The number of partitions should be determined based on the amount of data and the size of the cluster: typically, the number of partitions should be proportional to the CPU cores and memory size of the cluster. In general, each partition should contain at least 128MB of data.
Determine the number of partitions based on the type of task and data skew: If there is data skew in the task, increase the number of partitions to reduce its impact on performance.
Consider the data compression situation: if the data is compressed, it may be necessary to adjust the number of partitions to accommodate the compressed data volume.
Consider the situation of data skew: if the data skew is severe, you may want to consider using a custom partitioning strategy to evenly distribute the data across different partitions, in order to improve task parallelism and performance.
Monitoring job performance and dynamically adjusting the number of partitions: During the operation of the job, you can monitor the execution and performance of tasks in real-time, and dynamically adjust the number of partitions to achieve optimal performance.
