March 2021 Summaries

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.

Apache Kafka is a distributed, partitioned, replicated commit log service used by enterprises for high throughput, low latency data streaming applications. Consumer lag, the gap between the latest offset and consumer offset, can occur due to multiple reasons such as complex application logic, stuck consumers, slow message processing, uneven distribution of partitions among consumers, and rebalance events. Monitoring tools are available to help identify and address consumer lag issues in Kafka environments.