Iceberg Table Analyzer

This measures the time it takes a query engine like Apache Trino to read the entire table. Improving this metric can lead to faster queries, especially those that need to scan and process large portions of or the entire dataset.

This is the time it takes to scan the worst-performing partition of the table. Partitions with a high scan overhead can be a bottleneck for queries that target specific segments of data.

This indicates the number of files in the table. A large number of files requires maintaining significantly more metadata resulting in slow query planning and lower perceived query performance.

This shows the number of files in the most bloated partition. A large number of files in a partition can cause slower performance due to increased overhead in managing those files during query execution.

It’s the average size of the files in the table. Small files can lead to a “small file problem,” where the overhead of opening and closing files can dominate query execution time degrading overall query performance

This represents the total amount of data stored in the table. While this metric doesn’t directly affect performance, it gives a sense of the scale of the data and potential storage costs.