What are some other "newer" methodologies?
Really interested to understand what is possible here as this is a topic
came up in this forum time and again.
On Thu, 6 May 2021 at 5:13 pm, Gourav Sengupta <
gourav.sengupta.develo...@gmail.com> wrote:
> Hi Mich,
>
> thanks a ton for your kind response, looks like we are still using the
> earlier methodologies for stopping a spark streaming program gracefully.
>
>
> Regards,
> Gourav Sengupta
>
> On Wed, May 5, 2021 at 6:04 PM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>>
>> Hi,
>>
>>
>> I believe I discussed this in this forum. I sent the following to
>> spark-dev forum as an add-on to Spark functionality. This is the gist of
>> it.
>>
>>
>> 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 interrupts 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 (meaning waiting for graceful
>> completion)
>>
>> 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.
>>
>> HTH
>>
>> view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
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>>
>>
>>
>>
>> On Wed, 5 May 2021 at 17:30, Gourav Sengupta <
>> gourav.sengupta.develo...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> just thought of reaching out once again and seeking out your kind help
>>> to find out what is the best way to stop SPARK streaming gracefully. Do we
>>> still use the methods of creating a file as in SPARK 2.4.x which is several
>>> years old method or do we have a better approach in SPARK 3.1?
>>>
>>> Regards,
>>> Gourav Sengupta
>>>
>>> ---------- Forwarded message ---------
>>> From: Gourav Sengupta <gourav.sengupta.develo...@gmail.com>
>>> Date: Wed, Apr 21, 2021 at 10:06 AM
>>> Subject: Graceful shutdown SPARK Structured Streaming
>>> To: <user@spark.apache.org>
>>>
>>>
>>> Dear friends,
>>>
>>> is there any documentation available for gracefully stopping SPARK
>>> Structured Streaming in 3.1.x?
>>>
>>> I am referring to articles which are 4 to 5 years old and was wondering
>>> whether there is a better way available today to gracefully shutdown a
>>> SPARK streaming job.
>>>
>>> Thanks a ton in advance for all your kind help.
>>>
>>> Regards,
>>> Gourav Sengupta
>>>
>> --
Best Regards,
Ayan Guha
