Have you tried partitionBy?
Something like
hiveWindowsEvents.foreachRDD( rdd => {
val eventsDataFrame = rdd.toDF()
eventsDataFrame.write.mode(SaveMode.Append).partitionBy("
windows_event_time_bin").saveAsTable("windows_event")
})
On Wed, Oct 28, 2015 at 7:41 AM, Bryan Jeffrey <bryan.jeff...@gmail.com>
wrote:
> Hello.
>
> I am working to get a simple solution working using Spark SQL. I am
> writing streaming data to persistent tables using a HiveContext. Writing
> to a persistent non-partitioned table works well - I update the table using
> Spark streaming, and the output is available via Hive Thrift/JDBC.
>
> I create a table that looks like the following:
>
> 0: jdbc:hive2://localhost:10000> describe windows_event;
> describe windows_event;
> +--------------------------+---------------------+----------+
> | col_name | data_type | comment |
> +--------------------------+---------------------+----------+
> | target_entity | string | NULL |
> | target_entity_type | string | NULL |
> | date_time_utc | timestamp | NULL |
> | machine_ip | string | NULL |
> | event_id | string | NULL |
> | event_data | map<string,string> | NULL |
> | description | string | NULL |
> | event_record_id | string | NULL |
> | level | string | NULL |
> | machine_name | string | NULL |
> | sequence_number | string | NULL |
> | source | string | NULL |
> | source_machine_name | string | NULL |
> | task_category | string | NULL |
> | user | string | NULL |
> | additional_data | map<string,string> | NULL |
> | windows_event_time_bin | timestamp | NULL |
> | # Partition Information | | |
> | # col_name | data_type | comment |
> | windows_event_time_bin | timestamp | NULL |
> +--------------------------+---------------------+----------+
>
>
> However, when I create a partitioned table and write data using the
> following:
>
> hiveWindowsEvents.foreachRDD( rdd => {
> val eventsDataFrame = rdd.toDF()
>
> eventsDataFrame.write.mode(SaveMode.Append).saveAsTable("windows_event")
> })
>
> The data is written as though the table is not partitioned (so everything
> is written to /user/hive/warehouse/windows_event/file.gz.paquet. Because
> the data is not following the partition schema, it is not accessible (and
> not partitioned).
>
> Is there a straightforward way to write to partitioned tables using Spark
> SQL? I understand that the read performance for partitioned data is far
> better - are there other performance improvements that might be better to
> use instead of partitioning?
>
> Regards,
>
> Bryan Jeffrey
>
