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hadoop old API CombineFileInputFormat
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简介
本文主要介绍下面4个方面
1.为什么要使用CombineFileInputFormat
2.CombineFileInputFormat实现原理
3.怎样使用CombineFileInputFormat
4.现存的问题
使用CombineFileInputFormat的目的
在开发MR的程序时,mapper的主要作用是对数据的收集。一般情况下,为了能让mapper更快的运行,我们会对文件进行split,以便多个mapper同时运行。在这种情况下,为了让程序更好更快的运行,我们需要控制mapper的个数。Mapper的个数主要由文件的大小及我们所设置的mapred.min.split.size以及blockSize所决定(详细参考:http://ai-longyu.iteye.com/blog/1566633)
上面所说的在我们使用TextInputFormat和分析单个文件时是没有问题的,基本上mapper的个数能够控制在我们所预期的范围内。但是当我们使用多个文件作为input的时候,mapper的个数就不再是我们所期望的那样了,因为TextInputFormat继承的是FileInputFormat,而FileInputFormat的split操作是只针对单个文件,对于多个文件,是将每个文件进行split,而不能做一些合并的操作(尤其是大量的小文件)。
你会想为什么不能进行合并呢,有没有实现合并的split呢?在这个时候,CombineFileInputFormat就闪亮登场了。这里所说的CombineFileInputFormat是由官方提供的,只要我们搞清楚了官方是怎么实现的,就能够自己也实现一个了。接下来将逐步分析CombineFileInputFormat的实现了。
CombineFileInputFormat实现步骤
这里插一句,官方的CombineFileInputFormat并不是线程安全的。
先申明一下,这里分析所采用的源码是apache的1.0.3,分析的在org.apache.hadoop.mapred.lib.CombineFileInputFormat而不是org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat,这里分析的旧API,而没有分析新的API
生成split的信息是由
- public InputSplit[] getSplits(JobConf job, int numSplits)
Job参数:job的配置信息
numSplits参数:期望的mapper数目,在这里根本就没有使用
- //每个DN的最小split大小
- long minSizeNode = 0;
- //同机架的最小split大小
- long minSizeRack = 0;
- //最大的split大小
- long maxSize = 0;
这几个变量都可以从job的配置信息中获取
接下来就是获取input的路径列表,判断每个路径时候被Filter所允许,然后对允许的路径列表生成split信息列表,进入该类的核心方法
- /**
- * Return all the splits in the specified set of paths
- *
- * @param job Job的配置信息
- * @param paths 输入源的路径列表
- * @param maxSize 最大的split大小
- * @param minSizeNode 每个DN最小的split大小
- * @param minSizeRack 每个rack最小的split大小
- * @param splits split信息列表
- * @throws IOException
- */
- private void getMoreSplits(JobConf job, Path[] paths,
- long maxSize, long minSizeNode, long minSizeRack,
- List<CombineFileSplit> splits)
生成每个文件的OneFileInfo对象
- // populate all the blocks for all files
- long totLength = 0;
- for (int i = 0; i < paths.length; i++) {
- //构建每个input文件的信息,并将文件中的每个
- //block信息收集到rackToBlocks、blockToNodes、nodeToBlocks中
- files = new OneFileInfo(paths, job,
- rackToBlocks, blockToNodes, nodeToBlocks);
- //增加所有文件的大小
- totLength += files.getLength();
- }
在下面就开始真正的生成Split信息了
第一次:将同DN上的所有block生成Split,生成方式:
1.循环nodeToBlocks,获得每个DN上有哪些block
2.循环这些block列表
3.将block从blockToNodes中移除,避免同一个block被包含在多个split中
4.将该block添加到一个有效block的列表中,这个列表主要是保留哪些block已经从blockToNodes中被移除了,方便后面恢复到blockToNodes中
5.向临时变量curSplitSize增加block的大小
6.判断curSplitSize是否已经超过了设置的maxSize
a) 如果超过,执行并添加split信息,并重置curSplitSize和validBlocks
b) 没有超过,继续循环block列表,跳到第2步
7.当前DN上的block列表循环完成,判断剩余的block是否允许被split(剩下的block大小之和是否大于每个DN的最小split大小)
a) 如果允许,执行并添加split信息
b) 如果不被允许,将这些剩余的block归还blockToNodes
8.重置
9.跳到步骤1
- // process all nodes and create splits that are local
- // to a node.
- //创建同一个DN上的split
- for (Iterator<Map.Entry<String,
- List<OneBlockInfo>>> iter = nodeToBlocks.entrySet().iterator();
- iter.hasNext() {
- Map.Entry<String, List<OneBlockInfo>> one = iter.next();
- nodes.add(one.getKey());
- List<OneBlockInfo> blocksInNode = one.getValue();
- // for each block, copy it into validBlocks. Delete it from
- // blockToNodes so that the same block does not appear in
- // two different splits.
- for (OneBlockInfo oneblock : blocksInNode) {
- if (blockToNodes.containsKey(oneblock)) {
- validBlocks.add(oneblock);
- blockToNodes.remove(oneblock);
- curSplitSize += oneblock.length;
- // if the accumulated split size exceeds the maximum, then
- // create this split.
- if (maxSize != 0 && curSplitSize >= maxSize) {
- // create an input split and add it to the splits array
- //创建这些block合并后的split,并将其split添加到split列表中
- addCreatedSplit(job, splits, nodes, validBlocks);
- //重置
- curSplitSize = 0;
- validBlocks.clear();
- }
- }
- }
- // if there were any blocks left over and their combined size is
- // larger than minSplitNode, then combine them into one split.
- // Otherwise add them back to the unprocessed pool. It is likely
- // that they will be combined with other blocks from the same rack later on.
- //其实这里的注释已经说的很清楚,我再按照我的理解说一下
- /**
- * 这里有几种情况:
- * 1、在这个DN上还有没有被split的block,
- * 而且这些block的大小大于了在一个DN上的split最小值(没有达到最大值),
- * 将把这些block合并成一个split
- * 2、剩余的block的大小还是没有达到,将剩余的这些block
- * 归还给blockToNodes,等以后统一处理
- */
- if (minSizeNode != 0 && curSplitSize >= minSizeNode) {
- // create an input split and add it to the splits array
- addCreatedSplit(job, splits, nodes, validBlocks);
- } else {
- for (OneBlockInfo oneblock : validBlocks) {
- blockToNodes.put(oneblock, oneblock.hosts);
- }
- }
- validBlocks.clear();
- nodes.clear();
- curSplitSize = 0;
- }
第二次:对不再同一个DN上但是在同一个Rack上的block进行合并(只是之前还剩下的block)
- // if blocks in a rack are below the specified minimum size, then keep them
- // in ‘overflow‘. After the processing of all racks is complete, these overflow
- // blocks will be combined into splits.
- ArrayList<OneBlockInfo> overflowBlocks = new ArrayList<OneBlockInfo>();
- ArrayList<String> racks = new ArrayList<String>();
- // Process all racks over and over again until there is no more work to do.
- //这里处理的就不再是同一个DN上的block
- //同一个DN上的已经被处理过了(上面的代码),这里是一些
- //还没有被处理的block
- while (blockToNodes.size() > 0) {
- // Create one split for this rack before moving over to the next rack.
- // Come back to this rack after creating a single split for each of the
- // remaining racks.
- // Process one rack location at a time, Combine all possible blocks that
- // reside on this rack as one split. (constrained by minimum and maximum
- // split size).
- // iterate over all racks
- //创建同机架的split
- for (Iterator<Map.Entry<String, List<OneBlockInfo>>> iter =
- rackToBlocks.entrySet().iterator(); iter.hasNext() {
- Map.Entry<String, List<OneBlockInfo>> one = iter.next();
- racks.add(one.getKey());
- List<OneBlockInfo> blocks = one.getValue();
- // for each block, copy it into validBlocks. Delete it from
- // blockToNodes so that the same block does not appear in
- // two different splits.
- boolean createdSplit = false;
- for (OneBlockInfo oneblock : blocks) {
- //这里很重要,现在的blockToNodes说明的是还有哪些block没有被split
- if (blockToNodes.containsKey(oneblock)) {
- validBlocks.add(oneblock);
- blockToNodes.remove(oneblock);
- curSplitSize += oneblock.length;
- // if the accumulated split size exceeds the maximum, then
- // create this split.
- if (maxSize != 0 && curSplitSize >= maxSize) {
- // create an input split and add it to the splits array
- addCreatedSplit(job, splits, getHosts(racks), validBlocks);
- createdSplit = true;
- break;
- }
- }
- }
- // if we created a split, then just go to the next rack
- if (createdSplit) {
- curSplitSize = 0;
- validBlocks.clear();
- racks.clear();
- continue;
- }
- //还有没有被split的block
- //如果这些block的大小大于了同机架的最小split,
- //则创建split
- //否则,将这些block留到后面处理
- if (!validBlocks.isEmpty()) {
- if (minSizeRack != 0 && curSplitSize >= minSizeRack) {
- // if there is a mimimum size specified, then create a single split
- // otherwise, store these blocks into overflow data structure
- addCreatedSplit(job, splits, getHosts(racks), validBlocks);
- } else {
- // There were a few blocks in this rack that remained to be processed.
- // Keep them in ‘overflow‘ block list. These will be combined later.
- overflowBlocks.addAll(validBlocks);
- }
- }
- curSplitSize = 0;
- validBlocks.clear();
- racks.clear();
- }
- }
最后,对于既不在同DN也不在同rack的block进行合并(经过前两步还剩下的block),这里源码就没有什么了,就不再贴了
源码总结:
合并,经过了3个步骤。同DN----》同rack不同DN-----》不同rack
将可以合并的block写到同一个split中
使用自定义的CombineFileInputFormat
MultiFileCombineInputFormat
- package org.rollinkin.hadoop;
- import java.io.IOException;
- import org.apache.hadoop.io.LongWritable;
- import org.apache.hadoop.io.Text;
- import org.apache.hadoop.mapred.InputSplit;
- import org.apache.hadoop.mapred.JobConf;
- import org.apache.hadoop.mapred.RecordReader;
- import org.apache.hadoop.mapred.Reporter;
- import org.apache.hadoop.mapred.lib.CombineFileInputFormat;
- import org.apache.hadoop.mapred.lib.CombineFileRecordReader;
- import org.apache.hadoop.mapred.lib.CombineFileSplit;
- /**
- * 多文件合并split的输入format
- *
- * @author rollinkin
- * @date 2012-10-29
- * @version 1.0
- * @since 1.0
- */
- public class MultiFileCombineInputFormat extends
- CombineFileInputFormat<LongWritable, Text> {
- @Override
- public RecordReader<LongWritable, Text> getRecordReader(
- InputSplit split, JobConf job, Reporter reporter)
- throws IOException {
- @SuppressWarnings({ "rawtypes", "unchecked" })
- Class<RecordReader<LongWritable, Text>> rrClass = (Class)CombineLineRecordReader.class;
- return new CombineFileRecordReader<LongWritable, Text>(job,(CombineFileSplit) split, reporter,rrClass);
- }
- }
CombineLineRecordReader,这个其实没有什么内容,就是包装了一个Reader
- package org.rollinkin.hadoop;
- import java.io.IOException;
- import org.apache.hadoop.conf.Configuration;
- import org.apache.hadoop.io.LongWritable;
- import org.apache.hadoop.io.Text;
- import org.apache.hadoop.mapred.FileSplit;
- import org.apache.hadoop.mapred.LineRecordReader;
- import org.apache.hadoop.mapred.RecordReader;
- import org.apache.hadoop.mapred.Reporter;
- import org.apache.hadoop.mapred.lib.CombineFileSplit;
- public class CombineLineRecordReader implements
- RecordReader<LongWritable, Text> {
- private LineRecordReader delegate;
- public CombineLineRecordReader(CombineFileSplit split, Configuration conf,
- Reporter reporter, Integer idx) throws IOException {
- FileSplit fileSplit = new FileSplit(split.getPath(idx),
- split.getOffset(idx), split.getLength(idx),
- split.getLocations());
- delegate = new LineRecordReader(conf, fileSplit);
- }
- @Override
- public boolean next(LongWritable key, Text value) throws IOException {
- return delegate.next(key, value);
- }
- @Override
- public LongWritable createKey() {
- return delegate.createKey();
- }
- @Override
- public Text createValue() {
- return delegate.createValue();
- }
- @Override
- public long getPos() throws IOException {
- return delegate.getPos();
- }
- @Override
- public void close() throws IOException {
- delegate.close();
- }
- @Override
- public float getProgress() throws IOException {
- return delegate.getProgress();
- }
- }
具体的使用我就不再留了,其实很简单,就是把你的InputFormat设置成MultiFileCombineInputFormat 就可以了(在2012-11-09之前提供了一个reader实际上是不可用,他存在跨块读取的问题,
这里就不在提供了。如果使用了,请更新一下。哎,又传播错误的消息了)
现存问题
- 合并后会造成mapper不能本地化,带来mapper的额外开销,需要权衡
- 这里只实现了简单的Text的方式的合并,对于可压缩的、二进制等文件没有提供
- 这里提供的自定义的实现,只是简单的按行读取
hadoop old API CombineFileInputFormat