Commit c481bdf significantly modified the way Spark SQL estimates the output data size of query plans. I've found that—with the new table query partition pruning support in 2.1—this has lead to in some cases underestimation of join plan child size statistics to a degree that makes executing such queries impossible without disabling automatic broadcast conversion.
In one case we debugged, the query planner had estimated the size of a join child to be 3,854 bytes. In the execution of this child query, Spark reads 20 million rows in 1 GB of data from parquet files and shuffles 722.9 MB of data, outputting 17 million rows. In planning the original join query, Spark converts the child to a BroadcastExchange. This query execution fails unless automatic broadcast conversion is disabled.
This particular query is complex and very specific to our data and schema. I have not yet developed a reproducible test case that can be shared. I realize this ticket does not give the Spark team a lot to work with to reproduce and test this issue, but I'm available to help. At the moment I can suggest running a join where one side is an aggregation selecting a few fields over a large table with a wide schema including many string columns.
This issue exists in Spark 2.0, but we never encountered it because in that version it only manifests itself for partitioned relations read from the filesystem, and we rarely use this feature. We've encountered this issue in 2.1 because 2.1 does partition pruning for metastore tables now.
As a back stop, we've patched our branch of Spark 2.1 to revert the reductions in default data type size for string, binary and user-defined types. We also removed the override of the statistics method in UnaryNode which reduces the output size of a plan based on the ratio of that plan's output schema size versus its children's. We have not had this problem since.