The main bottlenecks of hadoop cluster are data transmission bottleneck and resource utilization bottleneck.

In Hadoop cluster, data transmission is a major bottleneck. In MapReduce task, data needs to be read from distributed storage system and transmitted between nodes, which will lead to network bandwidth bottleneck and delay. In order to optimize data transmission, we can use compression algorithm to reduce the amount of data. For example, you can use the Gzip compression algorithm to compress and decompress data.

In Hadoop cluster, resource utilization is also an important bottleneck. Due to the limited resources of the cluster, the task may be limited due to insufficient resources. In order to optimize the utilization of resources, we can use containerization technology to manage and isolate tasks. This can make better use of cluster resources and allocate appropriate resources for each task.

Hadoop cluster

Hadoop cluster is a distributed system composed of multiple computers, which work together to store and process large-scale data sets. Hadoop software framework based on Apache includes two core components, namely Hadoop distributed file system and Hadoop distributed computing framework. Advantages of Hadoop cluster include high reliability, high scalability and high cost performance. It can handle large-scale data sets and provides a powerful distributed computing framework for analyzing and processing these data sets.

Hadoop distributed file system is a reliable and highly extensible file system, which aims to store large data sets and provide data access and processing methods. HDFS divides data into blocks, and stores each block on different nodes in the cluster, thus realizing redundant backup and fault tolerance of data. HDFS also provides high scalability because it can easily add new nodes to expand storage capacity.