airflow-provider-ducklake 0.0.10

DuckLake provider for Apache Airflow (based on DuckDB)

DuckLake Provider for Apache Airflow

This is a custom provider for integrating DuckLake (based on DuckDB) with Apache Airflow.

DuckLake Configuration

The DuckLakeHook uses Airflow connection fields and extras to configure the connection. Standard fields are relabeled for common use:

• Host: Used for metadata host (e.g., Postgres/MySQL host) or file path (e.g., for DuckDB/SQLite metadata file).
• Login: Username (for Postgres/MySQL).
• Password: Password (for Postgres/MySQL).
• Schema: Metadata schema (defaults to 'duckdb').
• Extra: JSON dict for all other settings (required for engine, storage_type, and conditional fields).

Example extras JSON (adjust based on engine and storage_type):

{
  "engine": "postgres",
  "dbname": "my_ducklake",
  "pgdbname": "dev_nophiml_db",
  "storage_type": "s3",
  "s3_bucket": "your-s3-bucket",
  "s3_path": "your/s3/path/",
  "aws_access_key_id": "your-access-key-id",
  "aws_secret_access_key": "your-secret-access-key",
  "aws_region": "us-east-1",
  "install_extensions": ["spatial"],  # Optional: Inherited from DuckDB provider
  "load_extensions": ["spatial"],     # Optional
  "connect_stack": [                  # Optional: override default DuckLake install/load commands
    "INSTALL httpfs;",
    "LOAD httpfs;",
    "INSTALL ducklake;",
    "LOAD ducklake;"
  ]
}

Supported Engines (set in extras['engine'])

• duckdb: Requires 'metadata_file' in extras or host as file path.
• sqlite: Requires 'metadata_file' in extras or host as file path.
• postgres: Requires host, login, password, and 'pgdbname' in extras.
• mysql: Requires host, login, password, and 'mysqldbname' in extras.

Supported Storage Types (set in extras['storage_type'], default 's3')

• s3: Requires 's3_bucket', 's3_path'; optional AWS creds.
• azure: Requires 'azure_account_name', 'azure_container', 'azure_path'; optional connection_string.
• gcs: Requires 'gcs_bucket', 'gcs_path'; optional service_account_key (JSON string).
• local: Requires 'local_data_path'.

The UI shows core fields; use extras for engine/storage-specific ones. For dynamic behavior, select engine/storage in extras and provide corresponding keys. If you need to customize the static DuckLake connection commands (for example to install additional extensions), provide a connect_stack list in extras. Commands that depend on runtime variables (secrets, thread settings, attachments, etc.) are always appended automatically by the hook.

Performance and Resource Controls

The hook exposes a few knobs for tuning concurrency and memory usage:

• threads: (int/string) Overrides DuckDB's worker thread count. Non-numeric/blank values are ignored and the default of 4 is used.
• memory_limit: (string) A DuckDB-formatted limit such as "4GB" or "512MB". If provided, this always wins.
• memory_plan: ("conservative", "midtier", "aggressive") Lets the hook auto-size memory_limit based on available RAM. Defaults to "midtier" if not configured.

When memory_limit is omitted, DuckLake estimates available physical memory (using psutil, /proc/meminfo, POSIX sysconf, or Windows APIs), applies the selected plan’s fraction, and clamps within defined min/max bounds. This ensures the hook never grabs more than the machine can spare and still caps to sane maxima. If the machine’s free memory cannot be determined, DuckDB’s default memory settings are used.

You can also pass these parameters directly when instantiating the hook in a DAG:

from ducklake_provider.hooks.ducklake_hook import DuckLakeHook

hook = DuckLakeHook(
    ducklake_conn_id="ducklake_default",
    memory_plan="conservative",  # or set memory_limit="6GB"
    threads=8,
)