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jayadeep_jayara
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Re: ORC Table Timestamp PySpark 2.1 CASTIssue

by Contributor in Support Questions
‎06-13-2017 07:59 AM
‎06-13-2017 07:59 AM
Hi @Daniel Kozlowski, I resolved the issue it was because the input table I had defined had string datatype, I used a cast function inside my spark code and now everything is working fine. Thanks for your help. ... View more

Re: ORC Table Timestamp PySpark 2.1 CASTIssue

by Contributor in Support Questions
‎06-13-2017 05:52 AM
‎06-13-2017 05:52 AM
Also see the below the structure of the dataframe before the write method is called DataFrame[vehicle_hdr: string, vehicle_no: string, incident_timestamp: string] ... View more

Re: ORC Table Timestamp PySpark 2.1 CASTIssue

by Contributor in Support Questions
‎06-13-2017 05:43 AM
‎06-13-2017 05:43 AM
Hi @Daniel Kozlowski, I have tested the above case and it works fine...on my end as well. Also, I created a table with the timestamp column as string and then from this temp table I inserted the data into the main table with timestamp datatype and from spark I am able to read the data without any issues. I guess the issue is when I am inserting data from spark into hive and reading it back. ... View more

Re: ORC Table Timestamp PySpark 2.1 CASTIssue

by Contributor in Support Questions
‎06-13-2017 05:23 AM
‎06-13-2017 05:23 AM
Thanks @Daniel, the timestamp in my case are real time stamps that are coming from our sensors. As can be seen the timestamp values are 1969-06-1906:57:26.485 and 1988-06-2105:36:22.35 are in my table. I inserted the data from a pyspark program, code snippet below write_df = final_df.where(col(first_partitioned_column).isin(format(first_partition))) write_df.drop(first_partitioned_column) write_df.write.mode("overwrite").format("orc").partitionBy(first_partitioned_column).save(path) One thing I observed was the timestamp column in write_df was of string datatype and not timestamp but then my assumption is that spark will do the cast internally where a dataframe column is string and the table column is of timestamp value. Another thing to note is from beeline I am able to query the results without any issues. Thanks in advance. ... View more

ORC Table Timestamp PySpark 2.1 CASTIssue

by Contributor in Support Questions
‎06-12-2017 07:10 AM
‎06-12-2017 07:10 AM
All, I have a table which has 3 columns and is in ORC format, the data is as below +--------------+-------------+--------------------------+--+ | vehicle_hdr | vehicle_no | incident_timestamp | +--------------+-------------+--------------------------+--+ | XXXX | 3911 | 1969-06-19 06:57:26.485 | | XXXX | 3911 | 1988-06-21 05:36:22.35 | The DDL for the table is as below create table test (vehicle_hdr string,vehicle_no string,incident_timestamp timestamp)stored as ORC; From the hive beeline I am able to view the results but when I am using PySpark 2.1 and running the below code o1 = sqlContext.sql("select vehicle_hdr, incident_timestamp from test") I am getting the below error Caused by: java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to org.apache.hadoop.hive.serde2.io.TimestampWritable at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableTimestampObjectInspector.getPrimitiveJavaObject(WritableTimestampObjectInspector.java:39) at org.apache.spark.sql.hive.HadoopTableReader$anonfun$14$anonfun$apply$11.apply(TableReader.scala:393) at org.apache.spark.sql.hive.HadoopTableReader$anonfun$14$anonfun$apply$11.apply(TableReader.scala:392) at org.apache.spark.sql.hive.HadoopTableReader$anonfun$fillObject$2.apply(TableReader.scala:416) at org.apache.spark.sql.hive.HadoopTableReader$anonfun$fillObject$2.apply(TableReader.scala:408) at scala.collection.Iterator$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$anon$11.next(Iterator.scala:328) ... View more
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Re: Spark 2.1 Hive Partition Adding Issue ORC Form...

by Contributor in Support Questions
‎06-12-2017 07:05 AM
‎06-12-2017 07:05 AM
I resolved it by removing the column on which the table was partitioned from the dataframe ... View more

Spark 2.1 Hive Partition Adding Issue ORC Format

by Contributor in Support Questions
‎06-01-2017 04:35 PM
‎06-01-2017 04:35 PM
I am using pyspark 2.1 to create partitions dynamically from table A to table B. Below are the DDL's <code>create table A ( objid bigint, occur_date timestamp) STORED AS ORC; create table B ( objid bigint, occur_date timestamp) PARTITIONED BY ( occur_date_pt date) STORED AS ORC; I am then using a pyspark code where I am trying to determine the partitions that need to be merged, below is the portion of code where I am actually doing that <code>for row in incremental_df.select(partitioned_column).distinct().collect(): path = '/apps/hive/warehouse/B/' + partitioned_column + '=' + format(row[0]) old_df = merge_df.where(col(partitioned_column).isin(format(row[0]))) new_df = incremental_df.where(col(partitioned_column).isin(format(row[0]))) output_df = old_df.subtract(new_df) output_df = output_df.unionAll(new_df) output_df.write.option("compression","none").mode("overwrite").format("orc").save(path) refresh_metadata_sql = 'MSCK REPAIR TABLE ' + table_name sqlContext.sql(refresh_metadata_sql) On Execution of the code I am able to see the partitions in HDFS Found 3 items drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-01 drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-02 drwx------ - 307010265 hdfs 0 2017-06-01 10:31 /apps/hive/warehouse/B/occur_date_pt=2017-06-03 But when I am trying to access the table inside Spark I am getting array out of bound error <code>>> merge_df = sqlContext.sql('select * from B') DataFrame[] >>> merge_df.show() 17/06/01 10:33:13 ERROR Executor: Exception in task 0.0 in stage 200.0 (TID 4827) java.lang.IndexOutOfBoundsException: toIndex = 3 at java.util.ArrayList.subListRangeCheck(ArrayList.java:1004) at java.util.ArrayList.subList(ArrayList.java:996) at org.apache.hadoop.hive.ql.io.orc.RecordReaderFactory.getSchemaOnRead(RecordReaderFactory.java:161) at org.apache.hadoop.hive.ql.io.orc.RecordReaderFactory.createTreeReader(RecordReaderFactory.java:66) at org.apache.hadoop.hive.ql.io.orc.RecordReaderImpl.<init>(RecordReaderImpl.java:202) at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.rowsOptions(ReaderImpl.java:539) at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger$ReaderPair.<init>(OrcRawRecordMerger.java:183) at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger$OriginalReaderPair.<init>(OrcRawRecordMerger.java:226) at org.apache.hadoop.hive.ql.io.orc.OrcRawRecordMerger.<init>(OrcRawRecordMerger.java:437) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getReader(OrcInputFormat.java:1215) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getRecordReader(OrcInputFormat.java:1113) at org.apache.spark.rdd.HadoopRDD$anon$1.liftedTree1$1(HadoopRDD.scala:252) at org.apache.spark.rdd.HadoopRDD$anon$1.<init>(HadoopRDD.scala:251) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:211) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:102) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) ... View more
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Re: Hive LLAP in Hive View Ambari

by Contributor in Support Questions
‎03-01-2017 09:13 AM
‎03-01-2017 09:13 AM
Thanks @Jay SenSharma ... View more

Hive LLAP in Hive View Ambari

by Contributor in Support Questions
‎03-01-2017 08:07 AM
‎03-01-2017 08:07 AM
Hi All, I have installed LLAP in my HDP 2.5 cluster. I believe the Hive View in Ambari by default uses hive on tez, how can I make it use HIVE LLAP ? Thanks, Jayadeep ... View more
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Re: Zeppelin Home Page Blank

by Contributor in Support Questions
‎02-27-2017 06:42 PM
‎02-27-2017 06:42 PM
I resolved it, by adding few extra lines in the rewrite rule to handle websocket interaction RewriteRule ^/ws(.*)$ ws://localhost:9995/ws [P] ... View more
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