Thanks Reynold.
Not sure why doExecute is not invoked, since CollectLimit does not support
wholeStage
case class CollectLimit(limit: Int, child: SparkPlan) extends UnaryNode {
I will dig further into this.
Zhan Zhang
On Apr 18, 2016, at 10:36 PM, Reynold Xin
<r...@databricks.com<mailto:r...@databricks.com>> wrote:
Anyway we can verify this easily. I just added a println to each row and
verified that only limit + 1 row was printed after the join and before the
limit.
It'd be great if you do some debugging yourself and see if it is going through
some other code path.
On Mon, Apr 18, 2016 at 10:35 PM, Reynold Xin
<r...@databricks.com<mailto:r...@databricks.com>> wrote:
But doExecute is not called?
On Mon, Apr 18, 2016 at 10:32 PM, Zhan Zhang
<zzh...@hortonworks.com<mailto:zzh...@hortonworks.com>> wrote:
Hi Reynold,
I just check the code for CollectLimit, there is a shuffle happening to collect
them in one partition.
protected override def doExecute(): RDD[InternalRow] = {
val shuffled = new ShuffledRowRDD(
ShuffleExchange.prepareShuffleDependency(
child.execute(), child.output, SinglePartition, serializer))
shuffled.mapPartitionsInternal(_.take(limit))
}
Thus, there is no way to avoid processing all data before the shuffle. I think
that is the reason. Do I understand correctly?
Thanks.
Zhan Zhang
On Apr 18, 2016, at 10:08 PM, Reynold Xin
<r...@databricks.com<mailto:r...@databricks.com>> wrote:
Unless I'm really missing something I don't think so. As I said, it goes
through an iterator and after processing each stream side we do a shouldStop
check. The generated code looks like
/* 094 */ protected void processNext() throws java.io.IOException {
/* 095 */ /*** PRODUCE: Project [id#79L] */
/* 096 */
/* 097 */ /*** PRODUCE: BroadcastHashJoin [id#79L], [id#82L], Inner,
BuildRight, None */
/* 098 */
/* 099 */ /*** PRODUCE: Range 0, 1, 8, 100, [id#79L] */
/* 100 */
/* 101 */ // initialize Range
/* 102 */ if (!range_initRange) {
/* 103 */ range_initRange = true;
/* 104 */ initRange(partitionIndex);
/* 105 */ }
/* 106 */
/* 107 */ while (!range_overflow && range_number < range_partitionEnd) {
/* 108 */ long range_value = range_number;
/* 109 */ range_number += 1L;
/* 110 */ if (range_number < range_value ^ 1L < 0) {
/* 111 */ range_overflow = true;
/* 112 */ }
/* 113 */
/* 114 */ /*** CONSUME: BroadcastHashJoin [id#79L], [id#82L], Inner,
BuildRight, None */
/* 115 */
/* 116 */ // generate join key for stream side
/* 117 */
/* 118 */ // find matches from HashedRelation
/* 119 */ UnsafeRow bhj_matched = false ? null:
(UnsafeRow)bhj_relation.getValue(range_value);
/* 120 */ if (bhj_matched == null) continue;
/* 121 */
/* 122 */ bhj_metricValue.add(1);
/* 123 */
/* 124 */ /*** CONSUME: Project [id#79L] */
/* 125 */
/* 126 */ System.out.println("i got one row");
/* 127 */
/* 128 */ /*** CONSUME: WholeStageCodegen */
/* 129 */
/* 130 */ project_rowWriter.write(0, range_value);
/* 131 */ append(project_result);
/* 132 */
/* 133 */ if (shouldStop()) return;
/* 134 */ }
/* 135 */ }
/* 136 */ }
shouldStop is false once we go pass the limit.
On Mon, Apr 18, 2016 at 9:44 PM, Zhan Zhang
<zzh...@hortonworks.com<mailto:zzh...@hortonworks.com>> wrote:
>From the physical plan, the limit is one level up than the WholeStageCodegen,
>Thus, I don’t think shouldStop would work here. To move it work, the limit has
>to be part of the wholeStageCodeGen.
Correct me if I am wrong.
Thanks.
Zhan Zhang
On Apr 18, 2016, at 11:09 AM, Reynold Xin
<r...@databricks.com<mailto:r...@databricks.com>> wrote:
I could be wrong but I think we currently do that through whole stage codegen.
After processing every row on the stream side, the generated code for broadcast
join checks whether it has hit the limit or not (through this thing called
shouldStop).
It is not the most optimal solution, because a single stream side row might
output multiple hits, but it is usually not a problem.
On Mon, Apr 18, 2016 at 10:46 AM, Andrew Ray
<ray.and...@gmail.com<mailto:ray.and...@gmail.com>> wrote:
While you can't automatically push the limit *through* the join, we could push
it *into* the join (stop processing after generating 10 records). I believe
that is what Rajesh is suggesting.
On Tue, Apr 12, 2016 at 7:46 AM, Herman van Hövell tot Westerflier
<hvanhov...@questtec.nl<mailto:hvanhov...@questtec.nl>> wrote:
I am not sure if you can push a limit through a join. This becomes problematic
if not all keys are present on both sides; in such a case a limit can produce
fewer rows than the set limit.
This might be a rare case in which whole stage codegen is slower, due to the
fact that we need to buffer the result of such a stage. You could try to
disable it by setting "spark.sql.codegen.wholeStage" to false.
2016-04-12 14:32 GMT+02:00 Rajesh Balamohan
<rajesh.balamo...@gmail.com<mailto:rajesh.balamo...@gmail.com>>:
Hi,
I ran the following query in spark (latest master codebase) and it took a lot
of time to complete even though it was a broadcast hash join.
It appears that limit computation is done only after computing complete join
condition. Shouldn't the limit condition be pushed to BroadcastHashJoin
(wherein it would have to stop processing after generating 10 rows?). Please
let me know if my understanding on this is wrong.
select l_partkey from lineitem, partsupp where ps_partkey=l_partkey limit 10;
>>>>
| == Physical Plan ==
CollectLimit 10
+- WholeStageCodegen
: +- Project [l_partkey#893]
: +- BroadcastHashJoin [l_partkey#893], [ps_partkey#908], Inner,
BuildRight, None
: :- Project [l_partkey#893]
: : +- Filter isnotnull(l_partkey#893)
: : +- Scan HadoopFiles[l_partkey#893] Format: ORC,
PushedFilters: [IsNotNull(l_partkey)], ReadSchema: struct<l_partkey:int>
: +- INPUT
+- BroadcastExchange
HashedRelationBroadcastMode(true,List(cast(ps_partkey#908 as
bigint)),List(ps_partkey#908))
+- WholeStageCodegen
: +- Project [ps_partkey#908]
: +- Filter isnotnull(ps_partkey#908)
: +- Scan HadoopFiles[ps_partkey#908] Format: ORC,
PushedFilters: [IsNotNull(ps_partkey)], ReadSchema: struct<ps_partkey:int> |
>>>>
--
~Rajesh.B
