Hi Ali,
On a practical side, I have used both the old DStreams and the newer Spark
structured streaming (SSS).
SSS does a good job at micro-batch level in the form of
foreachBatch(SendToSink)
"foreach" performs custom write logic on each row and "foreachBatch" *performs
custom write logic *on each micro-batch through SendToSink function.
foreachBatch(SendToSink) expects 2 parameters, first: micro-batch as
DataFrame or Dataset and second: unique id for each batch. Using
foreachBatch, we write each micro batch eventually to storage defined in
our custom logic. In this case, we store the output of our streaming
application to Redis or Google BigQuery table or any other sink
In Dstream world you would have done something like below
// Work on every Stream
dstream.foreachRDD
{ pricesRDD =>
if (!pricesRDD.isEmpty) // data exists in RDD
{
and after some work from that RDD you would have created a DF (df)
With regard to SSS, it allows you to use the passed DataFrame for your
work. However, say in my case if you were interested in individual rows of
micro-batch (say different collection of prices for different tickers
(securities), you could create RDD from the dataframe
for row in df.rdd.collect():
ticker = row.ticker
price = row.price
With regard to foreach(process_row), I have not really tried it as we don't
have a use case for it, so I assume your mileage varies as usual.
HTH
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On Sun, 4 Apr 2021 at 16:27, Ali Gouta <ali.go...@gmail.com> wrote:
> Thank you guys for your answers, I will dig more this new way of doing
> things and why not consider leaving the old Dstreams and use instead
> structured streaming. Hope that strucrured streaming + spark on Kubernetes
> works well and the combination is production ready.
>
> Best regards,
> Ali Gouta.
>
> Le dim. 4 avr. 2021 à 12:52, Jacek Laskowski <ja...@japila.pl> a écrit :
>
>> Hi,
>>
>> Just to add it to Gabor's excellent answer that checkpointing and offsets
>> are infrastructure-related and should not really be in the hands of Spark
>> devs who should instead focus on the business purpose of the code (not
>> offsets that are very low-level and not really important).
>>
>> BTW That's what happens in Kafka Streams too
>>
>> Pozdrawiam,
>> Jacek Laskowski
>> ----
>> https://about.me/JacekLaskowski
>> "The Internals Of" Online Books <https://books.japila.pl/>
>> Follow me on https://twitter.com/jaceklaskowski
>>
>> <https://twitter.com/jaceklaskowski>
>>
>>
>> On Sun, Apr 4, 2021 at 12:28 PM Gabor Somogyi <gabor.g.somo...@gmail.com>
>> wrote:
>>
>>> There is no way to store offsets in Kafka and restart from the stored
>>> offset. Structured Streaming stores offset in checkpoint and it restart
>>> from there without any user code.
>>>
>>> Offsets can be stored with a listener but it can be only used for lag
>>> calculation.
>>>
>>> BR,
>>> G
>>>
>>>
>>> On Sat, 3 Apr 2021, 21:09 Ali Gouta, <ali.go...@gmail.com> wrote:
>>>
>>>> Hello,
>>>>
>>>> I was reading the spark docs about spark structured streaming, since we
>>>> are thinking about updating our code base that today uses Dstreams, hence
>>>> spark streaming. Also, one main reason for this change that we want to
>>>> realize is that reading headers in kafka messages is only supported in
>>>> spark structured streaming and not in Dstreams.
>>>>
>>>> I was surprised to not see an obvious way to handle manually the
>>>> offsets by committing the offsets to kafka. In spark streaming we used to
>>>> do it with something similar to these lines of code:
>>>>
>>>> stream.foreachRDD { rdd =>
>>>> val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
>>>>
>>>> // some time later, after outputs have completed
>>>> stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)}
>>>>
>>>>
>>>> And this works perfectly ! Especially, this works very nice in case of
>>>> job failure/restart... I am wondering how this can be achieved in spark
>>>> structured streaming ?
>>>>
>>>> I read about checkpoints, and this reminds me the old way of doing
>>>> things in spark 1.5/kafka0.8 and is not perfect since we are not deciding
>>>> when to commit offsets by ourselves.
>>>>
>>>> Did I miss anything ? What would be the best way of committing offsets
>>>> to kafka with spark structured streaming to the concerned consumer group ?
>>>>
>>>> Best regards,
>>>> Ali Gouta.
>>>>
>>>
