Achieving optimal performance for data lakes stored on object storage like S3 using formats like Apache Iceberg can present challenges, particularly as tables grow and evolve. Over time, numerous small data files accumulate due to frequent writes, leading to fragmented data. This fragmentation severely impacts read query performance because query engines must scan and process many files, increasing read amplification and reducing data locality.
Fortunately, Iceberg provides powerful features to combat this degradation: compaction. Compaction is the process of merging smaller data files into larger ones and reorganizing data within those files to improve read efficiency. Two particularly effective compaction strategies for boosting performance are sort compaction and Z-order compaction.
Sort compaction involves physically sorting the data within each data file based on one or more specified columns. When queries filter or range over these sorted columns, the query engine can quickly prune irrelevant files and blocks within files, drastically reducing the amount of data that needs to be read from S3. This is especially beneficial for range scans and equality lookups on high-cardinality columns.
For queries involving multiple filtering columns, Z-order compaction offers a superior approach. Z-ordering is a multi-dimensional spatial indexing technique that arranges data points along a space-filling curve. By applying Z-order compaction based on several key query dimensions, data points that are close together in multi-dimensional space are stored near each other physically on S3. This significantly improves data locality for multi-column filters, allowing query engines to skip large portions of data that do not satisfy the predicate across multiple columns simultaneously.
Implementing these compaction strategies regularly is crucial for maintaining high query performance. It involves leveraging Iceberg’s built-in capabilities, often orchestrated through engines like Spark, Flink, or Trino, to run scheduled compaction jobs. By reducing the number of files and organizing data intelligently through sort and Z-order compaction, you minimize read amplification, lower S3 costs associated with data retrieval, and ensure your Apache Iceberg tables remain fast and efficient for analytical workloads. Proactively managing file layout through strategic compaction is key to unlocking the full potential of your Iceberg data lake on S3.
Source: https://aws.amazon.com/blogs/aws/new-improve-apache-iceberg-query-performance-in-amazon-s3-with-sort-and-z-order-compaction/
