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> wrote:
> But doExecute is not called?
>
> On Mon, Apr 18, 2016 at 10:32 PM, Zhan Zhang <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> 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>
>> 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> 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>
>>> 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> 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>:
>>>>>
>>>>>> 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
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
