Thanks for the reply.
Now I saved the part_movies as parquet file.
Then created new dataframe from the saved parquet file and I did not
persist it. The i ran the same query. It still read all 20 partitions and
this time from hdfs.
So what will be exact scenario when it will prune partitions. I am bit
confused now. Isnt there a way to see the exact partition pruning?
Thanks
On Tue, Apr 5, 2016 at 8:59 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> 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
>>>>
>>>>
>>>>
>>>> --
>>>> View this message in context:
>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Partition-pruning-in-spark-1-5-2-tp26682.html
>>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>>
>>>> ---------------------------------------------------------------------
>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>>> For additional commands, e-mail: user-h...@spark.apache.org
>>>>
>>>>
>>>
>>
>
