Great analysis of what iceberg does but don’t agree with so much criticism.

It is very basic compared to a database, and even when you go into details of databases there are many things that don’t make sense in terms of doing the absolute best thing.

You could ciritisize parquet in a similar way if you go through the spec but because it is open and so popular people are going to use it no matter what.

If you need more performance/efficiency simplicity etc. just don’t use parquet but have conversion between your format and parquet.

Or you can build on top of parquet with external indices, keeping metadata in memory and having a separate WAL for consistency.

Similarly it should be possible to build on top of iceberg spec to create something like a db server that is efficient.

It is unlikely for something so usable for so many use cases to be the technically pure and most sensible option.

I am currently considering whether it is worth moving our stack from Hive type tables to Iceberg. Iceberg is obviously technically more competent, but the Hive tables are just so nice because the data is almost orthogonal from the tables.

You can throw away a table and recreate it in minutes and vice versa you can edit the data and the table will adapt.

I am so used to this and I am worried of loosing this flexibility with Iceberg.

Maybe a mix is the way to go.

TFA is very well written by the way. From my perspective I see Iceberg as Hive tables 2.0. Solving a lot of the Hive related problems but not all generic database problems. So all new features are positive for me.

But my only gripe is - is the added complexity worth it?

I really don't get a lot of this criticism. For example, who is using iceberg with hundreds of concurrent committers, especially at the scale mentioned in the article (10k rows per second)? Using iceberg or any table format over object storage would be insane in that case. But for your typical spark application, you have one main writer (the spark driver) appending or merging a large number of records in > 1 minute microbatches and maybe a handful of maintenance jobs for compaction and retention; Iceberg's concurrency system works fine there.

If you have any use case like one the author describes, maybe use an in-memory cloud database with tiered storage or a plain RDBMS. Iceberg (and similar formats) work great for the use cases for which they're designed.