How to Manage Small File Problems in Your Data Lake

Big Data systems face a small file problem that hampers productivity and wastes valuable resources. This issue is caused by the inefficient handling of numerous small files, leading to poor Namenode memory utilization, RPC calls, and reduced application layer performance. The problem affects distributed file systems like HDFS, where smaller file sizes mean more overhead when reading those files. Slow files can slow down reads, processing jobs, and waste storage space, resulting in stale data and slower decision-making processes. To manage small files effectively, it is crucial to identify their sources, perform cleanup tasks such as compaction and deletion, and use appropriate tools for monitoring and optimization.