Have you ever come across the small file problem? What are the issues?

Small file problems manifest in several ways: 1) Metadata overhead: HDFS stores metadata for each file in the NameNode, so thousands of small files can exhaust memory and slow down name resolution. 2) Job scheduling overhead: MapReduce and Spark launch a task per file or per split, so many small files increase the number of tasks, leading to higher scheduling latency and resource contention. 3) Poor compression: Small files cannot be compressed efficiently, resulting in higher storage usage and slower I/O. 4) Skewed data distribution: Small files often indicate uneven data partitioning, causing some executors to be idle while others are overloaded. 5) Increased network traffic: Each file read may involve separate network calls, amplifying latency. Addressing these issues involves consolidating files, using appropriate partitioning, enabling file merging during write, and employing compaction jobs to maintain optimal file sizes.