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?
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.
Can you reply with your table's Hive metastore schema, including partition
schema? Where are the table's files located? 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>".
Cheers,
Michael
> On Jan 17, 2017, at 12:02 AM, Raju Bairishetti <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/>
