HappenLee commented on issue #3438:
URL:
https://github.com/apache/incubator-doris/issues/3438#issuecomment-625759402
### About the POC
At present, only aggregatenode has been verified .
* 1. First, we do tuple convert columnVector and **time consuming of
conversion operation is recorded.**
```
{
Timer time;
FOREACH_ROW_LIMIT(in_batch, group_start, cache_size,
in_batch_iter) {
// Hoist lookups out of non-null branch to speed up non-null
case.
TupleRow *in_row = in_batch_iter.get();
for (int i = 0; i < agg_fn_evals_.size(); ++i) {
void** values = static_cast<void
**>(valuesVector[i].col_data());
values[count] = agg_fn_evals_[i]->getAggValue(in_row);
}
for (int i = 0; i < ht_ctx->get_key_size(); ++i) {
void** keys = static_cast<void
**>(keysVector[i].col_data());
keys[count] = ht_ctx->get_key(in_row, i);
}
count++;
}
_cost_time += time.stop();
}
```
* 2. Then we do aggregate use column storage
```
EvalAndHashPrefetchGroup(keysVector, count, ht_ctx,
remaining_capacity, tuples);
for (int i = 0; i < agg_fn_evals_.size(); ++i) {
UpdateTuple(agg_fn_evals_[i], tuples,
static_cast<void**>(valuesVector[i].col_data()), count);
}
```
* 3. Execute the following query statement in a table of 70
million。```select max(C_PHONE) from customer group by C_MKTSEGMENT;```
Count the time of aggratetation. we can find deducte the time of row
conversion, **the time cut in half**.
Statistic|origin| row convert to column |
:-:|:-:|:-:
Time | 4.19 Sec | 2.47 Sec |
* 4. In this case, the performance is improved through the **execution model
of the column store**。
In addition to the aggregatenode, another part of POC is the expression
calculation and filtering part, such as AND OR filtering calculations whether
can be improved by vectorization.
**Join and sort can be ignored in this period, so if the query contains join
or sort, it needs to be able to identify and use non vectorized execution path
for execution by query planning.**
### Implementation and Design Detail
There is three important parts: **Rewriting of scannode, Rewriting of
expression calculation, Rewriting of aggregatenode**.
#### Rewrite the scanner section first:
* At present, for agg key and unique key, it is necessary to perform data
pre aggregation and
other operations by heap sort. **It seems default to avoid the
transformation of columns and
rows. So we only implement vectorized execution in dup_key.**
we can use **Rowblockv2 to be an abstraction of the column storage model, it
is used to transfer data between each node**。we don't need convert **Rowblockv2
to old Rowblock**.
* Implement the vectorization of individual expression (For example, the
first phase can only
implement simple expressions such as =/</>).
We can make a judgment when querying the plan. If query on dup_key and only
have simple expressions,
the vectorization interface will be called. The pseudo code is as follows:
```
VectorizedBatch vbatch = _reader->next_batch_with_aggregation();
vector<rowId> selectedRowId = Expr.eval(vbatch);
convertVecBatchToRowBatch(vbatch, selectedRowId, output_rowbatch);
```
**If there are other expressions that are not supported temporarily,
continue to use the old interface.** The input of new and old interfaces is
different (one is row, the other is vbatch), but the output is the same (both
are the original rowbatch of query layer)
* Support more expr calculations, such as IN, FUNCTION and so on. **Finally
implement all vectorization
execution in scanner stage.** The output of scanner to the upper layer is
still the old rowbatch, **but the internal layer and the storage layer is
vectorized.**
#### Rewrite the scannode section second:
* **Rewrite the interface between scan node and scanner to vectorization.**
The ultimate goal is output of the upper layer of the sctopan node is the old
rowbatch, but the output of the lower layer of the scan node (scanner and
storage layer) is vectorized。
So far, the changes of scan part and expr part are basically completed. We
can rewrite the aggregate node now.
#### Rewrite the aggregatenode section finally:
* Design and implement a new hash table structure that is compatible with
the column storage model
* Rewrite the computing model of the corresponding aggregation operator to
support the vectorization
* After all of that，**the final goal of the first phase is to vectorize the
aggregate query of a single table.**
If there is a join of multiple tables or a sort node, the old execution
logic is still used.
Later, we can gradually extend the vectorization calculation to other
execution nodes. **It's going to be a long-term plan.**
#### RelationShip between Rowblock, Rowblockv2, Columnblock
@kangkaisen
* **Rowblock is an old definition. At present, the storage layer and query
layer still use rowblock interface to pass data.** The query layer accesses the
data inside rowblock through rowcursor. Data in rowblock is stored
by row.
* **Rowblockv2 is a new definition which is vectorized. It contains multiple
columnblocks.** Each columnblock is a column of data. Rowblockv2 is actually
the newly defined vectorizedrowbatch.New segment V2 data is read in rowblockv2
format. The BetaRowsetReader will convert rowblockv2 to rowblock to returns the
data.
* The reader gets the next line at a time through the internal collect
iterator. The collect iter will merge all rowsetreaders and return by row.
**The merger operations here are based on rowcursor and rowblock.So it needs to
be rewrite reader first.**
* Now Doris has dup_key, agg_key and unique_key storage models. Dup_key does
not need to do aggregation logic. **So we can first transform this part that
directly deliver the corresponding data to the upper layer through columnvector
rather than row_cursor.** At present, for agg key and unique key, it is
necessary to perform data pre aggregation and other operationsby heap sort. It
seems default to avoid the transformation of columns and rows.
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