On Mon, 26 Apr 2021 at 10:21, Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
>
> Spark Structured Streaming AKA SSS is a very useful tool in dealing with
> Event Driven Architecture. In an Event Driven Architecture, there is
> generally a main loop that listens for events and then triggers a call-back
> function when one of those events is detected. In a streaming application
> the application waits to receive the source messages in a set interval or
> whenever they happen and reacts accordingly.
>
> There are occasions that you may want to stop the Spark program
> gracefully. Gracefully meaning that Spark application handles the last
> streaming message completely and terminates the application. This is
> different from invoking interrupt such as CTRL-C. Of course one can
> terminate the process based on the following
>
>
> 1.
>
> query.awaitTermination() # Waits for the termination of this query,
> with stop() or with error
> 2.
>
> query.awaitTermination(timeoutMs) # Returns true if this query is
> terminated within the timeout in milliseconds.
>
> So the first one above waits until an interrupt signal is received. The
> second one will count the timeout and will exit when timeout in
> milliseconds is reached
>
> The issue is that one needs to predict how long the streaming job needs to
> run. Clearly any interrupt at the terminal or OS level (kill process), may
> end up the processing terminated without a proper completion of the
> streaming process.
>
> I have devised a method that allows one to terminate the spark application
> internally after processing the last received message. Within say 2 seconds
> of the confirmation of shutdown, the process will invoke
>
> How to shutdown the topic doing work for the message being processed, wait
> for it to complete and shutdown the streaming process for a given topic.
>
>
> I thought about this and looked at options. Using sensors to
> implement this like airflow would be expensive as for example reading a
> file from object storage or from an underlying database would have incurred
> additional I/O overheads through continuous polling.
>
>
> So the design had to be incorporated into the streaming process itself.
> What I came up with was an addition of a control topic (I call it newtopic
> below), which keeps running triggered every 2 seconds say and is in json
> format with the following structure
>
>
> root
>
> |-- newtopic_value: struct (nullable = true)
>
> | |-- uuid: string (nullable = true)
>
> | |-- timeissued: timestamp (nullable = true)
>
> | |-- queue: string (nullable = true)
>
> | |-- status: string (nullable = true)
>
> In above the queue refers to the business topic) and status is set to
> 'true', meaning carry on processing the business stream. This control topic
> streaming can be restarted anytime, and status can be set to false if we
> want to stop the streaming queue for a given business topic
>
> ac7d0b2e-dc71-4b3f-a17a-500cd9d38efe
> {"uuid":"ac7d0b2e-dc71-4b3f-a17a-500cd9d38efe",
> "timeissued":"2021-04-23T08:54:06", "queue":"md", "status":"true"}
>
> 64a8321c-1593-428b-ae65-89e45ddf0640
> {"uuid":"64a8321c-1593-428b-ae65-89e45ddf0640",
> "timeissued":"2021-04-23T09:49:37", "queue":"md", "status":"false"}
>
> So how can I stop the business queue when the current business topic
> message has been processed? Let us say the source is sending data for a
> business topic every 30 seconds. Our control topic sends a one liner as
> above every 2 seconds.
>
> In your writestream add the following line to be able to identify topic
> name
>
> trigger(processingTime='30 seconds'). \
> *queryName('md'). *\
>
> Next the controlling topic (called newtopic) has the following
>
> foreachBatch(*sendToControl*). \
> trigger(processingTime='2 seconds'). \
> queryName('newtopic'). \
>
> That method sendToControl does what is needed
>
> def sendToControl(dfnewtopic, batchId):
> if(len(dfnewtopic.take(1))) > 0:
> #print(f"""newtopic batchId is {batchId}""")
> #dfnewtopic.show(10,False)
> queue = dfnewtopic.select(col("queue")).collect()[0][0]
> status = dfnewtopic.select(col("status")).collect()[0][0]
>
> if((queue == 'md')) & (status == 'false')):
> spark_session = s.spark_session(config['common']['appName'])
> active = spark_session.streams.active
> for e in active:
> #print(e)
> name = e.name
> if(name == 'md'):
> print(f"""Terminating streaming process {name}""")
> e.stop()
> else:
> print("DataFrame newtopic is empty")
>
> This seems to work as I checked it to ensure that in this case data was
> written and saved to the target sink (BigQuery table). It will wait until
> data is written completely meaning the current streaming message is
> processed and there is a latency there.
>
> This is the output
>
> Terminating streaming process md
> wrote to DB ## this is the flag I added to ensure the current
> micro-bath was completed
> 2021-04-23 09:59:18,029 ERROR streaming.MicroBatchExecution: Query md [id
> = 6bbccbfe-e770-4fb0-b83d-0dedd0ee571b, runId =
> 2ae55673-6bc2-4dbe-af60-9fdc0447bff5] terminated with error
>
> The various termination processes are described in
>
> Structured Streaming Programming Guide - Spark 3.1.1 Documentation
> (apache.org)
> <http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#managing-streaming-queries>
>
> This is the idea I came up with which allows ending the streaming process
> with least cost.
>
> Ideas, opinions are welcome
>
>
> Cheers
>
> On Mon, 26 Apr 2021 at 09:44, Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Hi,
>>
>> Apologies. I just want to ensure that my subscription to
>> d...@spark.apache.org works OK.
>>
>>
>> Regards,
>>
>> Mich
>>
>>
>>
>> view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
> --
view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
https://en.everybodywiki.com/Mich_Talebzadeh
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
