This documentation is for an older version (1.4.7) of Dagster. You can view the version of this page from our latest release below.
from abc import abstractmethod
from contextlib import contextmanager
from typing import Optional, Sequence, Type, cast
import duckdb
from dagster import IOManagerDefinition, OutputContext, io_manager
from dagster._config.pythonic_config import (
ConfigurableIOManagerFactory,
)
from dagster._core.definitions.time_window_partitions import TimeWindow
from dagster._core.storage.db_io_manager import (
DbClient,
DbIOManager,
DbTypeHandler,
TablePartitionDimension,
TableSlice,
)
from dagster._core.storage.io_manager import dagster_maintained_io_manager
from dagster._utils.backoff import backoff
from pydantic import Field
DUCKDB_DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
[docs]def build_duckdb_io_manager(
type_handlers: Sequence[DbTypeHandler], default_load_type: Optional[Type] = None
) -> IOManagerDefinition:
"""Builds an IO manager definition that reads inputs from and writes outputs to DuckDB.
Args:
type_handlers (Sequence[DbTypeHandler]): Each handler defines how to translate between
DuckDB tables and an in-memory type - e.g. a Pandas DataFrame. If only
one DbTypeHandler is provided, it will be used as teh default_load_type.
default_load_type (Type): When an input has no type annotation, load it as this type.
Returns:
IOManagerDefinition
Examples:
.. code-block:: python
from dagster_duckdb import build_duckdb_io_manager
from dagster_duckdb_pandas import DuckDBPandasTypeHandler
@asset(
key_prefix=["my_schema"] # will be used as the schema in duckdb
)
def my_table() -> pd.DataFrame: # the name of the asset will be the table name
...
duckdb_io_manager = build_duckdb_io_manager([DuckDBPandasTypeHandler()])
@repository
def my_repo():
return with_resources(
[my_table],
{"io_manager": duckdb_io_manager.configured({"database": "my_db.duckdb"})}
)
If you do not provide a schema, Dagster will determine a schema based on the assets and ops using
the IO Manager. For assets, the schema will be determined from the asset key. For ops, the schema can be
specified by including a "schema" entry in output metadata. If none of these is provided, the schema will
default to "public".
.. code-block:: python
@op(
out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pd.DataFrame:
...
To only use specific columns of a table as input to a downstream op or asset, add the metadata "columns" to the
In or AssetIn.
.. code-block:: python
@asset(
ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pd.DataFrame):
# my_table will just contain the data from column "a"
...
"""
@dagster_maintained_io_manager
@io_manager(config_schema=DuckDBIOManager.to_config_schema())
def duckdb_io_manager(init_context):
"""IO Manager for storing outputs in a DuckDB database.
Assets will be stored in the schema and table name specified by their AssetKey.
Subsequent materializations of an asset will overwrite previous materializations of that asset.
Op outputs will be stored in the schema specified by output metadata (defaults to public) in a
table of the name of the output.
"""
return DbIOManager(
type_handlers=type_handlers,
db_client=DuckDbClient(),
io_manager_name="DuckDBIOManager",
database=init_context.resource_config["database"],
schema=init_context.resource_config.get("schema"),
default_load_type=default_load_type,
)
return duckdb_io_manager
[docs]class DuckDBIOManager(ConfigurableIOManagerFactory):
"""Base class for an IO manager definition that reads inputs from and writes outputs to DuckDB.
Examples:
.. code-block:: python
from dagster_duckdb import DuckDBIOManager
from dagster_duckdb_pandas import DuckDBPandasTypeHandler
class MyDuckDBIOManager(DuckDBIOManager):
@staticmethod
def type_handlers() -> Sequence[DbTypeHandler]:
return [DuckDBPandasTypeHandler()]
@asset(
key_prefix=["my_schema"] # will be used as the schema in duckdb
)
def my_table() -> pd.DataFrame: # the name of the asset will be the table name
...
defs = Definitions(
assets=[my_table],
resources={"io_manager": MyDuckDBIOManager(database="my_db.duckdb")}
)
If you do not provide a schema, Dagster will determine a schema based on the assets and ops using
the IO Manager. For assets, the schema will be determined from the asset key, as in the above example.
For ops, the schema can be specified by including a "schema" entry in output metadata. If none
of these is provided, the schema will default to "public".
.. code-block:: python
@op(
out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pd.DataFrame:
...
To only use specific columns of a table as input to a downstream op or asset, add the metadata "columns" to the
In or AssetIn.
.. code-block:: python
@asset(
ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pd.DataFrame):
# my_table will just contain the data from column "a"
...
"""
database: str = Field(description="Path to the DuckDB database.")
schema_: Optional[str] = Field(
default=None, alias="schema", description="Name of the schema to use."
) # schema is a reserved word for pydantic
@staticmethod
@abstractmethod
def type_handlers() -> Sequence[DbTypeHandler]:
...
@staticmethod
def default_load_type() -> Optional[Type]:
return None
def create_io_manager(self, context) -> DbIOManager:
return DbIOManager(
db_client=DuckDbClient(),
database=self.database,
schema=self.schema_,
type_handlers=self.type_handlers(),
default_load_type=self.default_load_type(),
io_manager_name="DuckDBIOManager",
)
class DuckDbClient(DbClient):
@staticmethod
def delete_table_slice(context: OutputContext, table_slice: TableSlice, connection) -> None:
try:
connection.execute(_get_cleanup_statement(table_slice))
except duckdb.CatalogException:
# table doesn't exist yet, so ignore the error
pass
@staticmethod
def ensure_schema_exists(context: OutputContext, table_slice: TableSlice, connection) -> None:
connection.execute(f"create schema if not exists {table_slice.schema};")
@staticmethod
def get_select_statement(table_slice: TableSlice) -> str:
col_str = ", ".join(table_slice.columns) if table_slice.columns else "*"
if table_slice.partition_dimensions and len(table_slice.partition_dimensions) > 0:
query = f"SELECT {col_str} FROM {table_slice.schema}.{table_slice.table} WHERE\n"
return query + _partition_where_clause(table_slice.partition_dimensions)
else:
return f"""SELECT {col_str} FROM {table_slice.schema}.{table_slice.table}"""
@staticmethod
@contextmanager
def connect(context, _):
conn = backoff(
fn=duckdb.connect,
retry_on=(RuntimeError, duckdb.IOException),
kwargs={"database": context.resource_config["database"], "read_only": False},
max_retries=10,
)
yield conn
conn.close()
def _get_cleanup_statement(table_slice: TableSlice) -> str:
"""Returns a SQL statement that deletes data in the given table to make way for the output data
being written.
"""
if table_slice.partition_dimensions and len(table_slice.partition_dimensions) > 0:
query = f"DELETE FROM {table_slice.schema}.{table_slice.table} WHERE\n"
return query + _partition_where_clause(table_slice.partition_dimensions)
else:
return f"DELETE FROM {table_slice.schema}.{table_slice.table}"
def _partition_where_clause(partition_dimensions: Sequence[TablePartitionDimension]) -> str:
return " AND\n".join(
(
_time_window_where_clause(partition_dimension)
if isinstance(partition_dimension.partitions, TimeWindow)
else _static_where_clause(partition_dimension)
)
for partition_dimension in partition_dimensions
)
def _time_window_where_clause(table_partition: TablePartitionDimension) -> str:
partition = cast(TimeWindow, table_partition.partitions)
start_dt, end_dt = partition
start_dt_str = start_dt.strftime(DUCKDB_DATETIME_FORMAT)
end_dt_str = end_dt.strftime(DUCKDB_DATETIME_FORMAT)
return f"""{table_partition.partition_expr} >= '{start_dt_str}' AND {table_partition.partition_expr} < '{end_dt_str}'"""
def _static_where_clause(table_partition: TablePartitionDimension) -> str:
partitions = ", ".join(f"'{partition}'" for partition in table_partition.partitions)
return f"""{table_partition.partition_expr} in ({partitions})"""