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[jira] [Created] (FLINK-10100) Optimizer pushes partitioning past Null-Filter

Fabian Hueske created FLINK-10100:

             Summary: Optimizer pushes partitioning past Null-Filter
                 Key: FLINK-10100
                 URL: https://issues.apache.org/jira/browse/FLINK-10100
             Project: Flink
          Issue Type: Bug
          Components: DataSet API, Optimizer
    Affects Versions: 1.5.2, 1.4.2, 1.3.3, 1.6.0, 1.7.0
            Reporter: Fabian Hueske

The DataSet optimizer pushes certain operations like partitioning or sorting past Filter operators.
It does that because it knows that a {{FilterFunction}} cannot modify the records but only indicate whether a record should be forwarded or not. 

However, this causes problems if the filter should remove records with null keys. In that case, the partitioning can be pushed past the filter such that the partitioner has to deal with null keys. This can fail with a {{NullPointerException}}.

The following code produces an affected plan.

List<Row> rowList = new ArrayList<>();
rowList.add(Row.of(null, 1L));
rowList.add(Row.of(2L, 2L));
rowList.add(Row.of(2L, 2L));
rowList.add(Row.of(3L, 3L));
rowList.add(Row.of(null, 3L));

DataSet<Row> rows = env.fromCollection(rowList, Types.ROW(Types.LONG, Types.LONG));

DataSet<Long> result = rows
  .filter(r -> r.getField(0) != null)
  .reduceGroup((Iterable<Row> vals, Collector<Long> out) -> {
      long cnt = 0L;
      for(Row v : vals) { cnt++; }

result.output(new DiscardingOutputFormat());

To resolve the problem, we could remove the field-forward property of {{FilterFunction}}. In general, it is typically more efficient to filter before shipping or sorting data. So this might also improve the performance of certain plans.

As a *workaround* until this bug is fix, users can implement the filter with a {{FlatMapFunction}}. {{FlatMapFunction}} is a more generic interface and the optimizer cannot automatically infer how the function behaves and won't push partitionings or sorts past the function.

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