| /* |
| * 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. |
| */ |
| package org.apache.accumulo.examples.mapreduce; |
| |
| import java.io.IOException; |
| import java.util.HashMap; |
| import java.util.HashSet; |
| |
| import org.apache.accumulo.core.client.AccumuloClient; |
| import org.apache.accumulo.core.data.ByteSequence; |
| import org.apache.accumulo.core.data.Key; |
| import org.apache.accumulo.core.data.Value; |
| import org.apache.accumulo.examples.cli.ClientOpts; |
| import org.apache.accumulo.hadoop.mapreduce.AccumuloInputFormat; |
| import org.apache.hadoop.fs.Path; |
| import org.apache.hadoop.io.Text; |
| import org.apache.hadoop.mapreduce.Job; |
| import org.apache.hadoop.mapreduce.Mapper; |
| import org.apache.hadoop.mapreduce.Reducer; |
| import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; |
| |
| import com.beust.jcommander.Parameter; |
| |
| /** |
| * A simple map reduce job that computes the unique column families and column qualifiers in a |
| * table. This example shows one way to run against an offline table. |
| */ |
| public class UniqueColumns { |
| |
| private static final Text EMPTY = new Text(); |
| |
| public static class UMapper extends Mapper<Key,Value,Text,Text> { |
| private Text temp = new Text(); |
| private static final Text CF = new Text("cf:"); |
| private static final Text CQ = new Text("cq:"); |
| |
| @Override |
| public void map(Key key, Value value, Context context) |
| throws IOException, InterruptedException { |
| temp.set(CF); |
| ByteSequence cf = key.getColumnFamilyData(); |
| temp.append(cf.getBackingArray(), cf.offset(), cf.length()); |
| context.write(temp, EMPTY); |
| |
| temp.set(CQ); |
| ByteSequence cq = key.getColumnQualifierData(); |
| temp.append(cq.getBackingArray(), cq.offset(), cq.length()); |
| context.write(temp, EMPTY); |
| } |
| } |
| |
| public static class UReducer extends Reducer<Text,Text,Text,Text> { |
| @Override |
| public void reduce(Text key, Iterable<Text> values, Context context) |
| throws IOException, InterruptedException { |
| context.write(key, EMPTY); |
| } |
| } |
| |
| static class Opts extends ClientOpts { |
| @Parameter(names = {"-t", "--table"}, required = true, description = "table to use") |
| String tableName; |
| @Parameter(names = "--output", description = "output directory") |
| String output; |
| @Parameter(names = "--reducers", description = "number of reducers to use", required = true) |
| int reducers; |
| @Parameter(names = "--offline", description = "run against an offline table") |
| boolean offline = false; |
| } |
| |
| public static void main(String[] args) throws Exception { |
| Opts opts = new Opts(); |
| opts.parseArgs(UniqueColumns.class.getName(), args); |
| |
| try (AccumuloClient client = opts.createAccumuloClient()) { |
| |
| Job job = Job.getInstance(opts.getHadoopConfig()); |
| String jobName = UniqueColumns.class.getSimpleName() + "_" + System.currentTimeMillis(); |
| job.setJobName(UniqueColumns.class.getSimpleName() + "_" + System.currentTimeMillis()); |
| job.setJarByClass(UniqueColumns.class); |
| job.setInputFormatClass(AccumuloInputFormat.class); |
| |
| String table = opts.tableName; |
| if (opts.offline) { |
| /* |
| * this example clones the table and takes it offline. If you plan to run map reduce jobs |
| * over a table many times, it may be more efficient to compact the table, clone it, and |
| * then keep using the same clone as input for map reduce. |
| */ |
| table = opts.tableName + "_" + jobName; |
| client.tableOperations().clone(opts.tableName, table, true, new HashMap<>(), |
| new HashSet<>()); |
| client.tableOperations().offline(table); |
| } |
| |
| AccumuloInputFormat.configure().clientProperties(opts.getClientProperties()).table(table) |
| .offlineScan(opts.offline).store(job); |
| job.setMapperClass(UMapper.class); |
| job.setMapOutputKeyClass(Text.class); |
| job.setMapOutputValueClass(Text.class); |
| |
| job.setCombinerClass(UReducer.class); |
| job.setReducerClass(UReducer.class); |
| job.setNumReduceTasks(opts.reducers); |
| job.setOutputFormatClass(TextOutputFormat.class); |
| TextOutputFormat.setOutputPath(job, new Path(opts.output)); |
| job.waitForCompletion(true); |
| if (opts.offline) { |
| client.tableOperations().delete(table); |
| } |
| System.exit(job.isSuccessful() ? 0 : 1); |
| } |
| System.exit(1); |
| } |
| } |