To set the number of maps by the size of the input shard

Abstract set the number of maps by the size of the input shard

Map Inputsplit Hadoop

Preface: In the specific implementation of the Hadoop program, we have to set the number of maps according to different circumstances. In addition to setting the maximum number of maps that can be run on each node, we also need to control the number of tasks that actually perform the map operation.
1. How to control the number of map tasks actually running
We know that when the file is uploaded to the HDFs file system, it is cut into different block blocks (the default size is 64MB). However, each map processing block is sometimes not the physical block size of the system. The actual processing of the input block size is based on Inputsplit to set, then inputsplit how to get it?

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InputSplit=Math.max(minSize, Math.min(maxSize, blockSize) 其中：minSize=mapred.min.split.size     maxSize=mapred.max.split.size

We control how many maps are actually used by changing the number of shards in the InputFormat, while controlling how many shards in the InputFormat need to control the size of each Inputsplit shard
2. How to control the size of each split shard
The default input format for Hadoop is Textinputformat, which defines how files are read and how they are fragmented. We open his source file (in the Org.apache.hadoop.mapreduce.lib.input package):

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package org.apache.hadoop.mapreduce.lib.input;importorg.apache.hadoop.fs.Path;importorg.apache.hadoop.io.LongWritable;importorg.apache.hadoop.io.Text;importorg.apache.hadoop.io.compress.CompressionCodec;importorg.apache.hadoop.io.compress.CompressionCodecFactory;import org.apache.hadoop.io.compress.SplittableCompressionCodec;importorg.apache.hadoop.mapreduce.InputFormat;importorg.apache.hadoop.mapreduce.InputSplit;importorg.apache.hadoop.mapreduce.JobContext;importorg.apache.hadoop.mapreduce.RecordReader;importorg.apache.hadoop.mapreduce.TaskAttemptContext;public classTextInputFormat extendsFileInputFormat<LongWritable, Text> {   @Override   publicRecordReader<LongWritable, Text>      createRecordReader(InputSplit split,                       TaskAttemptContext context) {     returnnewLineRecordReader();   }   @Override   protectedboolean isSplitable(JobContext context, Path file) {     CompressionCodec codec =        newCompressionCodecFactory(context.getConfiguration()).getCodec(file);     if(null== codec) {       returntrue;     }     returncodec instanceofSplittableCompressionCodec;   }}

Through the source code, we found that Textinputformat inherited the Fileinputformat, and in Textinputformat, we did not find a specific section for file segmentation, The Textinputformat should be using the Fileinputformat default Inputsplit method. So, we open the source code for Fileinputformat, where we found:

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publicstaticvoidsetMinInputSplitSize(Job job,longsize) {     job.getConfiguration().setLong("mapred.min.split.size", size);   }   publicstaticlonggetMinSplitSize(JobContext job) {     returnjob.getConfiguration().getLong("mapred.min.split.size", 1L);   }    public staticvoidsetMaxInputSplitSize(Job job,longsize) {     job.getConfiguration().setLong("mapred.max.split.size", size);   }   publicstatic longgetMaxSplitSize(JobContext context) {     returncontext.getConfiguration().getLong("mapred.max.split.size",Long.MAX_VALUE);   }

As we can see, Hadoop implements the definition of mapred.min.split.size and mapred.max.split.size here, with the default values of 1 and the largest of long. Therefore, we can control the size of the Inputsplit shard by simply re-assigning the values to these two values in the program.
3. If we want to set the Shard size to 10MB
Then we can add the following code to the driver section of the MapReduce program:

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123	TextInputFormat.setMinInputSplitSize(job,1024L);//设置最小分片大小TextInputFormat.setMaxInputSplitSize(job,1024×1024×10L);//设置最大分片大小

(go) Set the number of maps by the size of the input shard