monotonically_increasing_id on streaming dataFrames will be really helpful to me and I believe to many more users. Adding this functionality in Spark would be efficient in terms of performance as compared to implementing this functionality inside the applications.On Thu, Apr 26, 2018 at 11:59 PM, Michael Armbrust <firstname.lastname@example.org> wrote:The basic tenet of structured streaming is that a query should return the same answer in streaming or batch mode. We support sorting in complete mode because we have all the data and can sort it correctly and return the full answer. In update or append mode, sorting would only return a correct answer if we could promise that records that sort lower are going to arrive later (and we can't). Therefore, it is disallowed.If you are just looking for a unique, stable id and you are already using kafka as the source, you could just combine the partition id and the offset. The structured streaming connector to Kafka exposes both of these in the schema of the streaming DataFrame. (similarly for kinesis you can use the shard id and sequence number)If you need the IDs to be contiguous, then this is a somewhat fundamentally hard problem. I think the best we could do is add support for monotonically_increasing_id() in streaming dataframes.On Tue, Apr 24, 2018 at 1:38 PM, Chayapan Khannabha <email@example.com> wrote:Perhaps your use case fits to Apache Kafka better.More info at:Everything really comes down to the architecture design and algorithm spec. However, from my experience with Spark, there are many good reasons why this requirement is not supported ;)Best,Chayapan (A)On Apr 24, 2018, at 2:18 PM, Hemant Bhanawat <firstname.lastname@example.org> wrote: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.HemantOn Mon, Apr 16, 2018 at 6:09 PM, Bowden, Chris <email@example.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 <firstname.lastname@example.org>
Sent: Thursday, April 12, 2018 11:42:59 PM
To: Reynold Xin
Subject: Re: Sorting on a streaming dataframeWell, 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 <email@example.com> wrote:
Can you describe your use case more?
On Thu, Apr 12, 2018 at 11:12 PM Hemant Bhanawat <firstname.lastname@example.org> wrote:
Why is sorting on streaming dataframes not supported(unless it is complete mode)? My downstream needs me to sort the streaming dataframe.