Optimizing Apache Iceberg Tables

Posted in Technical | February 14, 2024

Apache Iceberg has gained popularity for adding data warehouse-like capabilities to data lakes, making it easier to analyze both structured and unstructured data. This blog delves into strategies to optimize Iceberg tables to improve performance and productivity for data engineers and analysts.

Problem with Too Many Snapshots

Regularly expiring snapshots can help to delete unnecessary data files and keep the size of table metadata small, resulting in faster read/write times. The ‘expire_snapshots’ operation can be used to achieve this.

Problem with Suboptimal Manifests

The ‘rewrite_manifests’ operation can help to organize manifest files into a well-balanced hierarchical tree, improving query planning and runtime of metadata queries.

Problem with Delete Files

Iceberg V2 offers two options for handling updates: copy-on-write and merge-on-read. Considerations for isolation guarantees and performance implications are discussed, as well as strategies for managing delete files, such as compacting position delete files and performing a major compaction to physically remove delete files.

Problem with Small Files

The use of distribution mode in write properties can address partition amplification and file size issues by controlling the distribution of data during write operations.