#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Functionality to perform file loads into BigQuery for Batch and Streaming
pipelines.
This source is able to work around BigQuery load quotas and limitations. When
destinations are dynamic, or when data for a single job is too large, the data
will be split into multiple jobs.
NOTHING IN THIS FILE HAS BACKWARDS COMPATIBILITY GUARANTEES.
"""
# pytype: skip-file
import hashlib
import io
import logging
import random
import time
import uuid
import apache_beam as beam
from apache_beam import pvalue
from apache_beam.io import filesystems as fs
from apache_beam.io.gcp import bigquery_tools
from apache_beam.io.gcp.bigquery_io_metadata import create_bigquery_io_metadata
from apache_beam.options import value_provider as vp
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.transforms import trigger
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.util import GroupIntoBatches
from apache_beam.transforms.window import GlobalWindows
# Protect against environments where bigquery library is not available.
# pylint: disable=wrong-import-order, wrong-import-position
try:
from apitools.base.py.exceptions import HttpError
except ImportError:
pass
_LOGGER = logging.getLogger(__name__)
ONE_TERABYTE = (1 << 40)
# The maximum file size for imports is 5TB. We keep our files under that.
_DEFAULT_MAX_FILE_SIZE = 4 * ONE_TERABYTE
_DEFAULT_MAX_WRITERS_PER_BUNDLE = 20
# The maximum size for a single load job is one terabyte
_MAXIMUM_LOAD_SIZE = 15 * ONE_TERABYTE
# Big query only supports up to 10 thousand URIs for a single load job.
_MAXIMUM_SOURCE_URIS = 10 * 1000
# If triggering_frequency is supplied, we will trigger the file write after
# this many records are written.
_FILE_TRIGGERING_RECORD_COUNT = 500000
# If using auto-sharding for unbounded data, we batch the records before
# triggering file write to avoid generating too many small files.
_FILE_TRIGGERING_BATCHING_DURATION_SECS = 1
def _generate_job_name(job_name, job_type, step_name):
return bigquery_tools.generate_bq_job_name(
job_name=job_name,
step_id=step_name,
job_type=job_type,
random=random.randint(0, 1000))
[docs]def file_prefix_generator(
with_validation=True, pipeline_gcs_location=None, temp_location=None):
def _generate_file_prefix(unused_elm):
# If a gcs location is provided to the pipeline, then we shall use that.
# Otherwise, we shall use the temp_location from pipeline options.
gcs_base = pipeline_gcs_location.get()
if not gcs_base:
gcs_base = temp_location
# This will fail at pipeline execution time, but will fail early, as this
# step doesn't have any dependencies (and thus will be one of the first
# stages to be run).
if with_validation and (not gcs_base or not gcs_base.startswith('gs://')):
raise ValueError(
'Invalid GCS location: %r.\n'
'Writing to BigQuery with FILE_LOADS method requires a'
' GCS location to be provided to write files to be loaded'
' into BigQuery. Please provide a GCS bucket through'
' custom_gcs_temp_location in the constructor of WriteToBigQuery'
' or the fallback option --temp_location, or pass'
' method="STREAMING_INSERTS" to WriteToBigQuery.' % gcs_base)
prefix_uuid = _bq_uuid()
return fs.FileSystems.join(gcs_base, 'bq_load', prefix_uuid)
return _generate_file_prefix
def _make_new_file_writer(
file_prefix,
destination,
file_format,
schema=None,
schema_side_inputs=tuple()):
destination = bigquery_tools.get_hashable_destination(destination)
# Windows does not allow : on filenames. Replacing with underscore.
# Other disallowed characters are:
# https://docs.microsoft.com/en-us/windows/desktop/fileio/naming-a-file
destination = destination.replace(':', '.')
directory = fs.FileSystems.join(file_prefix, destination)
if not fs.FileSystems.exists(directory):
fs.FileSystems.mkdirs(directory)
file_name = str(uuid.uuid4())
file_path = fs.FileSystems.join(file_prefix, destination, file_name)
if file_format == bigquery_tools.FileFormat.AVRO:
if callable(schema):
schema = schema(destination, *schema_side_inputs)
elif isinstance(schema, vp.ValueProvider):
schema = schema.get()
writer = bigquery_tools.AvroRowWriter(
fs.FileSystems.create(file_path, "application/avro"), schema)
elif file_format == bigquery_tools.FileFormat.JSON:
writer = bigquery_tools.JsonRowWriter(
fs.FileSystems.create(file_path, "application/text"))
else:
raise ValueError((
'Only AVRO and JSON are supported as intermediate formats for '
'BigQuery WriteRecordsToFile, got: {}.').format(file_format))
return file_path, writer
def _bq_uuid(seed=None):
if not seed:
return str(uuid.uuid4()).replace("-", "")
else:
return str(hashlib.md5(seed.encode('utf8')).hexdigest())
class _ShardDestinations(beam.DoFn):
"""Adds a shard number to the key of the KV element.
Experimental; no backwards compatibility guarantees."""
DEFAULT_SHARDING_FACTOR = 10
def __init__(self, sharding_factor=DEFAULT_SHARDING_FACTOR):
self.sharding_factor = sharding_factor
def start_bundle(self):
self._shard_count = random.randrange(self.sharding_factor)
def process(self, element):
destination = element[0]
row = element[1]
sharded_destination = (
destination, self._shard_count % self.sharding_factor)
self._shard_count += 1
yield (sharded_destination, row)
[docs]class WriteRecordsToFile(beam.DoFn):
"""Write input records to files before triggering a load job.
This transform keeps up to ``max_files_per_bundle`` files open to write to. It
receives (destination, record) tuples, and it writes the records to different
files for each destination.
If there are more than ``max_files_per_bundle`` destinations that we need to
write to, then those records are grouped by their destination, and later
written to files by ``WriteGroupedRecordsToFile``.
It outputs two PCollections.
"""
UNWRITTEN_RECORD_TAG = 'UnwrittenRecords'
WRITTEN_FILE_TAG = 'WrittenFiles'
def __init__(
self,
schema,
max_files_per_bundle=_DEFAULT_MAX_WRITERS_PER_BUNDLE,
max_file_size=_DEFAULT_MAX_FILE_SIZE,
file_format=None):
"""Initialize a :class:`WriteRecordsToFile`.
Args:
max_files_per_bundle (int): The maximum number of files that can be kept
open during execution of this step in a worker. This is to avoid over-
whelming the worker memory.
max_file_size (int): The maximum size in bytes for a file to be used in
an export job.
"""
self.schema = schema
self.max_files_per_bundle = max_files_per_bundle
self.max_file_size = max_file_size
self.file_format = file_format or bigquery_tools.FileFormat.JSON
[docs] def display_data(self):
return {
'max_files_per_bundle': self.max_files_per_bundle,
'max_file_size': str(self.max_file_size),
'file_format': self.file_format,
}
[docs] def start_bundle(self):
self._destination_to_file_writer = {}
[docs] def process(self, element, file_prefix, *schema_side_inputs):
"""Take a tuple with (destination, row) and write to file or spill out.
Destination may be a ``TableReference`` or a string, and row is a
Python dictionary for a row to be inserted to BigQuery."""
destination = bigquery_tools.get_hashable_destination(element[0])
row = element[1]
if destination not in self._destination_to_file_writer:
if len(self._destination_to_file_writer) < self.max_files_per_bundle:
self._destination_to_file_writer[destination] = _make_new_file_writer(
file_prefix,
destination,
self.file_format,
self.schema,
schema_side_inputs)
else:
yield pvalue.TaggedOutput(
WriteRecordsToFile.UNWRITTEN_RECORD_TAG, element)
return
(file_path, writer) = self._destination_to_file_writer[destination]
# TODO(pabloem): Is it possible for this to throw exception?
writer.write(row)
file_size = writer.tell()
if file_size > self.max_file_size:
writer.close()
self._destination_to_file_writer.pop(destination)
yield pvalue.TaggedOutput(
WriteRecordsToFile.WRITTEN_FILE_TAG,
(destination, (file_path, file_size)))
[docs] def finish_bundle(self):
for destination, file_path_writer in \
self._destination_to_file_writer.items():
(file_path, writer) = file_path_writer
file_size = writer.tell()
writer.close()
yield pvalue.TaggedOutput(
WriteRecordsToFile.WRITTEN_FILE_TAG,
GlobalWindows.windowed_value((destination, (file_path, file_size))))
self._destination_to_file_writer = {}
[docs]class WriteGroupedRecordsToFile(beam.DoFn):
"""Receives collection of dest-iterable(records), writes it to files.
This is different from ``WriteRecordsToFile`` because it receives records
grouped by destination. This means that it's not necessary to keep multiple
file descriptors open, because we know for sure when records for a single
destination have been written out.
Experimental; no backwards compatibility guarantees.
"""
def __init__(
self, schema, max_file_size=_DEFAULT_MAX_FILE_SIZE, file_format=None):
self.schema = schema
self.max_file_size = max_file_size
self.file_format = file_format or bigquery_tools.FileFormat.JSON
[docs] def process(self, element, file_prefix, *schema_side_inputs):
destination = bigquery_tools.get_hashable_destination(element[0])
rows = element[1]
file_path, writer = None, None
for row in rows:
if writer is None:
(file_path, writer) = _make_new_file_writer(
file_prefix,
destination,
self.file_format,
self.schema,
schema_side_inputs)
writer.write(row)
file_size = writer.tell()
if file_size > self.max_file_size:
writer.close()
yield (destination, (file_path, file_size))
file_path, writer = None, None
if writer is not None:
writer.close()
yield (destination, (file_path, file_size))
[docs]class UpdateDestinationSchema(beam.DoFn):
"""Update destination schema based on data that is about to be copied into it.
Unlike load and query jobs, BigQuery copy jobs do not support schema field
addition or relaxation on the destination table. This DoFn fills that gap by
updating the destination table schemas to be compatible with the data coming
from the source table so that schema field modification options are respected
regardless of whether data is loaded directly to the destination table or
loaded into temporary tables before being copied into the destination.
This tranform takes as input a (destination, job_reference) pair where the
job_reference refers to a completed load job into a temporary table.
This transform emits (destination, job_reference) pairs where the
job_reference refers to a submitted load job for performing the schema
modification. Note that the input and output job references are not the same.
Experimental; no backwards compatibility guarantees.
"""
def __init__(
self,
write_disposition=None,
test_client=None,
additional_bq_parameters=None,
step_name=None):
self._test_client = test_client
self._write_disposition = write_disposition
self._additional_bq_parameters = additional_bq_parameters or {}
self._step_name = step_name
[docs] def setup(self):
self._bq_wrapper = bigquery_tools.BigQueryWrapper(client=self._test_client)
self._bq_io_metadata = create_bigquery_io_metadata(self._step_name)
[docs] def process(self, element, schema_mod_job_name_prefix):
destination = element[0]
temp_table_load_job_reference = element[1]
if callable(self._additional_bq_parameters):
additional_parameters = self._additional_bq_parameters(destination)
elif isinstance(self._additional_bq_parameters, vp.ValueProvider):
additional_parameters = self._additional_bq_parameters.get()
else:
additional_parameters = self._additional_bq_parameters
# When writing to normal tables WRITE_TRUNCATE will overwrite the schema but
# when writing to a partition, care needs to be taken to update the schema
# even on WRITE_TRUNCATE.
if (self._write_disposition not in ('WRITE_TRUNCATE', 'WRITE_APPEND') or
not additional_parameters or
not additional_parameters.get("schemaUpdateOptions")):
# No need to modify schema of destination table
return
table_reference = bigquery_tools.parse_table_reference(destination)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
try:
# Check if destination table exists
destination_table = self._bq_wrapper.get_table(
project_id=table_reference.projectId,
dataset_id=table_reference.datasetId,
table_id=table_reference.tableId)
except HttpError as exn:
if exn.status_code == 404:
# Destination table does not exist, so no need to modify its schema
# ahead of the copy jobs.
return
else:
raise
temp_table_load_job = self._bq_wrapper.get_job(
project=temp_table_load_job_reference.projectId,
job_id=temp_table_load_job_reference.jobId,
location=temp_table_load_job_reference.location)
temp_table_schema = temp_table_load_job.configuration.load.schema
if bigquery_tools.check_schema_equal(temp_table_schema,
destination_table.schema,
ignore_descriptions=True,
ignore_field_order=True):
# Destination table schema is already the same as the temp table schema,
# so no need to run a job to update the destination table schema.
return
destination_hash = _bq_uuid(
'%s:%s.%s' % (
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId))
uid = _bq_uuid()
job_name = '%s_%s_%s' % (schema_mod_job_name_prefix, destination_hash, uid)
_LOGGER.debug(
'Triggering schema modification job %s on %s',
job_name,
table_reference)
# Trigger potential schema modification by loading zero rows into the
# destination table with the temporary table schema.
schema_update_job_reference = self._bq_wrapper.perform_load_job(
destination=table_reference,
source_stream=io.BytesIO(), # file with zero rows
job_id=job_name,
schema=temp_table_schema,
write_disposition='WRITE_APPEND',
create_disposition='CREATE_NEVER',
additional_load_parameters=additional_parameters,
job_labels=self._bq_io_metadata.add_additional_bq_job_labels())
yield (destination, schema_update_job_reference)
[docs]class TriggerCopyJobs(beam.DoFn):
"""Launches jobs to copy from temporary tables into the main target table.
When a job needs to write to multiple destination tables, or when a single
destination table needs to have multiple load jobs to write to it, files are
loaded into temporary tables, and those tables are later copied to the
destination tables.
This transform emits (destination, job_reference) pairs.
TODO(BEAM-7822): In file loads method of writing to BigQuery,
copying from temp_tables to destination_table is not atomic.
See: https://issues.apache.org/jira/browse/BEAM-7822
"""
def __init__(
self,
create_disposition=None,
write_disposition=None,
test_client=None,
step_name=None):
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self.test_client = test_client
self._observed_tables = set()
self.bq_io_metadata = None
self._step_name = step_name
[docs] def display_data(self):
return {
'launchesBigQueryJobs': DisplayDataItem(
True, label="This Dataflow job launches bigquery jobs.")
}
[docs] def start_bundle(self):
self._observed_tables = set()
self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
if not self.bq_io_metadata:
self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
[docs] def process(self, element, job_name_prefix=None, unused_schema_mod_jobs=None):
destination = element[0]
job_reference = element[1]
copy_to_reference = bigquery_tools.parse_table_reference(destination)
if copy_to_reference.projectId is None:
copy_to_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
copy_from_reference = bigquery_tools.parse_table_reference(destination)
copy_from_reference.tableId = job_reference.jobId
if copy_from_reference.projectId is None:
copy_from_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
copy_job_name = '%s_%s' % (
job_name_prefix,
_bq_uuid(
'%s:%s.%s' % (
copy_from_reference.projectId,
copy_from_reference.datasetId,
copy_from_reference.tableId)))
_LOGGER.info(
"Triggering copy job from %s to %s",
copy_from_reference,
copy_to_reference)
if copy_to_reference.tableId not in self._observed_tables:
# When the write_disposition for a job is WRITE_TRUNCATE,
# multiple copy jobs to the same destination can stump on
# each other, truncate data, and write to the BQ table over and
# over.
# Thus, the first copy job runs with the user's write_disposition,
# but afterwards, all jobs must always WRITE_APPEND to the table.
# If they do not, subsequent copy jobs will clear out data appended
# by previous jobs.
write_disposition = self.write_disposition
wait_for_job = True
self._observed_tables.add(copy_to_reference.tableId)
else:
wait_for_job = False
write_disposition = 'WRITE_APPEND'
if not self.bq_io_metadata:
self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
job_reference = self.bq_wrapper._insert_copy_job(
copy_to_reference.projectId,
copy_job_name,
copy_from_reference,
copy_to_reference,
create_disposition=self.create_disposition,
write_disposition=write_disposition,
job_labels=self.bq_io_metadata.add_additional_bq_job_labels())
if wait_for_job:
self.bq_wrapper.wait_for_bq_job(job_reference, sleep_duration_sec=10)
yield (destination, job_reference)
[docs]class TriggerLoadJobs(beam.DoFn):
"""Triggers the import jobs to BQ.
Experimental; no backwards compatibility guarantees.
"""
TEMP_TABLES = 'TemporaryTables'
def __init__(
self,
schema=None,
create_disposition=None,
write_disposition=None,
test_client=None,
temporary_tables=False,
additional_bq_parameters=None,
source_format=None,
step_name=None):
self.schema = schema
self.test_client = test_client
self.temporary_tables = temporary_tables
self.additional_bq_parameters = additional_bq_parameters or {}
self.source_format = source_format
self.bq_io_metadata = None
self._step_name = step_name
if self.temporary_tables:
# If we are loading into temporary tables, we rely on the default create
# and write dispositions, which mean that a new table will be created.
self.create_disposition = None
self.write_disposition = None
else:
self.create_disposition = create_disposition
self.write_disposition = write_disposition
[docs] def display_data(self):
result = {
'create_disposition': str(self.create_disposition),
'write_disposition': str(self.write_disposition),
'additional_bq_params': str(self.additional_bq_parameters),
'schema': str(self.schema),
'launchesBigQueryJobs': DisplayDataItem(
True, label="This Dataflow job launches bigquery jobs.")
}
return result
[docs] def start_bundle(self):
self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
if not self.bq_io_metadata:
self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
[docs] def process(self, element, load_job_name_prefix, *schema_side_inputs):
# Each load job is assumed to have files respecting these constraints:
# 1. Total size of all files < 15 TB (Max size for load jobs)
# 2. Total no. of files in a single load job < 10,000
# This assumption means that there will always be a single load job
# triggered for each partition of files.
destination = element[0]
files = element[1]
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
if callable(self.additional_bq_parameters):
additional_parameters = self.additional_bq_parameters(destination)
elif isinstance(self.additional_bq_parameters, vp.ValueProvider):
additional_parameters = self.additional_bq_parameters.get()
else:
additional_parameters = self.additional_bq_parameters
table_reference = bigquery_tools.parse_table_reference(destination)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
# Load jobs for a single destination are always triggered from the same
# worker. This means that we can generate a deterministic numbered job id,
# and not need to worry.
destination_hash = _bq_uuid(
'%s:%s.%s' % (
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId))
uid = _bq_uuid()
job_name = '%s_%s_%s' % (load_job_name_prefix, destination_hash, uid)
_LOGGER.debug(
'Load job has %s files. Job name is %s.', len(files), job_name)
create_disposition = self.create_disposition
if self.temporary_tables:
# If we are using temporary tables, then we must always create the
# temporary tables, so we replace the create_disposition.
create_disposition = 'CREATE_IF_NEEDED'
# For temporary tables, we create a new table with the name with JobId.
table_reference.tableId = job_name
yield pvalue.TaggedOutput(
TriggerLoadJobs.TEMP_TABLES,
bigquery_tools.get_hashable_destination(table_reference))
_LOGGER.info(
'Triggering job %s to load data to BigQuery table %s.'
'Schema: %s. Additional parameters: %s',
job_name,
table_reference,
schema,
additional_parameters)
if not self.bq_io_metadata:
self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
job_reference = self.bq_wrapper.perform_load_job(
destination=table_reference,
source_uris=files,
job_id=job_name,
schema=schema,
write_disposition=self.write_disposition,
create_disposition=create_disposition,
additional_load_parameters=additional_parameters,
source_format=self.source_format,
job_labels=self.bq_io_metadata.add_additional_bq_job_labels())
yield (destination, job_reference)
[docs]class PartitionFiles(beam.DoFn):
MULTIPLE_PARTITIONS_TAG = 'MULTIPLE_PARTITIONS'
SINGLE_PARTITION_TAG = 'SINGLE_PARTITION'
[docs] class Partition(object):
def __init__(self, max_size, max_files, files=None, size=0):
self.max_size = max_size
self.max_files = max_files
self.files = files if files is not None else []
self.size = size
[docs] def can_accept(self, file_size, no_of_files=1):
if (((self.size + file_size) <= self.max_size) and
((len(self.files) + no_of_files) <= self.max_files)):
return True
else:
return False
[docs] def add(self, file_path, file_size):
self.files.append(file_path)
self.size += file_size
def __init__(self, max_partition_size, max_files_per_partition):
self.max_partition_size = max_partition_size
self.max_files_per_partition = max_files_per_partition
[docs] def process(self, element):
destination = element[0]
files = element[1]
partitions = []
latest_partition = PartitionFiles.Partition(
self.max_partition_size, self.max_files_per_partition)
for file_path, file_size in files:
if latest_partition.can_accept(file_size):
latest_partition.add(file_path, file_size)
else:
partitions.append(latest_partition.files)
latest_partition = PartitionFiles.Partition(
self.max_partition_size, self.max_files_per_partition)
latest_partition.add(file_path, file_size)
partitions.append(latest_partition.files)
if len(partitions) > 1:
output_tag = PartitionFiles.MULTIPLE_PARTITIONS_TAG
else:
output_tag = PartitionFiles.SINGLE_PARTITION_TAG
for partition in partitions:
yield pvalue.TaggedOutput(output_tag, (destination, partition))
[docs]class WaitForBQJobs(beam.DoFn):
"""Takes in a series of BQ job names as side input, and waits for all of them.
If any job fails, it will fail. If all jobs succeed, it will succeed.
Experimental; no backwards compatibility guarantees.
"""
def __init__(self, test_client=None):
self.test_client = test_client
[docs] def start_bundle(self):
self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
[docs] def process(self, element, dest_ids_list):
job_references = [elm[1] for elm in dest_ids_list]
for ref in job_references:
# We must poll repeatedly until the job finishes or fails, thus setting
# max_retries to 0.
self.bq_wrapper.wait_for_bq_job(ref, sleep_duration_sec=10, max_retries=0)
return dest_ids_list # Pass the list of destination-jobs downstream
[docs]class DeleteTablesFn(beam.DoFn):
def __init__(self, test_client=None):
self.test_client = test_client
[docs] def start_bundle(self):
self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
[docs] def process(self, table_reference):
_LOGGER.info("Deleting table %s", table_reference)
table_reference = bigquery_tools.parse_table_reference(table_reference)
self.bq_wrapper._delete_table(
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId)
[docs]class BigQueryBatchFileLoads(beam.PTransform):
"""Takes in a set of elements, and inserts them to BigQuery via batch loads.
"""
DESTINATION_JOBID_PAIRS = 'destination_load_jobid_pairs'
DESTINATION_FILE_PAIRS = 'destination_file_pairs'
DESTINATION_COPY_JOBID_PAIRS = 'destination_copy_jobid_pairs'
COUNT = 0
def __init__(
self,
destination,
schema=None,
custom_gcs_temp_location=None,
create_disposition=None,
write_disposition=None,
triggering_frequency=None,
with_auto_sharding=False,
temp_file_format=None,
max_file_size=None,
max_files_per_bundle=None,
max_partition_size=None,
max_files_per_partition=None,
additional_bq_parameters=None,
table_side_inputs=None,
schema_side_inputs=None,
test_client=None,
validate=True,
is_streaming_pipeline=False):
self.destination = destination
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self.triggering_frequency = triggering_frequency
self.with_auto_sharding = with_auto_sharding
self.max_file_size = max_file_size or _DEFAULT_MAX_FILE_SIZE
self.max_files_per_bundle = (
max_files_per_bundle or _DEFAULT_MAX_WRITERS_PER_BUNDLE)
self.max_partition_size = max_partition_size or _MAXIMUM_LOAD_SIZE
self.max_files_per_partition = (
max_files_per_partition or _MAXIMUM_SOURCE_URIS)
if (isinstance(custom_gcs_temp_location, str) or
custom_gcs_temp_location is None):
self._custom_gcs_temp_location = vp.StaticValueProvider(
str, custom_gcs_temp_location or '')
elif isinstance(custom_gcs_temp_location, vp.ValueProvider):
self._custom_gcs_temp_location = custom_gcs_temp_location
else:
raise ValueError('custom_gcs_temp_location must be str or ValueProvider')
self.test_client = test_client
self.schema = schema
self._temp_file_format = temp_file_format or bigquery_tools.FileFormat.JSON
# If we have multiple destinations, then we will have multiple load jobs,
# thus we will need temporary tables for atomicity.
self.dynamic_destinations = bool(callable(destination))
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.is_streaming_pipeline = is_streaming_pipeline
self._validate = validate
if self._validate:
self.verify()
[docs] def verify(self):
if (isinstance(self._custom_gcs_temp_location.get(), vp.StaticValueProvider)
and not self._custom_gcs_temp_location.get().startswith('gs://')):
# Only fail if the custom location is provided, and it is not a GCS
# location.
raise ValueError(
'Invalid GCS location: %r.\n'
'Writing to BigQuery with FILE_LOADS method requires a '
'GCS location to be provided to write files to be '
'loaded into BigQuery. Please provide a GCS bucket, or '
'pass method="STREAMING_INSERTS" to WriteToBigQuery.' %
self._custom_gcs_temp_location.get())
if self.is_streaming_pipeline and not self.triggering_frequency:
raise ValueError(
'triggering_frequency must be specified to use file'
'loads in streaming')
elif not self.is_streaming_pipeline and self.triggering_frequency:
raise ValueError(
'triggering_frequency can only be used with file'
'loads in streaming')
if not self.is_streaming_pipeline and self.with_auto_sharding:
return ValueError(
'with_auto_sharding can only be used with file loads in streaming.')
def _window_fn(self):
"""Set the correct WindowInto PTransform"""
# The user-supplied triggering_frequency is often chosen to control how
# many BigQuery load jobs are triggered, to prevent going over BigQuery's
# daily quota for load jobs. If this is set to a large value, currently we
# have to buffer all the data until the trigger fires. Instead we ensure
# that the files are written if a threshold number of records are ready.
# We use only the user-supplied trigger on the actual BigQuery load.
# This allows us to offload the data to the filesystem.
#
# In the case of dynamic sharding, however, we use a default trigger since
# the transform performs sharding also batches elements to avoid generating
# too many tiny files. User trigger is applied right after writes to limit
# the number of load jobs.
if self.is_streaming_pipeline and not self.with_auto_sharding:
return beam.WindowInto(beam.window.GlobalWindows(),
trigger=trigger.Repeatedly(
trigger.AfterAny(
trigger.AfterProcessingTime(
self.triggering_frequency),
trigger.AfterCount(
_FILE_TRIGGERING_RECORD_COUNT))),
accumulation_mode=trigger.AccumulationMode\
.DISCARDING)
else:
return beam.WindowInto(beam.window.GlobalWindows())
def _maybe_apply_user_trigger(self, destination_file_kv_pc):
if self.is_streaming_pipeline:
# Apply the user's trigger back before we start triggering load jobs
return (
destination_file_kv_pc
| "ApplyUserTrigger" >> beam.WindowInto(
beam.window.GlobalWindows(),
trigger=trigger.Repeatedly(
trigger.AfterAll(
trigger.AfterProcessingTime(self.triggering_frequency),
trigger.AfterCount(1))),
accumulation_mode=trigger.AccumulationMode.DISCARDING))
else:
return destination_file_kv_pc
def _write_files(self, destination_data_kv_pc, file_prefix_pcv):
outputs = (
destination_data_kv_pc
| beam.ParDo(
WriteRecordsToFile(
schema=self.schema,
max_files_per_bundle=self.max_files_per_bundle,
max_file_size=self.max_file_size,
file_format=self._temp_file_format),
file_prefix_pcv,
*self.schema_side_inputs).with_outputs(
WriteRecordsToFile.UNWRITTEN_RECORD_TAG,
WriteRecordsToFile.WRITTEN_FILE_TAG))
# A PCollection of (destination, file) tuples. It lists files with records,
# and the destination each file is meant to be imported into.
destination_files_kv_pc = outputs[WriteRecordsToFile.WRITTEN_FILE_TAG]
# A PCollection of (destination, record) tuples. These are later sharded,
# grouped, and all records for each destination-shard is written to files.
# This PCollection is necessary because not all records can be written into
# files in ``WriteRecordsToFile``.
unwritten_records_pc = outputs[WriteRecordsToFile.UNWRITTEN_RECORD_TAG]
more_destination_files_kv_pc = (
unwritten_records_pc
| beam.ParDo(_ShardDestinations())
| "GroupShardedRows" >> beam.GroupByKey()
| "DropShardNumber" >> beam.Map(lambda x: (x[0][0], x[1]))
| "WriteGroupedRecordsToFile" >> beam.ParDo(
WriteGroupedRecordsToFile(
schema=self.schema, file_format=self._temp_file_format),
file_prefix_pcv,
*self.schema_side_inputs))
# TODO(BEAM-9494): Remove the identity transform. We flatten both
# PCollection paths and use an identity function to work around a
# flatten optimization issue where the wrong coder is being used.
all_destination_file_pairs_pc = (
(destination_files_kv_pc, more_destination_files_kv_pc)
| "DestinationFilesUnion" >> beam.Flatten()
| "IdentityWorkaround" >> beam.Map(lambda x: x))
return self._maybe_apply_user_trigger(all_destination_file_pairs_pc)
def _write_files_with_auto_sharding(
self, destination_data_kv_pc, file_prefix_pcv):
clock = self.test_client.test_clock if self.test_client else time.time
# 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, table references
# are restored.
destination_files_kv_pc = (
destination_data_kv_pc
|
'ToHashableTableRef' >> beam.Map(bigquery_tools.to_hashable_table_ref)
| 'WithAutoSharding' >> GroupIntoBatches.WithShardedKey(
batch_size=_FILE_TRIGGERING_RECORD_COUNT,
max_buffering_duration_secs=_FILE_TRIGGERING_BATCHING_DURATION_SECS,
clock=clock)
| 'FromHashableTableRefAndDropShard' >> beam.Map(
lambda kvs:
(bigquery_tools.parse_table_reference(kvs[0].key), kvs[1]))
| beam.ParDo(
WriteGroupedRecordsToFile(
schema=self.schema, file_format=self._temp_file_format),
file_prefix_pcv,
*self.schema_side_inputs))
return self._maybe_apply_user_trigger(destination_files_kv_pc)
def _load_data(
self,
partitions_using_temp_tables,
partitions_direct_to_destination,
load_job_name_pcv,
schema_mod_job_name_pcv,
copy_job_name_pcv,
p,
step_name):
"""Load data to BigQuery
Data is loaded into BigQuery in the following two ways:
1. Single partition:
When there is a single partition of files destined to a single
destination, a single load job is triggered.
2. Multiple partitions and/or Dynamic Destinations:
When there are multiple partitions of files destined for a single
destination or when Dynamic Destinations are used, multiple load jobs
need to be triggered for each partition/destination. Load Jobs are
triggered to temporary tables, and those are later copied to the actual
appropriate destination table. This ensures atomicity when only some
of the load jobs would fail but not other. If any of them fails, then
copy jobs are not triggered.
"""
# Load data using temp tables
trigger_loads_outputs = (
partitions_using_temp_tables
| "TriggerLoadJobsWithTempTables" >> beam.ParDo(
TriggerLoadJobs(
schema=self.schema,
write_disposition=self.write_disposition,
create_disposition=self.create_disposition,
test_client=self.test_client,
temporary_tables=True,
additional_bq_parameters=self.additional_bq_parameters,
source_format=self._temp_file_format,
step_name=step_name),
load_job_name_pcv,
*self.schema_side_inputs).with_outputs(
TriggerLoadJobs.TEMP_TABLES, main='main'))
temp_tables_load_job_ids_pc = trigger_loads_outputs['main']
temp_tables_pc = trigger_loads_outputs[TriggerLoadJobs.TEMP_TABLES]
finished_temp_tables_load_jobs_pc = (
p
| "ImpulseMonitorLoadJobs" >> beam.Create([None])
| "WaitForTempTableLoadJobs" >> beam.ParDo(
WaitForBQJobs(self.test_client),
pvalue.AsList(temp_tables_load_job_ids_pc)))
schema_mod_job_ids_pc = (
finished_temp_tables_load_jobs_pc
| beam.ParDo(
UpdateDestinationSchema(
write_disposition=self.write_disposition,
test_client=self.test_client,
additional_bq_parameters=self.additional_bq_parameters,
step_name=step_name),
schema_mod_job_name_pcv))
finished_schema_mod_jobs_pc = (
p
| "ImpulseMonitorSchemaModJobs" >> beam.Create([None])
| "WaitForSchemaModJobs" >> beam.ParDo(
WaitForBQJobs(self.test_client),
pvalue.AsList(schema_mod_job_ids_pc)))
destination_copy_job_ids_pc = (
finished_temp_tables_load_jobs_pc
| beam.ParDo(
TriggerCopyJobs(
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
test_client=self.test_client,
step_name=step_name),
copy_job_name_pcv,
pvalue.AsIter(finished_schema_mod_jobs_pc)))
finished_copy_jobs_pc = (
p
| "ImpulseMonitorCopyJobs" >> beam.Create([None])
| "WaitForCopyJobs" >> beam.ParDo(
WaitForBQJobs(self.test_client),
pvalue.AsList(destination_copy_job_ids_pc)))
_ = (
p
| "RemoveTempTables/Impulse" >> beam.Create([None])
| "RemoveTempTables/PassTables" >> beam.FlatMap(
lambda _,
unused_copy_jobs,
deleting_tables: deleting_tables,
pvalue.AsIter(finished_copy_jobs_pc),
pvalue.AsIter(temp_tables_pc))
| "RemoveTempTables/AddUselessValue" >> beam.Map(lambda x: (x, None))
| "RemoveTempTables/DeduplicateTables" >> beam.GroupByKey()
| "RemoveTempTables/GetTableNames" >> beam.Keys()
| "RemoveTempTables/Delete" >> beam.ParDo(
DeleteTablesFn(self.test_client)))
# Load data directly to destination table
destination_load_job_ids_pc = (
partitions_direct_to_destination
| "TriggerLoadJobsWithoutTempTables" >> beam.ParDo(
TriggerLoadJobs(
schema=self.schema,
write_disposition=self.write_disposition,
create_disposition=self.create_disposition,
test_client=self.test_client,
temporary_tables=False,
additional_bq_parameters=self.additional_bq_parameters,
source_format=self._temp_file_format,
step_name=step_name),
load_job_name_pcv,
*self.schema_side_inputs))
_ = (
p
| "ImpulseMonitorDestinationLoadJobs" >> beam.Create([None])
| "WaitForDestinationLoadJobs" >> beam.ParDo(
WaitForBQJobs(self.test_client),
pvalue.AsList(destination_load_job_ids_pc)))
destination_load_job_ids_pc = (
(temp_tables_load_job_ids_pc, destination_load_job_ids_pc)
| beam.Flatten())
return destination_load_job_ids_pc, destination_copy_job_ids_pc
[docs] def expand(self, pcoll):
p = pcoll.pipeline
try:
step_name = self.label
except AttributeError:
step_name = 'BigQueryBatchFileLoads_%d' % BigQueryBatchFileLoads.COUNT
BigQueryBatchFileLoads.COUNT += 1
temp_location = p.options.view_as(GoogleCloudOptions).temp_location
job_name = (
p.options.view_as(GoogleCloudOptions).job_name or 'AUTOMATIC_JOB_NAME')
empty_pc = p | "ImpulseEmptyPC" >> beam.Create([])
singleton_pc = p | "ImpulseSingleElementPC" >> beam.Create([None])
load_job_name_pcv = pvalue.AsSingleton(
singleton_pc
| "LoadJobNamePrefix" >> beam.Map(
lambda _: _generate_job_name(
job_name, bigquery_tools.BigQueryJobTypes.LOAD, 'LOAD_STEP')))
schema_mod_job_name_pcv = pvalue.AsSingleton(
singleton_pc
| "SchemaModJobNamePrefix" >> beam.Map(
lambda _: _generate_job_name(
job_name,
bigquery_tools.BigQueryJobTypes.LOAD,
'SCHEMA_MOD_STEP')))
copy_job_name_pcv = pvalue.AsSingleton(
singleton_pc
| "CopyJobNamePrefix" >> beam.Map(
lambda _: _generate_job_name(
job_name, bigquery_tools.BigQueryJobTypes.COPY, 'COPY_STEP')))
file_prefix_pcv = pvalue.AsSingleton(
singleton_pc
| "GenerateFilePrefix" >> beam.Map(
file_prefix_generator(
self._validate, self._custom_gcs_temp_location, temp_location)))
destination_data_kv_pc = (
pcoll
| "RewindowIntoGlobal" >> self._window_fn()
| "AppendDestination" >> beam.ParDo(
bigquery_tools.AppendDestinationsFn(self.destination),
*self.table_side_inputs))
if not self.with_auto_sharding:
all_destination_file_pairs_pc = self._write_files(
destination_data_kv_pc, file_prefix_pcv)
else:
all_destination_file_pairs_pc = self._write_files_with_auto_sharding(
destination_data_kv_pc, file_prefix_pcv)
grouped_files_pc = (
all_destination_file_pairs_pc
| "GroupFilesByTableDestinations" >> beam.GroupByKey())
partitions = (
grouped_files_pc
| beam.ParDo(
PartitionFiles(
self.max_partition_size,
self.max_files_per_partition)).with_outputs(
PartitionFiles.MULTIPLE_PARTITIONS_TAG,
PartitionFiles.SINGLE_PARTITION_TAG))
multiple_partitions_per_destination_pc = partitions[
PartitionFiles.MULTIPLE_PARTITIONS_TAG]
single_partition_per_destination_pc = partitions[
PartitionFiles.SINGLE_PARTITION_TAG]
# When using dynamic destinations, elements with both single as well as
# multiple partitions are loaded into BigQuery using temporary tables to
# ensure atomicity.
if self.dynamic_destinations:
all_partitions = ((
multiple_partitions_per_destination_pc,
single_partition_per_destination_pc)
| "FlattenPartitions" >> beam.Flatten())
destination_load_job_ids_pc, destination_copy_job_ids_pc = (
self._load_data(all_partitions,
empty_pc,
load_job_name_pcv,
schema_mod_job_name_pcv,
copy_job_name_pcv,
p,
step_name))
else:
destination_load_job_ids_pc, destination_copy_job_ids_pc = (
self._load_data(multiple_partitions_per_destination_pc,
single_partition_per_destination_pc,
load_job_name_pcv,
schema_mod_job_name_pcv,
copy_job_name_pcv,
p,
step_name))
return {
self.DESTINATION_JOBID_PAIRS: destination_load_job_ids_pc,
self.DESTINATION_FILE_PAIRS: all_destination_file_pairs_pc,
self.DESTINATION_COPY_JOBID_PAIRS: destination_copy_job_ids_pc,
}