| # 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 filter_function(test=None): |
| # [START filter_function] |
| import apache_beam as beam |
| |
| def is_perennial(plant): |
| return plant['duration'] == 'perennial' |
| |
| with beam.Pipeline() as pipeline: |
| 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'}, |
| ]) |
| | 'Filter perennials' >> beam.Filter(is_perennial) |
| | beam.Map(print) |
| ) |
| # [END filter_function] |
| if test: |
| test(perennials) |
| |
| |
| def filter_lambda(test=None): |
| # [START filter_lambda] |
| import apache_beam as beam |
| |
| with beam.Pipeline() as pipeline: |
| 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'}, |
| ]) |
| | 'Filter perennials' >> beam.Filter( |
| lambda plant: plant['duration'] == 'perennial') |
| | beam.Map(print) |
| ) |
| # [END filter_lambda] |
| if test: |
| test(perennials) |
| |
| |
| def filter_multiple_arguments(test=None): |
| # [START filter_multiple_arguments] |
| import apache_beam as beam |
| |
| def has_duration(plant, duration): |
| return plant['duration'] == duration |
| |
| with beam.Pipeline() as pipeline: |
| 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'}, |
| ]) |
| | 'Filter perennials' >> beam.Filter(has_duration, 'perennial') |
| | beam.Map(print) |
| ) |
| # [END filter_multiple_arguments] |
| if test: |
| test(perennials) |
| |
| |
| def filter_side_inputs_singleton(test=None): |
| # [START filter_side_inputs_singleton] |
| import apache_beam as beam |
| |
| with beam.Pipeline() as pipeline: |
| perennial = pipeline | 'Perennial' >> beam.Create(['perennial']) |
| |
| 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'}, |
| ]) |
| | 'Filter perennials' >> beam.Filter( |
| lambda plant, duration: plant['duration'] == duration, |
| duration=beam.pvalue.AsSingleton(perennial), |
| ) |
| | beam.Map(print) |
| ) |
| # [END filter_side_inputs_singleton] |
| if test: |
| test(perennials) |
| |
| |
| def filter_side_inputs_iter(test=None): |
| # [START filter_side_inputs_iter] |
| import apache_beam as beam |
| |
| with beam.Pipeline() as pipeline: |
| valid_durations = pipeline | 'Valid durations' >> beam.Create([ |
| 'annual', |
| 'biennial', |
| 'perennial', |
| ]) |
| |
| valid_plants = ( |
| 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'}, |
| ]) |
| | 'Filter valid plants' >> beam.Filter( |
| lambda plant, valid_durations: plant['duration'] in valid_durations, |
| valid_durations=beam.pvalue.AsIter(valid_durations), |
| ) |
| | beam.Map(print) |
| ) |
| # [END filter_side_inputs_iter] |
| if test: |
| test(valid_plants) |
| |
| |
| def filter_side_inputs_dict(test=None): |
| # [START filter_side_inputs_dict] |
| import apache_beam as beam |
| |
| with beam.Pipeline() as pipeline: |
| keep_duration = pipeline | 'Duration filters' >> beam.Create([ |
| ('annual', False), |
| ('biennial', False), |
| ('perennial', True), |
| ]) |
| |
| 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'}, |
| ]) |
| | 'Filter plants by duration' >> beam.Filter( |
| lambda plant, keep_duration: keep_duration[plant['duration']], |
| keep_duration=beam.pvalue.AsDict(keep_duration), |
| ) |
| | beam.Map(print) |
| ) |
| # [END filter_side_inputs_dict] |
| if test: |
| test(perennials) |