| # coding=utf-8 |
| # |
| # 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. |
| # |
| |
| from __future__ import absolute_import |
| from __future__ import print_function |
| |
| |
| def partition_function(test=None): |
| # [START partition_function] |
| import apache_beam as beam |
| |
| durations = ['annual', 'biennial', 'perennial'] |
| |
| def by_duration(plant, num_partitions): |
| return durations.index(plant['duration']) |
| |
| with beam.Pipeline() as pipeline: |
| annuals, biennials, perennials = ( |
| pipeline |
| | 'Gardening plants' >> beam.Create([ |
| {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'}, |
| {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'}, |
| {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'}, |
| {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'}, |
| {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'}, |
| ]) |
| | 'Partition' >> beam.Partition(by_duration, len(durations)) |
| ) |
| _ = ( |
| annuals |
| | 'Annuals' >> beam.Map(lambda x: print('annual: ' + str(x))) |
| ) |
| _ = ( |
| biennials |
| | 'Biennials' >> beam.Map(lambda x: print('biennial: ' + str(x))) |
| ) |
| _ = ( |
| perennials |
| | 'Perennials' >> beam.Map(lambda x: print('perennial: ' + str(x))) |
| ) |
| # [END partition_function] |
| if test: |
| test(annuals, biennials, perennials) |
| |
| |
| def partition_lambda(test=None): |
| # [START partition_lambda] |
| import apache_beam as beam |
| |
| durations = ['annual', 'biennial', 'perennial'] |
| |
| with beam.Pipeline() as pipeline: |
| annuals, biennials, perennials = ( |
| pipeline |
| | 'Gardening plants' >> beam.Create([ |
| {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'}, |
| {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'}, |
| {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'}, |
| {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'}, |
| {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'}, |
| ]) |
| | 'Partition' >> beam.Partition( |
| lambda plant, num_partitions: durations.index(plant['duration']), |
| len(durations), |
| ) |
| ) |
| _ = ( |
| annuals |
| | 'Annuals' >> beam.Map(lambda x: print('annual: ' + str(x))) |
| ) |
| _ = ( |
| biennials |
| | 'Biennials' >> beam.Map(lambda x: print('biennial: ' + str(x))) |
| ) |
| _ = ( |
| perennials |
| | 'Perennials' >> beam.Map(lambda x: print('perennial: ' + str(x))) |
| ) |
| # [END partition_lambda] |
| if test: |
| test(annuals, biennials, perennials) |
| |
| |
| def partition_multiple_arguments(test=None): |
| # [START partition_multiple_arguments] |
| import apache_beam as beam |
| import json |
| |
| def split_dataset(plant, num_partitions, ratio): |
| assert num_partitions == len(ratio) |
| bucket = sum(map(ord, json.dumps(plant))) % sum(ratio) |
| total = 0 |
| for i, part in enumerate(ratio): |
| total += part |
| if bucket < total: |
| return i |
| return len(ratio) - 1 |
| |
| with beam.Pipeline() as pipeline: |
| train_dataset, test_dataset = ( |
| pipeline |
| | 'Gardening plants' >> beam.Create([ |
| {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'}, |
| {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'}, |
| {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'}, |
| {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'}, |
| {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'}, |
| ]) |
| | 'Partition' >> beam.Partition(split_dataset, 2, ratio=[8, 2]) |
| ) |
| _ = ( |
| train_dataset |
| | 'Train' >> beam.Map(lambda x: print('train: ' + str(x))) |
| ) |
| _ = ( |
| test_dataset |
| | 'Test' >> beam.Map(lambda x: print('test: ' + str(x))) |
| ) |
| # [END partition_multiple_arguments] |
| if test: |
| test(train_dataset, test_dataset) |