from abc import abstractmethod
from contextlib import contextmanager
from typing import Generator, Optional, Sequence, Type, cast
from dagster import IOManagerDefinition, OutputContext, io_manager
from dagster._annotations import experimental
from dagster._config.pythonic_config import (
ConfigurableIOManagerFactory,
)
from dagster._core.storage.db_io_manager import (
DbClient,
DbIOManager,
DbTypeHandler,
TablePartitionDimension,
TableSlice,
TimeWindow,
)
from dagster._core.storage.io_manager import dagster_maintained_io_manager
from google.api_core.exceptions import NotFound
from google.cloud import bigquery
from pydantic import Field
from .utils import setup_gcp_creds
BIGQUERY_DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
[docs]@experimental
def build_bigquery_io_manager(
type_handlers: Sequence[DbTypeHandler], default_load_type: Optional[Type] = None
) -> IOManagerDefinition:
"""Builds an I/O manager definition that reads inputs from and writes outputs to BigQuery.
Args:
type_handlers (Sequence[DbTypeHandler]): Each handler defines how to translate between
slices of BigQuery tables and an in-memory type - e.g. a Pandas DataFrame.
If only one DbTypeHandler is provided, it will be used as the 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_gcp import build_bigquery_io_manager
from dagster_bigquery_pandas import BigQueryPandasTypeHandler
from dagster import Definitions
@asset(
key_prefix=["my_prefix"],
metadata={"schema": "my_dataset"} # will be used as the dataset in BigQuery
)
def my_table() -> pd.DataFrame: # the name of the asset will be the table name
...
@asset(
key_prefix=["my_dataset"] # my_dataset will be used as the dataset in BigQuery
)
def my_second_table() -> pd.DataFrame: # the name of the asset will be the table name
...
bigquery_io_manager = build_bigquery_io_manager([BigQueryPandasTypeHandler()])
defs = Definitions(
assets=[my_table, my_second_table],
resources={
"io_manager": bigquery_io_manager.configured({
"project" : {"env": "GCP_PROJECT"}
})
}
)
You can set a default dataset to store the assets using the ``dataset`` configuration value of the BigQuery I/O
Manager. This dataset will be used if no other dataset is specified directly on an asset or op.
.. code-block:: python
defs = Definitions(
assets=[my_table],
resources={
"io_manager": bigquery_io_manager.configured({
"project" : {"env": "GCP_PROJECT"}
"dataset": "my_dataset"
})
}
)
On individual assets, you an also specify the dataset where they should be stored using metadata or
by adding a ``key_prefix`` to the asset key. If both ``key_prefix`` and metadata are defined, the metadata will
take precedence.
.. code-block:: python
@asset(
key_prefix=["my_dataset"] # will be used as the dataset in BigQuery
)
def my_table() -> pd.DataFrame:
...
@asset(
# note that the key needs to be "schema"
metadata={"schema": "my_dataset"} # will be used as the dataset in BigQuery
)
def my_other_table() -> pd.DataFrame:
...
For ops, the dataset can be specified by including a "schema" entry in output metadata.
.. code-block:: python
@op(
out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pd.DataFrame:
...
If none of these is provided, the dataset will default to "public".
To only use specific columns of a table as input to a downstream op or asset, add the metadata ``columns`` to the
:py:class:`~dagster.In` or :py:class:`~dagster.AssetIn`.
.. code-block:: python
@asset(
ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pd.DataFrame) -> pd.DataFrame:
# my_table will just contain the data from column "a"
...
If you cannot upload a file to your Dagster deployment, or otherwise cannot
`authenticate with GCP <https://cloud.google.com/docs/authentication/provide-credentials-adc>`_
via a standard method, you can provide a service account key as the ``gcp_credentials`` configuration.
Dagster willstore this key in a temporary file and set ``GOOGLE_APPLICATION_CREDENTIALS`` to point to the file.
After the run completes, the file will be deleted, and ``GOOGLE_APPLICATION_CREDENTIALS`` will be
unset. The key must be base64 encoded to avoid issues with newlines in the keys. You can retrieve
the base64 encoded with this shell command: ``cat $GOOGLE_APPLICATION_CREDENTIALS | base64``
"""
@dagster_maintained_io_manager
@io_manager(config_schema=BigQueryIOManager.to_config_schema())
def bigquery_io_manager(init_context):
"""I/O Manager for storing outputs in a BigQuery database.
Assets will be stored in the dataset 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 dataset specified by output metadata (defaults to public) in a
table of the name of the output.
Note that the BigQuery config is mapped to the DB IO manager table hierarchy as follows:
BigQuery DB IO
* project -> database
* dataset -> schema
* table -> table
"""
mgr = DbIOManager(
type_handlers=type_handlers,
db_client=BigQueryClient(),
io_manager_name="BigQueryIOManager",
database=init_context.resource_config["project"],
schema=init_context.resource_config.get("dataset"),
default_load_type=default_load_type,
)
if init_context.resource_config.get("gcp_credentials"):
with setup_gcp_creds(init_context.resource_config.get("gcp_credentials")):
yield mgr
else:
yield mgr
return bigquery_io_manager
[docs]class BigQueryIOManager(ConfigurableIOManagerFactory):
"""Base class for an I/O manager definition that reads inputs from and writes outputs to BigQuery.
Examples:
.. code-block:: python
from dagster_gcp import BigQueryIOManager
from dagster_bigquery_pandas import BigQueryPandasTypeHandler
from dagster import Definitions, EnvVar
class MyBigQueryIOManager(BigQueryIOManager):
@staticmethod
def type_handlers() -> Sequence[DbTypeHandler]:
return [BigQueryPandasTypeHandler()]
@asset(
key_prefix=["my_dataset"] # my_dataset will be used as the dataset in BigQuery
)
def my_table() -> pd.DataFrame: # the name of the asset will be the table name
...
defs = Definitions(
assets=[my_table],
resources={
"io_manager": MyBigQueryIOManager(project=EnvVar("GCP_PROJECT"))
}
)
You can set a default dataset to store the assets using the ``dataset`` configuration value of the BigQuery I/O
Manager. This dataset will be used if no other dataset is specified directly on an asset or op.
.. code-block:: python
defs = Definitions(
assets=[my_table],
resources={
"io_manager": MyBigQueryIOManager(project=EnvVar("GCP_PROJECT"), dataset="my_dataset")
}
)
On individual assets, you an also specify the dataset where they should be stored using metadata or
by adding a ``key_prefix`` to the asset key. If both ``key_prefix`` and metadata are defined, the metadata will
take precedence.
.. code-block:: python
@asset(
key_prefix=["my_dataset"] # will be used as the dataset in BigQuery
)
def my_table() -> pd.DataFrame:
...
@asset(
# note that the key needs to be "schema"
metadata={"schema": "my_dataset"} # will be used as the dataset in BigQuery
)
def my_other_table() -> pd.DataFrame:
...
For ops, the dataset can be specified by including a "schema" entry in output metadata.
.. code-block:: python
@op(
out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pd.DataFrame:
...
If none of these is provided, the dataset will default to "public".
To only use specific columns of a table as input to a downstream op or asset, add the metadata ``columns`` to the
:py:class:`~dagster.In` or :py:class:`~dagster.AssetIn`.
.. code-block:: python
@asset(
ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pd.DataFrame) -> pd.DataFrame:
# my_table will just contain the data from column "a"
...
If you cannot upload a file to your Dagster deployment, or otherwise cannot
`authenticate with GCP <https://cloud.google.com/docs/authentication/provide-credentials-adc>`_
via a standard method, you can provide a service account key as the ``gcp_credentials`` configuration.
Dagster will store this key in a temporary file and set ``GOOGLE_APPLICATION_CREDENTIALS`` to point to the file.
After the run completes, the file will be deleted, and ``GOOGLE_APPLICATION_CREDENTIALS`` will be
unset. The key must be base64 encoded to avoid issues with newlines in the keys. You can retrieve
the base64 encoded with this shell command: ``cat $GOOGLE_APPLICATION_CREDENTIALS | base64``
"""
project: str = Field(description="The GCP project to use.")
dataset: Optional[str] = Field(
default=None,
description=(
"Name of the BigQuery dataset to use. If not provided, the last prefix before"
" the asset name will be used."
),
)
location: Optional[str] = Field(
default=None,
description=(
"The GCP location. Note: When using PySpark DataFrames, the default"
" location of the project will be used. A custom location can be specified in"
" your SparkSession configuration."
),
)
gcp_credentials: Optional[str] = Field(
default=None,
description=(
"GCP authentication credentials. If provided, a temporary file will be created"
" with the credentials and ``GOOGLE_APPLICATION_CREDENTIALS`` will be set to the"
" temporary file. To avoid issues with newlines in the keys, you must base64"
" encode the key. You can retrieve the base64 encoded key with this shell"
" command: ``cat $GOOGLE_AUTH_CREDENTIALS | base64``"
),
)
temporary_gcs_bucket: Optional[str] = Field(
default=None,
description=(
"When using PySpark DataFrames, optionally specify a temporary GCS bucket to"
" store data. If not provided, data will be directly written to BigQuery."
),
)
timeout: Optional[float] = Field(
default=None,
description=(
"When using Pandas DataFrames, optionally specify a timeout for the BigQuery"
" queries (loading and reading from tables)."
),
)
@staticmethod
@abstractmethod
def type_handlers() -> Sequence[DbTypeHandler]: ...
@staticmethod
def default_load_type() -> Optional[Type]:
return None
def create_io_manager(self, context) -> Generator:
mgr = DbIOManager(
db_client=BigQueryClient(),
io_manager_name="BigQueryIOManager",
database=self.project,
schema=self.dataset,
type_handlers=self.type_handlers(),
default_load_type=self.default_load_type(),
)
if self.gcp_credentials:
with setup_gcp_creds(self.gcp_credentials):
yield mgr
else:
yield mgr
class BigQueryClient(DbClient):
@staticmethod
def delete_table_slice(context: OutputContext, table_slice: TableSlice, connection) -> None:
try:
connection.query(_get_cleanup_statement(table_slice)).result()
except NotFound:
# table doesn't exist yet, so ignore the error
pass
@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"
f" `{table_slice.database}.{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.database}.{table_slice.schema}.{table_slice.table}`"""
@staticmethod
def ensure_schema_exists(context: OutputContext, table_slice: TableSlice, connection) -> None:
connection.query(f"CREATE SCHEMA IF NOT EXISTS {table_slice.schema}").result()
@staticmethod
@contextmanager
def connect(context, _):
conn = bigquery.Client(
project=context.resource_config.get("project"),
location=context.resource_config.get("location"),
)
yield conn
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.database}.{table_slice.schema}.{table_slice.table}` WHERE\n"
)
return query + _partition_where_clause(table_slice.partition_dimensions)
else:
return f"TRUNCATE TABLE `{table_slice.database}.{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(BIGQUERY_DATETIME_FORMAT)
end_dt_str = end_dt.strftime(BIGQUERY_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})"""