Spark UI中‘input size/Record’列Memory与Network区别咨询

Let’s break down what these two labels mean, and connect them directly to your slow task scenario:

• Memory
  When this shows up, it means the task is pulling input data straight from the local Executor’s in-memory cache. This could be data from a persisted RDD (like using persist(StorageLevel.MEMORY_ONLY)), or shuffle output that’s stored locally after a shuffle operation. Since there’s no network transfer involved—data is already in the Executor’s memory—these tasks usually run much faster, skipping the overhead of pulling data over the wire.

• Network
  This label tells you the task has to fetch its input data across the network. That could mean pulling shuffle data from a remote Executor, or grabbing blocks from an external storage system (like HDFS) on a distant DataNode. Network I/O adds significant latency: bandwidth bottlenecks, high load on the remote node, or uneven data partitioning (forcing some tasks to pull way more remote data than others) can all drag these tasks down. This directly explains why your non-speculative tasks are running sluggishly.

Why this ties to your slow tasks #

Your issue with slow non-speculative tasks showing "Network" points to a few likely causes:

• Skewed data partitioning: If your shuffle operations created lopsided partitions, some tasks end up having to pull data from dozens of remote nodes instead of just a handful.
• Missing or evicted cache: If you didn’t persist intermediate RDDs to memory, or if the cache was evicted due to memory pressure, tasks have to re-fetch data from remote sources instead of using local memory.
• Remote node/storage overload: If the DataNodes or Executors holding the required data are under heavy load, network transfers will be throttled, slowing down your tasks.

内容的提问来源于stack exchange，提问作者pigpigAutumn