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"""BigQuery sources and sinks.
This module implements reading from and writing to BigQuery tables. It relies
on several classes exposed by the BigQuery API: TableSchema, TableFieldSchema,
TableRow, and TableCell. The default mode is to return table rows read from a
BigQuery source as dictionaries. Similarly a Write transform to a BigQuerySink
accepts PCollections of dictionaries. This is done for more convenient
programming. If desired, the native TableRow objects can be used throughout to
represent rows (use an instance of TableRowJsonCoder as a coder argument when
creating the sources or sinks respectively).
Also, for programming convenience, instances of TableReference and TableSchema
have a string representation that can be used for the corresponding arguments:
- TableReference can be a PROJECT:DATASET.TABLE or DATASET.TABLE string.
- TableSchema can be a NAME:TYPE{,NAME:TYPE}* string
(e.g. 'month:STRING,event_count:INTEGER').
The syntax supported is described here:
https://cloud.google.com/bigquery/bq-command-line-tool-quickstart
BigQuery sources can be used as main inputs or side inputs. A main input
(common case) is expected to be massive and will be split into manageable chunks
and processed in parallel. Side inputs are expected to be small and will be read
completely every time a ParDo DoFn gets executed. In the example below the
lambda function implementing the DoFn for the Map transform will get on each
call *one* row of the main table and *all* rows of the side table. The runner
may use some caching techniques to share the side inputs between calls in order
to avoid excessive reading:::
main_table = pipeline | 'VeryBig' >> beam.io.ReadFromBigQuery(...)
side_table = pipeline | 'NotBig' >> beam.io.ReadFromBigQuery(...)
results = (
main_table
| 'ProcessData' >> beam.Map(
lambda element, side_input: ..., AsList(side_table)))
There is no difference in how main and side inputs are read. What makes the
side_table a 'side input' is the AsList wrapper used when passing the table
as a parameter to the Map transform. AsList signals to the execution framework
that its input should be made available whole.
The main and side inputs are implemented differently. Reading a BigQuery table
as main input entails exporting the table to a set of GCS files (in AVRO or in
JSON format) and then processing those files.
Users may provide a query to read from rather than reading all of a BigQuery
table. If specified, the result obtained by executing the specified query will
be used as the data of the input transform.::
query_results = pipeline | beam.io.gcp.bigquery.ReadFromBigQuery(
query='SELECT year, mean_temp FROM samples.weather_stations')
When creating a BigQuery input transform, users should provide either a query
or a table. Pipeline construction will fail with a validation error if neither
or both are specified.
When reading via `ReadFromBigQuery`, bytes are returned decoded as bytes.
This is due to the fact that ReadFromBigQuery uses Avro exports by default.
When reading from BigQuery using `apache_beam.io.BigQuerySource`, bytes are
returned as base64-encoded bytes. To get base64-encoded bytes using
`ReadFromBigQuery`, you can use the flag `use_json_exports` to export
data as JSON, and receive base64-encoded bytes.
ReadAllFromBigQuery
-------------------
Beam 2.27.0 introduces a new transform called `ReadAllFromBigQuery` which
allows you to define table and query reads from BigQuery at pipeline
runtime.:::
read_requests = p | beam.Create([
ReadFromBigQueryRequest(query='SELECT * FROM mydataset.mytable'),
ReadFromBigQueryRequest(table='myproject.mydataset.mytable')])
results = read_requests | ReadAllFromBigQuery()
A good application for this transform is in streaming pipelines to
refresh a side input coming from BigQuery. This would work like so:::
side_input = (
p
| 'PeriodicImpulse' >> PeriodicImpulse(
first_timestamp, last_timestamp, interval, True)
| 'MapToReadRequest' >> beam.Map(
lambda x: ReadFromBigQueryRequest(table='dataset.table'))
| beam.io.ReadAllFromBigQuery())
main_input = (
p
| 'MpImpulse' >> beam.Create(sample_main_input_elements)
|
'MapMpToTimestamped' >> beam.Map(lambda src: TimestampedValue(src, src))
| 'WindowMpInto' >> beam.WindowInto(
window.FixedWindows(main_input_windowing_interval)))
result = (
main_input
| 'ApplyCrossJoin' >> beam.FlatMap(
cross_join, rights=beam.pvalue.AsIter(side_input)))
**Note**: This transform is supported on Portable and Dataflow v2 runners.
**Note**: This transform does not currently clean up temporary datasets
created for its execution. (BEAM-11359)
Writing Data to BigQuery
========================
The `WriteToBigQuery` transform is the recommended way of writing data to
BigQuery. It supports a large set of parameters to customize how you'd like to
write to BigQuery.
Table References
----------------
This transform allows you to provide static `project`, `dataset` and `table`
parameters which point to a specific BigQuery table to be created. The `table`
parameter can also be a dynamic parameter (i.e. a callable), which receives an
element to be written to BigQuery, and returns the table that that element
should be sent to.
You may also provide a tuple of PCollectionView elements to be passed as side
inputs to your callable. For example, suppose that one wishes to send
events of different types to different tables, and the table names are
computed at pipeline runtime, one may do something like the following::
with Pipeline() as p:
elements = (p | beam.Create([
{'type': 'error', 'timestamp': '12:34:56', 'message': 'bad'},
{'type': 'user_log', 'timestamp': '12:34:59', 'query': 'flu symptom'},
]))
table_names = (p | beam.Create([
('error', 'my_project:dataset1.error_table_for_today'),
('user_log', 'my_project:dataset1.query_table_for_today'),
])
table_names_dict = beam.pvalue.AsDict(table_names)
elements | beam.io.gcp.bigquery.WriteToBigQuery(
table=lambda row, table_dict: table_dict[row['type']],
table_side_inputs=(table_names_dict,))
In the example above, the `table_dict` argument passed to the function in
`table_dict` is the side input coming from `table_names_dict`, which is passed
as part of the `table_side_inputs` argument.
Schemas
---------
This transform also allows you to provide a static or dynamic `schema`
parameter (i.e. a callable).
If providing a callable, this should take in a table reference (as returned by
the `table` parameter), and return the corresponding schema for that table.
This allows to provide different schemas for different tables::
def compute_table_name(row):
...
errors_schema = {'fields': [
{'name': 'type', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'message', 'type': 'STRING', 'mode': 'NULLABLE'}]}
queries_schema = {'fields': [
{'name': 'type', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'query', 'type': 'STRING', 'mode': 'NULLABLE'}]}
with Pipeline() as p:
elements = (p | beam.Create([
{'type': 'error', 'timestamp': '12:34:56', 'message': 'bad'},
{'type': 'user_log', 'timestamp': '12:34:59', 'query': 'flu symptom'},
]))
elements | beam.io.gcp.bigquery.WriteToBigQuery(
table=compute_table_name,
schema=lambda table: (errors_schema
if 'errors' in table
else queries_schema))
It may be the case that schemas are computed at pipeline runtime. In cases
like these, one can also provide a `schema_side_inputs` parameter, which is
a tuple of PCollectionViews to be passed to the schema callable (much like
the `table_side_inputs` parameter).
Additional Parameters for BigQuery Tables
-----------------------------------------
This sink is able to create tables in BigQuery if they don't already exist. It
also relies on creating temporary tables when performing file loads.
The WriteToBigQuery transform creates tables using the BigQuery API by
inserting a load job (see the API reference [1]), or by inserting a new table
(see the API reference for that [2][3]).
When creating a new BigQuery table, there are a number of extra parameters
that one may need to specify. For example, clustering, partitioning, data
encoding, etc. It is possible to provide these additional parameters by
passing a Python dictionary as `additional_bq_parameters` to the transform.
As an example, to create a table that has specific partitioning, and
clustering properties, one would do the following::
additional_bq_parameters = {
'timePartitioning': {'type': 'DAY'},
'clustering': {'fields': ['country']}}
with Pipeline() as p:
elements = (p | beam.Create([
{'country': 'mexico', 'timestamp': '12:34:56', 'query': 'acapulco'},
{'country': 'canada', 'timestamp': '12:34:59', 'query': 'influenza'},
]))
elements | beam.io.gcp.bigquery.WriteToBigQuery(
table='project_name1:dataset_2.query_events_table',
additional_bq_parameters=additional_bq_parameters)
Much like the schema case, the parameter with `additional_bq_parameters` can
also take a callable that receives a table reference.
[1] https://cloud.google.com/bigquery/docs/reference/rest/v2/Job\
#jobconfigurationload
[2] https://cloud.google.com/bigquery/docs/reference/rest/v2/tables/insert
[3] https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource
*** Short introduction to BigQuery concepts ***
Tables have rows (TableRow) and each row has cells (TableCell).
A table has a schema (TableSchema), which in turn describes the schema of each
cell (TableFieldSchema). The terms field and cell are used interchangeably.
TableSchema: Describes the schema (types and order) for values in each row.
Has one attribute, 'field', which is list of TableFieldSchema objects.
TableFieldSchema: Describes the schema (type, name) for one field.
Has several attributes, including 'name' and 'type'. Common values for
the type attribute are: 'STRING', 'INTEGER', 'FLOAT', 'BOOLEAN', 'NUMERIC',
'GEOGRAPHY'.
All possible values are described at:
https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
TableRow: Holds all values in a table row. Has one attribute, 'f', which is a
list of TableCell instances.
TableCell: Holds the value for one cell (or field). Has one attribute,
'v', which is a JsonValue instance. This class is defined in
apitools.base.py.extra_types.py module.
As of Beam 2.7.0, the NUMERIC data type is supported. This data type supports
high-precision decimal numbers (precision of 38 digits, scale of 9 digits).
The GEOGRAPHY data type works with Well-Known Text (See
https://en.wikipedia.org/wiki/Well-known_text) format for reading and writing
to BigQuery.
BigQuery IO requires values of BYTES datatype to be encoded using base64
encoding when writing to BigQuery.
"""
# pytype: skip-file
import collections
import itertools
import json
import logging
import random
import time
import uuid
from typing import Dict
from typing import Union
import apache_beam as beam
from apache_beam import coders
from apache_beam import pvalue
from apache_beam.internal.gcp.json_value import from_json_value
from apache_beam.internal.gcp.json_value import to_json_value
from apache_beam.io.avroio import _create_avro_source as create_avro_source
from apache_beam.io.filesystems import CompressionTypes
from apache_beam.io.filesystems import FileSystems
from apache_beam.io.gcp import bigquery_tools
from apache_beam.io.gcp.bigquery_io_metadata import create_bigquery_io_metadata
from apache_beam.io.gcp.bigquery_read_internal import _BigQueryReadSplit
from apache_beam.io.gcp.bigquery_read_internal import _JsonToDictCoder
from apache_beam.io.gcp.bigquery_read_internal import _PassThroughThenCleanup
from apache_beam.io.gcp.bigquery_read_internal import bigquery_export_destination_uri
from apache_beam.io.gcp.bigquery_tools import RetryStrategy
from apache_beam.io.gcp.internal.clients import bigquery
from apache_beam.io.iobase import BoundedSource
from apache_beam.io.iobase import RangeTracker
from apache_beam.io.iobase import SDFBoundedSourceReader
from apache_beam.io.iobase import SourceBundle
from apache_beam.io.textio import _TextSource as TextSource
from apache_beam.metrics import Metrics
from apache_beam.options import value_provider as vp
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.value_provider import StaticValueProvider
from apache_beam.options.value_provider import ValueProvider
from apache_beam.options.value_provider import check_accessible
from apache_beam.runners.dataflow.native_io import iobase as dataflow_io
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.sideinputs import SIDE_INPUT_PREFIX
from apache_beam.transforms.sideinputs import get_sideinput_index
from apache_beam.transforms.util import ReshufflePerKey
from apache_beam.transforms.window import GlobalWindows
from apache_beam.utils import retry
from apache_beam.utils.annotations import deprecated
from apache_beam.utils.annotations import experimental
try:
from apache_beam.io.gcp.internal.clients.bigquery import DatasetReference
from apache_beam.io.gcp.internal.clients.bigquery import TableReference
except ImportError:
DatasetReference = None
TableReference = None
__all__ = [
'TableRowJsonCoder',
'BigQueryDisposition',
'BigQuerySource',
'BigQuerySink',
'WriteToBigQuery',
'ReadFromBigQuery',
'ReadFromBigQueryRequest',
'ReadAllFromBigQuery',
'SCHEMA_AUTODETECT',
]
_LOGGER = logging.getLogger(__name__)
"""
Template for BigQuery jobs created by BigQueryIO. This template is:
`"beam_bq_job_{job_type}_{job_id}_{step_id}_{random}"`, where:
- `job_type` represents the BigQuery job type (e.g. extract / copy / load /
query).
- `job_id` is the Beam job name.
- `step_id` is a UUID representing the the Dataflow step that created the
BQ job.
- `random` is a random string.
NOTE: This job name template does not have backwards compatibility guarantees.
"""
BQ_JOB_NAME_TEMPLATE = "beam_bq_job_{job_type}_{job_id}_{step_id}{random}"
@deprecated(since='2.11.0', current="bigquery_tools.parse_table_reference")
def _parse_table_reference(table, dataset=None, project=None):
return bigquery_tools.parse_table_reference(table, dataset, project)
@deprecated(
since='2.11.0', current="bigquery_tools.parse_table_schema_from_json")
def parse_table_schema_from_json(schema_string):
return bigquery_tools.parse_table_schema_from_json(schema_string)
@deprecated(since='2.11.0', current="bigquery_tools.default_encoder")
def default_encoder(obj):
return bigquery_tools.default_encoder(obj)
@deprecated(since='2.11.0', current="bigquery_tools.RowAsDictJsonCoder")
def RowAsDictJsonCoder(*args, **kwargs):
return bigquery_tools.RowAsDictJsonCoder(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryReader")
def BigQueryReader(*args, **kwargs):
return bigquery_tools.BigQueryReader(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryWriter")
def BigQueryWriter(*args, **kwargs):
return bigquery_tools.BigQueryWriter(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryWrapper")
def BigQueryWrapper(*args, **kwargs):
return bigquery_tools.BigQueryWrapper(*args, **kwargs)
[docs]class TableRowJsonCoder(coders.Coder):
"""A coder for a TableRow instance to/from a JSON string.
Note that the encoding operation (used when writing to sinks) requires the
table schema in order to obtain the ordered list of field names. Reading from
sources on the other hand does not need the table schema.
"""
def __init__(self, table_schema=None):
# The table schema is needed for encoding TableRows as JSON (writing to
# sinks) because the ordered list of field names is used in the JSON
# representation.
self.table_schema = table_schema
# Precompute field names since we need them for row encoding.
if self.table_schema:
self.field_names = tuple(fs.name for fs in self.table_schema.fields)
self.field_types = tuple(fs.type for fs in self.table_schema.fields)
[docs] def encode(self, table_row):
if self.table_schema is None:
raise AttributeError(
'The TableRowJsonCoder requires a table schema for '
'encoding operations. Please specify a table_schema argument.')
try:
return json.dumps(
collections.OrderedDict(
zip(
self.field_names,
[from_json_value(f.v) for f in table_row.f])),
allow_nan=False,
default=bigquery_tools.default_encoder)
except ValueError as e:
raise ValueError('%s. %s' % (e, bigquery_tools.JSON_COMPLIANCE_ERROR))
[docs] def decode(self, encoded_table_row):
od = json.loads(
encoded_table_row, object_pairs_hook=collections.OrderedDict)
return bigquery.TableRow(
f=[bigquery.TableCell(v=to_json_value(e)) for e in od.values()])
[docs]class BigQueryDisposition(object):
"""Class holding standard strings used for create and write dispositions."""
CREATE_NEVER = 'CREATE_NEVER'
CREATE_IF_NEEDED = 'CREATE_IF_NEEDED'
WRITE_TRUNCATE = 'WRITE_TRUNCATE'
WRITE_APPEND = 'WRITE_APPEND'
WRITE_EMPTY = 'WRITE_EMPTY'
[docs] @staticmethod
def validate_create(disposition):
values = (
BigQueryDisposition.CREATE_NEVER, BigQueryDisposition.CREATE_IF_NEEDED)
if disposition not in values:
raise ValueError(
'Invalid create disposition %s. Expecting %s' % (disposition, values))
return disposition
[docs] @staticmethod
def validate_write(disposition):
values = (
BigQueryDisposition.WRITE_TRUNCATE,
BigQueryDisposition.WRITE_APPEND,
BigQueryDisposition.WRITE_EMPTY)
if disposition not in values:
raise ValueError(
'Invalid write disposition %s. Expecting %s' % (disposition, values))
return disposition
# -----------------------------------------------------------------------------
# BigQuerySource, BigQuerySink.
[docs]@deprecated(since='2.25.0', current="ReadFromBigQuery")
def BigQuerySource(
table=None,
dataset=None,
project=None,
query=None,
validate=False,
coder=None,
use_standard_sql=False,
flatten_results=True,
kms_key=None,
use_dataflow_native_source=False):
if use_dataflow_native_source:
return _BigQuerySource(
table,
dataset,
project,
query,
validate,
coder,
use_standard_sql,
flatten_results,
kms_key)
else:
return ReadFromBigQuery(
table=table,
dataset=dataset,
project=project,
query=query,
validate=validate,
coder=coder,
use_standard_sql=use_standard_sql,
flatten_results=flatten_results,
use_json_exports=True,
kms_key=kms_key)
@deprecated(since='2.25.0', current="ReadFromBigQuery")
class _BigQuerySource(dataflow_io.NativeSource):
"""A source based on a BigQuery table."""
def __init__(
self,
table=None,
dataset=None,
project=None,
query=None,
validate=False,
coder=None,
use_standard_sql=False,
flatten_results=True,
kms_key=None,
temp_dataset=None):
"""Initialize a :class:`BigQuerySource`.
Args:
table (str): The ID of a BigQuery table. If specified all data of the
table will be used as input of the current source. The ID must contain
only letters ``a-z``, ``A-Z``, numbers ``0-9``, or underscores
``_``. If dataset and query arguments are :data:`None` then the table
argument must contain the entire table reference specified as:
``'DATASET.TABLE'`` or ``'PROJECT:DATASET.TABLE'``.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument or a query is specified.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument or a query is specified.
query (str): A query to be used instead of arguments table, dataset, and
project.
validate (bool): If :data:`True`, various checks will be done when source
gets initialized (e.g., is table present?). This should be
:data:`True` for most scenarios in order to catch errors as early as
possible (pipeline construction instead of pipeline execution). It
should be :data:`False` if the table is created during pipeline
execution by a previous step.
coder (~apache_beam.coders.coders.Coder): The coder for the table
rows if serialized to disk. If :data:`None`, then the default coder is
:class:`~apache_beam.io.gcp.bigquery_tools.RowAsDictJsonCoder`,
which will interpret every line in a file as a JSON serialized
dictionary. This argument needs a value only in special cases when
returning table rows as dictionaries is not desirable.
use_standard_sql (bool): Specifies whether to use BigQuery's standard SQL
dialect for this query. The default value is :data:`False`.
If set to :data:`True`, the query will use BigQuery's updated SQL
dialect with improved standards compliance.
This parameter is ignored for table inputs.
flatten_results (bool): Flattens all nested and repeated fields in the
query results. The default value is :data:`True`.
kms_key (str): Optional Cloud KMS key name for use when creating new
tables.
temp_dataset (``google.cloud.bigquery.dataset.DatasetReference``):
The dataset in which to create temporary tables when performing file
loads. By default, a new dataset is created in the execution project for
temporary tables.
Raises:
ValueError: if any of the following is true:
1) the table reference as a string does not match the expected format
2) neither a table nor a query is specified
3) both a table and a query is specified.
"""
# Import here to avoid adding the dependency for local running scenarios.
try:
# pylint: disable=wrong-import-order, wrong-import-position
from apitools.base import py # pylint: disable=unused-import
except ImportError:
raise ImportError(
'Google Cloud IO not available, '
'please install apache_beam[gcp]')
if table is not None and query is not None:
raise ValueError(
'Both a BigQuery table and a query were specified.'
' Please specify only one of these.')
elif table is None and query is None:
raise ValueError('A BigQuery table or a query must be specified')
elif table is not None:
self.table_reference = bigquery_tools.parse_table_reference(
table, dataset, project)
self.query = None
self.use_legacy_sql = True
else:
self.query = query
# TODO(BEAM-1082): Change the internal flag to be standard_sql
self.use_legacy_sql = not use_standard_sql
self.table_reference = None
self.validate = validate
self.flatten_results = flatten_results
self.coder = coder or bigquery_tools.RowAsDictJsonCoder()
self.kms_key = kms_key
self.temp_dataset = temp_dataset
def display_data(self):
if self.query is not None:
res = {'query': DisplayDataItem(self.query, label='Query')}
else:
if self.table_reference.projectId is not None:
tableSpec = '{}:{}.{}'.format(
self.table_reference.projectId,
self.table_reference.datasetId,
self.table_reference.tableId)
else:
tableSpec = '{}.{}'.format(
self.table_reference.datasetId, self.table_reference.tableId)
res = {'table': DisplayDataItem(tableSpec, label='Table')}
res['validation'] = DisplayDataItem(
self.validate, label='Validation Enabled')
return res
@property
def format(self):
"""Source format name required for remote execution."""
return 'bigquery'
def reader(self, test_bigquery_client=None):
return bigquery_tools.BigQueryReader(
source=self,
test_bigquery_client=test_bigquery_client,
use_legacy_sql=self.use_legacy_sql,
flatten_results=self.flatten_results,
kms_key=self.kms_key)
class _CustomBigQuerySource(BoundedSource):
def __init__(
self,
gcs_location=None,
table=None,
dataset=None,
project=None,
query=None,
validate=False,
pipeline_options=None,
coder=None,
use_standard_sql=False,
flatten_results=True,
kms_key=None,
bigquery_job_labels=None,
use_json_exports=False,
job_name=None,
step_name=None,
unique_id=None,
temp_dataset=None):
if table is not None and query is not None:
raise ValueError(
'Both a BigQuery table and a query were specified.'
' Please specify only one of these.')
elif table is None and query is None:
raise ValueError('A BigQuery table or a query must be specified')
elif table is not None:
self.table_reference = bigquery_tools.parse_table_reference(
table, dataset, project)
self.query = None
self.use_legacy_sql = True
else:
if isinstance(query, str):
query = StaticValueProvider(str, query)
self.query = query
# TODO(BEAM-1082): Change the internal flag to be standard_sql
self.use_legacy_sql = not use_standard_sql
self.table_reference = None
self.gcs_location = gcs_location
self.project = project
self.validate = validate
self.flatten_results = flatten_results
self.coder = coder or _JsonToDictCoder
self.kms_key = kms_key
self.split_result = None
self.options = pipeline_options
self.bq_io_metadata = None # Populate in setup, as it may make an RPC
self.bigquery_job_labels = bigquery_job_labels or {}
self.use_json_exports = use_json_exports
self.temp_dataset = temp_dataset
self._job_name = job_name or 'BQ_EXPORT_JOB'
self._step_name = step_name
self._source_uuid = unique_id
def _get_bq_metadata(self):
if not self.bq_io_metadata:
self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
return self.bq_io_metadata
def display_data(self):
export_format = 'JSON' if self.use_json_exports else 'AVRO'
return {
'table': str(self.table_reference),
'query': str(self.query),
'project': str(self.project),
'use_legacy_sql': self.use_legacy_sql,
'bigquery_job_labels': json.dumps(self.bigquery_job_labels),
'export_file_format': export_format,
'launchesBigQueryJobs': DisplayDataItem(
True, label="This Dataflow job launches bigquery jobs."),
}
def estimate_size(self):
bq = bigquery_tools.BigQueryWrapper()
if self.table_reference is not None:
table_ref = self.table_reference
if (isinstance(self.table_reference, vp.ValueProvider) and
self.table_reference.is_accessible()):
table_ref = bigquery_tools.parse_table_reference(
self.table_reference.get(), project=self._get_project())
elif isinstance(self.table_reference, vp.ValueProvider):
# Size estimation is best effort. We return None as we have
# no access to the table that we're querying.
return None
if not table_ref.projectId:
table_ref.projectId = self._get_project()
table = bq.get_table(
table_ref.projectId, table_ref.datasetId, table_ref.tableId)
return int(table.numBytes)
elif self.query is not None and self.query.is_accessible():
project = self._get_project()
query_job_name = bigquery_tools.generate_bq_job_name(
self._job_name,
self._source_uuid,
bigquery_tools.BigQueryJobTypes.QUERY,
'%s_%s' % (int(time.time()), random.randint(0, 1000)))
job = bq._start_query_job(
project,
self.query.get(),
self.use_legacy_sql,
self.flatten_results,
job_id=query_job_name,
dry_run=True,
kms_key=self.kms_key,
job_labels=self._get_bq_metadata().add_additional_bq_job_labels(
self.bigquery_job_labels))
size = int(job.statistics.totalBytesProcessed)
return size
else:
# Size estimation is best effort. We return None as we have
# no access to the query that we're running.
return None
def _get_project(self):
"""Returns the project that queries and exports will be billed to."""
project = self.options.view_as(GoogleCloudOptions).project
if isinstance(project, vp.ValueProvider):
project = project.get()
if self.temp_dataset:
return self.temp_dataset.projectId
if not project:
project = self.project
return project
def _create_source(self, path, schema):
if not self.use_json_exports:
return create_avro_source(path, use_fastavro=True)
else:
return TextSource(
path,
min_bundle_size=0,
compression_type=CompressionTypes.UNCOMPRESSED,
strip_trailing_newlines=True,
coder=self.coder(schema))
def split(self, desired_bundle_size, start_position=None, stop_position=None):
if self.split_result is None:
bq = bigquery_tools.BigQueryWrapper(
temp_dataset_id=(
self.temp_dataset.datasetId if self.temp_dataset else None))
if self.query is not None:
self._setup_temporary_dataset(bq)
self.table_reference = self._execute_query(bq)
if not self.table_reference.projectId:
self.table_reference.projectId = self._get_project()
schema, metadata_list = self._export_files(bq)
self.split_result = [
self._create_source(metadata.path, schema)
for metadata in metadata_list
]
if self.query is not None:
bq.clean_up_temporary_dataset(self._get_project())
for source in self.split_result:
yield SourceBundle(
weight=1.0, source=source, start_position=None, stop_position=None)
def get_range_tracker(self, start_position, stop_position):
class CustomBigQuerySourceRangeTracker(RangeTracker):
"""A RangeTracker that always returns positions as None."""
def start_position(self):
return None
def stop_position(self):
return None
return CustomBigQuerySourceRangeTracker()
def read(self, range_tracker):
raise NotImplementedError('BigQuery source must be split before being read')
@check_accessible(['query'])
def _setup_temporary_dataset(self, bq):
if self.temp_dataset:
# Temp dataset was provided by the user so we can just return.
return
location = bq.get_query_location(
self._get_project(), self.query.get(), self.use_legacy_sql)
bq.create_temporary_dataset(self._get_project(), location)
@check_accessible(['query'])
def _execute_query(self, bq):
query_job_name = bigquery_tools.generate_bq_job_name(
self._job_name,
self._source_uuid,
bigquery_tools.BigQueryJobTypes.QUERY,
'%s_%s' % (int(time.time()), random.randint(0, 1000)))
job = bq._start_query_job(
self._get_project(),
self.query.get(),
self.use_legacy_sql,
self.flatten_results,
job_id=query_job_name,
kms_key=self.kms_key,
job_labels=self._get_bq_metadata().add_additional_bq_job_labels(
self.bigquery_job_labels))
job_ref = job.jobReference
bq.wait_for_bq_job(job_ref, max_retries=0)
return bq._get_temp_table(self._get_project())
def _export_files(self, bq):
"""Runs a BigQuery export job.
Returns:
bigquery.TableSchema instance, a list of FileMetadata instances
"""
job_labels = self._get_bq_metadata().add_additional_bq_job_labels(
self.bigquery_job_labels)
export_job_name = bigquery_tools.generate_bq_job_name(
self._job_name,
self._source_uuid,
bigquery_tools.BigQueryJobTypes.EXPORT,
'%s_%s' % (int(time.time()), random.randint(0, 1000)))
temp_location = self.options.view_as(GoogleCloudOptions).temp_location
gcs_location = bigquery_export_destination_uri(
self.gcs_location, temp_location, self._source_uuid)
if self.use_json_exports:
job_ref = bq.perform_extract_job([gcs_location],
export_job_name,
self.table_reference,
bigquery_tools.FileFormat.JSON,
project=self._get_project(),
job_labels=job_labels,
include_header=False)
else:
job_ref = bq.perform_extract_job([gcs_location],
export_job_name,
self.table_reference,
bigquery_tools.FileFormat.AVRO,
project=self._get_project(),
include_header=False,
job_labels=job_labels,
use_avro_logical_types=True)
bq.wait_for_bq_job(job_ref)
metadata_list = FileSystems.match([gcs_location])[0].metadata_list
if isinstance(self.table_reference, vp.ValueProvider):
table_ref = bigquery_tools.parse_table_reference(
self.table_reference.get(), project=self.project)
else:
table_ref = self.table_reference
table = bq.get_table(
table_ref.projectId, table_ref.datasetId, table_ref.tableId)
return table.schema, metadata_list
[docs]@deprecated(since='2.11.0', current="WriteToBigQuery")
class BigQuerySink(dataflow_io.NativeSink):
"""A sink based on a BigQuery table.
This BigQuery sink triggers a Dataflow native sink for BigQuery
that only supports batch pipelines.
Instead of using this sink directly, please use WriteToBigQuery
transform that works for both batch and streaming pipelines.
"""
def __init__(
self,
table,
dataset=None,
project=None,
schema=None,
create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=BigQueryDisposition.WRITE_EMPTY,
validate=False,
coder=None,
kms_key=None):
"""Initialize a BigQuerySink.
Args:
table (str): The ID of the table. The ID must contain only letters
``a-z``, ``A-Z``, numbers ``0-9``, or underscores ``_``. If
**dataset** argument is :data:`None` then the table argument must
contain the entire table reference specified as: ``'DATASET.TABLE'`` or
``'PROJECT:DATASET.TABLE'``.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
schema (str): The schema to be used if the BigQuery table to write has
to be created. This can be either specified as a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` object or a single string of the form
``'field1:type1,field2:type2,field3:type3'`` that defines a comma
separated list of fields. Here ``'type'`` should specify the BigQuery
type of the field. Single string based schemas do not support nested
fields, repeated fields, or specifying a BigQuery mode for fields (mode
will always be set to ``'NULLABLE'``).
create_disposition (BigQueryDisposition): A string describing what
happens if the table does not exist. Possible values are:
* :attr:`BigQueryDisposition.CREATE_IF_NEEDED`: create if does not
exist.
* :attr:`BigQueryDisposition.CREATE_NEVER`: fail the write if does not
exist.
write_disposition (BigQueryDisposition): A string describing what
happens if the table has already some data. Possible values are:
* :attr:`BigQueryDisposition.WRITE_TRUNCATE`: delete existing rows.
* :attr:`BigQueryDisposition.WRITE_APPEND`: add to existing rows.
* :attr:`BigQueryDisposition.WRITE_EMPTY`: fail the write if table not
empty.
validate (bool): If :data:`True`, various checks will be done when sink
gets initialized (e.g., is table present given the disposition
arguments?). This should be :data:`True` for most scenarios in order to
catch errors as early as possible (pipeline construction instead of
pipeline execution). It should be :data:`False` if the table is created
during pipeline execution by a previous step.
coder (~apache_beam.coders.coders.Coder): The coder for the
table rows if serialized to disk. If :data:`None`, then the default
coder is :class:`~apache_beam.io.gcp.bigquery_tools.RowAsDictJsonCoder`,
which will interpret every element written to the sink as a dictionary
that will be JSON serialized as a line in a file. This argument needs a
value only in special cases when writing table rows as dictionaries is
not desirable.
kms_key (str): Optional Cloud KMS key name for use when creating new
tables.
Raises:
TypeError: if the schema argument is not a :class:`str` or a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` object.
ValueError: if the table reference as a string does not
match the expected format.
"""
# Import here to avoid adding the dependency for local running scenarios.
try:
# pylint: disable=wrong-import-order, wrong-import-position
from apitools.base import py # pylint: disable=unused-import
except ImportError:
raise ImportError(
'Google Cloud IO not available, '
'please install apache_beam[gcp]')
self.table_reference = bigquery_tools.parse_table_reference(
table, dataset, project)
# Transform the table schema into a bigquery.TableSchema instance.
if isinstance(schema, str):
# TODO(silviuc): Should add a regex-based validation of the format.
table_schema = bigquery.TableSchema()
schema_list = [s.strip(' ') for s in schema.split(',')]
for field_and_type in schema_list:
field_name, field_type = field_and_type.split(':')
field_schema = bigquery.TableFieldSchema()
field_schema.name = field_name
field_schema.type = field_type
field_schema.mode = 'NULLABLE'
table_schema.fields.append(field_schema)
self.table_schema = table_schema
elif schema is None:
# TODO(silviuc): Should check that table exists if no schema specified.
self.table_schema = schema
elif isinstance(schema, bigquery.TableSchema):
self.table_schema = schema
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
self.create_disposition = BigQueryDisposition.validate_create(
create_disposition)
self.write_disposition = BigQueryDisposition.validate_write(
write_disposition)
self.validate = validate
self.coder = coder or bigquery_tools.RowAsDictJsonCoder()
self.kms_key = kms_key
[docs] def display_data(self):
res = {}
if self.table_reference is not None:
tableSpec = '{}.{}'.format(
self.table_reference.datasetId, self.table_reference.tableId)
if self.table_reference.projectId is not None:
tableSpec = '{}:{}'.format(self.table_reference.projectId, tableSpec)
res['table'] = DisplayDataItem(tableSpec, label='Table')
res['validation'] = DisplayDataItem(
self.validate, label="Validation Enabled")
return res
[docs] def schema_as_json(self):
"""Returns the TableSchema associated with the sink as a JSON string."""
def schema_list_as_object(schema_list):
"""Returns a list of TableFieldSchema objects as a list of dicts."""
fields = []
for f in schema_list:
fs = {'name': f.name, 'type': f.type}
if f.description is not None:
fs['description'] = f.description
if f.mode is not None:
fs['mode'] = f.mode
if f.type.lower() == 'record':
fs['fields'] = schema_list_as_object(f.fields)
fields.append(fs)
return fields
return json.dumps(
{'fields': schema_list_as_object(self.table_schema.fields)})
@property
def format(self):
"""Sink format name required for remote execution."""
return 'bigquery'
[docs] def writer(self, test_bigquery_client=None, buffer_size=None):
return bigquery_tools.BigQueryWriter(
sink=self,
test_bigquery_client=test_bigquery_client,
buffer_size=buffer_size)
_KNOWN_TABLES = set()
class BigQueryWriteFn(DoFn):
"""A ``DoFn`` that streams writes to BigQuery once the table is created."""
DEFAULT_MAX_BUFFERED_ROWS = 2000
DEFAULT_MAX_BATCH_SIZE = 500
FAILED_ROWS = 'FailedRows'
STREAMING_API_LOGGING_FREQUENCY_SEC = 300
def __init__(
self,
batch_size,
schema=None,
create_disposition=None,
write_disposition=None,
kms_key=None,
test_client=None,
max_buffered_rows=None,
retry_strategy=None,
additional_bq_parameters=None,
ignore_insert_ids=False,
with_batched_input=False):
"""Initialize a WriteToBigQuery transform.
Args:
batch_size: Number of rows to be written to BQ per streaming API insert.
schema: The schema to be used if the BigQuery table to write has to be
created. This can be either specified as a 'bigquery.TableSchema' object
or a single string of the form 'field1:type1,field2:type2,field3:type3'
that defines a comma separated list of fields. Here 'type' should
specify the BigQuery type of the field. Single string based schemas do
not support nested fields, repeated fields, or specifying a BigQuery
mode for fields (mode will always be set to 'NULLABLE').
create_disposition: A string describing what happens if the table does not
exist. Possible values are:
- BigQueryDisposition.CREATE_IF_NEEDED: create if does not exist.
- BigQueryDisposition.CREATE_NEVER: fail the write if does not exist.
write_disposition: A string describing what happens if the table has
already some data. Possible values are:
- BigQueryDisposition.WRITE_TRUNCATE: delete existing rows.
- BigQueryDisposition.WRITE_APPEND: add to existing rows.
- BigQueryDisposition.WRITE_EMPTY: fail the write if table not empty.
For streaming pipelines WriteTruncate can not be used.
kms_key: Optional Cloud KMS key name for use when creating new tables.
test_client: Override the default bigquery client used for testing.
max_buffered_rows: The maximum number of rows that are allowed to stay
buffered when running dynamic destinations. When destinations are
dynamic, it is important to keep caches small even when a single
batch has not been completely filled up.
retry_strategy: The strategy to use when retrying streaming inserts
into BigQuery. Options are shown in bigquery_tools.RetryStrategy attrs.
additional_bq_parameters (dict, callable): A set of additional parameters
to be passed when creating a BigQuery table. These are passed when
triggering a load job for FILE_LOADS, and when creating a new table for
STREAMING_INSERTS.
ignore_insert_ids: When using the STREAMING_INSERTS method to write data
to BigQuery, `insert_ids` are a feature of BigQuery that support
deduplication of events. If your use case is not sensitive to
duplication of data inserted to BigQuery, set `ignore_insert_ids`
to True to increase the throughput for BQ writing. See:
https://cloud.google.com/bigquery/streaming-data-into-bigquery#disabling_best_effort_de-duplication
with_batched_input: Whether the input has already been batched per
destination. If not, perform best-effort batching per destination within
a bunble.
"""
self.schema = schema
self.test_client = test_client
self.create_disposition = create_disposition
self.write_disposition = write_disposition
if write_disposition in (BigQueryDisposition.WRITE_EMPTY,
BigQueryDisposition.WRITE_TRUNCATE):
raise ValueError(
'Write disposition %s is not supported for'
' streaming inserts to BigQuery' % write_disposition)
self._rows_buffer = []
self._reset_rows_buffer()
self._total_buffered_rows = 0
self.kms_key = kms_key
self._max_batch_size = batch_size or BigQueryWriteFn.DEFAULT_MAX_BATCH_SIZE
self._max_buffered_rows = (
max_buffered_rows or BigQueryWriteFn.DEFAULT_MAX_BUFFERED_ROWS)
self._retry_strategy = retry_strategy or RetryStrategy.RETRY_ALWAYS
self.ignore_insert_ids = ignore_insert_ids
self.with_batched_input = with_batched_input
self.additional_bq_parameters = additional_bq_parameters or {}
# accumulate the total time spent in exponential backoff
self._throttled_secs = Metrics.counter(
BigQueryWriteFn, "cumulativeThrottlingSeconds")
self.batch_size_metric = Metrics.distribution(self.__class__, "batch_size")
self.batch_latency_metric = Metrics.distribution(
self.__class__, "batch_latency_ms")
self.failed_rows_metric = Metrics.distribution(
self.__class__, "rows_failed_per_batch")
self.bigquery_wrapper = None
self.streaming_api_logging_frequency_sec = (
BigQueryWriteFn.STREAMING_API_LOGGING_FREQUENCY_SEC)
def display_data(self):
return {
'max_batch_size': self._max_batch_size,
'max_buffered_rows': self._max_buffered_rows,
'retry_strategy': self._retry_strategy,
'create_disposition': str(self.create_disposition),
'write_disposition': str(self.write_disposition),
'additional_bq_parameters': str(self.additional_bq_parameters),
'ignore_insert_ids': str(self.ignore_insert_ids)
}
def _reset_rows_buffer(self):
self._rows_buffer = collections.defaultdict(lambda: [])
@staticmethod
def get_table_schema(schema):
"""Transform the table schema into a bigquery.TableSchema instance.
Args:
schema: The schema to be used if the BigQuery table to write has to be
created. This is a dictionary object created in the WriteToBigQuery
transform.
Returns:
table_schema: The schema to be used if the BigQuery table to write has
to be created but in the bigquery.TableSchema format.
"""
if schema is None:
return schema
elif isinstance(schema, str):
return bigquery_tools.parse_table_schema_from_json(schema)
elif isinstance(schema, dict):
return bigquery_tools.parse_table_schema_from_json(json.dumps(schema))
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
def start_bundle(self):
self._reset_rows_buffer()
if not self.bigquery_wrapper:
self.bigquery_wrapper = bigquery_tools.BigQueryWrapper(
client=self.test_client)
(
bigquery_tools.BigQueryWrapper.HISTOGRAM_METRIC_LOGGER.
minimum_logging_frequency_msec
) = self.streaming_api_logging_frequency_sec * 1000
self._backoff_calculator = iter(
retry.FuzzedExponentialIntervals(
initial_delay_secs=0.2, num_retries=10000, max_delay_secs=1500))
def _create_table_if_needed(self, table_reference, schema=None):
str_table_reference = '%s:%s.%s' % (
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId)
if str_table_reference in _KNOWN_TABLES:
return
if self.create_disposition == BigQueryDisposition.CREATE_NEVER:
# If we never want to create the table, we assume it already exists,
# and avoid the get-or-create step.
return
_LOGGER.debug(
'Creating or getting table %s with schema %s.', table_reference, schema)
table_schema = self.get_table_schema(schema)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
self.bigquery_wrapper.get_or_create_table(
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId,
table_schema,
self.create_disposition,
self.write_disposition,
additional_create_parameters=self.additional_bq_parameters)
_KNOWN_TABLES.add(str_table_reference)
def process(self, element, *schema_side_inputs):
destination = bigquery_tools.get_hashable_destination(element[0])
if callable(self.schema):
schema = self.schema(destination, *schema_side_inputs)
elif isinstance(self.schema, vp.ValueProvider):
schema = self.schema.get()
else:
schema = self.schema
self._create_table_if_needed(
bigquery_tools.parse_table_reference(destination), schema)
if not self.with_batched_input:
row_and_insert_id = element[1]
self._rows_buffer[destination].append(row_and_insert_id)
self._total_buffered_rows += 1
if len(self._rows_buffer[destination]) >= self._max_batch_size:
return self._flush_batch(destination)
elif self._total_buffered_rows >= self._max_buffered_rows:
return self._flush_all_batches()
else:
# The input is already batched per destination, flush the rows now.
batched_rows = element[1]
self._rows_buffer[destination].extend(batched_rows)
return self._flush_batch(destination)
def finish_bundle(self):
bigquery_tools.BigQueryWrapper.HISTOGRAM_METRIC_LOGGER.log_metrics(
reset_after_logging=True)
return self._flush_all_batches()
def _flush_all_batches(self):
_LOGGER.debug(
'Attempting to flush to all destinations. Total buffered: %s',
self._total_buffered_rows)
return itertools.chain(
*[
self._flush_batch(destination)
for destination in list(self._rows_buffer.keys())
if self._rows_buffer[destination]
])
def _flush_batch(self, destination):
# Flush the current batch of rows to BigQuery.
rows_and_insert_ids = self._rows_buffer[destination]
table_reference = bigquery_tools.parse_table_reference(destination)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
_LOGGER.debug(
'Flushing data to %s. Total %s rows.',
destination,
len(rows_and_insert_ids))
self.batch_size_metric.update(len(rows_and_insert_ids))
rows = [r[0] for r in rows_and_insert_ids]
if self.ignore_insert_ids:
insert_ids = [None for r in rows_and_insert_ids]
else:
insert_ids = [r[1] for r in rows_and_insert_ids]
while True:
start = time.time()
passed, errors = self.bigquery_wrapper.insert_rows(
project_id=table_reference.projectId,
dataset_id=table_reference.datasetId,
table_id=table_reference.tableId,
rows=rows,
insert_ids=insert_ids,
skip_invalid_rows=True)
self.batch_latency_metric.update((time.time() - start) * 1000)
failed_rows = [rows[entry['index']] for entry in errors]
should_retry = any(
RetryStrategy.should_retry(
self._retry_strategy, entry['errors'][0]['reason'])
for entry in errors)
if not passed:
self.failed_rows_metric.update(len(failed_rows))
message = (
'There were errors inserting to BigQuery. Will{} retry. '
'Errors were {}'.format(("" if should_retry else " not"), errors))
if should_retry:
_LOGGER.warning(message)
else:
_LOGGER.error(message)
rows = failed_rows
if not should_retry:
break
else:
retry_backoff = next(self._backoff_calculator)
_LOGGER.info(
'Sleeping %s seconds before retrying insertion.', retry_backoff)
time.sleep(retry_backoff)
self._throttled_secs.inc(retry_backoff)
self._total_buffered_rows -= len(self._rows_buffer[destination])
del self._rows_buffer[destination]
return [
pvalue.TaggedOutput(
BigQueryWriteFn.FAILED_ROWS,
GlobalWindows.windowed_value((destination, row)))
for row in failed_rows
]
# The number of shards per destination when writing via streaming inserts.
DEFAULT_SHARDS_PER_DESTINATION = 500
# The max duration a batch of elements is allowed to be buffered before being
# flushed to BigQuery.
DEFAULT_BATCH_BUFFERING_DURATION_LIMIT_SEC = 0.2
class _StreamToBigQuery(PTransform):
def __init__(
self,
table_reference,
table_side_inputs,
schema_side_inputs,
schema,
batch_size,
create_disposition,
write_disposition,
kms_key,
retry_strategy,
additional_bq_parameters,
ignore_insert_ids,
with_auto_sharding,
test_client=None):
self.table_reference = table_reference
self.table_side_inputs = table_side_inputs
self.schema_side_inputs = schema_side_inputs
self.schema = schema
self.batch_size = batch_size
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self.kms_key = kms_key
self.retry_strategy = retry_strategy
self.test_client = test_client
self.additional_bq_parameters = additional_bq_parameters
self.ignore_insert_ids = ignore_insert_ids
self.with_auto_sharding = with_auto_sharding
class InsertIdPrefixFn(DoFn):
def start_bundle(self):
self.prefix = str(uuid.uuid4())
self._row_count = 0
def process(self, element):
key = element[0]
value = element[1]
insert_id = '%s-%s' % (self.prefix, self._row_count)
self._row_count += 1
yield (key, (value, insert_id))
def expand(self, input):
bigquery_write_fn = BigQueryWriteFn(
schema=self.schema,
batch_size=self.batch_size,
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
kms_key=self.kms_key,
retry_strategy=self.retry_strategy,
test_client=self.test_client,
additional_bq_parameters=self.additional_bq_parameters,
ignore_insert_ids=self.ignore_insert_ids,
with_batched_input=self.with_auto_sharding)
def _add_random_shard(element):
key = element[0]
value = element[1]
return ((key, random.randint(0, DEFAULT_SHARDS_PER_DESTINATION)), value)
def _restore_table_ref(sharded_table_ref_elems_kv):
sharded_table_ref = sharded_table_ref_elems_kv[0]
table_ref = bigquery_tools.parse_table_reference(sharded_table_ref)
return (table_ref, sharded_table_ref_elems_kv[1])
tagged_data = (
input
| 'AppendDestination' >> beam.ParDo(
bigquery_tools.AppendDestinationsFn(self.table_reference),
*self.table_side_inputs)
| 'AddInsertIds' >> beam.ParDo(_StreamToBigQuery.InsertIdPrefixFn())
|
'ToHashableTableRef' >> beam.Map(bigquery_tools.to_hashable_table_ref))
if not self.with_auto_sharding:
tagged_data = (
tagged_data
| 'WithFixedSharding' >> beam.Map(_add_random_shard)
| 'CommitInsertIds' >> ReshufflePerKey()
| 'DropShard' >> beam.Map(lambda kv: (kv[0][0], kv[1])))
else:
# Auto-sharding is achieved via GroupIntoBatches.WithShardedKey
# transform which shards, groups and at the same time batches the table
# rows to be inserted to BigQuery.
# Firstly the keys of tagged_data (table references) are converted to a
# hashable format. This is needed to work with the keyed states used by
# GroupIntoBatches. After grouping and batching is done, original table
# references are restored.
tagged_data = (
tagged_data
| 'WithAutoSharding' >> beam.GroupIntoBatches.WithShardedKey(
(self.batch_size or BigQueryWriteFn.DEFAULT_MAX_BUFFERED_ROWS),
DEFAULT_BATCH_BUFFERING_DURATION_LIMIT_SEC)
| 'DropShard' >> beam.Map(lambda kv: (kv[0].key, kv[1])))
return (
tagged_data
| 'FromHashableTableRef' >> beam.Map(_restore_table_ref)
| 'StreamInsertRows' >> ParDo(
bigquery_write_fn, *self.schema_side_inputs).with_outputs(
BigQueryWriteFn.FAILED_ROWS, main='main'))
# Flag to be passed to WriteToBigQuery to force schema autodetection
SCHEMA_AUTODETECT = 'SCHEMA_AUTODETECT'
[docs]class WriteToBigQuery(PTransform):
"""Write data to BigQuery.
This transform receives a PCollection of elements to be inserted into BigQuery
tables. The elements would come in as Python dictionaries, or as `TableRow`
instances.
"""
[docs] class Method(object):
DEFAULT = 'DEFAULT'
STREAMING_INSERTS = 'STREAMING_INSERTS'
FILE_LOADS = 'FILE_LOADS'
def __init__(
self,
table,
dataset=None,
project=None,
schema=None,
create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=BigQueryDisposition.WRITE_APPEND,
kms_key=None,
batch_size=None,
max_file_size=None,
max_files_per_bundle=None,
test_client=None,
custom_gcs_temp_location=None,
method=None,
insert_retry_strategy=None,
additional_bq_parameters=None,
table_side_inputs=None,
schema_side_inputs=None,
triggering_frequency=None,
validate=True,
temp_file_format=None,
ignore_insert_ids=False,
# TODO(BEAM-11857): Switch the default when the feature is mature.
with_auto_sharding=False):
"""Initialize a WriteToBigQuery transform.
Args:
table (str, callable, ValueProvider): The ID of the table, or a callable
that returns it. The ID must contain only letters ``a-z``, ``A-Z``,
numbers ``0-9``, or connectors ``-_``. If dataset argument is
:data:`None` then the table argument must contain the entire table
reference specified as: ``'DATASET.TABLE'``
or ``'PROJECT:DATASET.TABLE'``. If it's a callable, it must receive one
argument representing an element to be written to BigQuery, and return
a TableReference, or a string table name as specified above.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
schema (str,dict,ValueProvider,callable): The schema to be used if the
BigQuery table to write has to be created. This can be either specified
as a :class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema`. or a `ValueProvider` that has a JSON string,
or a python dictionary, or the string or dictionary itself,
object or a single string of the form
``'field1:type1,field2:type2,field3:type3'`` that defines a comma
separated list of fields. Here ``'type'`` should specify the BigQuery
type of the field. Single string based schemas do not support nested
fields, repeated fields, or specifying a BigQuery mode for fields
(mode will always be set to ``'NULLABLE'``).
If a callable, then it should receive a destination (in the form of
a str, and return a str, dict or TableSchema).
One may also pass ``SCHEMA_AUTODETECT`` here when using JSON-based
file loads, and BigQuery will try to infer the schema for the files
that are being loaded.
create_disposition (BigQueryDisposition): A string describing what
happens if the table does not exist. Possible values are:
* :attr:`BigQueryDisposition.CREATE_IF_NEEDED`: create if does not
exist.
* :attr:`BigQueryDisposition.CREATE_NEVER`: fail the write if does not
exist.
write_disposition (BigQueryDisposition): A string describing what happens
if the table has already some data. Possible values are:
* :attr:`BigQueryDisposition.WRITE_TRUNCATE`: delete existing rows.
* :attr:`BigQueryDisposition.WRITE_APPEND`: add to existing rows.
* :attr:`BigQueryDisposition.WRITE_EMPTY`: fail the write if table not
empty.
For streaming pipelines WriteTruncate can not be used.
kms_key (str): Optional Cloud KMS key name for use when creating new
tables.
batch_size (int): Number of rows to be written to BQ per streaming API
insert. The default is 500.
test_client: Override the default bigquery client used for testing.
max_file_size (int): The maximum size for a file to be written and then
loaded into BigQuery. The default value is 4TB, which is 80% of the
limit of 5TB for BigQuery to load any file.
max_files_per_bundle(int): The maximum number of files to be concurrently
written by a worker. The default here is 20. Larger values will allow
writing to multiple destinations without having to reshard - but they
increase the memory burden on the workers.
custom_gcs_temp_location (str): A GCS location to store files to be used
for file loads into BigQuery. By default, this will use the pipeline's
temp_location, but for pipelines whose temp_location is not appropriate
for BQ File Loads, users should pass a specific one.
method: The method to use to write to BigQuery. It may be
STREAMING_INSERTS, FILE_LOADS, or DEFAULT. An introduction on loading
data to BigQuery: https://cloud.google.com/bigquery/docs/loading-data.
DEFAULT will use STREAMING_INSERTS on Streaming pipelines and
FILE_LOADS on Batch pipelines.
insert_retry_strategy: The strategy to use when retrying streaming inserts
into BigQuery. Options are shown in bigquery_tools.RetryStrategy attrs.
Default is to retry always. This means that whenever there are rows
that fail to be inserted to BigQuery, they will be retried indefinitely.
Other retry strategy settings will produce a deadletter PCollection
as output. Appropriate values are:
* `RetryStrategy.RETRY_ALWAYS`: retry all rows if
there are any kind of errors. Note that this will hold your pipeline
back if there are errors until you cancel or update it.
* `RetryStrategy.RETRY_NEVER`: rows with errors
will not be retried. Instead they will be output to a dead letter
queue under the `'FailedRows'` tag.
* `RetryStrategy.RETRY_ON_TRANSIENT_ERROR`: retry
rows with transient errors (e.g. timeouts). Rows with permanent errors
will be output to dead letter queue under `'FailedRows'` tag.
additional_bq_parameters (callable): A function that returns a dictionary
with additional parameters to pass to BQ when creating / loading data
into a table. These can be 'timePartitioning', 'clustering', etc. They
are passed directly to the job load configuration. See
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationload
table_side_inputs (tuple): A tuple with ``AsSideInput`` PCollections to be
passed to the table callable (if one is provided).
schema_side_inputs: A tuple with ``AsSideInput`` PCollections to be
passed to the schema callable (if one is provided).
triggering_frequency (int): Every triggering_frequency duration, a
BigQuery load job will be triggered for all the data written since
the last load job. BigQuery has limits on how many load jobs can be
triggered per day, so be careful not to set this duration too low, or
you may exceed daily quota. Often this is set to 5 or 10 minutes to
ensure that the project stays well under the BigQuery quota.
See https://cloud.google.com/bigquery/quota-policy for more information
about BigQuery quotas.
validate: Indicates whether to perform validation checks on
inputs. This parameter is primarily used for testing.
temp_file_format: The format to use for file loads into BigQuery. The
options are NEWLINE_DELIMITED_JSON or AVRO, with NEWLINE_DELIMITED_JSON
being used by default. For advantages and limitations of the two
formats, see
https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-avro
and
https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-json.
ignore_insert_ids: When using the STREAMING_INSERTS method to write data
to BigQuery, `insert_ids` are a feature of BigQuery that support
deduplication of events. If your use case is not sensitive to
duplication of data inserted to BigQuery, set `ignore_insert_ids`
to True to increase the throughput for BQ writing. See:
https://cloud.google.com/bigquery/streaming-data-into-bigquery#disabling_best_effort_de-duplication
with_auto_sharding: Experimental. If true, enables using a dynamically
determined number of shards to write to BigQuery. This can be used for
both FILE_LOADS and STREAMING_INSERTS. Only applicable to unbounded
input.
"""
self._table = table
self._dataset = dataset
self._project = project
self.table_reference = bigquery_tools.parse_table_reference(
table, dataset, project)
self.create_disposition = BigQueryDisposition.validate_create(
create_disposition)
self.write_disposition = BigQueryDisposition.validate_write(
write_disposition)
if schema == SCHEMA_AUTODETECT:
self.schema = schema
else:
self.schema = bigquery_tools.get_dict_table_schema(schema)
self.batch_size = batch_size
self.kms_key = kms_key
self.test_client = test_client
# TODO(pabloem): Consider handling ValueProvider for this location.
self.custom_gcs_temp_location = custom_gcs_temp_location
self.max_file_size = max_file_size
self.max_files_per_bundle = max_files_per_bundle
self.method = method or WriteToBigQuery.Method.DEFAULT
self.triggering_frequency = triggering_frequency
self.with_auto_sharding = with_auto_sharding
self.insert_retry_strategy = insert_retry_strategy
self._validate = validate
self._temp_file_format = temp_file_format or bigquery_tools.FileFormat.JSON
self.additional_bq_parameters = additional_bq_parameters or {}
self.table_side_inputs = table_side_inputs or ()
self.schema_side_inputs = schema_side_inputs or ()
self._ignore_insert_ids = ignore_insert_ids
# Dict/schema methods were moved to bigquery_tools, but keep references
# here for backward compatibility.
get_table_schema_from_string = \
staticmethod(bigquery_tools.get_table_schema_from_string)
table_schema_to_dict = staticmethod(bigquery_tools.table_schema_to_dict)
get_dict_table_schema = staticmethod(bigquery_tools.get_dict_table_schema)
def _compute_method(self, experiments, is_streaming_pipeline):
# If the new BQ sink is not activated for experiment flags, then we use
# streaming inserts by default (it gets overridden in dataflow_runner.py).
if self.method == self.Method.DEFAULT and is_streaming_pipeline:
return self.Method.STREAMING_INSERTS
elif self.method == self.Method.DEFAULT and not is_streaming_pipeline:
return self.Method.FILE_LOADS
else:
return self.method
[docs] def expand(self, pcoll):
p = pcoll.pipeline
if (isinstance(self.table_reference, TableReference) and
self.table_reference.projectId is None):
self.table_reference.projectId = pcoll.pipeline.options.view_as(
GoogleCloudOptions).project
# TODO(pabloem): Use a different method to determine if streaming or batch.
is_streaming_pipeline = p.options.view_as(StandardOptions).streaming
if not is_streaming_pipeline and self.with_auto_sharding:
raise ValueError(
'with_auto_sharding is not applicable to batch pipelines.')
experiments = p.options.view_as(DebugOptions).experiments or []
method_to_use = self._compute_method(experiments, is_streaming_pipeline)
if method_to_use == WriteToBigQuery.Method.STREAMING_INSERTS:
if self.schema == SCHEMA_AUTODETECT:
raise ValueError(
'Schema auto-detection is not supported for streaming '
'inserts into BigQuery. Only for File Loads.')
if self.triggering_frequency:
raise ValueError(
'triggering_frequency can only be used with '
'FILE_LOADS method of writing to BigQuery.')
outputs = pcoll | _StreamToBigQuery(
table_reference=self.table_reference,
table_side_inputs=self.table_side_inputs,
schema_side_inputs=self.schema_side_inputs,
schema=self.schema,
batch_size=self.batch_size,
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
kms_key=self.kms_key,
retry_strategy=self.insert_retry_strategy,
additional_bq_parameters=self.additional_bq_parameters,
ignore_insert_ids=self._ignore_insert_ids,
with_auto_sharding=self.with_auto_sharding,
test_client=self.test_client)
return {BigQueryWriteFn.FAILED_ROWS: outputs[BigQueryWriteFn.FAILED_ROWS]}
else:
if self._temp_file_format == bigquery_tools.FileFormat.AVRO:
if self.schema == SCHEMA_AUTODETECT:
raise ValueError(
'Schema auto-detection is not supported when using Avro based '
'file loads into BigQuery. Please specify a schema or set '
'temp_file_format="NEWLINE_DELIMITED_JSON"')
if self.schema is None:
raise ValueError(
'A schema must be provided when writing to BigQuery using '
'Avro based file loads')
from apache_beam.io.gcp import bigquery_file_loads
return pcoll | bigquery_file_loads.BigQueryBatchFileLoads(
destination=self.table_reference,
schema=self.schema,
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
triggering_frequency=self.triggering_frequency,
with_auto_sharding=self.with_auto_sharding,
temp_file_format=self._temp_file_format,
max_file_size=self.max_file_size,
max_files_per_bundle=self.max_files_per_bundle,
custom_gcs_temp_location=self.custom_gcs_temp_location,
test_client=self.test_client,
table_side_inputs=self.table_side_inputs,
schema_side_inputs=self.schema_side_inputs,
additional_bq_parameters=self.additional_bq_parameters,
validate=self._validate,
is_streaming_pipeline=is_streaming_pipeline)
[docs] def display_data(self):
res = {}
if self.table_reference is not None and isinstance(self.table_reference,
TableReference):
tableSpec = '{}.{}'.format(
self.table_reference.datasetId, self.table_reference.tableId)
if self.table_reference.projectId is not None:
tableSpec = '{}:{}'.format(self.table_reference.projectId, tableSpec)
res['table'] = DisplayDataItem(tableSpec, label='Table')
return res
[docs] def to_runner_api_parameter(self, context):
from apache_beam.internal import pickler
# It'd be nice to name these according to their actual
# names/positions in the orignal argument list, but such a
# transformation is currently irreversible given how
# remove_objects_from_args and insert_values_in_args
# are currently implemented.
def serialize(side_inputs):
return {(SIDE_INPUT_PREFIX + '%s') % ix:
si.to_runner_api(context).SerializeToString()
for ix,
si in enumerate(side_inputs)}
table_side_inputs = serialize(self.table_side_inputs)
schema_side_inputs = serialize(self.schema_side_inputs)
config = {
'table': self._table,
'dataset': self._dataset,
'project': self._project,
'schema': self.schema,
'create_disposition': self.create_disposition,
'write_disposition': self.write_disposition,
'kms_key': self.kms_key,
'batch_size': self.batch_size,
'max_file_size': self.max_file_size,
'max_files_per_bundle': self.max_files_per_bundle,
'custom_gcs_temp_location': self.custom_gcs_temp_location,
'method': self.method,
'insert_retry_strategy': self.insert_retry_strategy,
'additional_bq_parameters': self.additional_bq_parameters,
'table_side_inputs': table_side_inputs,
'schema_side_inputs': schema_side_inputs,
'triggering_frequency': self.triggering_frequency,
'validate': self._validate,
'temp_file_format': self._temp_file_format,
'ignore_insert_ids': self._ignore_insert_ids,
'with_auto_sharding': self.with_auto_sharding,
}
return 'beam:transform:write_to_big_query:v0', pickler.dumps(config)
[docs] @PTransform.register_urn('beam:transform:write_to_big_query:v0', bytes)
def from_runner_api(unused_ptransform, payload, context):
from apache_beam.internal import pickler
from apache_beam.portability.api.beam_runner_api_pb2 import SideInput
config = pickler.loads(payload)
def deserialize(side_inputs):
deserialized_side_inputs = {}
for k, v in side_inputs.items():
side_input = SideInput()
side_input.ParseFromString(v)
deserialized_side_inputs[k] = side_input
# This is an ordered list stored as a dict (see the comments in
# to_runner_api_parameter above).
indexed_side_inputs = [(
get_sideinput_index(tag),
pvalue.AsSideInput.from_runner_api(si, context)) for tag,
si in deserialized_side_inputs.items()]
return [si for _, si in sorted(indexed_side_inputs)]
config['table_side_inputs'] = deserialize(config['table_side_inputs'])
config['schema_side_inputs'] = deserialize(config['schema_side_inputs'])
return WriteToBigQuery(**config)
[docs]class ReadFromBigQuery(PTransform):
"""Read data from BigQuery.
This PTransform uses a BigQuery export job to take a snapshot of the table
on GCS, and then reads from each produced file. File format is Avro by
default.
Args:
table (str, callable, ValueProvider): The ID of the table, or a callable
that returns it. The ID must contain only letters ``a-z``, ``A-Z``,
numbers ``0-9``, or underscores ``_``. If dataset argument is
:data:`None` then the table argument must contain the entire table
reference specified as: ``'DATASET.TABLE'``
or ``'PROJECT:DATASET.TABLE'``. If it's a callable, it must receive one
argument representing an element to be written to BigQuery, and return
a TableReference, or a string table name as specified above.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
project (str): The ID of the project containing this table.
query (str, ValueProvider): A query to be used instead of arguments
table, dataset, and project.
validate (bool): If :data:`True`, various checks will be done when source
gets initialized (e.g., is table present?). This should be
:data:`True` for most scenarios in order to catch errors as early as
possible (pipeline construction instead of pipeline execution). It
should be :data:`False` if the table is created during pipeline
execution by a previous step.
coder (~apache_beam.coders.coders.Coder): The coder for the table
rows. If :data:`None`, then the default coder is
_JsonToDictCoder, which will interpret every row as a JSON
serialized dictionary.
use_standard_sql (bool): Specifies whether to use BigQuery's standard SQL
dialect for this query. The default value is :data:`False`.
If set to :data:`True`, the query will use BigQuery's updated SQL
dialect with improved standards compliance.
This parameter is ignored for table inputs.
flatten_results (bool): Flattens all nested and repeated fields in the
query results. The default value is :data:`True`.
kms_key (str): Optional Cloud KMS key name for use when creating new
temporary tables.
gcs_location (str, ValueProvider): The name of the Google Cloud Storage
bucket where the extracted table should be written as a string or
a :class:`~apache_beam.options.value_provider.ValueProvider`. If
:data:`None`, then the temp_location parameter is used.
bigquery_job_labels (dict): A dictionary with string labels to be passed
to BigQuery export and query jobs created by this transform. See:
https://cloud.google.com/bigquery/docs/reference/rest/v2/\
Job#JobConfiguration
use_json_exports (bool): By default, this transform works by exporting
BigQuery data into Avro files, and reading those files. With this
parameter, the transform will instead export to JSON files. JSON files
are slower to read due to their larger size.
When using JSON exports, the BigQuery types for DATE, DATETIME, TIME, and
TIMESTAMP will be exported as strings. This behavior is consistent with
BigQuerySource.
When using Avro exports, these fields will be exported as native Python
types (datetime.date, datetime.datetime, datetime.datetime,
and datetime.datetime respectively). Avro exports are recommended.
To learn more about BigQuery types, and Time-related type
representations, see: https://cloud.google.com/bigquery/docs/reference/\
standard-sql/data-types
To learn more about type conversions between BigQuery and Avro, see:
https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-avro\
#avro_conversions
temp_dataset (``apache_beam.io.gcp.internal.clients.bigquery.\
DatasetReference``):
Temporary dataset reference to use when reading from BigQuery using a
query. When reading using a query, BigQuery source will create a
temporary dataset and a temporary table to store the results of the
query. With this option, you can set an existing dataset to create the
temporary table in. BigQuery source will create a temporary table in
that dataset, and will remove it once it is not needed. Job needs access
to create and delete tables within the given dataset. Dataset name
should *not* start with the reserved prefix `beam_temp_dataset_`.
"""
COUNTER = 0
def __init__(self, gcs_location=None, *args, **kwargs):
if gcs_location:
if not isinstance(gcs_location, (str, ValueProvider)):
raise TypeError(
'%s: gcs_location must be of type string'
' or ValueProvider; got %r instead' %
(self.__class__.__name__, type(gcs_location)))
if isinstance(gcs_location, str):
gcs_location = StaticValueProvider(str, gcs_location)
self.gcs_location = gcs_location
self._args = args
self._kwargs = kwargs
[docs] def expand(self, pcoll):
temp_location = pcoll.pipeline.options.view_as(
GoogleCloudOptions).temp_location
job_name = pcoll.pipeline.options.view_as(GoogleCloudOptions).job_name
gcs_location_vp = self.gcs_location
unique_id = str(uuid.uuid4())[0:10]
def file_path_to_remove(unused_elm):
gcs_location = bigquery_export_destination_uri(
gcs_location_vp, temp_location, unique_id, True)
return gcs_location + '/'
files_to_remove_pcoll = beam.pvalue.AsList(
pcoll.pipeline
| 'FilesToRemoveImpulse' >> beam.Create([None])
| 'MapFilesToRemove' >> beam.Map(file_path_to_remove))
try:
step_name = self.label
except AttributeError:
step_name = 'ReadFromBigQuery_%d' % ReadFromBigQuery.COUNTER
ReadFromBigQuery.COUNTER += 1
return (
pcoll
| beam.io.Read(
_CustomBigQuerySource(
gcs_location=self.gcs_location,
pipeline_options=pcoll.pipeline.options,
job_name=job_name,
step_name=step_name,
unique_id=unique_id,
*self._args,
**self._kwargs))
| _PassThroughThenCleanup(files_to_remove_pcoll))
[docs]class ReadFromBigQueryRequest:
"""
Class that defines data to read from BQ.
"""
def __init__(
self,
query: str = None,
use_standard_sql: bool = True,
table: Union[str, TableReference] = None,
flatten_results: bool = False):
"""
Only one of query or table should be specified.
:param query: SQL query to fetch data.
:param use_standard_sql:
Specifies whether to use BigQuery's standard SQL dialect for this query.
The default value is :data:`True`. If set to :data:`False`,
the query will use BigQuery's legacy SQL dialect.
This parameter is ignored for table inputs.
:param table:
The ID of the table to read. The ID must contain only letters
``a-z``, ``A-Z``, numbers ``0-9``, or underscores ``_``. Table should
define project and dataset (ex.: ``'PROJECT:DATASET.TABLE'``).
:param flatten_results:
Flattens all nested and repeated fields in the query results.
The default value is :data:`False`.
"""
self.flatten_results = flatten_results
self.query = query
self.use_standard_sql = use_standard_sql
self.table = table
self.validate()
# We use this internal object ID to generate BigQuery export directories.
self.obj_id = random.randint(0, 100000)
[docs] def validate(self):
if self.table is not None and self.query is not None:
raise ValueError(
'Both a BigQuery table and a query were specified.'
' Please specify only one of these.')
elif self.table is None and self.query is None:
raise ValueError('A BigQuery table or a query must be specified')
if self.table is not None:
if isinstance(self.table, str):
assert self.table.find('.'), (
'Expected a table reference '
'(PROJECT:DATASET.TABLE or DATASET.TABLE) instead of %s'
% self.table)
[docs]@experimental()
class ReadAllFromBigQuery(PTransform):
"""Read data from BigQuery.
PTransform:ReadFromBigQueryRequest->Rows
This PTransform uses a BigQuery export job to take a snapshot of the table
on GCS, and then reads from each produced file. Data is exported into
a new subdirectory for each export using UUIDs generated in
`ReadFromBigQueryRequest` objects.
It is recommended not to use this PTransform for streaming jobs on
GlobalWindow, since it will not be able to cleanup snapshots.
Args:
gcs_location (str): The name of the Google Cloud Storage
bucket where the extracted table should be written as a string. If
:data:`None`, then the temp_location parameter is used.
validate (bool): If :data:`True`, various checks will be done when source
gets initialized (e.g., is table present?).
kms_key (str): Experimental. Optional Cloud KMS key name for use when
creating new temporary tables.
"""
COUNTER = 0
def __init__(
self,
gcs_location: Union[str, ValueProvider] = None,
validate: bool = False,
kms_key: str = None,
temp_dataset: Union[str, DatasetReference] = None,
bigquery_job_labels: Dict[str, str] = None):
if gcs_location:
if not isinstance(gcs_location, (str, ValueProvider)):
raise TypeError(
'%s: gcs_location must be of type string'
' or ValueProvider; got %r instead' %
(self.__class__.__name__, type(gcs_location)))
self.gcs_location = gcs_location
self.validate = validate
self.kms_key = kms_key
self.bigquery_job_labels = bigquery_job_labels
self.temp_dataset = temp_dataset
[docs] def expand(self, pcoll):
job_name = pcoll.pipeline.options.view_as(GoogleCloudOptions).job_name
project = pcoll.pipeline.options.view_as(GoogleCloudOptions).project
unique_id = str(uuid.uuid4())[0:10]
try:
step_name = self.label
except AttributeError:
step_name = 'ReadAllFromBigQuery_%d' % ReadAllFromBigQuery.COUNTER
ReadAllFromBigQuery.COUNTER += 1
sources_to_read, cleanup_locations = (
pcoll
| beam.ParDo(
_BigQueryReadSplit(
options=pcoll.pipeline.options,
gcs_location=self.gcs_location,
bigquery_job_labels=self.bigquery_job_labels,
job_name=job_name,
step_name=step_name,
unique_id=unique_id,
kms_key=self.kms_key,
project=project,
temp_dataset=self.temp_dataset)).with_outputs(
"location_to_cleanup", main="files_to_read")
)
return (
sources_to_read
| SDFBoundedSourceReader(data_to_display=self.display_data())
| _PassThroughThenCleanup(beam.pvalue.AsIter(cleanup_locations)))