Source code for

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Functionality to perform file loads into BigQuery for Batch and Streaming

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.


# pytype: skip-file

from __future__ import absolute_import

import datetime
import hashlib
import logging
import random
import uuid

from future.utils import iteritems

import apache_beam as beam
from apache_beam import pvalue
from import filesystems as fs
from import bigquery_tools
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.window import GlobalWindows

_LOGGER = logging.getLogger(__name__)

ONE_TERABYTE = (1 << 40)

# The maximum file size for imports is 5TB. We keep our files under that.


# The maximum size for a single load job is one terabyte

# Big query only supports up to 10 thousand URIs for a single load job.

# If triggering_frequency is supplied, we will trigger the file write after
# this many records are written.

def _generate_load_job_name():
  datetime_component ="%Y_%m_%d_%H%M%S")
  # TODO(pabloem): include job id / pipeline component?
  return 'beam_load_%s_%s' % (datetime_component, random.randint(0, 100))

[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' ' loaded into BigQuery. Please provide a GCS bucket, 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: # 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, (element[0], (file_path, file_size)))
[docs] def finish_bundle(self): for destination, file_path_writer in \ iteritems(self._destination_to_file_writer): (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 = 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 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: """ def __init__( self, create_disposition=None, write_disposition=None, test_client=None): self.create_disposition = create_disposition self.write_disposition = write_disposition self.test_client = test_client self._observed_tables = set()
[docs] def start_bundle(self): self._observed_tables = set() self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
[docs] def process(self, element, job_name_prefix=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_copy_%s_to_%s' % ( job_name_prefix, _bq_uuid( '%s:%s.%s' % ( copy_from_reference.projectId, copy_from_reference.datasetId, copy_from_reference.tableId)), _bq_uuid( '%s:%s.%s' % ( copy_to_reference.projectId, copy_to_reference.datasetId, copy_to_reference.tableId))) "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' 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) 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): 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 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) } result['schema'] = str(self.schema) return result
[docs] def start_bundle(self): self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
[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, table_reference) 'Triggering job %s to load data to BigQuery table %s.' 'Schema: %s. Additional parameters: %s', job_name, table_reference, schema, additional_parameters) job_reference = self.bq_wrapper.perform_load_job( table_reference, files, job_name, schema=schema, write_disposition=self.write_disposition, create_disposition=create_disposition, additional_load_parameters=additional_parameters, source_format=self.source_format) yield (destination, job_reference)
[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):"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' def __init__( self, destination, schema=None, custom_gcs_temp_location=None, create_disposition=None, write_disposition=None, triggering_frequency=None, 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.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')
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. if self.is_streaming_pipeline: 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 _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)) if self.is_streaming_pipeline: # Apply the user's trigger back before we start triggering load jobs all_destination_file_pairs_pc = ( all_destination_file_pairs_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)) return all_destination_file_pairs_pc def _load_data( self, partitions_using_temp_tables, partitions_direct_to_destination, load_job_name_pcv, singleton_pc): """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), 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] destination_copy_job_ids_pc = ( singleton_pc | "WaitForTempTableLoadJobs" >> beam.ParDo( WaitForBQJobs(self.test_client), beam.pvalue.AsList(temp_tables_load_job_ids_pc)) | beam.ParDo( TriggerCopyJobs( create_disposition=self.create_disposition, write_disposition=self.write_disposition, test_client=self.test_client), load_job_name_pcv)) finished_copy_jobs_pc = ( singleton_pc | "WaitForCopyJobs" >> beam.ParDo( WaitForBQJobs(self.test_client), beam.pvalue.AsList(destination_copy_job_ids_pc))) _ = ( finished_copy_jobs_pc | "RemoveTempTables/PassTables" >> beam.FlatMap( lambda x, deleting_tables: deleting_tables, pvalue.AsIter(temp_tables_pc)) | "RemoveTempTables/AddUselessValue" >> beam.Map(lambda x: (x, None)) | "RemoveTempTables/DeduplicateTables" >> beam.GroupByKey() | "RemoveTempTables/GetTableNames" >> beam.Map(lambda elm: elm[0]) | "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), load_job_name_pcv, *self.schema_side_inputs)) _ = ( singleton_pc | "WaitForDestinationLoadJobs" >> beam.ParDo( WaitForBQJobs(self.test_client), beam.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 temp_location = p.options.view_as(GoogleCloudOptions).temp_location empty_pc = p | "ImpulseEmptyPC" >> beam.Create([]) singleton_pc = p | "ImpulseSingleElementPC" >> beam.Create([None]) load_job_name_pcv = pvalue.AsSingleton( singleton_pc | beam.Map(lambda _: _generate_load_job_name())) 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)) all_destination_file_pairs_pc = self._write_files( 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, singleton_pc) 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, singleton_pc) 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, }