[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Attachment: sparkStructuredStreaming01.png
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.2.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
> Attachments: sparkStructuredStreaming01.png
>
>
> 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
> # 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 a graceful shutdown.
> {color:#00}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{color}
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Affects Version/s: 3.2.2
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.2.2, 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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
> # 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 a graceful shutdown.
> {color:#00}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{color}
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Affects Version/s: (was: 3.3.2)
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.2.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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
> # 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 a graceful shutdown.
> {color:#00}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{color}
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Target Version/s: (was: 3.3.2)
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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
> # 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 a graceful shutdown.
> {color:#00}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{color}
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-42485:
--
Shepherd: (was: Dongjoon Hyun)
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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
> # 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 a graceful shutdown.
> {color:#00}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{color}
--
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Description:
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
# 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 a graceful shutdown.
{color:#00}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{color}
was:
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 a graceful shutdown.
{color:#00}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{color}
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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
[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Description:
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 a graceful shutdown.
{color:#00}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{color}
was:
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 a graceful shutdown.
{color:#00}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{color}
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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.
[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Description:
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 a graceful shutdown.
{color:#00}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{color}
was:{color:#00}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.{color}
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> 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 a graceful shutdown.
> {color:#00}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{color}
--
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[jira] [Updated] (SPARK-42485) SPIP: Shutting down spark structured streaming when the streaming process completed current process
[
https://issues.apache.org/jira/browse/SPARK-42485?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mich Talebzadeh updated SPARK-42485:
Shepherd: Dongjoon Hyun
Target Version/s: 3.3.2
Labels: SPIP (was: )
> SPIP: Shutting down spark structured streaming when the streaming process
> completed current process
> ---
>
> Key: SPARK-42485
> URL: https://issues.apache.org/jira/browse/SPARK-42485
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
>Affects Versions: 3.3.2
>Reporter: Mich Talebzadeh
>Priority: Major
> Labels: SPIP
>
> {color:#00}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.{color}
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