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https://issues.apache.org/jira/browse/SPARK-14927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16162522#comment-16162522
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Rajesh Chandramohan commented on SPARK-14927:
---------------------------------------------
[~ctang.ma] , I was talking here about HIVE-1.2 version & Spark-2.1.0. In Our
Environment we are not yet moved to HIVE-2.0
*Once Spark Updates the HIVE table , later we can't access hive table via hive*
spark-sql> insert into table zeta_dev_gdw_tables.rajesh_user1 values ( 1002);
Once above command executed the HDFS file structure changes. Then we can't
access via hive , it throws error.
------------------
hive> select * from zeta_dev_gdw_tables.rajesh_user1 ;
OK
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further
details.
Failed with exception java.io.IOException:parquet.io.ParquetDecodingException:
Can not read value at 0 in block 0 in file
hdfs://cluster/user/hive/warehouse/zeta_dev_gdw_tables.db/rajesh_user1/part-00000-d4d0b321-3e7e-4525-9de0-d64fb223e9b2.snappy.parquet
Time taken: 1.495 seconds
hive>
Hive log
Caused by: parquet.io.ParquetDecodingException: Can not read value at 0 in
block 0 in file
hdfs://cluster/user/hive/warehouse/zeta_dev_gdw_tables.db/rajesh_user1/part-00000-d4d0b321-3e7e-4525-9de0-d64fb223e9b2.snappy.parquet
at
parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
at
parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
at
org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:122)
at
org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:85)
at
org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat.getRecordReader(MapredParquetInputFormat.java:72)
at
org.apache.hadoop.hive.ql.exec.FetchOperator$FetchInputFormatSplit.getRecordReader(FetchOperator.java:682)
at
org.apache.hadoop.hive.ql.exec.FetchOperator.getRecordReader(FetchOperator.java:328)
at
org.apache.hadoop.hive.ql.exec.FetchOperator.getNextRow(FetchOperator.java:450)
... 15 more
Caused by: java.lang.UnsupportedOperationException:
org.apache.hadoop.hive.ql.io.parquet.convert.ETypeConverter$8$1Should be
related to this Bug: HIVE-15082
> DataFrame. saveAsTable creates RDD partitions but not Hive partitions
> ---------------------------------------------------------------------
>
> Key: SPARK-14927
> URL: https://issues.apache.org/jira/browse/SPARK-14927
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.2, 1.6.1
> Environment: Mac OS X 10.11.4 local
> Reporter: Sasha Ovsankin
>
> This is a followup to
> http://stackoverflow.com/questions/31341498/save-spark-dataframe-as-dynamic-partitioned-table-in-hive
> . I tried to use suggestions in the answers but couldn't make it to work in
> Spark 1.6.1
> I am trying to create partitions programmatically from `DataFrame. Here is
> the relevant code (adapted from a Spark test):
> hc.setConf("hive.metastore.warehouse.dir", "tmp/tests")
> // hc.setConf("hive.exec.dynamic.partition", "true")
> // hc.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
> hc.sql("create database if not exists tmp")
> hc.sql("drop table if exists tmp.partitiontest1")
> Seq(2012 -> "a").toDF("year", "val")
> .write
> .partitionBy("year")
> .mode(SaveMode.Append)
> .saveAsTable("tmp.partitiontest1")
> hc.sql("show partitions tmp.partitiontest1").show
> Full file is here:
> https://gist.github.com/SashaOv/7c65f03a51c7e8f9c9e018cd42aa4c4a
> I get the error that the table is not partitioned:
> ======================
> HIVE FAILURE OUTPUT
> ======================
> SET hive.support.sql11.reserved.keywords=false
> SET hive.metastore.warehouse.dir=tmp/tests
> OK
> OK
> FAILED: Execution Error, return code 1 from
> org.apache.hadoop.hive.ql.exec.DDLTask. Table tmp.partitiontest1 is not a
> partitioned table
> ======================
> It looks like the root cause is that
> `org.apache.spark.sql.hive.HiveMetastoreCatalog.newSparkSQLSpecificMetastoreTable`
> always creates table with empty partitions.
> Any help to move this forward is appreciated.
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