Hi Dongjoon., This was an oversight from my side. I confused your involvement with docker build stuff.
HTH 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. On Sun, 19 Feb 2023 at 01:02, Dongjoon Hyun <dongjoon.h...@gmail.com> wrote: > Thank you for considering me, but may I ask what makes you think to put me > there, Mich? I'm curious about your reason. > > > I have put dongjoon.hyun as a shepherd. > > BTW, unfortunately, I cannot help you with that due to my on-going > personal stuff. I'll adjust the JIRA first. > > Thanks, > Dongjoon. > > > On Sat, Feb 18, 2023 at 10:51 AM Mich Talebzadeh < > mich.talebza...@gmail.com> wrote: > >> https://issues.apache.org/jira/browse/SPARK-42485 >> >> >> Spark Structured Streaming 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 >> >> >> 1. 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 the 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 a >> graceful shutdown. >> >> This new feature proposes a solution to handle the topic doing work for >> the message being processed gracefully, wait for it to complete and >> shutdown the streaming process for a given topic without loss of data or >> orphaned transactions >> >> >> I have put dongjoon.hyun as a shepherd. Kindly advise me if that is the >> correct approach. >> >> JIRA ticket https://issues.apache.org/jira/browse/SPARK-42485 >> >> SPIP doc: TBC >> >> Discussion thread: in >> >> https://lists.apache.org/list.html?dev@spark.apache.org >> >> >> Thanks. >> >> >> 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. >> >> >> >
