Apache Spark What happens when repartition($"key") is called when size of all records per key is greater than the size of a single partition?

Question

Suppose I have a dataframe of 10GB with one of the column's "c1" having same value for every record. Each single partition is maximum 128 MB(default value). Suppose i call repartition($"c1"), then will all the records be shuffled to the same partition? If so, wouldn't it exceed the maximum size per partition? How would repartition work in this case?

Answer 1

The configuration spark.sql.files.maxPartitionBytes is effective only when reading files from file-based sources. So when you execute repartition, you reshuffle your existing Dataframe and the number of output partitions will be defined by repartition logic, which in your case will be 1.

Answer 2

The value of 128 MB comes from the spark property spark.sql.files.maxPartitionBytes which is only applicable when you create a dataframe after reading a file based source. Refer there for details https://spark.apache.org/docs/latest/sql-performance-tuning.html#other-configuration-options. This is to achieve max parallelism while reading. So, if you create a dataframe after transforming another dataframe or joining two dataframe, the partitions are not affect be this value. For example, you can read 10 GB of data and to a df.repartition(1) and this should work without any issues(assuming your executor has enough memory)