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From Mich Talebzadeh <>
Subject Re: Graceful shutdown SPARK Structured Streaming
Date Wed, 05 May 2021 17:04:18 GMT

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


   query.awaitTermination() # Waits for the termination of this query, with
   stop() or with error

   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


 |-- 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

"timeissued":"2021-04-23T08:54:06", "queue":"md", "status":"true"}

"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}"""),False)
        queue ="queue")).collect()[0][0]
        status ="status")).collect()[0][0]

        if((queue == 'md')) & (status == 'false')):
          spark_session = s.spark_session(config['common']['appName'])
          active =
          for e in active:
             name =
             if(name == 'md'):
                print(f"""Terminating streaming process {name}""")
        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

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

This is the idea I came up with which allows ending the streaming process
with least cost.


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On Wed, 5 May 2021 at 17:30, Gourav Sengupta <> 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 <>
> Date: Wed, Apr 21, 2021 at 10:06 AM
> Subject: Graceful shutdown SPARK Structured Streaming
> To: <>
> 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

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