I think I understand. Partition pruning for the case where spark.sql.hive.convertMetastoreParquet is true was not added to Spark until 2.1.0. I think that in previous versions it only worked when spark.sql.hive.convertMetastoreParquet is false. Unfortunately, that configuration gives you data decoding errors. If it's possible for you to write all of your data with Hive, then you should be able to read it without decoding errors and with partition pruning turned on. Another possibility is running your Spark app with a very large maximum heap configuration, like 8g or even 16g. However, loading all of that partition metadata can be quite slow for very large tables. I'm sorry I can't think of a better solution for you.
Michael > On Jan 17, 2017, at 8:59 PM, Raju Bairishetti <r...@apache.org> wrote: > > Tested on both 1.5.2 and 1.61. > > On Wed, Jan 18, 2017 at 12:52 PM, Michael Allman <mich...@videoamp.com > <mailto:mich...@videoamp.com>> wrote: > What version of Spark are you running? > >> On Jan 17, 2017, at 8:42 PM, Raju Bairishetti <r...@apache.org >> <mailto:r...@apache.org>> wrote: >> >> describe dummy; >> >> OK >> >> sample string >> >> year string >> >> month string >> >> # Partition Information >> >> # col_name data_type comment >> >> year string >> >> >> month string >> >> >> >> val df = sqlContext.sql("select count(1) from rajub.dummy where year='2017'") >> >> df: org.apache.spark.sql.DataFrame = [_c0: bigint] >> >> scala> df.explain >> >> == Physical Plan == >> >> TungstenAggregate(key=[], >> functions=[(count(1),mode=Final,isDistinct=false)], output=[_c0#3070L]) >> >> +- TungstenExchange SinglePartition, None >> >> +- TungstenAggregate(key=[], >> functions=[(count(1),mode=Partial,isDistinct=false)], output=[count#3076L]) >> >> +- Scan ParquetRelation: rajub.dummy[] InputPaths: >> maprfs:/user/rajub/dummy/sample/year=2016/month=10, >> maprfs:/user/rajub/dummy/sample/year=2016/month=11, >> maprfs:/user/rajub/dummy/sample/year=2016/month=9, >> maprfs:/user/rajub/dummy/sample/year=2017/month=10, >> maprfs:/user/rajub/dummy/sample/year=2017/month=11, >> maprfs:/user/rajub/dummy/sample/year=2017/month=9 >> >> >> On Wed, Jan 18, 2017 at 12:25 PM, Michael Allman <mich...@videoamp.com >> <mailto:mich...@videoamp.com>> wrote: >> Can you paste the actual query plan here, please? >> >>> On Jan 17, 2017, at 7:38 PM, Raju Bairishetti <r...@apache.org >>> <mailto:r...@apache.org>> wrote: >>> >>> >>> On Wed, Jan 18, 2017 at 11:13 AM, Michael Allman <mich...@videoamp.com >>> <mailto:mich...@videoamp.com>> wrote: >>> What is the physical query plan after you set >>> spark.sql.hive.convertMetastoreParquet to true? >>> Physical plan continas all the partition locations >>> >>> Michael >>> >>>> On Jan 17, 2017, at 6:51 PM, Raju Bairishetti <r...@apache.org >>>> <mailto:r...@apache.org>> wrote: >>>> >>>> Thanks Michael for the respopnse. >>>> >>>> >>>> On Wed, Jan 18, 2017 at 2:45 AM, Michael Allman <mich...@videoamp.com >>>> <mailto:mich...@videoamp.com>> wrote: >>>> Hi Raju, >>>> >>>> I'm sorry this isn't working for you. I helped author this functionality >>>> and will try my best to help. >>>> >>>> First, I'm curious why you set spark.sql.hive.convertMetastoreParquet to >>>> false? >>>> I had set as suggested in SPARK-6910 and corresponsing pull reqs. It did >>>> not work for me without setting spark.sql.hive.convertMetastoreParquet >>>> property. >>>> >>>> Can you link specifically to the jira issue or spark pr you referred to? >>>> The first thing I would try is setting >>>> spark.sql.hive.convertMetastoreParquet to true. Setting that to false >>>> might also explain why you're getting parquet decode errors. If you're >>>> writing your table data with Spark's parquet file writer and reading with >>>> Hive's parquet file reader, there may be an incompatibility accounting for >>>> the decode errors you're seeing. >>>> >>>> https://issues.apache.org/jira/browse/SPARK-6910 >>>> <https://issues.apache.org/jira/browse/SPARK-6910> . My main motivation is >>>> to avoid fetching all the partitions. We reverted >>>> spark.sql.hive.convertMetastoreParquet setting to true to decoding >>>> errors. After reverting this it is fetching all partiitons from the table. >>>> >>>> Can you reply with your table's Hive metastore schema, including partition >>>> schema? >>>> col1 string >>>> col2 string >>>> year int >>>> month int >>>> day int >>>> hour int >>>> # Partition Information >>>> >>>> # col_name data_type comment >>>> >>>> year int >>>> >>>> month int >>>> >>>> day int >>>> >>>> hour int >>>> >>>> venture string >>>> >>>> >>>> Where are the table's files located? >>>> In hadoop. Under some user directory. >>>> If you do a "show partitions <dbname>.<tablename>" in the spark-sql shell, >>>> does it show the partitions you expect to see? If not, run "msck repair >>>> table <dbname>.<tablename>". >>>> Yes. It is listing the partitions >>>> Cheers, >>>> >>>> Michael >>>> >>>> >>>>> On Jan 17, 2017, at 12:02 AM, Raju Bairishetti <r...@apache.org >>>>> <mailto:r...@apache.org>> wrote: >>>>> >>>>> Had a high level look into the code. Seems getHiveQlPartitions method >>>>> from HiveMetastoreCatalog is getting called irrespective of >>>>> metastorePartitionPruning conf value. >>>>> >>>>> It should not fetch all partitions if we set metastorePartitionPruning >>>>> to true (Default value for this is false) >>>>> def getHiveQlPartitions(predicates: Seq[Expression] = Nil): >>>>> Seq[Partition] = { >>>>> val rawPartitions = if (sqlContext.conf.metastorePartitionPruning) { >>>>> table.getPartitions(predicates) >>>>> } else { >>>>> allPartitions >>>>> } >>>>> ... >>>>> def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = >>>>> client.getPartitionsByFilter(this, predicates) >>>>> lazy val allPartitions = table.getAllPartitions >>>>> But somehow getAllPartitions is getting called eventough after setting >>>>> metastorePartitionPruning to true. >>>>> Am I missing something or looking at wrong place? >>>>> >>>>> On Tue, Jan 17, 2017 at 4:01 PM, Raju Bairishetti <r...@apache.org >>>>> <mailto:r...@apache.org>> wrote: >>>>> Hello, >>>>> >>>>> Spark sql is generating query plan with all partitions information >>>>> even though if we apply filters on partitions in the query. Due to this, >>>>> sparkdriver/hive metastore is hitting with OOM as each table is with lots >>>>> of partitions. >>>>> >>>>> We can confirm from hive audit logs that it tries to fetch all partitions >>>>> from hive metastore. >>>>> >>>>> 2016-12-28 07:18:33,749 INFO [pool-4-thread-184]: HiveMetaStore.audit >>>>> (HiveMetaStore.java:logAuditEvent(371)) - ugi=rajub ip=/x.x.x.x >>>>> cmd=get_partitions : db=xxxx tbl=xxxxx >>>>> >>>>> >>>>> Configured the following parameters in the spark conf to fix the above >>>>> issue(source: from spark-jira & github pullreq): >>>>> spark.sql.hive.convertMetastoreParquet false >>>>> spark.sql.hive.metastorePartitionPruning true >>>>> >>>>> plan: rdf.explain >>>>> == Physical Plan == >>>>> HiveTableScan [rejection_reason#626], MetastoreRelation dbname, >>>>> tablename, None, [(year#314 = 2016),(month#315 = 12),(day#316 = >>>>> 28),(hour#317 = 2),(venture#318 = DEFAULT)] >>>>> >>>>> get_partitions_by_filter method is called and fetching only required >>>>> partitions. >>>>> >>>>> But we are seeing parquetDecode errors in our applications frequently >>>>> after this. Looks like these decoding errors were because of changing >>>>> serde fromspark-builtin to hive serde. >>>>> >>>>> I feel like, fixing query plan generation in the spark-sql is the right >>>>> approach instead of forcing users to use hive serde. >>>>> >>>>> Is there any workaround/way to fix this issue? I would like to hear more >>>>> thoughts on this :) >>>>> >>>>> >>>>> On Tue, Jan 17, 2017 at 4:00 PM, Raju Bairishetti <r...@apache.org >>>>> <mailto:r...@apache.org>> wrote: >>>>> Had a high level look into the code. Seems getHiveQlPartitions method >>>>> from HiveMetastoreCatalog is getting called irrespective of >>>>> metastorePartitionPruning conf value. >>>>> >>>>> It should not fetch all partitions if we set metastorePartitionPruning >>>>> to true (Default value for this is false) >>>>> def getHiveQlPartitions(predicates: Seq[Expression] = Nil): >>>>> Seq[Partition] = { >>>>> val rawPartitions = if (sqlContext.conf.metastorePartitionPruning) { >>>>> table.getPartitions(predicates) >>>>> } else { >>>>> allPartitions >>>>> } >>>>> ... >>>>> def getPartitions(predicates: Seq[Expression]): Seq[HivePartition] = >>>>> client.getPartitionsByFilter(this, predicates) >>>>> lazy val allPartitions = table.getAllPartitions >>>>> But somehow getAllPartitions is getting called eventough after setting >>>>> metastorePartitionPruning to true. >>>>> Am I missing something or looking at wrong place? >>>>> >>>>> On Mon, Jan 16, 2017 at 12:53 PM, Raju Bairishetti <r...@apache.org >>>>> <mailto:r...@apache.org>> wrote: >>>>> Waiting for suggestions/help on this... >>>>> >>>>> On Wed, Jan 11, 2017 at 12:14 PM, Raju Bairishetti <r...@apache.org >>>>> <mailto:r...@apache.org>> wrote: >>>>> Hello, >>>>> >>>>> Spark sql is generating query plan with all partitions information >>>>> even though if we apply filters on partitions in the query. Due to this, >>>>> spark driver/hive metastore is hitting with OOM as each table is with >>>>> lots of partitions. >>>>> >>>>> We can confirm from hive audit logs that it tries to fetch all partitions >>>>> from hive metastore. >>>>> >>>>> 2016-12-28 07:18:33,749 INFO [pool-4-thread-184]: HiveMetaStore.audit >>>>> (HiveMetaStore.java:logAuditEvent(371)) - ugi=rajub ip=/x.x.x.x >>>>> cmd=get_partitions : db=xxxx tbl=xxxxx >>>>> >>>>> >>>>> Configured the following parameters in the spark conf to fix the above >>>>> issue(source: from spark-jira & github pullreq): >>>>> spark.sql.hive.convertMetastoreParquet false >>>>> spark.sql.hive.metastorePartitionPruning true >>>>> >>>>> plan: rdf.explain >>>>> == Physical Plan == >>>>> HiveTableScan [rejection_reason#626], MetastoreRelation dbname, >>>>> tablename, None, [(year#314 = 2016),(month#315 = 12),(day#316 = >>>>> 28),(hour#317 = 2),(venture#318 = DEFAULT)] >>>>> >>>>> get_partitions_by_filter method is called and fetching only required >>>>> partitions. >>>>> >>>>> But we are seeing parquetDecode errors in our applications frequently >>>>> after this. Looks like these decoding errors were because of changing >>>>> serde from spark-builtin to hive serde. >>>>> >>>>> I feel like, fixing query plan generation in the spark-sql is the right >>>>> approach instead of forcing users to use hive serde. >>>>> >>>>> Is there any workaround/way to fix this issue? I would like to hear more >>>>> thoughts on this :) >>>>> >>>>> ------ >>>>> Thanks, >>>>> Raju Bairishetti, >>>>> www.lazada.com <http://www.lazada.com/> >>>>> >>>>> >>>>> -- >>>>> >>>>> ------ >>>>> Thanks, >>>>> Raju Bairishetti, >>>>> www.lazada.com <http://www.lazada.com/> >>>>> >>>>> >>>>> -- >>>>> >>>>> ------ >>>>> Thanks, >>>>> Raju Bairishetti, >>>>> www.lazada.com <http://www.lazada.com/> >>>>> >>>>> >>>>> -- >>>>> >>>>> ------ >>>>> Thanks, >>>>> Raju Bairishetti, >>>>> www.lazada.com <http://www.lazada.com/> >>>>> >>>>> >>>>> -- >>>>> >>>>> ------ >>>>> Thanks, >>>>> Raju Bairishetti, >>>>> www.lazada.com <http://www.lazada.com/> >>>> >>>> >>>> >>>> -- >>>> >>>> ------ >>>> Thanks, >>>> Raju Bairishetti, >>>> www.lazada.com <http://www.lazada.com/> >>> >>> >>> >>> -- >>> >>> ------ >>> Thanks, >>> Raju Bairishetti, >>> www.lazada.com <http://www.lazada.com/> >> >> >> >> -- >> >> ------ >> Thanks, >> Raju Bairishetti, >> www.lazada.com <http://www.lazada.com/> > > > > -- > > ------ > Thanks, > Raju Bairishetti, > www.lazada.com <http://www.lazada.com/>
