ic4y commented on issue #956:
URL:
https://github.com/apache/arrow-datafusion/issues/956#issuecomment-987649287
@alamb In the count sql, I use pprof to count the time consumed by the
following methods. The long time consumption is mainly in `make slice`,
`hashbrown get_mut`, `create_hashes`. The time consumed by creating the final
array and aggregate updates method is relatively small.
| func | time |
| ---- | ----|
|arrow::array::array::Array::slice|26% of all
|hashbrown::raw::inner::RawTable<T,A>::get_mut|22% of all
|datafusion::physical_plan::hash_utils::create_hashes|6.5% of all
|datafusion::physical_plan::AccumulatorFly>::update_batch|2.5% of all
|datafusion::physical_plan::hash_aggregate_fly::create_batch_from_map|<0.1%
of all
Corresponding to the following code snippets
1、array slice and update_batch
```rust
groups_with_rows.iter()
.zip(offsets.windows(2))
.try_for_each(|(group_idx, offsets)| {
accumulators.group_indices[*group_idx].clear();
accumulators.accumulator_items.iter_mut()
.zip(values.iter())
.try_for_each(|(accumulator, aggr_array)| {
let values = aggr_array
.iter()
.map(|array| {
array.slice(offsets[0], offsets[1] - offsets[0])
})
.collect::<Vec<ArrayRef>>();
match mode {
AggregateMode::Partial =>
accumulator.update_batch(*group_idx, &values),
AggregateMode::FinalPartitioned |
AggregateMode::Final => {
accumulator.merge_batch(*group_idx, &values)
}
}
})
});
```
2、hashbrown get_mut
```rust
let entry = map.get_mut(hash, |(_hash, group_idx)| {
let group_state_c = &group_by_values[*group_idx];
group_values
.iter()
.zip(group_state_c.iter())
.all(|(array, scalar)| scalar.eq_array(array, row)) //
eq_array takes a long time
});
```
3、create_hashes
```rust
create_hashes(&group_values, random_state, &mut batch_hashes)?;
```
4、create_batch_from_map
```rust
let batch = create_batch_from_map(&mode, &accumulators, group_expr.len(),
&schema);
```
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