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From Michael Armbrust <>
Subject Re: Sorting on a streaming dataframe
Date Thu, 26 Apr 2018 18:29:52 GMT
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 <>

> 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 <> 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.
> Hemant
> On Mon, Apr 16, 2018 at 6:09 PM, Bowden, Chris <
>> 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 <>
>> *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 <>
>> wrote:
>> Can you describe your use case more?
>> On Thu, Apr 12, 2018 at 11:12 PM Hemant Bhanawat <>
>> 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

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