How to optimize HDFS on CentOS

Optimizing HDFS (Hadoop Distributed File System) on CentOS can be done from multiple aspects, including configuration adjustment, hardware optimization, performance optimization, etc. Here are some specific optimization steps and tips:

1. Configuration adjustment

• Adjust block size : Adjust block size according to workload. Larger blocks can improve read efficiency but increase data localization difficulty.
• Increase number of replicas : Increase data reliability, but increases storage costs. Adjust the number of replicas based on the importance of the data and the frequency of access.
• Avoid small files : Small files will increase NameNode load and reduce performance, and should be avoided as much as possible.
• Use compression technology : Reduce storage space and network transfer time, but consider CPU overhead.
• Hardware upgrade : Use faster CPU, memory, hard disk and network devices.
• Cluster horizontal scaling : expand the cluster by adding NameNode and DataNode to improve processing power.

2. Performance Tuning

• Heartbeat concurrency optimization : Edit the hdfs-site.xml file and increase the value of dfs.namenode.handler.count appropriately to improve the concurrency ability of NameNode to handle DataNode heartbeat and client metadata operations.
• Turn on the HDFS Recycle Bin : Modify the fs.trash.interval and fs.trash.checkpoint.interval values ​​in core-site.xml to enable and manage the Recycle Bin function to protect data from being deleted by mistake and allow recovery.
• Data locality : By increasing the number of DataNodes, data blocks are stored near the client as much as possible, reducing network transmission.
• Read and write performance optimization : Optimize NameNode RPC response delay and use efficient transmission protocols.
• Cache optimization : utilizes the block caching mechanism to improve read performance by reasonably setting cache size and policies.

3. Operating system optimization

• Turn off unnecessary services : reduce the use of system resources.
• Adjust file descriptor limits : Add file descriptor limits to improve the system's concurrent processing capabilities.
• Manage sudo permissions : Make sure Hadoop runs in an optimized system environment.

4. Hardware planning

• CPU, memory and hard disk ratio : hardware selection is made according to application needs and budget.
• Network Throughput : It is recommended that each node provide sufficient network bandwidth to support the needs of data transmission and task scheduling.

When performing the above optimization, it is recommended to adjust according to the specific business needs and cluster size, and conduct sufficient testing in the production environment to ensure the effectiveness of optimization measures.

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