InfluxDB to Iceberg plugin

The InfluxDB to Iceberg Plugin enables data transfer from InfluxDB 3 to Apache Iceberg tables. Transfer time series data to Iceberg for long-term storage, analytics, or integration with data lake architectures. The plugin supports both scheduled batch transfers of historical data and on-demand transfers via HTTP API.

Configuration

Scheduler trigger parameters

Required parameters

Optional parameters

HTTP trigger parameters

Request body structure

Schema management

• Automatically creates Iceberg table schema from the first batch of data
• Maps pandas data types to Iceberg types:
  • int64 → IntegerType
  • float64 → FloatType
  • datetime64[us] → TimestampType
  • object → StringType
• Fields with no null values are marked as required
• The time column is converted to datetime64[us] for Iceberg compatibility
• Tables are created in format: <namespace>.<table_name>

Schema requirements

The plugin assumes that the Iceberg table schema is already defined in the database, as it relies on this schema to retrieve field and tag names required for processing.

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.

Example TOML configuration

influxdb_to_iceberg_config_scheduler.toml

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

Installation steps

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

2. Install required Python packages:

   • pandas (for data manipulation)
   • pyarrow (for Parquet support)
   • pyiceberg[catalog-options] (for Iceberg integration)

   influxdb3 install package pandas
   influxdb3 install package pyarrow
   influxdb3 install package "pyiceberg[s3fs,hive,sql-sqlite]"

   Note: Include the appropriate PyIceberg extras based on your catalog type:

   • [s3fs] for S3 storage
   • [hive] for Hive metastore
   • [sql-sqlite] for SQL catalog with SQLite
   • See PyIceberg documentation for all options

Trigger setup

Scheduled data transfer

Periodically transfer data from InfluxDB 3 to Iceberg:

influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/influxdb_to_iceberg/influxdb_to_iceberg.py \
  --trigger-spec "every:1h" \
  --trigger-arguments 'measurement=cpu,window=1h,catalog_configs="eyJ1cmkiOiAiaHR0cDovL25lc3NpZTo5MDAwIn0=",namespace=monitoring,table_name=cpu_metrics' \
  hourly_iceberg_transfer

HTTP API endpoint

Create an on-demand transfer endpoint:

influxdb3 create trigger \
  --database mydb \
  --plugin-filename gh:influxdata/influxdb_to_iceberg/influxdb_to_iceberg.py \
  --trigger-spec "request:replicate" \
  iceberg_http_transfer

Enable the trigger:

influxdb3 enable trigger --database mydb iceberg_http_transfer

The endpoint is registered at /api/v3/engine/replicate.

Example usage

Example 1: Basic scheduled transfer

Transfer CPU metrics to Iceberg every hour:

# Create trigger with base64-encoded catalog config
# Original JSON: {"uri": "http://nessie:9000"}
# Base64: eyJ1cmkiOiAiaHR0cDovL25lc3NpZTo5MDAwIn0=
influxdb3 create trigger \
  --database metrics \
  --plugin-filename gh:influxdata/influxdb_to_iceberg/influxdb_to_iceberg.py \
  --trigger-spec "every:1h" \
  --trigger-arguments 'measurement=cpu,window=24h,catalog_configs="eyJ1cmkiOiAiaHR0cDovL25lc3NpZTo5MDAwIn0="' \
  cpu_to_iceberg

# Write test data
influxdb3 write \
  --database metrics \
  "cpu,host=server1 usage_user=45.2,usage_system=12.1"

# After trigger runs, data is available in Iceberg table "default.cpu"

Expected results

• Creates Iceberg table default.cpu with schema matching the measurement
• Transfers all CPU data from the last 24 hours
• Appends new data on each hourly run

Example 2: HTTP backfill with field filtering

Backfill specific fields from historical data:

# Create and enable HTTP trigger
influxdb3 create trigger \
  --database metrics \
  --plugin-filename gh:influxdata/influxdb_to_iceberg/influxdb_to_iceberg.py \
  --trigger-spec "request:replicate" \
  iceberg_backfill

influxdb3 enable trigger --database metrics iceberg_backfill

# Request backfill via HTTP
curl -X POST http://localhost:8181/api/v3/engine/replicate \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "measurement": "temperature",
    "catalog_configs": {
      "type": "sql",
      "uri": "sqlite:///path/to/catalog.db"
    },
    "included_fields": ["temp_celsius", "humidity"],
    "namespace": "weather",
    "table_name": "temperature_history",
    "batch_size": "12h",
    "backfill_start": "2024-01-01T00:00:00+00:00",
    "backfill_end": "2024-01-07T00:00:00+00:00"
  }'

Expected results

• Creates Iceberg table weather.temperature_history
• Transfers only temp_celsius and humidity fields
• Processes data in 12-hour batches for the specified week
• Returns status of the backfill operation

Example 3: S3-backed Iceberg catalog

Transfer data to Iceberg tables stored in S3:

# Create catalog config JSON
cat > catalog_config.json << EOF
{
  "type": "sql",
  "uri": "sqlite:///iceberg/catalog.db",
  "warehouse": "s3://my-bucket/iceberg-warehouse/",
  "s3.endpoint": "http://minio:9000",
  "s3.access-key-id": "minioadmin",
  "s3.secret-access-key": "minioadmin",
  "s3.path-style-access": true
}
EOF

# Encode to base64
CATALOG_CONFIG=$(base64 < catalog_config.json)

# Create trigger
influxdb3 create trigger \
  --database metrics \
  --plugin-filename gh:influxdata/influxdb_to_iceberg/influxdb_to_iceberg.py \
  --trigger-spec "every:30m" \
  --trigger-arguments "measurement=sensor_data,window=1h,catalog_configs=\"$CATALOG_CONFIG\",namespace=iot,table_name=sensors" \
  s3_iceberg_transfer

Using TOML Configuration Files

This plugin supports using TOML configuration files to specify all plugin arguments. This is useful for complex configurations or when you want to version control your plugin settings.

Important Requirements

To use TOML configuration files, you must set the PLUGIN_DIR environment variable in the InfluxDB 3 host environment. This is required in addition to the --plugin-dir flag when starting InfluxDB 3:

• --plugin-dir tells InfluxDB 3 where to find plugin Python files
• PLUGIN_DIR environment variable tells the plugins where to find TOML configuration files

Setting Up TOML Configuration

1. Start InfluxDB 3 with the PLUGIN_DIR environment variable set:

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

2. Copy the example TOML configuration file to your plugin directory:

   cp influxdb_to_iceberg_config_scheduler.toml ~/.plugins/

3. Edit the TOML file to match your requirements:

   # Required parameters
   measurement = "cpu"
   window = "1h"
   
   # Optional parameters
   namespace = "monitoring"
   table_name = "cpu_metrics"
   
   # Iceberg catalog configuration
   [catalog_configs]
   type = "sql"
   uri = "http://nessie:9000"
   warehouse = "s3://iceberg-warehouse/"

4. Create a trigger using the config_file_path argument:

   influxdb3 create trigger \
     --database mydb \
     --plugin-filename influxdb_to_iceberg.py \
     --trigger-spec "every:1h" \
     --trigger-arguments config_file_path=influxdb_to_iceberg_config_scheduler.toml \
     iceberg_toml_trigger

Code overview

Files

• influxdb_to_iceberg.py: The main plugin code containing handlers for scheduled and HTTP triggers
• influxdb_to_iceberg_config_scheduler.toml: Example TOML configuration file for scheduled triggers

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

Main functions

process_scheduled_call(influxdb3_local, call_time, args)

Handles scheduled data transfers. Queries data within the specified window and appends to Iceberg tables.

Key operations:

1. Parses configuration and decodes catalog settings
2. Queries source measurement with optional field filtering
3. Creates Iceberg table if needed
4. Appends data to Iceberg table

process_http_request(influxdb3_local, request_body, args)

Handles on-demand data transfers via HTTP. Supports backfill operations with configurable batch sizes.

Key operations:

1. Validates request body parameters
2. Determines backfill time range
3. Processes data in batches
4. Returns transfer status

Troubleshooting

Common issues

Issue: “Failed to decode catalog_configs” error

Solution: Ensure the catalog configuration is properly base64-encoded:

# Create JSON file
echo '{"uri": "http://nessie:9000"}' > config.json
# Encode to base64
base64 config.json

Issue: “Failed to create Iceberg table” error

Solution:

1. Verify catalog configuration is correct
2. Check warehouse path permissions
3. Ensure required PyIceberg extras are installed:

   influxdb3 install package "pyiceberg[s3fs]"

Issue: No data in Iceberg table after transfer

Solution:

1. Check if source measurement contains data:

   influxdb3 query --database mydb "SELECT COUNT(*) FROM measurement"

2. Verify time window covers data:

   influxdb3 query --database mydb "SELECT MIN(time), MAX(time) FROM measurement"

3. Check logs for errors:

   influxdb3 query --database _internal "SELECT * FROM system.processing_engine_logs WHERE log_level = 'ERROR'"

Issue: “Schema evolution not supported” error

Solution: The plugin doesn’t handle schema changes. If fields change:

1. Create a new table with different name
2. Or manually update the Iceberg table schema

Debugging tips

1. Test catalog connectivity:

   from pyiceberg.catalog import load_catalog
   catalog = load_catalog("my_catalog", **catalog_configs)
   print(catalog.list_namespaces())

2. Verify field names:

   influxdb3 query --database mydb "SHOW FIELD KEYS FROM measurement"

3. Use smaller windows for initial testing:

   --trigger-arguments 'window=5m,...'

Performance considerations

• File sizing: Each scheduled run creates new Parquet files. Use appropriate window sizes to balance file count and size
• Batch processing: For HTTP transfers, adjust batch_size based on available memory
• Field filtering: Use included_fields to reduce data volume when only specific fields are needed
• Catalog choice: SQL catalogs (SQLite) are simpler but REST catalogs scale better

Report an issue

For plugin issues, see the Plugins repository issues page.

Find support for InfluxDB 3 Core

The InfluxDB Discord server is the best place to find support for InfluxDB 3 Core and InfluxDB 3 Enterprise. For other InfluxDB versions, see the Support and feedback options.