Java tutorial
/* * Copyright 2016 Cask Data, Inc. * * 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. */ package $package; import co.cask.cdap.api.annotation.Description; import co.cask.cdap.api.annotation.Macro; import co.cask.cdap.api.annotation.Name; import co.cask.cdap.api.annotation.Plugin; import co.cask.cdap.api.data.batch.Output; import co.cask.cdap.api.data.format.StructuredRecord; import co.cask.cdap.api.data.schema.Schema; import co.cask.cdap.api.dataset.lib.FileSet; import co.cask.cdap.api.dataset.lib.FileSetArguments; import co.cask.cdap.api.dataset.lib.FileSetProperties; import co.cask.cdap.api.dataset.lib.KeyValue; import co.cask.cdap.api.plugin.PluginConfig; import co.cask.cdap.etl.api.Emitter; import co.cask.cdap.etl.api.PipelineConfigurer; import co.cask.cdap.etl.api.batch.BatchRuntimeContext; import co.cask.cdap.etl.api.batch.BatchSink; import co.cask.cdap.etl.api.batch.BatchSinkContext; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import java.util.HashMap; import java.util.Iterator; import java.util.Map; import javax.annotation.Nullable; /** * Batch Sink that writes to a FileSet in text format. * Each record will be written as a single line, with record fields separated by a configurable separator. * * StructuredRecord is the first parameter because that is the input to the sink. * The second and third parameters are the key and value expected by Hadoop's {@link TextOutputFormat}. */ @Plugin(type = BatchSink.PLUGIN_TYPE) @Name(TextFileSetSink.NAME) @Description("Writes to a FileSet in text format.") public class TextFileSetSink extends BatchSink<StructuredRecord, NullWritable, Text> { public static final String NAME = "TextFileSet"; private final Conf config; /** * Config properties for the plugin. */ public static class Conf extends PluginConfig { public static final String FILESET_NAME = "fileSetName"; public static final String OUTPUT_DIR = "outputDir"; public static final String FIELD_SEPARATOR = "fieldSeparator"; // The name annotation tells CDAP what the property name is. It is optional, and defaults to the variable name. // Note: only primitives (including boxed types) and string are the types that are supported @Name(FILESET_NAME) @Description("The name of the FileSet to write to.") private String fileSetName; // Macro enabled properties can be set to a placeholder value ${key} when the pipeline is deployed. // At runtime, the value for 'key' can be given and substituted in. @Macro @Name(OUTPUT_DIR) @Description("The FileSet directory to write to.") private String outputDir; @Nullable @Name(FIELD_SEPARATOR) @Description("The separator to use to join input record fields together. Defaults to ','.") private String fieldSeparator; // Use a no-args constructor to set field defaults. public Conf() { fileSetName = ""; fieldSeparator = ","; } } // CDAP will pass in a config with its fields populated based on the configuration given when creating the pipeline. public TextFileSetSink(Conf config) { this.config = config; } // configurePipeline is called exactly once when the pipeline is being created. // Any static configuration should be performed here. @Override public void configurePipeline(PipelineConfigurer pipelineConfigurer) { // create the FileSet here. pipelineConfigurer.createDataset(config.fileSetName, FileSet.class, FileSetProperties.builder().setInputFormat(TextInputFormat.class) .setOutputFormat(TextOutputFormat.class).setEnableExploreOnCreate(true) .setExploreFormat("text").setExploreSchema("text string").build()); } // prepareRun is called before every pipeline run, and is used to configure what the input should be, // as well as any arguments the input should use. It is called by the client that is submitting the batch job. @Override public void prepareRun(BatchSinkContext context) throws Exception { Map<String, String> arguments = new HashMap<>(); FileSetArguments.setOutputPath(arguments, config.outputDir); context.addOutput(Output.ofDataset(config.fileSetName, arguments)); } // onRunFinish is called at the end of the pipeline run by the client that submitted the batch job. @Override public void onRunFinish(boolean succeeded, BatchSinkContext context) { // perform any actions that should happen at the end of the run. } // initialize is called by each job executor before any call to transform is made. // This occurs at the start of the batch job run, after the job has been successfully submitted. // For example, if mapreduce is the execution engine, each mapper will call initialize at the start of the program. @Override public void initialize(BatchRuntimeContext context) throws Exception { super.initialize(context); // create any resources required by transform() } // destroy is called by each job executor at the end of its life. // For example, if mapreduce is the execution engine, each mapper will call destroy at the end of the program. @Override public void destroy() { // clean up any resources created by initialize } @Override public void transform(StructuredRecord input, Emitter<KeyValue<NullWritable, Text>> emitter) throws Exception { StringBuilder joinedFields = new StringBuilder(); Iterator<Schema.Field> fieldIter = input.getSchema().getFields().iterator(); if (!fieldIter.hasNext()) { // shouldn't happen return; } Object val = input.get(fieldIter.next().getName()); if (val != null) { joinedFields.append(val); } while (fieldIter.hasNext()) { String fieldName = fieldIter.next().getName(); joinedFields.append(config.fieldSeparator); val = input.get(fieldName); if (val != null) { joinedFields.append(val); } } emitter.emit(new KeyValue<>(NullWritable.get(), new Text(joinedFields.toString()))); } }