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
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