table names
• Tag validation: Required/optional tags, allowed tag values
• Field validation: Required/optional fields, type checking (float, integer, string, boolean, uint64), allowed field values
• Field stripping: Extra tags/fields not defined in the schema are automatically stripped from the output
• Flexible targeting: Write to a different database, different table name, or add prefix/suffix
• Per-table schemas: Define different validation rules for each measurement
• Rejection logging: Optionally log rejected rows and/or write rejection details to a measurement
• Cached config: Schema file is cached for 5 minutes to avoid repeated file reads

Quick Start

1. Deploy the plugin files

The plugin code can be deployed via the InfluxDB CLI, Explorer, or GitHub (gh:) trigger paths. However, the schema JSON configuration file must be manually placed in the plugin directory on the server since there is no API for uploading non-plugin files.

• schema_validator.py - the plugin code (can be uploaded via CLI/Explorer/GitHub)
• schema_validator_config.json - your schema definition (must be manually copied to the plugin directory)

2. Create a trigger

Cross-database validation (raw_db -> clean_db):

influxdb3 create trigger \
  --database raw_db \
  --plugin-filename schema_validator.py \
  --trigger-spec "all_tables" \
  --trigger-arguments schema_file=schema_validator_config.json,target_database=clean_db \
  schema_validator_trigger

Same database, different table (with suffix):

influxdb3 create trigger \
  --database mydb \
  --plugin-filename schema_validator.py \
  --trigger-spec "table:weather" \
  --trigger-arguments schema_file=schema_validator_config.json,target_table_suffix=_clean \
  schema_validator_weather

Using a TOML config file:

influxdb3 create trigger \
  --database raw_db \
  --plugin-filename schema_validator.py \
  --trigger-spec "all_tables" \
  --trigger-arguments config_file_path=schema_validator_trigger_config.toml \
  schema_validator_trigger

3. Write data normally

Write to your raw database as usual. The plugin will automatically validate and forward conforming data.

# This row has all required fields -> will be written to clean_db
influxdb3 write --database raw_db \
  "weather,location=us-east,station_id=ST001 temperature=72.5,humidity=45.2"

# This row is missing required tag 'station_id' -> will be rejected
influxdb3 write --database raw_db \
  "weather,location=us-east temperature=72.5,humidity=45.2"

Schema Configuration (JSON)

The schema is defined in a JSON file. Here is the full structure:

{
    "allowed_measurements": ["weather", "cpu"],

    "tables": {
        "weather": {
            "target_table": "weather_clean",
            "tags": {
                "location": {
                    "required": true,
                    "allowed_values": ["us-east", "us-west", "eu-west"]
                },
                "station_id": {
                    "required": true
                },
                "region": {
                    "required": false
                }
            },
            "fields": {
                "temperature": {
                    "required": true,
                    "type": "float"
                },
                "humidity": {
                    "required": true,
                    "type": "float"
                },
                "condition": {
                    "required": false,
                    "type": "string",
                    "allowed_values": ["sunny", "cloudy", "rain", "snow"]
                }
            }
        }
    }
}

Schema Fields Reference

Top-level

Table Schema

Tag Definition

Field Definition

Trigger Arguments

These can be passed via --trigger-arguments or in a TOML config file.

Validation Logic

For each incoming row, the plugin checks (in order):

1. Measurement name: Is the table name in allowed_measurements? (if defined)
2. Table schema exists: Is there a schema definition for this table in tables? If not, the table is skipped.
3. Required tags: Are all required tags present?
4. Tag values: Are tag values in the allowed_values list? (if defined)
5. Required fields: Are all required fields present?
6. Field types: Do field values match the expected type? (if defined)
7. Field values: Are field values in the allowed_values list? (if defined)
8. Field stripping: Any extra tags/fields not defined in the schema are stripped from the output.

If any check fails, the row is rejected and not written to the target.

Examples

IoT Sensor Validation

Ensure sensor readings always have a device_id, valid sensor type, and a numeric value:

{
    "allowed_measurements": ["sensor_readings"],
    "tables": {
        "sensor_readings": {
            "target_table": "sensors_validated",
            "tags": {
                "device_id": { "required": true },
                "sensor_type": {
                    "required": true,
                    "allowed_values": ["temperature", "pressure", "humidity"]
                }
            },
            "fields": {
                "value": { "required": true, "type": "float" },
                "status": {
                    "required": false,
                    "type": "string",
                    "allowed_values": ["ok", "warning", "critical"]
                }
            }
        }
    }
}

Multi-table with Cross-database

Validate weather and cpu data from raw_db, writing clean data to clean_db:

influxdb3 create trigger \
  --database raw_db \
  --plugin-filename schema_validator.py \
  --trigger-spec "all_tables" \
  --trigger-arguments schema_file=schema_validator_config.json,target_database=clean_db,write_rejection_log=true \
  schema_validator_all

Monitoring Rejections

Query the rejection log to see what data is being rejected and why:

SELECT * FROM _schema_rejections
WHERE time > now() - INTERVAL '1 hour'
ORDER BY time DESC

File Structure

schema_validator/
  schema_validator.py                       # Main plugin code
  schema_validator_config.json              # Example schema definition
  schema_validator_trigger_config.toml      # Example TOML trigger config
  README.md                                 # This file

Notes

• The schema JSON file is cached for 5 minutes. To force a reload, restart the trigger or wait for the cache to expire.
• Extra tags/fields not defined in the schema are silently stripped from the output (not written to the target).
• Measurements without an entry in tables are skipped entirely (no data written).
• The target_table property in a table schema takes precedence over target_table_prefix/target_table_suffix.
• The _schema_rejections table (if write_rejection_log=true) is written to the target database.
• Uses write_sync / write_sync_to_db with no_sync=True for optimal memory performance.
• Valid rows are batched per table and written in a single call for efficiency.

Logging

Logs are stored in the _internal database (or the database where the trigger is created) in the system.processing_engine_logs table. To view logs:

influxdb3 query --database _internal "SELECT * FROM system.processing_engine_logs WHERE trigger_name = 'your_trigger_name'"

Log columns:

• event_time: Timestamp of the log event
• trigger_name: Name of the trigger that generated the log
• log_level: Severity level (INFO, WARN, ERROR)
• log_text: Message describing the action or error

Report an issue

For plugin issues, see the Plugins repository issues page.

Find support for InfluxDB 3 Enterprise

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