hadoop mapreduce:java.lang.UnsatisfiedLinkError:org.apache.h

时间：2023-05-04
本文介绍了hadoop mapreduce:java.lang.UnsatisfiedLinkError:org.apache.hadoop.util.NativeCodeLoader.buildSupportsSnappy()Z的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着跟版网的小编来一起学习吧！

问题描述

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我正在尝试从 map-reduce 作业中编写一个快速的块压缩序列文件.我在用hadoop 2.0.0-cdh4.5.0 和 snappy-java 1.0.4.1

I am trying to write a snappy block compressed sequence file from a map-reduce job. I am using hadoop 2.0.0-cdh4.5.0, and snappy-java 1.0.4.1

这是我的代码:

package jinvestor.jhouse.mr;

import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.OutputStream;
import java.util.Arrays;
import java.util.List;

import jinvestor.jhouse.core.House;
import jinvestor.jhouse.core.util.HouseAvroUtil;
import jinvestor.jhouse.download.HBaseHouseDAO;

import org.apache.commons.io.IOUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.LocatedFileStatus;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.RemoteIterator;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.SnappyCodec;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.NamedVector;
import org.apache.mahout.math.VectorWritable;

/**
 * Produces mahout vectors from House entries in HBase.
 * 
 * @author Michael Scott Knapp
 * 
 */
public class HouseVectorizer {

    private final Configuration configuration;
    private final House minimumHouse;
    private final House maximumHouse;

    public HouseVectorizer(final Configuration configuration,
            final House minimumHouse, final House maximumHouse) {
        this.configuration = configuration;
        this.minimumHouse = minimumHouse;
        this.maximumHouse = maximumHouse;
    }

    public void vectorize() throws IOException, ClassNotFoundException, InterruptedException {
        JobConf jobConf = new JobConf();
        jobConf.setMapOutputKeyClass(LongWritable.class);
        jobConf.setMapOutputValueClass(VectorWritable.class);

        // we want the vectors written straight to HDFS,
        // the order does not matter.
        jobConf.setNumReduceTasks(0);

        Path outputDir = new Path("/home/cloudera/house_vectors");
        FileSystem fs = FileSystem.get(configuration);
        if (fs.exists(outputDir)) {
            fs.delete(outputDir, true);
        }

        FileOutputFormat.setOutputPath(jobConf, outputDir);

        // I want the mappers to know the max and min value
        // so they can normalize the data.
        // I will add them as properties in the configuration,
        // by serializing them with avro.
        String minmax = HouseAvroUtil.toBase64String(Arrays.asList(minimumHouse,
                maximumHouse));
        jobConf.set("minmax", minmax);

        Job job = Job.getInstance(jobConf);
        Scan scan = new Scan();
        scan.addFamily(Bytes.toBytes("data"));
        TableMapReduceUtil.initTableMapperJob("homes", scan,
                HouseVectorizingMapper.class, LongWritable.class,
                VectorWritable.class, job);
        job.setOutputFormatClass(SequenceFileOutputFormat.class);
        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(VectorWritable.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(VectorWritable.class);

        SequenceFileOutputFormat.setOutputCompressionType(job, SequenceFile.CompressionType.BLOCK);
        SequenceFileOutputFormat.setOutputCompressorClass(job, SnappyCodec.class);
        SequenceFileOutputFormat.setOutputPath(job, outputDir);
        job.getConfiguration().setClass("mapreduce.map.output.compress.codec", 
                SnappyCodec.class, 
                CompressionCodec.class);

        job.waitForCompletion(true);
    }

当我运行它时，我得到了这个:

When I run it I get this:

java.lang.Exception: java.lang.UnsatisfiedLinkError: org.apache.hadoop.util.NativeCodeLoader.buildSupportsSnappy()Z
    at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:401)
Caused by: java.lang.UnsatisfiedLinkError: org.apache.hadoop.util.NativeCodeLoader.buildSupportsSnappy()Z
    at org.apache.hadoop.util.NativeCodeLoader.buildSupportsSnappy(Native Method)
    at org.apache.hadoop.io.compress.SnappyCodec.checkNativeCodeLoaded(SnappyCodec.java:62)
    at org.apache.hadoop.io.compress.SnappyCodec.getCompressorType(SnappyCodec.java:127)
    at org.apache.hadoop.io.compress.CodecPool.getCompressor(CodecPool.java:104)
    at org.apache.hadoop.io.compress.CodecPool.getCompressor(CodecPool.java:118)
    at org.apache.hadoop.io.SequenceFile$Writer.init(SequenceFile.java:1169)
    at org.apache.hadoop.io.SequenceFile$Writer.<init>(SequenceFile.java:1080)
    at org.apache.hadoop.io.SequenceFile$BlockCompressWriter.<init>(SequenceFile.java:1400)
    at org.apache.hadoop.io.SequenceFile.createWriter(SequenceFile.java:274)
    at org.apache.hadoop.io.SequenceFile.createWriter(SequenceFile.java:527)
    at org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.getSequenceWriter(SequenceFileOutputFormat.java:64)
    at org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.getRecordWriter(SequenceFileOutputFormat.java:75)
    at org.apache.hadoop.mapred.MapTask$NewDirectOutputCollector.<init>(MapTask.java:617)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:737)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:338)
    at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:233)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
    at java.util.concurrent.FutureTask.run(FutureTask.java:262)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:744)

如果我注释掉这些行，那么我的测试通过:

If I comment out these lines then my test passes:

SequenceFileOutputFormat.setOutputCompressionType(job, SequenceFile.CompressionType.BLOCK);
        SequenceFileOutputFormat.setOutputCompressorClass(job, SnappyCodec.class);
        job.getConfiguration().setClass("mapreduce.map.output.compress.coded", 
                SnappyCodec.class, 
                CompressionCodec.class);

但是，我真的很想在我的序列文件中使用 snappy 压缩.有人可以向我解释我做错了什么吗?

However, I really want to use snappy compression in my sequence files. Can somebody please explain to me what I am doing wrong?

推荐答案

从Cloudera 社区

  1. 确保 LD_LIBRARY_PATH 和 JAVA_LIBRARY_PATH 包含具有 libsnappy.so** 文件的本机目录路径.
  2. 确保已在 SPARK 环境中导出了 LD_LIBRARY_PATH 和 JAVA_LIBRARY 路径(spark-env.sh).
  1. Ensure that LD_LIBRARY_PATH and JAVA_LIBRARY_PATH contains the native directory path having the libsnappy.so** files.
  2. Ensure that LD_LIBRARY_PATH and JAVA_LIBRARY path have been exported in the SPARK environment(spark-env.sh).

例如，我使用 Hortonworks HDP，我的 spark-env.sh

For example I use Hortonworks HDP and I have the following configuration in my spark-env.sh

export JAVA_LIBRARY_PATH=$JAVA_LIBRARY_PATH:/usr/hdp/2.2.0.0-2041/hadoop/lib/native
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/hdp/2.2.0.0-2041/hadoop/lib/native
export SPARK_YARN_USER_ENV="JAVA_LIBRARY_PATH=$JAVA_LIBRARY_PATH,LD_LIBRARY_PATH=$LD_LIBRARY_PATH"

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