Hi, I had a few questions related to compaction support, in particular compaction for CDC destination iceberg tables. Perhaps this information is available somewhere else, but I could not find it readily, so responses appreciated.
1. I believe compaction for the CDC use case will require iceberg version >= 0.13 (to pick up the change that maintains the same sequence numbers after compaction) and Spark version >= 3.0 (for the actual compaction action support). But please correct me if I'm wrong. 2. How can the compaction action (via Spark) actually be triggered? Is it possible to specify filter predicate as well as the size and number of delete file thresholds for the compaction strategy via SQL statements or does one have to use the XXXRewriteDataFilesSparkAction classes directly from within a spark jar. 3. As far as I could understand from reading the code, the rewrite action processes all the data that matches a filter predicate (most likely a partition in practice). Internally the whole matched data is broken into smaller chunks which are processed concurrently. Any thoughts on setting a limit on the amount of work being done by the whole operation. I am worried about really large partitions where even though the whole operation is broken into chunks; it will take a long time to finish. 4. The regular compaction will remove the need for equality and position delete files, but those files will still be around. Is there a separate compaction action being planned to actually remove the equality and position delete files? Thanks, - Puneet
