huaxingao opened a new pull request, #6981:
URL: https://github.com/apache/iceberg/pull/6981
Push down min/max/count with group by if group by is on partition columns
For example:
```
CREATE TABLE test (id LONG, ts TIMESTAMP, data INT) USING iceberg
PARTITIONED BY (id, ts);
SELECT MIN(data), MAX(data), COUNT(data), COUNT(*) FROM test GROUP BY id, ts;
```
I have the following changes:
1. Added Expression `GroupBy`, `UnboundGroupBy`, `BoundGroupBy`
2. In `Aggregator`, I will take consideration of `GroupBy`. Use
`MaxAggregator` as an example:
- Add `List<BoundGroupBy>` in the constructor
- Add `private Map<StructLike, T> maxP` which contains max value for each
of the partition.
- The key of the above Map is `AggregateEvaluator.ArrayStructLike`, which
contains the partition values.
- Add `update(R value, StructLike partitionKey)` to update value for
specific partitions
- Add `Map<StructLike, R> currentPartition()` to get current value for
each partitions
4. Build the Scan schema to be: group by columns + aggregates (this is what
Spark is expecting)
Use `SELECT MIN(data), MAX(data), COUNT(data), COUNT(*) FROM test GROUP BY
id, ts` as an example:
the schema is
`LONG, TIMESTAMP, INT, INT, LONG, LONG`,
which corresponds to
`id, ts, MIN(data), MAX(data), COUNT(data), COUNT(*)`
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