Optimization Configuration for Multiple CPU Cores

Updated on 2024-07-19 GMT+08:00

View PDF

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.
NOTE:
- 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.
Map output and compression		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

Parent topic: MapReduce Performance Tuning

Previous topic: MapReduce Performance Tuning

Next topic: Determining the Job Baseline

Feedback

Was this page helpful?

Helpful Not helpful

Provide feedback

Thank you very much for your feedback. We will continue working to improve the documentation.See the reply and handling status in My Cloud VOC.

The system is busy. Please try again later.

Which of the following issues have you encountered?

Content is inconsistent with the product UI

Unclear descriptions

Lack of examples or code

Incorrect steps

Can't find what I need

Lack of best practices

Feedback (optional)

0/500

Select at least one type of issue, and enter your comments or suggestions.

Enter a maximum of 500 characters.

Submit Cancel

For any further questions, feel free to contact us through the chatbot.

Chatbot