| # |
| # 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. |
| # |
| |
| """Tools used by BigQuery sources and sinks. |
| |
| Classes, constants and functions in this file are experimental and have no |
| backwards compatibility guarantees. |
| |
| These tools include wrappers and clients to interact with BigQuery APIs. |
| |
| NOTHING IN THIS FILE HAS BACKWARDS COMPATIBILITY GUARANTEES. |
| """ |
| |
| # pytype: skip-file |
| |
| from __future__ import absolute_import |
| |
| import datetime |
| import decimal |
| import io |
| import json |
| import logging |
| import re |
| import sys |
| import time |
| import uuid |
| from builtins import object |
| |
| import fastavro |
| from future.utils import iteritems |
| from future.utils import raise_with_traceback |
| from past.builtins import unicode |
| |
| from apache_beam import coders |
| from apache_beam.internal.gcp import auth |
| 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.internal.http_client import get_new_http |
| from apache_beam.io.gcp import bigquery_avro_tools |
| from apache_beam.io.gcp.bigquery_io_metadata import create_bigquery_io_metadata |
| from apache_beam.io.gcp.internal.clients import bigquery |
| from apache_beam.options import value_provider |
| from apache_beam.options.pipeline_options import GoogleCloudOptions |
| from apache_beam.runners.dataflow.native_io import iobase as dataflow_io |
| from apache_beam.transforms import DoFn |
| from apache_beam.typehints.typehints import Any |
| from apache_beam.utils import retry |
| |
| # 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, HttpForbiddenError |
| except ImportError: |
| pass |
| |
| # pylint: enable=wrong-import-order, wrong-import-position |
| |
| _LOGGER = logging.getLogger(__name__) |
| |
| MAX_RETRIES = 3 |
| |
| JSON_COMPLIANCE_ERROR = 'NAN, INF and -INF values are not JSON compliant.' |
| |
| |
| class FileFormat(object): |
| CSV = 'CSV' |
| JSON = 'NEWLINE_DELIMITED_JSON' |
| AVRO = 'AVRO' |
| |
| |
| class ExportCompression(object): |
| GZIP = 'GZIP' |
| DEFLATE = 'DEFLATE' |
| SNAPPY = 'SNAPPY' |
| NONE = 'NONE' |
| |
| |
| def default_encoder(obj): |
| if isinstance(obj, decimal.Decimal): |
| return str(obj) |
| elif isinstance(obj, bytes): |
| # on python 3 base64-encoded bytes are decoded to strings |
| # before being sent to BigQuery |
| return obj.decode('utf-8') |
| elif isinstance(obj, (datetime.date, datetime.time)): |
| return str(obj) |
| elif isinstance(obj, datetime.datetime): |
| return obj.isoformat() |
| |
| _LOGGER.error("Unable to serialize %r to JSON", obj) |
| raise TypeError( |
| "Object of type '%s' is not JSON serializable" % type(obj).__name__) |
| |
| |
| def get_hashable_destination(destination): |
| """Parses a table reference into a (project, dataset, table) tuple. |
| |
| Args: |
| destination: Either a TableReference object from the bigquery API. |
| The object has the following attributes: projectId, datasetId, and |
| tableId. Or a string representing the destination containing |
| 'PROJECT:DATASET.TABLE'. |
| Returns: |
| A string representing the destination containing |
| 'PROJECT:DATASET.TABLE'. |
| """ |
| if isinstance(destination, bigquery.TableReference): |
| return '%s:%s.%s' % ( |
| destination.projectId, destination.datasetId, destination.tableId) |
| else: |
| return destination |
| |
| |
| def parse_table_schema_from_json(schema_string): |
| """Parse the Table Schema provided as string. |
| |
| Args: |
| schema_string: String serialized table schema, should be a valid JSON. |
| |
| Returns: |
| A TableSchema of the BigQuery export from either the Query or the Table. |
| """ |
| json_schema = json.loads(schema_string) |
| |
| def _parse_schema_field(field): |
| """Parse a single schema field from dictionary. |
| |
| Args: |
| field: Dictionary object containing serialized schema. |
| |
| Returns: |
| A TableFieldSchema for a single column in BigQuery. |
| """ |
| schema = bigquery.TableFieldSchema() |
| schema.name = field['name'] |
| schema.type = field['type'] |
| if 'mode' in field: |
| schema.mode = field['mode'] |
| else: |
| schema.mode = 'NULLABLE' |
| if 'description' in field: |
| schema.description = field['description'] |
| if 'fields' in field: |
| schema.fields = [_parse_schema_field(x) for x in field['fields']] |
| return schema |
| |
| fields = [_parse_schema_field(f) for f in json_schema['fields']] |
| return bigquery.TableSchema(fields=fields) |
| |
| |
| def parse_table_reference(table, dataset=None, project=None): |
| """Parses a table reference into a (project, dataset, table) tuple. |
| |
| Args: |
| table: The ID of the table. The ID must contain only letters |
| (a-z, A-Z), numbers (0-9), or underscores (_). If dataset argument is None |
| then the table argument must contain the entire table reference: |
| 'DATASET.TABLE' or 'PROJECT:DATASET.TABLE'. This argument can be a |
| bigquery.TableReference instance in which case dataset and project are |
| ignored and the reference is returned as a result. Additionally, for date |
| partitioned tables, appending '$YYYYmmdd' to the table name is supported, |
| e.g. 'DATASET.TABLE$YYYYmmdd'. |
| dataset: The ID of the dataset containing this table or null if the table |
| reference is specified entirely by the table argument. |
| project: The ID of the project containing this table or null if the table |
| reference is specified entirely by the table (and possibly dataset) |
| argument. |
| |
| Returns: |
| A TableReference object from the bigquery API. The object has the following |
| attributes: projectId, datasetId, and tableId. |
| If the input is a TableReference object, a new object will be returned. |
| |
| Raises: |
| ValueError: if the table reference as a string does not match the expected |
| format. |
| """ |
| |
| if isinstance(table, bigquery.TableReference): |
| return bigquery.TableReference( |
| projectId=table.projectId, |
| datasetId=table.datasetId, |
| tableId=table.tableId) |
| elif callable(table): |
| return table |
| elif isinstance(table, value_provider.ValueProvider): |
| return table |
| |
| table_reference = bigquery.TableReference() |
| # If dataset argument is not specified, the expectation is that the |
| # table argument will contain a full table reference instead of just a |
| # table name. |
| if dataset is None: |
| match = re.match( |
| r'^((?P<project>.+):)?(?P<dataset>\w+)\.(?P<table>[\w\$]+)$', table) |
| if not match: |
| raise ValueError( |
| 'Expected a table reference (PROJECT:DATASET.TABLE or ' |
| 'DATASET.TABLE) instead of %s.' % table) |
| table_reference.projectId = match.group('project') |
| table_reference.datasetId = match.group('dataset') |
| table_reference.tableId = match.group('table') |
| else: |
| table_reference.projectId = project |
| table_reference.datasetId = dataset |
| table_reference.tableId = table |
| return table_reference |
| |
| |
| # ----------------------------------------------------------------------------- |
| # BigQueryWrapper. |
| |
| |
| def _build_job_labels(input_labels): |
| """Builds job label protobuf structure.""" |
| input_labels = input_labels or {} |
| result = bigquery.JobConfiguration.LabelsValue() |
| |
| for k, v in input_labels.items(): |
| result.additionalProperties.append( |
| bigquery.JobConfiguration.LabelsValue.AdditionalProperty( |
| key=k, |
| value=v, |
| )) |
| return result |
| |
| |
| class BigQueryWrapper(object): |
| """BigQuery client wrapper with utilities for querying. |
| |
| The wrapper is used to organize all the BigQuery integration points and |
| offer a common place where retry logic for failures can be controlled. |
| In addition it offers various functions used both in sources and sinks |
| (e.g., find and create tables, query a table, etc.). |
| """ |
| |
| TEMP_TABLE = 'temp_table_' |
| TEMP_DATASET = 'temp_dataset_' |
| |
| def __init__(self, client=None): |
| self.client = client or bigquery.BigqueryV2( |
| http=get_new_http(), |
| credentials=auth.get_service_credentials(), |
| response_encoding=None if sys.version_info[0] < 3 else 'utf8') |
| self._unique_row_id = 0 |
| # For testing scenarios where we pass in a client we do not want a |
| # randomized prefix for row IDs. |
| self._row_id_prefix = '' if client else uuid.uuid4() |
| self._temporary_table_suffix = uuid.uuid4().hex |
| |
| @property |
| def unique_row_id(self): |
| """Returns a unique row ID (str) used to avoid multiple insertions. |
| |
| If the row ID is provided, BigQuery will make a best effort to not insert |
| the same row multiple times for fail and retry scenarios in which the insert |
| request may be issued several times. This comes into play for sinks executed |
| in a local runner. |
| |
| Returns: |
| a unique row ID string |
| """ |
| self._unique_row_id += 1 |
| return '%s_%d' % (self._row_id_prefix, self._unique_row_id) |
| |
| def _get_temp_table(self, project_id): |
| return parse_table_reference( |
| table=BigQueryWrapper.TEMP_TABLE + self._temporary_table_suffix, |
| dataset=BigQueryWrapper.TEMP_DATASET + self._temporary_table_suffix, |
| project=project_id) |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_query_location(self, project_id, query, use_legacy_sql): |
| """ |
| Get the location of tables referenced in a query. |
| |
| This method returns the location of the first available referenced |
| table for user in the query and depends on the BigQuery service to |
| provide error handling for queries that reference tables in multiple |
| locations. |
| """ |
| reference = bigquery.JobReference( |
| jobId=uuid.uuid4().hex, projectId=project_id) |
| request = bigquery.BigqueryJobsInsertRequest( |
| projectId=project_id, |
| job=bigquery.Job( |
| configuration=bigquery.JobConfiguration( |
| dryRun=True, |
| query=bigquery.JobConfigurationQuery( |
| query=query, |
| useLegacySql=use_legacy_sql, |
| )), |
| jobReference=reference)) |
| |
| response = self.client.jobs.Insert(request) |
| |
| if response.statistics is None: |
| # This behavior is only expected in tests |
| _LOGGER.warning( |
| "Unable to get location, missing response.statistics. Query: %s", |
| query) |
| return None |
| |
| referenced_tables = response.statistics.query.referencedTables |
| if referenced_tables: # Guards against both non-empty and non-None |
| for table in referenced_tables: |
| try: |
| location = self.get_table_location( |
| table.projectId, table.datasetId, table.tableId) |
| except HttpForbiddenError: |
| # Permission access for table (i.e. from authorized_view), |
| # try next one |
| continue |
| _LOGGER.info( |
| "Using location %r from table %r referenced by query %s", |
| location, |
| table, |
| query) |
| return location |
| |
| _LOGGER.debug( |
| "Query %s does not reference any tables or " |
| "you don't have permission to inspect them.", |
| query) |
| return None |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _insert_copy_job( |
| self, |
| project_id, |
| job_id, |
| from_table_reference, |
| to_table_reference, |
| create_disposition=None, |
| write_disposition=None, |
| job_labels=None): |
| reference = bigquery.JobReference() |
| reference.jobId = job_id |
| reference.projectId = project_id |
| request = bigquery.BigqueryJobsInsertRequest( |
| projectId=project_id, |
| job=bigquery.Job( |
| configuration=bigquery.JobConfiguration( |
| copy=bigquery.JobConfigurationTableCopy( |
| destinationTable=to_table_reference, |
| sourceTable=from_table_reference, |
| createDisposition=create_disposition, |
| writeDisposition=write_disposition, |
| ), |
| labels=_build_job_labels(job_labels), |
| ), |
| jobReference=reference, |
| )) |
| |
| _LOGGER.info("Inserting job request: %s", request) |
| response = self.client.jobs.Insert(request) |
| _LOGGER.info("Response was %s", response) |
| return response.jobReference |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _insert_load_job( |
| self, |
| project_id, |
| job_id, |
| table_reference, |
| source_uris, |
| schema=None, |
| write_disposition=None, |
| create_disposition=None, |
| additional_load_parameters=None, |
| source_format=None, |
| job_labels=None): |
| additional_load_parameters = additional_load_parameters or {} |
| job_schema = None if schema == 'SCHEMA_AUTODETECT' else schema |
| reference = bigquery.JobReference(jobId=job_id, projectId=project_id) |
| request = bigquery.BigqueryJobsInsertRequest( |
| projectId=project_id, |
| job=bigquery.Job( |
| configuration=bigquery.JobConfiguration( |
| load=bigquery.JobConfigurationLoad( |
| sourceUris=source_uris, |
| destinationTable=table_reference, |
| schema=job_schema, |
| writeDisposition=write_disposition, |
| createDisposition=create_disposition, |
| sourceFormat=source_format, |
| useAvroLogicalTypes=True, |
| autodetect=schema == 'SCHEMA_AUTODETECT', |
| **additional_load_parameters), |
| labels=_build_job_labels(job_labels), |
| ), |
| jobReference=reference, |
| )) |
| response = self.client.jobs.Insert(request) |
| return response.jobReference |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _start_query_job( |
| self, |
| project_id, |
| query, |
| use_legacy_sql, |
| flatten_results, |
| job_id, |
| dry_run=False, |
| kms_key=None, |
| job_labels=None): |
| reference = bigquery.JobReference(jobId=job_id, projectId=project_id) |
| request = bigquery.BigqueryJobsInsertRequest( |
| projectId=project_id, |
| job=bigquery.Job( |
| configuration=bigquery.JobConfiguration( |
| dryRun=dry_run, |
| query=bigquery.JobConfigurationQuery( |
| query=query, |
| useLegacySql=use_legacy_sql, |
| allowLargeResults=not dry_run, |
| destinationTable=self._get_temp_table(project_id) |
| if not dry_run else None, |
| flattenResults=flatten_results, |
| destinationEncryptionConfiguration=bigquery. |
| EncryptionConfiguration(kmsKeyName=kms_key)), |
| labels=_build_job_labels(job_labels), |
| ), |
| jobReference=reference)) |
| |
| response = self.client.jobs.Insert(request) |
| return response |
| |
| def wait_for_bq_job(self, job_reference, sleep_duration_sec=5, max_retries=0): |
| """Poll job until it is DONE. |
| |
| Args: |
| job_reference: bigquery.JobReference instance. |
| sleep_duration_sec: Specifies the delay in seconds between retries. |
| max_retries: The total number of times to retry. If equals to 0, |
| the function waits forever. |
| |
| Raises: |
| `RuntimeError`: If the job is FAILED or the number of retries has been |
| reached. |
| """ |
| retry = 0 |
| while True: |
| retry += 1 |
| job = self.get_job( |
| job_reference.projectId, job_reference.jobId, job_reference.location) |
| logging.info('Job status: %s', job.status.state) |
| if job.status.state == 'DONE' and job.status.errorResult: |
| raise RuntimeError( |
| 'BigQuery job {} failed. Error Result: {}'.format( |
| job_reference.jobId, job.status.errorResult)) |
| elif job.status.state == 'DONE': |
| return True |
| else: |
| time.sleep(sleep_duration_sec) |
| if max_retries != 0 and retry >= max_retries: |
| raise RuntimeError('The maximum number of retries has been reached') |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _get_query_results( |
| self, |
| project_id, |
| job_id, |
| page_token=None, |
| max_results=10000, |
| location=None): |
| request = bigquery.BigqueryJobsGetQueryResultsRequest( |
| jobId=job_id, |
| pageToken=page_token, |
| projectId=project_id, |
| maxResults=max_results, |
| location=location) |
| response = self.client.jobs.GetQueryResults(request) |
| return response |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_timeout_or_quota_issues_filter) |
| def _insert_all_rows( |
| self, project_id, dataset_id, table_id, rows, skip_invalid_rows=False): |
| """Calls the insertAll BigQuery API endpoint. |
| |
| Docs for this BQ call: https://cloud.google.com/bigquery/docs/reference\ |
| /rest/v2/tabledata/insertAll.""" |
| # The rows argument is a list of |
| # bigquery.TableDataInsertAllRequest.RowsValueListEntry instances as |
| # required by the InsertAll() method. |
| request = bigquery.BigqueryTabledataInsertAllRequest( |
| projectId=project_id, |
| datasetId=dataset_id, |
| tableId=table_id, |
| tableDataInsertAllRequest=bigquery.TableDataInsertAllRequest( |
| skipInvalidRows=skip_invalid_rows, |
| # TODO(silviuc): Should have an option for ignoreUnknownValues? |
| rows=rows)) |
| response = self.client.tabledata.InsertAll(request) |
| # response.insertErrors is not [] if errors encountered. |
| return not response.insertErrors, response.insertErrors |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_table(self, project_id, dataset_id, table_id): |
| """Lookup a table's metadata object. |
| |
| Args: |
| client: bigquery.BigqueryV2 instance |
| project_id: table lookup parameter |
| dataset_id: table lookup parameter |
| table_id: table lookup parameter |
| |
| Returns: |
| bigquery.Table instance |
| Raises: |
| HttpError: if lookup failed. |
| """ |
| request = bigquery.BigqueryTablesGetRequest( |
| projectId=project_id, datasetId=dataset_id, tableId=table_id) |
| response = self.client.tables.Get(request) |
| return response |
| |
| def _create_table( |
| self, |
| project_id, |
| dataset_id, |
| table_id, |
| schema, |
| additional_parameters=None): |
| additional_parameters = additional_parameters or {} |
| table = bigquery.Table( |
| tableReference=bigquery.TableReference( |
| projectId=project_id, datasetId=dataset_id, tableId=table_id), |
| schema=schema, |
| **additional_parameters) |
| request = bigquery.BigqueryTablesInsertRequest( |
| projectId=project_id, datasetId=dataset_id, table=table) |
| response = self.client.tables.Insert(request) |
| _LOGGER.debug("Created the table with id %s", table_id) |
| # The response is a bigquery.Table instance. |
| return response |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_or_create_dataset(self, project_id, dataset_id, location=None): |
| # Check if dataset already exists otherwise create it |
| try: |
| dataset = self.client.datasets.Get( |
| bigquery.BigqueryDatasetsGetRequest( |
| projectId=project_id, datasetId=dataset_id)) |
| return dataset |
| except HttpError as exn: |
| if exn.status_code == 404: |
| dataset_reference = bigquery.DatasetReference( |
| projectId=project_id, datasetId=dataset_id) |
| dataset = bigquery.Dataset(datasetReference=dataset_reference) |
| if location is not None: |
| dataset.location = location |
| request = bigquery.BigqueryDatasetsInsertRequest( |
| projectId=project_id, dataset=dataset) |
| response = self.client.datasets.Insert(request) |
| # The response is a bigquery.Dataset instance. |
| return response |
| else: |
| raise |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _is_table_empty(self, project_id, dataset_id, table_id): |
| request = bigquery.BigqueryTabledataListRequest( |
| projectId=project_id, |
| datasetId=dataset_id, |
| tableId=table_id, |
| maxResults=1) |
| response = self.client.tabledata.List(request) |
| # The response is a bigquery.TableDataList instance. |
| return response.totalRows == 0 |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _delete_table(self, project_id, dataset_id, table_id): |
| request = bigquery.BigqueryTablesDeleteRequest( |
| projectId=project_id, datasetId=dataset_id, tableId=table_id) |
| try: |
| self.client.tables.Delete(request) |
| except HttpError as exn: |
| if exn.status_code == 404: |
| _LOGGER.warning( |
| 'Table %s:%s.%s does not exist', project_id, dataset_id, table_id) |
| return |
| else: |
| raise |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def _delete_dataset(self, project_id, dataset_id, delete_contents=True): |
| request = bigquery.BigqueryDatasetsDeleteRequest( |
| projectId=project_id, |
| datasetId=dataset_id, |
| deleteContents=delete_contents) |
| try: |
| self.client.datasets.Delete(request) |
| except HttpError as exn: |
| if exn.status_code == 404: |
| _LOGGER.warning('Dataset %s:%s does not exist', project_id, dataset_id) |
| return |
| else: |
| raise |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_table_location(self, project_id, dataset_id, table_id): |
| table = self.get_table(project_id, dataset_id, table_id) |
| return table.location |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def create_temporary_dataset(self, project_id, location): |
| dataset_id = BigQueryWrapper.TEMP_DATASET + self._temporary_table_suffix |
| # Check if dataset exists to make sure that the temporary id is unique |
| try: |
| self.client.datasets.Get( |
| bigquery.BigqueryDatasetsGetRequest( |
| projectId=project_id, datasetId=dataset_id)) |
| if project_id is not None: |
| # Unittests don't pass projectIds so they can be run without error |
| raise RuntimeError( |
| 'Dataset %s:%s already exists so cannot be used as temporary.' % |
| (project_id, dataset_id)) |
| except HttpError as exn: |
| if exn.status_code == 404: |
| _LOGGER.warning( |
| 'Dataset %s:%s does not exist so we will create it as temporary ' |
| 'with location=%s', |
| project_id, |
| dataset_id, |
| location) |
| self.get_or_create_dataset(project_id, dataset_id, location=location) |
| else: |
| raise |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def clean_up_temporary_dataset(self, project_id): |
| temp_table = self._get_temp_table(project_id) |
| try: |
| self.client.datasets.Get( |
| bigquery.BigqueryDatasetsGetRequest( |
| projectId=project_id, datasetId=temp_table.datasetId)) |
| except HttpError as exn: |
| if exn.status_code == 404: |
| _LOGGER.warning( |
| 'Dataset %s:%s does not exist', project_id, temp_table.datasetId) |
| return |
| else: |
| raise |
| self._delete_dataset(temp_table.projectId, temp_table.datasetId, True) |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_job(self, project, job_id, location=None): |
| request = bigquery.BigqueryJobsGetRequest() |
| request.jobId = job_id |
| request.projectId = project |
| request.location = location |
| |
| return self.client.jobs.Get(request) |
| |
| def perform_load_job( |
| self, |
| destination, |
| files, |
| job_id, |
| schema=None, |
| write_disposition=None, |
| create_disposition=None, |
| additional_load_parameters=None, |
| source_format=None, |
| job_labels=None): |
| """Starts a job to load data into BigQuery. |
| |
| Returns: |
| bigquery.JobReference with the information about the job that was started. |
| """ |
| return self._insert_load_job( |
| destination.projectId, |
| job_id, |
| destination, |
| files, |
| schema=schema, |
| create_disposition=create_disposition, |
| write_disposition=write_disposition, |
| additional_load_parameters=additional_load_parameters, |
| source_format=source_format, |
| job_labels=job_labels) |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def perform_extract_job( |
| self, |
| destination, |
| job_id, |
| table_reference, |
| destination_format, |
| project=None, |
| include_header=True, |
| compression=ExportCompression.NONE, |
| use_avro_logical_types=False, |
| job_labels=None): |
| """Starts a job to export data from BigQuery. |
| |
| Returns: |
| bigquery.JobReference with the information about the job that was started. |
| """ |
| job_project = project or table_reference.projectId |
| job_reference = bigquery.JobReference(jobId=job_id, projectId=job_project) |
| request = bigquery.BigqueryJobsInsertRequest( |
| projectId=job_project, |
| job=bigquery.Job( |
| configuration=bigquery.JobConfiguration( |
| extract=bigquery.JobConfigurationExtract( |
| destinationUris=destination, |
| sourceTable=table_reference, |
| printHeader=include_header, |
| destinationFormat=destination_format, |
| compression=compression, |
| useAvroLogicalTypes=use_avro_logical_types, |
| ), |
| labels=_build_job_labels(job_labels), |
| ), |
| jobReference=job_reference, |
| )) |
| response = self.client.jobs.Insert(request) |
| return response.jobReference |
| |
| @retry.with_exponential_backoff( |
| num_retries=MAX_RETRIES, |
| retry_filter=retry.retry_on_server_errors_and_timeout_filter) |
| def get_or_create_table( |
| self, |
| project_id, |
| dataset_id, |
| table_id, |
| schema, |
| create_disposition, |
| write_disposition, |
| additional_create_parameters=None): |
| """Gets or creates a table based on create and write dispositions. |
| |
| The function mimics the behavior of BigQuery import jobs when using the |
| same create and write dispositions. |
| |
| Args: |
| project_id: The project id owning the table. |
| dataset_id: The dataset id owning the table. |
| table_id: The table id. |
| schema: A bigquery.TableSchema instance or None. |
| create_disposition: CREATE_NEVER or CREATE_IF_NEEDED. |
| write_disposition: WRITE_APPEND, WRITE_EMPTY or WRITE_TRUNCATE. |
| |
| Returns: |
| A bigquery.Table instance if table was found or created. |
| |
| Raises: |
| `RuntimeError`: For various mismatches between the state of the table and |
| the create/write dispositions passed in. For example if the table is not |
| empty and WRITE_EMPTY was specified then an error will be raised since |
| the table was expected to be empty. |
| """ |
| from apache_beam.io.gcp.bigquery import BigQueryDisposition |
| |
| found_table = None |
| try: |
| found_table = self.get_table(project_id, dataset_id, table_id) |
| except HttpError as exn: |
| if exn.status_code == 404: |
| if create_disposition == BigQueryDisposition.CREATE_NEVER: |
| raise RuntimeError( |
| 'Table %s:%s.%s not found but create disposition is CREATE_NEVER.' |
| % (project_id, dataset_id, table_id)) |
| else: |
| raise |
| |
| # If table exists already then handle the semantics for WRITE_EMPTY and |
| # WRITE_TRUNCATE write dispositions. |
| if found_table: |
| table_empty = self._is_table_empty(project_id, dataset_id, table_id) |
| if (not table_empty and |
| write_disposition == BigQueryDisposition.WRITE_EMPTY): |
| raise RuntimeError( |
| 'Table %s:%s.%s is not empty but write disposition is WRITE_EMPTY.' |
| % (project_id, dataset_id, table_id)) |
| # Delete the table and recreate it (later) if WRITE_TRUNCATE was |
| # specified. |
| if write_disposition == BigQueryDisposition.WRITE_TRUNCATE: |
| self._delete_table(project_id, dataset_id, table_id) |
| |
| # Create a new table potentially reusing the schema from a previously |
| # found table in case the schema was not specified. |
| if schema is None and found_table is None: |
| raise RuntimeError( |
| 'Table %s:%s.%s requires a schema. None can be inferred because the ' |
| 'table does not exist.' % (project_id, dataset_id, table_id)) |
| if found_table and write_disposition != BigQueryDisposition.WRITE_TRUNCATE: |
| return found_table |
| else: |
| created_table = None |
| try: |
| created_table = self._create_table( |
| project_id=project_id, |
| dataset_id=dataset_id, |
| table_id=table_id, |
| schema=schema or found_table.schema, |
| additional_parameters=additional_create_parameters) |
| except HttpError as exn: |
| if exn.status_code == 409: |
| _LOGGER.debug( |
| 'Skipping Creation. Table %s:%s.%s already exists.' % |
| (project_id, dataset_id, table_id)) |
| created_table = self.get_table(project_id, dataset_id, table_id) |
| else: |
| raise |
| _LOGGER.info( |
| 'Created table %s.%s.%s with schema %s. ' |
| 'Result: %s.', |
| project_id, |
| dataset_id, |
| table_id, |
| schema or found_table.schema, |
| created_table) |
| # if write_disposition == BigQueryDisposition.WRITE_TRUNCATE we delete |
| # the table before this point. |
| if write_disposition == BigQueryDisposition.WRITE_TRUNCATE: |
| # BigQuery can route data to the old table for 2 mins max so wait |
| # that much time before creating the table and writing it |
| _LOGGER.warning( |
| 'Sleeping for 150 seconds before the write as ' + |
| 'BigQuery inserts can be routed to deleted table ' + |
| 'for 2 mins after the delete and create.') |
| # TODO(BEAM-2673): Remove this sleep by migrating to load api |
| time.sleep(150) |
| return created_table |
| else: |
| return created_table |
| |
| def run_query( |
| self, |
| project_id, |
| query, |
| use_legacy_sql, |
| flatten_results, |
| dry_run=False, |
| job_labels=None): |
| job = self._start_query_job( |
| project_id, |
| query, |
| use_legacy_sql, |
| flatten_results, |
| job_id=uuid.uuid4().hex, |
| dry_run=dry_run, |
| job_labels=job_labels) |
| job_id = job.jobReference.jobId |
| location = job.jobReference.location |
| |
| if dry_run: |
| # If this was a dry run then the fact that we get here means the |
| # query has no errors. The start_query_job would raise an error otherwise. |
| return |
| page_token = None |
| while True: |
| response = self._get_query_results( |
| project_id, job_id, page_token, location=location) |
| if not response.jobComplete: |
| # The jobComplete field can be False if the query request times out |
| # (default is 10 seconds). Note that this is a timeout for the query |
| # request not for the actual execution of the query in the service. If |
| # the request times out we keep trying. This situation is quite possible |
| # if the query will return a large number of rows. |
| _LOGGER.info('Waiting on response from query: %s ...', query) |
| time.sleep(1.0) |
| continue |
| # We got some results. The last page is signalled by a missing pageToken. |
| yield response.rows, response.schema |
| if not response.pageToken: |
| break |
| page_token = response.pageToken |
| |
| def insert_rows( |
| self, |
| project_id, |
| dataset_id, |
| table_id, |
| rows, |
| insert_ids=None, |
| skip_invalid_rows=False): |
| """Inserts rows into the specified table. |
| |
| Args: |
| project_id: The project id owning the table. |
| dataset_id: The dataset id owning the table. |
| table_id: The table id. |
| rows: A list of plain Python dictionaries. Each dictionary is a row and |
| each key in it is the name of a field. |
| skip_invalid_rows: If there are rows with insertion errors, whether they |
| should be skipped, and all others should be inserted successfully. |
| |
| Returns: |
| A tuple (bool, errors). If first element is False then the second element |
| will be a bigquery.InserttErrorsValueListEntry instance containing |
| specific errors. |
| """ |
| |
| # Prepare rows for insertion. Of special note is the row ID that we add to |
| # each row in order to help BigQuery avoid inserting a row multiple times. |
| # BigQuery will do a best-effort if unique IDs are provided. This situation |
| # can happen during retries on failures. |
| # TODO(silviuc): Must add support to writing TableRow's instead of dicts. |
| final_rows = [] |
| for i, row in enumerate(rows): |
| json_row = self._convert_to_json_row(row) |
| insert_id = str(self.unique_row_id) if not insert_ids else insert_ids[i] |
| final_rows.append( |
| bigquery.TableDataInsertAllRequest.RowsValueListEntry( |
| insertId=insert_id, json=json_row)) |
| result, errors = self._insert_all_rows( |
| project_id, dataset_id, table_id, final_rows, skip_invalid_rows) |
| return result, errors |
| |
| def _convert_to_json_row(self, row): |
| json_object = bigquery.JsonObject() |
| for k, v in iteritems(row): |
| if isinstance(v, decimal.Decimal): |
| # decimal values are converted into string because JSON does not |
| # support the precision that decimal supports. BQ is able to handle |
| # inserts into NUMERIC columns by receiving JSON with string attrs. |
| v = str(v) |
| json_object.additionalProperties.append( |
| bigquery.JsonObject.AdditionalProperty(key=k, value=to_json_value(v))) |
| return json_object |
| |
| def _convert_cell_value_to_dict(self, value, field): |
| if field.type == 'STRING': |
| # Input: "XYZ" --> Output: "XYZ" |
| return value |
| elif field.type == 'BOOLEAN': |
| # Input: "true" --> Output: True |
| return value == 'true' |
| elif field.type == 'INTEGER': |
| # Input: "123" --> Output: 123 |
| return int(value) |
| elif field.type == 'FLOAT': |
| # Input: "1.23" --> Output: 1.23 |
| return float(value) |
| elif field.type == 'TIMESTAMP': |
| # The UTC should come from the timezone library but this is a known |
| # issue in python 2.7 so we'll just hardcode it as we're reading using |
| # utcfromtimestamp. |
| # Input: 1478134176.985864 --> Output: "2016-11-03 00:49:36.985864 UTC" |
| dt = datetime.datetime.utcfromtimestamp(float(value)) |
| return dt.strftime('%Y-%m-%d %H:%M:%S.%f UTC') |
| elif field.type == 'BYTES': |
| # Input: "YmJi" --> Output: "YmJi" |
| return value |
| elif field.type == 'DATE': |
| # Input: "2016-11-03" --> Output: "2016-11-03" |
| return value |
| elif field.type == 'DATETIME': |
| # Input: "2016-11-03T00:49:36" --> Output: "2016-11-03T00:49:36" |
| return value |
| elif field.type == 'TIME': |
| # Input: "00:49:36" --> Output: "00:49:36" |
| return value |
| elif field.type == 'RECORD': |
| # Note that a schema field object supports also a RECORD type. However |
| # when querying, the repeated and/or record fields are flattened |
| # unless we pass the flatten_results flag as False to the source |
| return self.convert_row_to_dict(value, field) |
| elif field.type == 'NUMERIC': |
| return decimal.Decimal(value) |
| elif field.type == 'GEOGRAPHY': |
| return value |
| else: |
| raise RuntimeError('Unexpected field type: %s' % field.type) |
| |
| def convert_row_to_dict(self, row, schema): |
| """Converts a TableRow instance using the schema to a Python dict.""" |
| result = {} |
| for index, field in enumerate(schema.fields): |
| value = None |
| if isinstance(schema, bigquery.TableSchema): |
| cell = row.f[index] |
| value = from_json_value(cell.v) if cell.v is not None else None |
| elif isinstance(schema, bigquery.TableFieldSchema): |
| cell = row['f'][index] |
| value = cell['v'] if 'v' in cell else None |
| if field.mode == 'REPEATED': |
| if value is None: |
| # Ideally this should never happen as repeated fields default to |
| # returning an empty list |
| result[field.name] = [] |
| else: |
| result[field.name] = [ |
| self._convert_cell_value_to_dict(x['v'], field) for x in value |
| ] |
| elif value is None: |
| if not field.mode == 'NULLABLE': |
| raise ValueError( |
| 'Received \'None\' as the value for the field %s ' |
| 'but the field is not NULLABLE.' % field.name) |
| result[field.name] = None |
| else: |
| result[field.name] = self._convert_cell_value_to_dict(value, field) |
| return result |
| |
| |
| # ----------------------------------------------------------------------------- |
| # BigQueryReader, BigQueryWriter. |
| |
| |
| class BigQueryReader(dataflow_io.NativeSourceReader): |
| """A reader for a BigQuery source.""" |
| def __init__( |
| self, |
| source, |
| test_bigquery_client=None, |
| use_legacy_sql=True, |
| flatten_results=True, |
| kms_key=None): |
| self.source = source |
| self.test_bigquery_client = test_bigquery_client |
| if auth.is_running_in_gce: |
| self.executing_project = auth.executing_project |
| elif hasattr(source, 'pipeline_options'): |
| self.executing_project = ( |
| source.pipeline_options.view_as(GoogleCloudOptions).project) |
| else: |
| self.executing_project = None |
| |
| # TODO(silviuc): Try to automatically get it from gcloud config info. |
| if not self.executing_project and test_bigquery_client is None: |
| raise RuntimeError( |
| 'Missing executing project information. Please use the --project ' |
| 'command line option to specify it.') |
| self.row_as_dict = isinstance(self.source.coder, RowAsDictJsonCoder) |
| # Schema for the rows being read by the reader. It is initialized the |
| # first time something gets read from the table. It is not required |
| # for reading the field values in each row but could be useful for |
| # getting additional details. |
| self.schema = None |
| self.use_legacy_sql = use_legacy_sql |
| self.flatten_results = flatten_results |
| self.kms_key = kms_key |
| self.bigquery_job_labels = {} |
| self.bq_io_metadata = None |
| |
| if self.source.table_reference is not None: |
| # If table schema did not define a project we default to executing |
| # project. |
| project_id = self.source.table_reference.projectId |
| if not project_id: |
| project_id = self.executing_project |
| self.query = 'SELECT * FROM [%s:%s.%s];' % ( |
| project_id, |
| self.source.table_reference.datasetId, |
| self.source.table_reference.tableId) |
| elif self.source.query is not None: |
| self.query = self.source.query |
| else: |
| # Enforce the "modes" enforced by BigQuerySource.__init__. |
| # If this exception has been raised, the BigQuerySource "modes" have |
| # changed and this method will need to be updated as well. |
| raise ValueError("BigQuerySource must have either a table or query") |
| |
| def _get_source_location(self): |
| """ |
| Get the source location (e.g. ``"EU"`` or ``"US"``) from either |
| |
| - :data:`source.table_reference` |
| or |
| - The first referenced table in :data:`source.query` |
| |
| See Also: |
| - :meth:`BigQueryWrapper.get_query_location` |
| - :meth:`BigQueryWrapper.get_table_location` |
| |
| Returns: |
| Optional[str]: The source location, if any. |
| """ |
| if self.source.table_reference is not None: |
| tr = self.source.table_reference |
| return self.client.get_table_location( |
| tr.projectId if tr.projectId is not None else self.executing_project, |
| tr.datasetId, |
| tr.tableId) |
| else: # It's a query source |
| return self.client.get_query_location( |
| self.executing_project, self.source.query, self.source.use_legacy_sql) |
| |
| def __enter__(self): |
| self.client = BigQueryWrapper(client=self.test_bigquery_client) |
| self.client.create_temporary_dataset( |
| self.executing_project, location=self._get_source_location()) |
| return self |
| |
| def __exit__(self, exception_type, exception_value, traceback): |
| self.client.clean_up_temporary_dataset(self.executing_project) |
| |
| def __iter__(self): |
| if not self.bq_io_metadata: |
| self.bq_io_metadata = create_bigquery_io_metadata() |
| for rows, schema in self.client.run_query( |
| project_id=self.executing_project, query=self.query, |
| use_legacy_sql=self.use_legacy_sql, |
| flatten_results=self.flatten_results, |
| job_labels=self.bq_io_metadata.add_additional_bq_job_labels( |
| self.bigquery_job_labels)): |
| if self.schema is None: |
| self.schema = schema |
| for row in rows: |
| # return base64 encoded bytes as byte type on python 3 |
| # which matches the behavior of Beam Java SDK |
| for i in range(len(row.f)): |
| if self.schema.fields[i].type == 'BYTES' and row.f[i].v: |
| row.f[i].v.string_value = row.f[i].v.string_value.encode('utf-8') |
| |
| if self.row_as_dict: |
| yield self.client.convert_row_to_dict(row, schema) |
| else: |
| yield row |
| |
| |
| class BigQueryWriter(dataflow_io.NativeSinkWriter): |
| """The sink writer for a BigQuerySink.""" |
| def __init__(self, sink, test_bigquery_client=None, buffer_size=None): |
| self.sink = sink |
| self.test_bigquery_client = test_bigquery_client |
| self.row_as_dict = isinstance(self.sink.coder, RowAsDictJsonCoder) |
| # Buffer used to batch written rows so we reduce communication with the |
| # BigQuery service. |
| self.rows_buffer = [] |
| self.rows_buffer_flush_threshold = buffer_size or 1000 |
| # Figure out the project, dataset, and table used for the sink. |
| self.project_id = self.sink.table_reference.projectId |
| |
| # If table schema did not define a project we default to executing project. |
| if self.project_id is None and hasattr(sink, 'pipeline_options'): |
| self.project_id = ( |
| sink.pipeline_options.view_as(GoogleCloudOptions).project) |
| |
| assert self.project_id is not None |
| |
| self.dataset_id = self.sink.table_reference.datasetId |
| self.table_id = self.sink.table_reference.tableId |
| |
| def _flush_rows_buffer(self): |
| if self.rows_buffer: |
| _LOGGER.info( |
| 'Writing %d rows to %s:%s.%s table.', |
| len(self.rows_buffer), |
| self.project_id, |
| self.dataset_id, |
| self.table_id) |
| passed, errors = self.client.insert_rows( |
| project_id=self.project_id, dataset_id=self.dataset_id, |
| table_id=self.table_id, rows=self.rows_buffer) |
| self.rows_buffer = [] |
| if not passed: |
| raise RuntimeError( |
| 'Could not successfully insert rows to BigQuery' |
| ' table [%s:%s.%s]. Errors: %s' % |
| (self.project_id, self.dataset_id, self.table_id, errors)) |
| |
| def __enter__(self): |
| self.client = BigQueryWrapper(client=self.test_bigquery_client) |
| self.client.get_or_create_table( |
| self.project_id, |
| self.dataset_id, |
| self.table_id, |
| self.sink.table_schema, |
| self.sink.create_disposition, |
| self.sink.write_disposition) |
| return self |
| |
| def __exit__(self, exception_type, exception_value, traceback): |
| self._flush_rows_buffer() |
| |
| def Write(self, row): |
| self.rows_buffer.append(row) |
| if len(self.rows_buffer) > self.rows_buffer_flush_threshold: |
| self._flush_rows_buffer() |
| |
| |
| class RowAsDictJsonCoder(coders.Coder): |
| """A coder for a table row (represented as a dict) to/from a JSON string. |
| |
| This is the default coder for sources and sinks if the coder argument is not |
| specified. |
| """ |
| def encode(self, table_row): |
| # The normal error when dumping NAN/INF values is: |
| # ValueError: Out of range float values are not JSON compliant |
| # This code will catch this error to emit an error that explains |
| # to the programmer that they have used NAN/INF values. |
| try: |
| return json.dumps( |
| table_row, allow_nan=False, default=default_encoder).encode('utf-8') |
| except ValueError as e: |
| raise ValueError( |
| '%s. %s. Row: %r' % (e, JSON_COMPLIANCE_ERROR, table_row)) |
| |
| def decode(self, encoded_table_row): |
| return json.loads(encoded_table_row.decode('utf-8')) |
| |
| def to_type_hint(self): |
| return Any |
| |
| |
| class JsonRowWriter(io.IOBase): |
| """ |
| A writer which provides an IOBase-like interface for writing table rows |
| (represented as dicts) as newline-delimited JSON strings. |
| """ |
| def __init__(self, file_handle): |
| """Initialize an JsonRowWriter. |
| |
| Args: |
| file_handle (io.IOBase): Output stream to write to. |
| """ |
| if not file_handle.writable(): |
| raise ValueError("Output stream must be writable") |
| |
| self._file_handle = file_handle |
| self._coder = RowAsDictJsonCoder() |
| |
| def close(self): |
| self._file_handle.close() |
| |
| @property |
| def closed(self): |
| return self._file_handle.closed |
| |
| def flush(self): |
| self._file_handle.flush() |
| |
| def read(self, size=-1): |
| raise io.UnsupportedOperation("JsonRowWriter is not readable") |
| |
| def tell(self): |
| return self._file_handle.tell() |
| |
| def writable(self): |
| return self._file_handle.writable() |
| |
| def write(self, row): |
| return self._file_handle.write(self._coder.encode(row) + b'\n') |
| |
| |
| class AvroRowWriter(io.IOBase): |
| """ |
| A writer which provides an IOBase-like interface for writing table rows |
| (represented as dicts) as Avro records. |
| """ |
| def __init__(self, file_handle, schema): |
| """Initialize an AvroRowWriter. |
| |
| Args: |
| file_handle (io.IOBase): Output stream to write Avro records to. |
| schema (Dict[Text, Any]): BigQuery table schema. |
| """ |
| if not file_handle.writable(): |
| raise ValueError("Output stream must be writable") |
| |
| self._file_handle = file_handle |
| avro_schema = fastavro.parse_schema( |
| get_avro_schema_from_table_schema(schema)) |
| self._avro_writer = fastavro.write.Writer(self._file_handle, avro_schema) |
| |
| def close(self): |
| if not self._file_handle.closed: |
| self.flush() |
| self._file_handle.close() |
| |
| @property |
| def closed(self): |
| return self._file_handle.closed |
| |
| def flush(self): |
| if self._file_handle.closed: |
| raise ValueError("flush on closed file") |
| |
| self._avro_writer.flush() |
| self._file_handle.flush() |
| |
| def read(self, size=-1): |
| raise io.UnsupportedOperation("AvroRowWriter is not readable") |
| |
| def tell(self): |
| # Flush the fastavro Writer to the underlying stream, otherwise there isn't |
| # a reliable way to determine how many bytes have been written. |
| self._avro_writer.flush() |
| return self._file_handle.tell() |
| |
| def writable(self): |
| return self._file_handle.writable() |
| |
| def write(self, row): |
| try: |
| self._avro_writer.write(row) |
| except (TypeError, ValueError) as ex: |
| raise_with_traceback( |
| ex.__class__( |
| "Error writing row to Avro: {}\nSchema: {}\nRow: {}".format( |
| ex, self._avro_writer.schema, row))) |
| |
| |
| class RetryStrategy(object): |
| RETRY_ALWAYS = 'RETRY_ALWAYS' |
| RETRY_NEVER = 'RETRY_NEVER' |
| RETRY_ON_TRANSIENT_ERROR = 'RETRY_ON_TRANSIENT_ERROR' |
| |
| _NON_TRANSIENT_ERRORS = {'invalid', 'invalidQuery', 'notImplemented'} |
| |
| @staticmethod |
| def should_retry(strategy, error_message): |
| if strategy == RetryStrategy.RETRY_ALWAYS: |
| return True |
| elif strategy == RetryStrategy.RETRY_NEVER: |
| return False |
| elif (strategy == RetryStrategy.RETRY_ON_TRANSIENT_ERROR and |
| error_message not in RetryStrategy._NON_TRANSIENT_ERRORS): |
| return True |
| else: |
| return False |
| |
| |
| class AppendDestinationsFn(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): |
| self.destination = AppendDestinationsFn._get_table_fn(destination) |
| |
| @staticmethod |
| def _value_provider_or_static_val(elm): |
| if isinstance(elm, value_provider.ValueProvider): |
| return elm |
| else: |
| # The type argument is a NoOp, because we assume the argument already has |
| # the proper formatting. |
| return value_provider.StaticValueProvider(lambda x: x, value=elm) |
| |
| @staticmethod |
| def _get_table_fn(destination): |
| if callable(destination): |
| return destination |
| else: |
| return lambda x: AppendDestinationsFn._value_provider_or_static_val( |
| destination).get() |
| |
| def process(self, element, *side_inputs): |
| yield (self.destination(element, *side_inputs), element) |
| |
| |
| def get_table_schema_from_string(schema): |
| """Transform the string table schema into a |
| :class:`~apache_beam.io.gcp.internal.clients.bigquery.\ |
| bigquery_v2_messages.TableSchema` instance. |
| |
| Args: |
| schema (str): The sting schema to be used if the BigQuery table to write |
| has to be created. |
| |
| Returns: |
| ~apache_beam.io.gcp.internal.clients.bigquery.\ |
| bigquery_v2_messages.TableSchema: |
| The schema to be used if the BigQuery table to write has to be created |
| but in the :class:`~apache_beam.io.gcp.internal.clients.bigquery.\ |
| bigquery_v2_messages.TableSchema` 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) |
| return table_schema |
| |
| |
| def table_schema_to_dict(table_schema): |
| """Create a dictionary representation of table schema for serialization |
| """ |
| def get_table_field(field): |
| """Create a dictionary representation of a table field |
| """ |
| result = {} |
| result['name'] = field.name |
| result['type'] = field.type |
| result['mode'] = getattr(field, 'mode', 'NULLABLE') |
| if hasattr(field, 'description') and field.description is not None: |
| result['description'] = field.description |
| if hasattr(field, 'fields') and field.fields: |
| result['fields'] = [get_table_field(f) for f in field.fields] |
| return result |
| |
| if not isinstance(table_schema, bigquery.TableSchema): |
| raise ValueError("Table schema must be of the type bigquery.TableSchema") |
| schema = {'fields': []} |
| for field in table_schema.fields: |
| schema['fields'].append(get_table_field(field)) |
| return schema |
| |
| |
| def get_dict_table_schema(schema): |
| """Transform the table schema into a dictionary instance. |
| |
| Args: |
| schema (str, dict, ~apache_beam.io.gcp.internal.clients.bigquery.\ |
| bigquery_v2_messages.TableSchema): |
| The schema to be used if the BigQuery table to write has to be created. |
| This can either be a dict or string or in the TableSchema format. |
| |
| Returns: |
| Dict[str, Any]: The schema to be used if the BigQuery table to write has |
| to be created but in the dictionary format. |
| """ |
| if (isinstance(schema, (dict, value_provider.ValueProvider)) or |
| callable(schema) or schema is None): |
| return schema |
| elif isinstance(schema, (str, unicode)): |
| table_schema = get_table_schema_from_string(schema) |
| return table_schema_to_dict(table_schema) |
| elif isinstance(schema, bigquery.TableSchema): |
| return table_schema_to_dict(schema) |
| else: |
| raise TypeError('Unexpected schema argument: %s.' % schema) |
| |
| |
| def get_avro_schema_from_table_schema(schema): |
| """Transform the table schema into an Avro schema. |
| |
| Args: |
| schema (str, dict, ~apache_beam.io.gcp.internal.clients.bigquery.\ |
| bigquery_v2_messages.TableSchema): |
| The TableSchema to convert to Avro schema. This can either be a dict or |
| string or in the TableSchema format. |
| |
| Returns: |
| Dict[str, Any]: An Avro schema, which can be used by fastavro. |
| """ |
| dict_table_schema = get_dict_table_schema(schema) |
| return bigquery_avro_tools.get_record_schema_from_dict_table_schema( |
| "root", dict_table_schema) |
| |
| |
| class BigQueryJobTypes: |
| EXPORT = 'EXPORT' |
| COPY = 'COPY' |
| LOAD = 'LOAD' |
| QUERY = 'QUERY' |
| |
| |
| def generate_bq_job_name(job_name, step_id, job_type, random=None): |
| from apache_beam.io.gcp.bigquery import BQ_JOB_NAME_TEMPLATE |
| random = ("_%s" % random) if random else "" |
| return str.format( |
| BQ_JOB_NAME_TEMPLATE, |
| job_type=job_type, |
| job_id=job_name.replace("-", ""), |
| step_id=step_id, |
| random=random) |