Dear Ryan, Thanks a lot for your answer. After having sent the e-mail we have investigated a bit more the data itself. It happened that for certain days it was very skewed and one of the row groups had much more records that all others. This was somehow related to the fact that we have sorted it using our object ids and by chance those that went first were smaller (or compressed better). So the Parquet file had a 6 rows groups where the first one had 300k rows and others only 30k rows. The search for a given object fell into the first row group and lasted very long time. The data itself was very much compressed as it contained a lot of zeros. To give some numbers the 600MB parquet file expanded to 56GB in JSON.
What we did is to sort the data not by object id but by the record timestamp which resulted in much more even data distribution among the row groups. This in fact helped a lot for the query time (using the timestamp & object id) I have to say that I haven't fully understood this phenomenon yet as I’m not a Parquet format & reader expert (at least not yet). Maybe it is just a simple function of how many records Spark has to scan and the level of parallelism (searching for a given object id when sorted by time needs to scan all/more the groups for larger times). One question here - is Parquet reader reading & decoding the projection columns even if the predicate columns should filter the record out? Unfortunately we have to have those big columns in the query as people want to do analysis on them. We will continue to investigate… Cheers, Jakub On 12 Jun 2018, at 22:51, Ryan Blue <rb...@netflix.com<mailto:rb...@netflix.com>> wrote: Jakub, You're right that Spark currently doesn't use the vectorized read path for nested data, but I'm not sure that's the problem here. With 50k elements in the f1 array, it could easily be that you're getting the significant speed-up from not reading or materializing that column. The non-vectorized path is slower, but it is more likely that the problem is the data if it is that much slower. I'd be happy to see vectorization for nested Parquet data move forward, but I think you might want to get an idea of how much it will help before you move forward with it. Can you use Impala to test whether vectorization would help here? rb On Mon, Jun 11, 2018 at 6:16 AM, Jakub Wozniak <jakub.wozn...@cern.ch<mailto:jakub.wozn...@cern.ch>> wrote: Hello, We have stumbled upon a quite degraded performance when reading a complex (struct, array) type columns stored in Parquet. A Parquet file is of around 600MB (snappy) with ~400k rows with a field of a complex type { f1: array of ints, f2: array of ints } where f1 array length is 50k elements. There are also other fields like entity_id: long, timestamp: long. A simple query that selects rows using predicates entity_id = X and timestamp >= T1 and timestamp <= T2 plus ds.show() takes 17 minutes to execute. If we remove the complex type columns from the query it is executed in a sub-second time. Now when looking at the implementation of the Parquet datasource the Vectorized* classes are used only if the read types are primitives. In other case the code falls back to the parquet-mr default implementation. In the VectorizedParquetRecordReader there is a TODO to handle complex types that "should be efficient & easy with codegen". For our CERN Spark usage the current execution times are pretty much prohibitive as there is a lot of data stored as arrays / complex types… The file of 600 MB represents 1 day of measurements and our data scientists would like to process sometimes months or even years of those. Could you please let me know if there is anybody currently working on it or maybe you have it in a roadmap for the future? Or maybe you could give me some suggestions how to avoid / resolve this problem? I’m using Spark 2.2.1. Best regards, Jakub Wozniak --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org<mailto:dev-unsubscr...@spark.apache.org> -- Ryan Blue Software Engineer Netflix
