The following should ensure partition pruning happens:
df.write.partitionBy("country").save("/path/to/data")
sqlContext.read.load("/path/to/data").where("country = 'UK'")
On Tue, Apr 5, 2016 at 1:13 PM, Darshan Singh <darshan.m...@gmail.com>
wrote:
> 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.
>>>>>
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>>>>>
>>>>>
>>>>
>>>
>>
>
