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https://issues.apache.org/jira/browse/BEAM-6553?focusedWorklogId=195882&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-195882
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ASF GitHub Bot logged work on BEAM-6553:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Feb/19 19:16
Start Date: 07/Feb/19 19:16
Worklog Time Spent: 10m
Work Description: chamikaramj commented on pull request #7655:
[BEAM-6553] A Python SDK sink that supports File Loads into BQ
URL: https://github.com/apache/beam/pull/7655#discussion_r254814480
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_file_loads.py
##########
@@ -0,0 +1,533 @@
+#
+# 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.
+
+NOTHING IN THIS FILE HAS BACKWARDS COMPATIBILITY GUARANTEES.
+"""
+
+from __future__ import absolute_import
+
+import datetime
+import logging
+import random
+import time
+import uuid
+
+from future.utils import iteritems
+
+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.internal.clients import bigquery as bigquery_api
+from apache_beam.options import value_provider as vp
+from apache_beam.transforms.combiners import Count
+
+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
+
+
+def _generate_load_job_name():
+ datetime_component = datetime.datetime.now().strftime("%Y_%m_%d_%H%M%S")
+ # TODO(pabloem): include job id / pipeline component?
+ return 'beam_load_%s_%s' % (datetime_component, random.randint(0, 100))
+
+
+def _generate_file_prefix(pipeline_gcs_location):
+ # 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 = str(pipeline_gcs_location or
+ vp.RuntimeValueProvider.get_value('temp_location', str, ''))
+ return fs.FileSystems.join(gcs_base, 'bq_load')
+
+
+def _make_new_file_writer(file_prefix, destination):
+ if isinstance(destination, bigquery_api.TableReference):
+ destination = '%s:%s.%s' % (
+ destination.projectId, destination.datasetId, destination.tableId)
+
+ 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)
+
+ return file_path, fs.FileSystems.create(file_path, 'application/text')
+
+
+def _bq_uuid():
+ return str(uuid.uuid4()).replace("-", "")
+
+
+class _AppendDestinationsFn(beam.DoFn):
+ """Adds the destination to an element, making it a KV pair.
+
+ Outputs a PCollection of KV-pairs where the key is a TableReference for the
+ destination, and the value is the record itself.
+
+ Experimental; no backwards compatibility guarantees.
+ """
+
+ def __init__(self, destination):
+ if callable(destination):
+ self.destination = destination
+ else:
+ self.destination = lambda x: destination
+
+ def process(self, element):
+ yield (self.destination(element), element)
+
+
+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)
+
+
+class WriteRecordsToFile(beam.DoFn):
+ """Write input records to files before triggering a load job.
+
+ It outputs two PCollections.
+ """
+
+ UNWRITTEN_RECORD_TAG = 'UnwrittenRecords'
+ WRITTEN_FILE_TAG = 'WrittenFiles'
+
+ def __init__(self,
+ max_files_per_bundle=_DEFAULT_MAX_WRITERS_PER_BUNDLE,
+ max_file_size=_DEFAULT_MAX_FILE_SIZE,
+ coder=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.max_files_per_bundle = max_files_per_bundle
+ self.max_file_size = max_file_size
+ self.coder = coder or bigquery_tools.RowAsDictJsonCoder()
+
+ def display_data(self):
+ return {
+ 'max_files_per_bundle': self.max_files_per_bundle,
+ 'max_file_size': str(self.max_file_size),
+ 'coder': self.coder.__class__.__name__
+ }
+
+ def start_bundle(self):
+ self._destination_to_file_writer = {}
+
+ def process(self, element, file_prefix):
+ destination = element[0]
+ row = element[1]
+
+ if destination in self._destination_to_file_writer:
+ writer = self._destination_to_file_writer[destination]
+ elif len(self._destination_to_file_writer) < self.max_files_per_bundle:
+ (file_path, writer) = _make_new_file_writer(file_prefix, destination)
+ self._destination_to_file_writer[destination] = writer
+ yield pvalue.TaggedOutput(WriteRecordsToFile.WRITTEN_FILE_TAG,
+ (destination, file_path))
+ else:
+ yield pvalue.TaggedOutput(
+ WriteRecordsToFile.UNWRITTEN_RECORD_TAG, element)
+ return
+
+ # TODO(pabloem): Is it possible for this to throw exception?
+ writer.write(self.coder.encode(row))
+ writer.write(b'\n')
+
+ if writer.tell() > self.max_file_size:
+ writer.close()
+ self._destination_to_file_writer.pop(destination)
+
+ def finish_bundle(self):
+ for _, writer in iteritems(self._destination_to_file_writer):
+ writer.close()
+ self._destination_to_file_writer = {}
+
+
+class WriteGroupedRecordsToFile(beam.DoFn):
+ """Receives collection of dest-iterable(records), writes it to files.
+
+ Experimental; no backwards compatibility guarantees.
+ """
+
+ def __init__(self, max_file_size=_DEFAULT_MAX_FILE_SIZE,
+ coder=None):
+ self.max_file_size = max_file_size
+ self.coder = coder or bigquery_tools.RowAsDictJsonCoder()
+
+ def process(self, element, file_prefix):
+ destination = element[0]
+ rows = element[1]
+
+ writer = None
+
+ for row in rows:
+ if writer is None:
+ (file_path, writer) = _make_new_file_writer(file_prefix, destination)
+ yield (destination, file_path)
+
+ writer.write(self.coder.encode(row))
+ writer.write(b'\n')
+
+ if writer.tell() > self.max_file_size:
+ writer.close()
+ writer = None
+
+
+class TriggerCopyJobs(beam.DoFn):
+ def __init__(self,
+ create_disposition=None,
+ write_disposition=None,
+ test_client=None,
+ temporary_tables=False):
+ self.create_disposition = create_disposition
+ self.write_disposition = write_disposition
+ self.test_client = test_client
+ self.temporary_tables = temporary_tables
+
+ def start_bundle(self):
+ self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
+
+ def process(self, element, load_job_name_prefix=None):
+ destination = element[0]
+ job_reference = element[1]
+
+ if not self.temporary_tables:
+ # If we did not use temporary tables, then we do not need to trigger any
+ # copy jobs.
+ return
+
+ copy_job_name = '%s_copy_%s' % (load_job_name_prefix, _bq_uuid())
+
+ 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, '')
+
+ logging.info("Triggering copy job from %s to %s",
+ copy_from_reference, copy_to_reference)
+ 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=self.write_disposition)
+
+ yield (destination, job_reference)
+
+
+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):
+ self.schema = schema
+ self.test_client = test_client
+ self.temporary_tables = temporary_tables
+ 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
+
+ def display_data(self):
+ result = {'create_disposition': str(self.create_disposition),
+ 'write_disposition': str(self.write_disposition)}
+ if self.schema is not None:
+ result['schema'] = str(self.schema)
+ else:
+ result['schema'] = 'AUTODETECT'
+
+ return result
+
+ def start_bundle(self):
+ self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
+
+ def process(self, element, load_job_name_prefix):
+ destination = element[0]
+ files = element[1]
+ job_name = '%s_%s' % (load_job_name_prefix, _bq_uuid())
+
+ table_reference = bigquery_tools.parse_table_reference(destination)
+ if table_reference.projectId is None:
+ table_reference.projectId = vp.RuntimeValueProvider.get_value(
+ 'project', str, '')
+
+ if self.temporary_tables:
+ # 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)
+
+ job_reference = self.bq_wrapper.perform_load_job(
+ table_reference, list(files), job_name,
+ schema=self.schema,
+ write_disposition=self.write_disposition,
+ create_disposition=self.create_disposition)
+ logging.info("Triggered job %s to load data to BigQuery table %s.",
+ job_reference, table_reference)
+ yield (destination, job_reference)
+
+
+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.
+ """
+ ALL_DONE = object()
+ FAILED = object()
+ WAITING = object()
+
+ def __init__(self, test_client):
+ self.test_client = test_client
+
+ def start_bundle(self):
+ self.bq_wrapper = bigquery_tools.BigQueryWrapper(client=self.test_client)
+
+ def process(self, element, dest_ids_list):
+ job_references = [elm[1] for elm in dest_ids_list]
+
+ while True:
+ status = self._check_job_states(job_references)
Review comment:
Catch exceptions here ? (unless we already catch them downstream).
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Issue Time Tracking
-------------------
Worklog Id: (was: 195882)
Time Spent: 3h 20m (was: 3h 10m)
> A BigQuery sink thta is SDK-implemented and supports file loads in Python
> -------------------------------------------------------------------------
>
> Key: BEAM-6553
> URL: https://issues.apache.org/jira/browse/BEAM-6553
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: Pablo Estrada
> Assignee: Pablo Estrada
> Priority: Major
> Labels: triaged
> Time Spent: 3h 20m
> Remaining Estimate: 0h
>
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