Add 3 counters for ExecNode:
ExecTime - Total execution time(excluding the execution time of children).
OutputBytes - The total number of bytes output to parent.
BlockCount - The total count of blocks output to parent.
in join node, if it's broadcast_join
and shared hash table, some counter/timer about build hash table is useless,
so we could add those counter/timer in faker profile, and those will not display in web profile.
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
1. Remove an exec node method corresponding to a span and replace it with an exec node corresponding to a span;
2. Fix some problems with tracing in pipeline.
Co-authored-by: yiguolei <yiguolei@gmail.com>
Currently, exec node save exprcontext**, but the object is in object pool, the code is very unclear. we could just use exprcontext*.
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
We have added logical project before, but to actually finish the prune to reduce the data IO, we need to add related supports in translator and BE.
This PR:
- add projections on each ExecNode in BE
- translate PhysicalProject into projections on PlanNode in FE
- do column prune on ScanNode in FE
Co-authored-by: HappenLee <happenlee@hotmail.com>
Copy most of profiles from VOlapScanNode and VOlapScanner to NewOlapScanNode and NewOlapScanner.
Fix some blocking bug of new scan framework.
TODO:
Memtracker
Opentelemetry spen
The new framework is still disabled by default, so it will not effect other feature.
Hash join node adds three new attributes.
The following will take an SQL as an example to illustrate the meaning of these three attributes
```
select t1. a from t1 left join t2 on t1. a=t2. b;
```
1. vOutputTupleDesc：Tuple2(a'')
2. vIntermediateTupleDescList: Tuple1(a', b'<nullable>)
2. vSrcToOutputSMap: <Tuple1(a'), Tuple2(a'')>
The slot in intermediatetuple corresponds to the slot in output tuple one by one through the expr calculation of the left child in vsrctooutputsmap.
This code mainly merges the contents of two PRs:
1. [fix](vectorized) Support outer join for vectorized exec engine (https://github.com/apache/doris/pull/10323)
2. [Fix](Join) Fix the bug of outer join function under vectorization #9954
The following is the specific description of the first PR
In a vectorized scenario, the query plan will generate a new tuple for the join node.
This tuple mainly describes the output schema of the join node.
Adding this tuple mainly solves the problem that the input schema of the join node is different from the output schema.
For example:
1. The case where the null side column caused by outer join is converted to nullable.
2. The projection of the outer tuple.
The following is the specific description of the second PR
This pr mainly fixes the following problems:
1. Solve the query combined with inline view and outer join. After adding a tuple to the join operator, the position of the `tupleisnull` function is inconsistent with the row storage. Currently the vectorized `tupleisnull` will be calculated in the HashJoinNode.computeOutputTuple() function.
2. Column nullable property error problem. At present, once the outer join occurs, the column on the null-side side will be planned to be nullable in the semantic parsing stage.
For example：
```
select * from (select a as k1 from test) tmp right join b on tmp.k1=b.k1
```
At this time, the nullable property of column k1 in the `tmp` inline view should be true.
In the vectorized code, the virtual `tableRef` of tmp will be used in constructing the output tuple of HashJoinNode (specifically, the function HashJoinNode.computeOutputTuple()). So the **correctness** of the column nullable property of this tableRef is very important.
In the above case, since the tmp table needs to perform a right join with the b table, as a null-side tmp side, it is necessary to change the column attributes involved in the tmp table to nullable.
In non-vectorized code, since the virtual tableRef tmp is not used at all, it uses the `TupleIsNull` function in `outputsmp` to ensure data correctness.
That is to say, the a column of the original table test is still non-null, and it does not affect the correctness of the result.
The vectorized nullable attribute requirements are very strict.
Outer join will change the nullable attribute of the join column, thereby changing the nullable attribute of the column in the upper operator layer by layer.
Since FE has no mechanism to modify the nullable attribute in the upper operator tuple layer by layer after the analyzer.
So at present, we can only preset the attributes before the lower join as nullable in the analyzer stage in advance, so as to avoid the problem.
(At the same time, be also wrote some evasive code in order to deal with the problem of null to non-null.)
Co-authored-by: EmmyMiao87
Co-authored-by: HappenLee
Co-authored-by: morrySnow
Co-authored-by: EmmyMiao87 <522274284@qq.com>
In a vectorized scenario, the query plan will generate a new tuple for the join node.
This tuple mainly describes the output schema of the join node.
Adding this tuple mainly solves the problem that the input schema of the join node is different from the output schema.
For example:
1. The case where the null side column caused by outer join is converted to nullable.
2. The projection of the outer tuple.
