Like to hear comments on this. Basically the ability to shutdown a running spark structured streaming process gracefully.
In a way it may be something worth integrating in Spark structured streaming. Much like Kafka team are working to get rid of zooKeeper and replacing it with a system type topic. I cannot think of any other way of shutting an event driven architecture externally except by brute force Control c etc which may need spark process aborted half way. Cheers 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. On Fri, 23 Apr 2021 at 10:36, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > > Hi, > > > This is the design that I came up with. > > > 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 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 BAU. 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 > > > 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. > > >
