courses/data_analysis/lab2/python/is_popular.py (42 lines of code) (raw):

#!/usr/bin/env python3 """ Copyright Google Inc. 2016 Licensed 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. """ import apache_beam as beam import argparse def startsWith(line, term): if line.startswith(term): yield line def splitPackageName(packageName): """e.g. given com.example.appname.library.widgetname returns com com.example com.example.appname etc. """ result = [] end = packageName.find('.') while end > 0: result.append(packageName[0:end]) end = packageName.find('.', end+1) result.append(packageName) return result def getPackages(line, keyword): start = line.find(keyword) + len(keyword) end = line.find(';', start) if start < end: packageName = line[start:end].strip() return splitPackageName(packageName) return [] def packageUse(line, keyword): packages = getPackages(line, keyword) for p in packages: yield (p, 1) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Find the most used Java packages') parser.add_argument('--output_prefix', default='/tmp/output', help='Output prefix') parser.add_argument('--input', default='../javahelp/src/main/java/com/google/cloud/training/dataanalyst/javahelp/', help='Input directory') options, pipeline_args = parser.parse_known_args() p = beam.Pipeline(argv=pipeline_args) input = '{0}*.java'.format(options.input) output_prefix = options.output_prefix keyword = 'import' # find most used packages (p | 'GetJava' >> beam.io.ReadFromText(input) | 'GetImports' >> beam.FlatMap(lambda line: startsWith(line, keyword)) | 'PackageUse' >> beam.FlatMap(lambda line: packageUse(line, keyword)) | 'TotalUse' >> beam.CombinePerKey(sum) | 'Top_5' >> beam.transforms.combiners.Top.Of(5, key=lambda kv: kv[1]) | 'write' >> beam.io.WriteToText(output_prefix) ) p.run().wait_until_finish()