Help Center/ MapReduce Service/ Component Operation Guide (ME-Abu Dhabi Region)/ Using MapReduce/ MapReduce Performance Tuning/ Optimization Configuration for Multiple CPU Cores
Updated on 2024-07-19 GMT+08:00
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Optimization Configuration for Multiple CPU Cores

Scenario

Optimization can be performed when the number of CPU cores is large, for example, the number of CPU cores is three times the number of disks.

Procedure

You can set the following parameters in either of the following ways:

  • Configuration on the server:

    On the All Configurations page of the Yarn service, enter a parameter name in the search box. For details, see Modifying Cluster Service Configuration Parameters.

  • Configuration on the client:
    Modify the corresponding configuration file on the client.
    • Path of configuration files on the HDFS client: Client installation directory/HDFS/hadoop/etc/hadoop/hdfs-site.xml
    • Path of configuration files on the Yarn client: Client installation directory/HDFS/hadoop/etc/hadoop/yarn-site.xml.
    • Path of configuration files on the MapReduce client: Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml.
Table 1 Settings of multiple CPU cores

Configuration

Description

Parameter

Default Value

Server/Client

Impact

Remarks

Number of slots in a node container

The combination of the following parameters determines the number of concurrent tasks (Map and Reduce tasks) of each node:

  • yarn.nodemanager.resource.memory-mb
  • mapreduce.map.memory.mb
  • mapreduce.reduce.memory.mb

yarn.nodemanager.resource.memory-mb

NOTE:

For versions earlier than MRS 3.x, configure this parameter on the MRS console.

For MRS 3.x or later: You need to configure this parameter on FusionInsight Manager.

Versions earlier than MRS 3.x:

8192

MRS 3.x or later:

16384

Server

If data needs to be read from and written into disks for all tasks (Map/Reduce tasks), a disk may be accessed by multiple processes at the same time, which leads to poor disk I/O performance. To ensure disk I/O performance, the number of concurrent access requests from a client to a disk cannot exceed 3.

The maximum number of concurrent containers must be [2.5 x Number of disks configured in Hadoop].

mapreduce.map.memory.mb

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml path.

4096

Client

mapreduce.reduce.memory.mb

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml path.

4096

Client

Map output and compression

The Map task output before being written into disks can be compressed. This can save disk space, offer faster data write, and reduce the data traffic delivered to Reducer. You need to configure the following parameters on the client:

  • mapreduce.map.output.compress: The Map task output can be compressed before it is transmitted over the network. It is a per-job configuration.
  • mapreduce.map.output.compress.codec: the codec used for data compression

mapreduce.map.output.compress

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml path.

true

Client

The disk I/O is the bottleneck. Therefore, use a compression algorithm with a high compression rate.

Snappy is used. The benchmark test results show that Snappy delivers high performance and efficiency.

mapreduce.map.output.compress.codec

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml path.

org.apache.hadoop.io.compress.Lz4Codec

Client

Spills

mapreduce.map.sort.spill.percent

mapreduce.map.sort.spill.percent

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/mapred-site.xml path.

0.8

Client

Disk I/Os are the bottleneck. You can set the value of mapreduce.task.io.sort.mb to minimize the memory spilled to the disk.

-

Data packet size

When the HDFS client writes data to a data node, the data will be accumulated until a packet is generated. Then, the packet is transmitted over the network. dfs.client-write-packet-size specifies the data packet size. It can be specified by each job.

dfs.client-write-packet-size

NOTE:

You need to set this parameter in the configuration file on the client in the Client installation directory/HDFS/hadoop/etc/hadoop/hdfs-site.xml/ path.

262144

Client

The data node receives data packets from the HDFS client and writes data into disks through single threads. When disks are in the concurrent write state, increasing the data packet size can reduce the disk seek time and improve the I/O performance.

dfs.client-write-packet-size = 262144