For the in-memory cache, we still launch tasks, we just skip blocks when
possible using statistics about those blocks.
On Tue, Apr 5, 2016 at 12:14 PM, Darshan Singh <darshan.m...@gmail.com>
wrote:
> Thanks. It is not my exact scenario but I have tried to reproduce it. I
> have used 1.5.2.
>
> I have a part-movies data-frame which has 20 partitions 1 each for a movie.
>
> I created following query
>
>
> val part_sql = sqlContext.sql("select * from part_movies where movie = 10")
> part_sql.count()
>
> I expect that this should just read from 1 partition i.e. partition 10.
> Other partitions it should max read metadata and not the data.
>
> here is physical plan. I could see the filter. From here i can not say
> whether this filter is causing any partition pruning. If actually pruning
> is happening i would like to see a operator which mentions the same.
>
> == Physical Plan ==
> TungstenAggregate(key=[], functions=[(count(1),mode=Final,isDistinct=false)],
> output=[count#75L])
> TungstenExchange SinglePartition
> TungstenAggregate(key=[],
> functions=[(count(1),mode=Partial,isDistinct=false)],
> output=[currentCount#93L])
> Project
> Filter (movie#33 = 10)
> InMemoryColumnarTableScan [movie#33], [(movie#33 = 10)],
> (InMemoryRelation [movie#33,title#34,genres#35], true, 10000,
> StorageLevel(true, true, false, true, 1), (Scan
> PhysicalRDD[movie#33,title#34,genres#35]), None)
>
>
> However, my assumption that partition is not pruned is not based on the
> above plan but when I look at the job and its stages. I could see that it
> has read full data of the dataframe. I should see around 65KB as that is
> almost average size of each partition.
>
> Aggregated Metrics by Executor
> Executor ID Address Task Time Total Tasks Failed Tasks Succeeded Tasks Input
> Size / Records Shuffle Write Size / Records
> driver localhost:53247 0.4 s 20 0 20 1289.0 KB / 20 840.0 B / 20
>
>
> Task details only first 7. Here I expect that except 1 task(which access
> the partitions data) all others should be either 0 KB or just the size of
> metadata after which it discarded that partition as its data was not
> needed. But i could see that all the partitions are read.
>
> This is small example so it doesnt make diff but for a large dataframe
> reading all the data even that in memory takes time.
>
> Tasks
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> 0 27 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 39 ms 12
> ms 9 ms 0 ms 0 ms 0.0 B 66.2 KB (memory) / 1 42.0 B / 1
> 1 28 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 41 ms 9
> ms 7 ms 0 ms 0 ms 0.0 B 63.9 KB (memory) / 1 1 ms 42.0 B / 1
> 2 29 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 40 ms 7
> ms 7 ms 0 ms 0 ms 0.0 B 65.9 KB (memory) / 1 1 ms 42.0 B / 1
> 3 30 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 6 ms 3
> ms 5 ms 0 ms 0 ms 0.0 B 62.0 KB (memory) / 1 42.0 B / 1
> 4 31 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 4 ms 4
> ms 6 ms 1 ms 0 ms 0.0 B 69.2 KB (memory) / 1 42.0 B / 1
> 5 32 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 5 ms 2
> ms 5 ms 0 ms 0 ms 0.0 B 60.3 KB (memory) / 1 42.0 B / 1
> 6 33 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 5 ms 3
> ms 4 ms 0 ms 0 ms 0.0 B 70.3 KB (memory) / 1 42.0 B / 1
> 7 34 0 SUCCESS PROCESS_LOCAL driver / localhost 2016/04/05 19:01:03 4 ms 5
> ms 4 ms 0 ms 0 ms 0.0 B 59.7 KB (memory) / 1 42.0 B / 1
>
> Let me know if you need anything else.
>
> Thanks
>
>
>
>
> On Tue, Apr 5, 2016 at 7:29 PM, Michael Armbrust <mich...@databricks.com>
> wrote:
>
>> Can you show your full code. How are you partitioning the data? How are
>> you reading it? What is the resulting query plan (run explain() or
>> EXPLAIN).
>>
>> On Tue, Apr 5, 2016 at 10:02 AM, dsing001 <darshan.m...@gmail.com> wrote:
>>
>>> HI,
>>>
>>> I am using 1.5.2. I have a dataframe which is partitioned based on the
>>> country. So I have around 150 partition in the dataframe. When I run
>>> sparksql and use country = 'UK' it still reads all partitions and not
>>> able
>>> to prune other partitions. Thus all the queries run for similar times
>>> independent of what country I pass. Is it desired?
>>>
>>> Is there a way to fix this in 1.5.2 by using some parameter or is it
>>> fixed
>>> in latest versions?
>>>
>>> Thanks
>>>
>>>
>>>
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>>
>
