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Updated on 2025-05-22 GMT+08:00
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PERF05-04 Optimizing Resources for Big Data Scenarios

  • Risk level

    Medium

  • Key strategies

    You can optimize resource usage and allocation to improve system performance and efficiency. The following are common methods to optimize resources:

    • Use distributed storage systems, such as Hadoop HDFS and Apache Cassandra, to store data on multiple nodes to improve data reliability and scalability.
    • Compress a large amount of data using compression algorithms to reduce the storage space and transmission bandwidth.
    • Use parallel computing frameworks, such as Apache Spark and Apache Flink, to distribute computing tasks to multiple nodes for parallel execution. This method improves the computing speed and efficiency.
    • Optimize the memory allocation and usage policies, such as using memory cache and memory mapping, to improve the data processing and computing speed and efficiency.
    • Use the load balancing technology to evenly distribute data and computing tasks to multiple nodes to prevent a single node overload and improve system availability and performance.
    • Divide data into multiple partitions according to certain rules to better process and compute data.
    • Optimize network parameters such as bandwidth and latency to improve data transmission speed and efficiency.
    • Clean and preprocess data to improve data quality and accuracy, and reduce the computing error rate and workload.
Parent topic: Resource Optimization