I guess sorting would make sense only when you have the complete data set. In streaming you don’t know what record is coming next so doesn’t make sense to sort it (except in the aggregated complete output mode where the entire result table is emitted each time and the results can be sorted).
Thanks, Arun From: Hemant Bhanawat <hemant9...@gmail.com> Date: Tuesday, April 24, 2018 at 12:18 AM To: "Bowden, Chris" <chris.bow...@microfocus.com> Cc: Reynold Xin <r...@databricks.com>, dev <dev@spark.apache.org> Subject: Re: Sorting on a streaming dataframe Thanks Chris. There are many ways in which I can solve this problem but they are cumbersome. The easiest way would have been to sort the streaming dataframe. The reason I asked this question is because I could not find a reason why sorting on streaming dataframe is disallowed. Hemant On Mon, Apr 16, 2018 at 6:09 PM, Bowden, Chris <chris.bow...@microfocus.com> wrote: You can happily sort the underlying RDD of InternalRow(s) inside a sink, assuming you are willing to implement and maintain your own sink(s). That is, just grabbing the parquet sink, etc. isn’t going to work out of the box. Alternatively map/flatMapGroupsWithState is probably sufficient and requires less working knowledge to make effective reuse of internals. Just group by foo and then sort accordingly and assign ids. The id counter can be stateful per group. Sometimes this problem may not need to be solved at all. For example, if you are using kafka, a proper partitioning scheme and message offsets may be “good enough”. From: Hemant Bhanawat <hemant9...@gmail.com> Sent: Thursday, April 12, 2018 11:42:59 PM To: Reynold Xin Cc: dev Subject: Re: Sorting on a streaming dataframe Well, we want to assign snapshot ids (incrementing counters) to the incoming records. For that, we are zipping the streaming rdds with that counter using a modified version of ZippedWithIndexRDD. We are ok if the records in the streaming dataframe gets counters in random order but the counter should always be incrementing. This is working fine until we have a failure. When we have a failure, we re-assign the records to snapshot ids and this time same snapshot id can get assigned to a different record. This is a problem because the primary key in our storage engine is <recordid, snapshotid>. So we want to sort the dataframe so that the records always get the same snapshot id. On Fri, Apr 13, 2018 at 11:43 AM, Reynold Xin <r...@databricks.com> wrote: Can you describe your use case more? On Thu, Apr 12, 2018 at 11:12 PM Hemant Bhanawat <hemant9...@gmail.com> wrote: Hi Guys, Why is sorting on streaming dataframes not supported(unless it is complete mode)? My downstream needs me to sort the streaming dataframe. Hemant
