org.apache.accumulo.examples.mapreduce.UniqueColumns.java Source code

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Here is the source code for org.apache.accumulo.examples.mapreduce.UniqueColumns.java

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/*
 * 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.Connector;
import org.apache.accumulo.core.client.mapreduce.AccumuloInputFormat;
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.MapReduceClientOnRequiredTable;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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 org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

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 extends Configured implements Tool {

    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 MapReduceClientOnRequiredTable {
        @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;
    }

    @Override
    public int run(String[] args) throws Exception {
        Opts opts = new Opts();
        opts.parseArgs(UniqueColumns.class.getName(), args);

        String jobName = this.getClass().getSimpleName() + "_" + System.currentTimeMillis();

        Job job = Job.getInstance(getConf());
        job.setJobName(jobName);
        job.setJarByClass(this.getClass());

        String clone = opts.getTableName();
        Connector conn = null;

        opts.setAccumuloConfigs(job);

        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.
             */

            conn = opts.getConnector();
            clone = opts.getTableName() + "_" + jobName;
            conn.tableOperations().clone(opts.getTableName(), clone, true, new HashMap<String, String>(),
                    new HashSet<String>());
            conn.tableOperations().offline(clone);

            AccumuloInputFormat.setOfflineTableScan(job, true);
            AccumuloInputFormat.setInputTableName(job, clone);
        }

        job.setInputFormatClass(AccumuloInputFormat.class);

        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) {
            conn.tableOperations().delete(clone);
        }

        return job.isSuccessful() ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new UniqueColumns(), args);
        System.exit(res);
    }
}