以编程方式将数据批量加载到HBase的最快方法是什么？

Question

我有一个纯文本文件,可能有数百万行需要自定义解析,我想尽快加载到HBase表中(使用Hadoop或HBase Java客户端).

我目前的解决方案是基于没有Reduce部分的MapReduce作业.我FileInputFormat用来读取文本文件,以便每行传递给map我的Mapper类的方法.此时,该行被解析以形成一个Put写入的对象context.然后,TableOutputFormat获取Put对象并将其插入表中.

该解决方案产生的平均插入速率为每秒1,000行,低于我的预期.我的HBase设置在单个服务器上处于伪分布式模式.

一个有趣的事情是,在插入1,000,000行时,会产生25个Mappers(任务),但它们会连续运行(一个接一个); 这是正常的吗？

这是我当前解决方案的代码:

public static class CustomMap extends Mapper<LongWritable, Text, ImmutableBytesWritable, Put> {

    protected void map(LongWritable key, Text value, Context context) throws IOException {
        Map<String, String> parsedLine = parseLine(value.toString());

        Put row = new Put(Bytes.toBytes(parsedLine.get(keys[1])));
        for (String currentKey : parsedLine.keySet()) {
            row.add(Bytes.toBytes(currentKey),Bytes.toBytes(currentKey),Bytes.toBytes(parsedLine.get(currentKey)));
        }

        try {
            context.write(new ImmutableBytesWritable(Bytes.toBytes(parsedLine.get(keys[1]))), row);
        } catch (InterruptedException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
}

public int run(String[] args) throws Exception {
    if (args.length != 2) {
        return -1;
    }

    conf.set("hbase.mapred.outputtable", args[1]);

    // I got these conf parameters from a presentation about Bulk Load
    conf.set("hbase.hstore.blockingStoreFiles", "25");
    conf.set("hbase.hregion.memstore.block.multiplier", "8");
    conf.set("hbase.regionserver.handler.count", "30");
    conf.set("hbase.regions.percheckin", "30");
    conf.set("hbase.regionserver.globalMemcache.upperLimit", "0.3");
    conf.set("hbase.regionserver.globalMemcache.lowerLimit", "0.15");

    Job job = new Job(conf);
    job.setJarByClass(BulkLoadMapReduce.class);
    job.setJobName(NAME);
    TextInputFormat.setInputPaths(job, new Path(args[0]));
    job.setInputFormatClass(TextInputFormat.class);
    job.setMapperClass(CustomMap.class);
    job.setOutputKeyClass(ImmutableBytesWritable.class);
    job.setOutputValueClass(Put.class);
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(TableOutputFormat.class);

    job.waitForCompletion(true);
    return 0;
}

public static void main(String[] args) throws Exception {
    Long startTime = Calendar.getInstance().getTimeInMillis();
    System.out.println("Start time : " + startTime);

    int errCode = ToolRunner.run(HBaseConfiguration.create(), new BulkLoadMapReduce(), args);

    Long endTime = Calendar.getInstance().getTimeInMillis();
    System.out.println("End time : " + endTime);
    System.out.println("Duration milliseconds: " + (endTime-startTime));

    System.exit(errCode);
}

Answer 1

我经历了一个与你的过程非常相似的过程,试图找到一种将数据从MR加载到HBase的有效方法.我发现工作的是HFileOutputFormat用作MR的OutputFormatClass.

下面是我必须生成的代码的基础job以及map写出数据的Mapper 函数.这很快.我们不再使用它了,所以我手边没有数字,但在一分钟内就有大约250万条记录.

这是我编写的(剥离)函数,用于为我的MapReduce进程生成作业以将数据放入HBase

private Job createCubeJob(...) {
    //Build and Configure Job
    Job job = new Job(conf);
    job.setJobName(jobName);
    job.setMapOutputKeyClass(ImmutableBytesWritable.class);
    job.setMapOutputValueClass(Put.class);
    job.setMapperClass(HiveToHBaseMapper.class);//Custom Mapper
    job.setJarByClass(CubeBuilderDriver.class);
    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(HFileOutputFormat.class);

    TextInputFormat.setInputPaths(job, hiveOutputDir);
    HFileOutputFormat.setOutputPath(job, cubeOutputPath);

    Configuration hConf = HBaseConfiguration.create(conf);
    hConf.set("hbase.zookeeper.quorum", hbaseZookeeperQuorum);
    hConf.set("hbase.zookeeper.property.clientPort", hbaseZookeeperClientPort);

    HTable hTable = new HTable(hConf, tableName);

    HFileOutputFormat.configureIncrementalLoad(job, hTable);
    return job;
}

这是我的HiveToHBaseMapper类中的map函数(稍加编辑).

public void map(WritableComparable key, Writable val, Context context)
        throws IOException, InterruptedException {
    try{
        Configuration config = context.getConfiguration();
        String[] strs = val.toString().split(Constants.HIVE_RECORD_COLUMN_SEPARATOR);
        String family = config.get(Constants.CUBEBUILDER_CONFIGURATION_FAMILY);
        String column = strs[COLUMN_INDEX];
        String Value = strs[VALUE_INDEX];
        String sKey = generateKey(strs, config);
        byte[] bKey = Bytes.toBytes(sKey);
        Put put = new Put(bKey);
        put.add(Bytes.toBytes(family), Bytes.toBytes(column), (value <= 0) 
                        ? Bytes.toBytes(Double.MIN_VALUE)
                        : Bytes.toBytes(value));

        ImmutableBytesWritable ibKey = new ImmutableBytesWritable(bKey);
        context.write(ibKey, put);

        context.getCounter(CubeBuilderContextCounters.CompletedMapExecutions).increment(1);
    }
    catch(Exception e){
        context.getCounter(CubeBuilderContextCounters.FailedMapExecutions).increment(1);    
    }

}

我很确定这不会是一个复制和粘贴解决方案.显然,我在这里使用的数据不需要任何自定义处理(在此之前在MR作业中完成).我想提供的主要内容是HFileOutputFormat.其余的只是我如何使用它的一个例子.:)
我希望它能让你走上一条通向良好解决方案的坚实道路.: