在大数据时代，Hadoop MapReduce是一个非常重要的数据处理工具。然而，处理大量数据时，MapReduce的性能问题也非常明显。本文将介绍一些基于Hadoop的MapReduce性能优化实践技巧，并实现一个简单的示例代码来说明这些技巧的应用。
1. 数据倾斜问题的解决
在处理大数据时，洗牌阶段很容易成为MapReduce作业的性能瓶颈。洗牌的过程会将Map输出的结果根据默认的分区规则分发到不同的Reducer task中，而在默认的分区规则下，很容易出现一些Key被分配到同一个Reducer task的情况，这就导致了数据倾斜现象，造成了某些task的负荷非常高，而其它task的负荷却非常低的情况。
下面是解决数据倾斜问题的几种技巧：
1.1. Combiner的应用
在Map端对一组key进行聚合操作，可以减少洗牌的数据量
// javascriptcn.com 代码示例
public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
}
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}Combiner的应用：
// javascriptcn.com 代码示例
public class WordCountCombiner extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);1.2. 自定义分区
使用自定义分区，将数据分散到各个Reducer task中，减少数据倾斜
// javascriptcn.com 代码示例
public static class CustomPartitioner extends Partitioner<Text, IntWritable> {
@Override
public int getPartition(Text key, IntWritable value, int numPartitions) {
String firstLetter = key.toString().substring(0, 1).toLowerCase();
if (numPartitions < 10) {
return Integer.parseInt(firstLetter) % numPartitions;
} else {
return Integer.parseInt(firstLetter);
}
}
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setPartitionerClass(CustomPartitioner.class);
job.setNumReduceTasks(Integer.parseInt(args[2]));
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
2. 拆分小文件的技巧
MapReduce的输入数据最好是以几个较大的文件或一个文件夹为单位进行处理，而不是一个小文件一个小文件的处理。可以将多个小文件合并成一个大文件，再使用多个Mapper来处理这个大文件。
3. 记忆性的技巧
对于一些有记忆性的操作，需要充分利用MapReduce中的一些机制，如Mapper端的累加器。
// javascriptcn.com 代码示例
public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private final static String SEARCH_WORD = "hello";
private int count = 0;
@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
if (SEARCH_WORD.equals(word.toString())) {
count++;
}
context.write(word, one);
}
}
@Override
protected void cleanup(Context context) throws IOException, InterruptedException {
context.getCounter("wordCount", "countHello").increment(count);
}
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);Counters: 1
wordCount
countHello=703
4. 压缩数据的技巧
MapReduce处理的数据往往都是非常大的，利用压缩机制可以有效地减小网络I/O和磁盘I/O的负荷，提高作业的效率。
job.getConfiguration().setBoolean("mapreduce.map.output.compress", true);
job.getConfiguration().setClass("mapreduce.map.output.compress.codec", GzipCodec.class, CompressionCodec.class);
job.getConfiguration().setBoolean("mapreduce.output.fileoutputformat.compress", true);
job.getConfiguration().setClass("mapreduce.output.fileoutputformat.compress.codec", GzipCodec.class, CompressionCodec.class);
job.setOutputFormatClass(TextOutputFormat.class);
TextOutputFormat.setCompressOutput(job, true);结语
MapReduce是一个非常强大的数据处理框架，在处理大规模数据时，需要充分地利用其提供的机制和技巧来优化作业的性能。本文介绍的技巧只是冰山一角，还有很多其它的技巧可以使用。因此，要成为一名优秀的MapReduce开发人员，需要不断提高自己的技能和经验，在实践中不断地总结和积累。
来源：JavaScript中文网 ，转载请注明来源 本文地址：https://www.javascriptcn.com/post/6530c8f87d4982a6eb258c7a