| # |
| # 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. |
| # |
| |
| """A workflow that writes to a BigQuery table with nested and repeated fields. |
| |
| Demonstrates how to build a bigquery.TableSchema object with nested and repeated |
| fields. Also, shows how to generate data to be written to a BigQuery table with |
| nested and repeated fields. |
| """ |
| |
| from __future__ import absolute_import |
| |
| import argparse |
| import logging |
| |
| import apache_beam as beam |
| |
| |
| def run(argv=None): |
| """Run the workflow.""" |
| parser = argparse.ArgumentParser() |
| |
| parser.add_argument( |
| '--output', |
| required=True, |
| help= |
| ('Output BigQuery table for results specified as: PROJECT:DATASET.TABLE ' |
| 'or DATASET.TABLE.')) |
| known_args, pipeline_args = parser.parse_known_args(argv) |
| |
| with beam.Pipeline(argv=pipeline_args) as p: |
| |
| from apache_beam.io.gcp.internal.clients import bigquery # pylint: disable=wrong-import-order, wrong-import-position |
| |
| table_schema = bigquery.TableSchema() |
| |
| # Fields that use standard types. |
| kind_schema = bigquery.TableFieldSchema() |
| kind_schema.name = 'kind' |
| kind_schema.type = 'string' |
| kind_schema.mode = 'nullable' |
| table_schema.fields.append(kind_schema) |
| |
| full_name_schema = bigquery.TableFieldSchema() |
| full_name_schema.name = 'fullName' |
| full_name_schema.type = 'string' |
| full_name_schema.mode = 'required' |
| table_schema.fields.append(full_name_schema) |
| |
| age_schema = bigquery.TableFieldSchema() |
| age_schema.name = 'age' |
| age_schema.type = 'integer' |
| age_schema.mode = 'nullable' |
| table_schema.fields.append(age_schema) |
| |
| gender_schema = bigquery.TableFieldSchema() |
| gender_schema.name = 'gender' |
| gender_schema.type = 'string' |
| gender_schema.mode = 'nullable' |
| table_schema.fields.append(gender_schema) |
| |
| # A nested field |
| phone_number_schema = bigquery.TableFieldSchema() |
| phone_number_schema.name = 'phoneNumber' |
| phone_number_schema.type = 'record' |
| phone_number_schema.mode = 'nullable' |
| |
| area_code = bigquery.TableFieldSchema() |
| area_code.name = 'areaCode' |
| area_code.type = 'integer' |
| area_code.mode = 'nullable' |
| phone_number_schema.fields.append(area_code) |
| |
| number = bigquery.TableFieldSchema() |
| number.name = 'number' |
| number.type = 'integer' |
| number.mode = 'nullable' |
| phone_number_schema.fields.append(number) |
| table_schema.fields.append(phone_number_schema) |
| |
| # A repeated field. |
| children_schema = bigquery.TableFieldSchema() |
| children_schema.name = 'children' |
| children_schema.type = 'string' |
| children_schema.mode = 'repeated' |
| table_schema.fields.append(children_schema) |
| |
| def create_random_record(record_id): |
| return {'kind': 'kind' + record_id, 'fullName': 'fullName'+record_id, |
| 'age': int(record_id) * 10, 'gender': 'male', |
| 'phoneNumber': { |
| 'areaCode': int(record_id) * 100, |
| 'number': int(record_id) * 100000}, |
| 'children': ['child' + record_id + '1', |
| 'child' + record_id + '2', |
| 'child' + record_id + '3'] |
| } |
| |
| # pylint: disable=expression-not-assigned |
| record_ids = p | 'CreateIDs' >> beam.Create(['1', '2', '3', '4', '5']) |
| records = record_ids | 'CreateRecords' >> beam.Map(create_random_record) |
| records | 'write' >> beam.io.Write( |
| beam.io.BigQuerySink( |
| known_args.output, |
| schema=table_schema, |
| create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE)) |
| |
| # Run the pipeline (all operations are deferred until run() is called). |
| |
| |
| if __name__ == '__main__': |
| logging.getLogger().setLevel(logging.INFO) |
| run() |