Apache Iceberg: The Definitive Guide

Chapter 4. Optimizing the Performance of Iceberg Tables

As you saw in Chapter 3, Apache Iceberg tables provide a layer of metadata that allows the query engine to create smarter query plans for better performance. However, this metadata is only the beginning of how you can optimize the performance of your data.

You have various optimization levers at your disposal, including reducing the number of datafiles, data sorting, table partitioning, row-level update handling, metrics collection, and external factors. These levers play a vital role in enhancing data performance, and this chapter explores each of them, addressing potential slowdowns and providing acceleration insights. Implementing robust monitoring with preferred tools is crucial for identifying optimization needs, including the use of Apache Iceberg metadata tables, which we will cover in Chapter 10.

Compaction

Every procedure or process comes at a cost in terms of time, meaning longer queries and higher compute costs. Stated differently, the more steps you need to take to do something, the longer it will take for you to do it. When you are querying your Apache Iceberg tables, you need to open and scan each file and then close the file when you’re done. The more files you have to scan for a query, the greater the cost these file operations will put on your query. This problem is magnified in the world of streaming or “real-time” data, where data is ingested as it is created, generating lots of files with only a few records ...