ducklake-client 0.5.1

Lightweight wrapper over DuckDB with convenience helpers for DuckLake.

ducklake-client

Lightweight Python helpers for opening DuckLake connections through DuckDB.

Install

pip install ducklake-client

Open a DuckLake connection

from ducklake_client import ColumnDef, DiskStorage, DuckDBCatalog, DuckLake

lake = DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
)

try:
    lake.schema.create("main")
    lake.table.create(
        "items",
        id=ColumnDef("INTEGER", nullable=False),
        name=ColumnDef("VARCHAR"),
    )
    rows = lake.connection.sql("SELECT * FROM lake.main.items").fetchall()
finally:
    lake.close()

DuckLake opens the underlying DuckDB connection lazily on first use. The client installs and loads the DuckDB ducklake and parquet extensions, attaches the catalog as lake, and exposes the native DuckDB connection through connection.

Context manager usage

from ducklake_client import DiskStorage, DuckDBCatalog, DuckLake

with DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
) as lake:
    lake.connection.execute("CREATE TABLE IF NOT EXISTS lake.main.events (id INTEGER)")
    lake.connection.execute("INSERT INTO lake.main.events VALUES (?)", [1])
    print(lake.connection.sql("SELECT count(*) FROM lake.main.events").fetchone())

Modules

DuckLake-specific helpers are grouped into modules. Native DuckDB behavior stays on lake.connection.

from ducklake_client import ColumnDef, DiskStorage, DuckDBCatalog, DuckLake

with DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
) as lake:
    lake.schema.create("main")
    lake.table.create_from_csv(
        "nl_train_stations",
        "https://blobs.duckdb.org/nl_stations.csv",
    )
    lake.table.comment("nl_train_stations", "Dutch railway stations")
    lake.table.comment(
        "nl_train_stations",
        "Full station name",
        column_name="name_long",
    )
    tables = lake.table.list()
    views = lake.view.list()
    info = lake.table.info("nl_train_stations")

Ad hoc SQL as dict rows

For quick queries with named parameters (DuckDB $param syntax), use sql_dicts:

from ducklake_client import DiskStorage, DuckDBCatalog, DuckLake

with DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
) as lake:
    rows = lake.sql_dicts("SELECT $n AS v", n=41)

Transactions

Use transaction() to automatically begin, commit, or roll back a block on the native DuckDB connection.

from ducklake_client import ColumnDef, DiskStorage, DuckDBCatalog, DuckLake

with DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
) as lake:
    with lake.transaction():
        lake.schema.create("main")
        lake.table.create(
            "items",
            id=ColumnDef("INTEGER", nullable=False),
            name=ColumnDef("VARCHAR"),
        )
        lake.connection.execute("INSERT INTO lake.main.items VALUES (?, ?)", [1, "example"])

Configuration

DuckLake requires explicit catalog and storage config objects:

from ducklake_client import DiskStorage, DuckDBCatalog, DuckLake

lake = DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
)

You can pass DuckDB runtime settings with DuckDBConfig:

from ducklake_client import DiskStorage, DuckDBConfig, DuckDBCatalog, DuckLake

lake = DuckLake(
    catalog=DuckDBCatalog("metadata.ducklake"),
    storage=DiskStorage("data"),
    duckdb=DuckDBConfig(
        database=":memory:",
        threads=4,
        memory_limit="2GB",
    ),
)

Catalogs can be DuckDBCatalog, SqliteCatalog, or PostgresCatalog. Storage can be DiskStorage or S3Storage.