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Table of Contents
Adjust system kernel parameters
Optimize HDFS configuration files
Improve hardware resources
Other optimization suggestions
Start and verify configuration
Home Operation and Maintenance CentOS How to optimize HDFS configuration on CentOS

How to optimize HDFS configuration on CentOS

May 19, 2025 pm 08:18 PM
centos compression technology red

Optimizing the performance of Hadoop distributed file system (HDFS) on CentOS systems can be achieved through a variety of methods, including adjusting system kernel parameters, optimizing HDFS configuration files, and improving hardware resources. Here are the detailed optimization steps and suggestions:

Adjust system kernel parameters

  • Increase the limit on the number of files opened by a single process : Use ulimit -n 65535 command to temporarily adjust. If it needs to take effect permanently, please edit /etc/security/limits.conf and /etc/pam.d/login files.
  • Optimize TCP parameters : Edit /etc/sysctl.conf file, add or modify the following content:
     <code>net.ipv4.tcp_tw_reuse = 1 net.core.somaxconn = 65535 net.ipv4.ip_local_port_range = 1024 65535</code>

    Then execute the sysctl -p command to apply these changes.

Optimize HDFS configuration files

  • core-site.xml :
     <code><configuration> <property> <name>fs.defaultFS</name> <value>hdfs://namenode:9020</value> </property> </configuration></code>
  • hdfs-site.xml :
     <code><configuration> <property> <name>dfs.replication</name> <value>3</value> </property> <property> <name>dfs.block.size</name> <value>128M</value> </property> <property> <name>dfs.namenode.handler.count</name> <value>20</value> </property> <property> <name>dfs.datanode.handler.count</name> <value>30</value> </property> </configuration></code>

Improve hardware resources

  • Using SSD : Replacing a hard drive (HDD) with a solid state drive (SSD) can significantly improve I/O performance.
  • Increase memory and CPU : Appropriately increase the server's memory and CPU resources according to the size and workload of the cluster.

Other optimization suggestions

  • Avoid small file problems : small files will increase the burden on NameNode, and the pressure on NameNode should be reduced by merging small files.
  • Data localization : By increasing the number of DataNodes, data blocks are stored near the client as much as possible, reducing network transmission.
  • Using compression technology : select the appropriate compression algorithm (such as Snappy, LZO or Bzip2), and enable compression by configuring mapreduce.map.output.compress parameters to reduce storage space and network transmission time.

Start and verify configuration

  • Format NameNode (on first execution):
     <code>hdfs namenode -format</code>
  • Start HDFS :
     <code>sbin/start-dfs.sh</code>
  • Verify the configuration : Use the jps command to view the process and make sure NameNode and DataNode have been started successfully.

When implementing these optimization measures, it is recommended to adjust according to specific business needs and cluster size, and conduct sufficient testing in the production environment to verify the effectiveness of the optimization effect.

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