drusso opened a new pull request #8222:
URL: https://github.com/apache/arrow/pull/8222
This is a proposal for an initial and partial implementation of the
`DISTINCT` keyword. Only `COUNT(DISTINCT)` is supported, with the following
conditions:
(a) only one argument, i.e. `COUNT(DISTINCT col)`, but not `COUNT(DISTINCT
col, other)`,
(b) the argument is an integer type, and
(c) the query must have a `GROUP BY` clause.
**Implementation Overview:**
The `Expr::AggregateFunction` variant has a new field, `distinct`, which
mirrors the `distinct` flag from `SQLExpr::Function` (up until now this flag
was unused). Any `Expr::AggregateFunction` may have its `distinct` flag
switched to `true` if the keyword is present in the SQL query. However, the
physical planner respects it only for `COUNT` expressions.
The count distinct aggregation slots into the existing physical plans as a
new set of `AggregateExpr`. To demonstrate, below are examples of the physical
plans for the following query, where `c1` may be any data type, and `c2` is a
`UInt8` column:
```
SELECT c1, COUNT(DISTINCT c2) FROM t1 GROUP BY c1
```
(a) Multiple Partitions:
HashAggregateExec:
mode: Final
group_expr:
Column(c1)
aggr_expr:
DistinctCountReduce(Column(c2))
schema:
c1: any
c2: UInt64
input:
MergeExec:
input:
HashAggregateExec:
mode: Partial
group_expr:
Column(c1)
aggr_expr:
DistinctCount(Column(c2))
schema:
c1: any
c2: LargeList(UInt8)
input:
CsvExec:
schema:
c1: any
c2: UInt8
The `DistinctCount` accumulates each `UInt8` into a list of distinct
`UInt8`. No counts are collected yet, this is a partial result: lists of
distinct values. In the `RecordBatch`, this is a `LargeListArray<UInt8>`
column. After the `MergeExec`, each list in `LargeListArray<UInt8>` is
accumulated by `DistinctCountReduce` (via `accumulate_batch()`), producing the
_final_ sets of distinct values. Finally, given the finalized sets of distinct
values, the counts are computed (always as `UInt64`).
(b) Single Partition:
HashAggregateExec:
mode: NoPartial
group_expr:
Column(c1)
aggr_expr:
DistinctCountReduce(Column(c2))
schema:
c1: any
c2: UInt64
input:
CsvExec:
schema:
c1: any
c2: UInt8
This scenario is unlike the multiple partition scenario: `DistinctCount` is
_not_ used, and there are no partial sets of distinct values. Rather, in a
single `HashAggregateExec` stage, each `UInt8` is accumulated into a distinct
value set, then the counts are computed at the end of the stage.
`DistinctCountReduce` is used, but note that unlike the multiple partition
case, it accumulates scalars via `accumulate_scalar()`.
There is a new aggregation mode: `NoPartial`. In summary, the modes are:
- `NoPartial`: used in single-stage aggregations
- `Partial`: used as the first stage of two-stage aggregations
- `Final`: used as the second stage of two-stage aggregaions
Prior to the new `NoPartial` mode, `Partial` was handling both of what are
now the responsibilities of `Partial` and `NoPartial`. No distinction was
required, because _non-distinct_ aggregations (such as count, sum, min, max,
and avg) do not need the distinction: the first aggregation stage is always the
same, regardless of whether the aggregation is one-stage or two-stage. This is
not the case for a _distinct_ count aggregation, and we can see that in the
physical plans above.
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