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https://issues.apache.org/jira/browse/SPARK-22163?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael N updated SPARK-22163:
------------------------------
Description:
The application objects can contain List and can be modified dynamically as
well. However, Spark Streaming framework asynchronously serializes the
application's objects as the application runs. Therefore, it causes random
run-time exception on the List when Spark Streaming framework happens to
serializes the application's objects while the application modifies a List in
its own object.
In fact, there are multiple bugs reported about
Caused by: java.util.ConcurrentModificationException
at java.util.ArrayList.writeObject
that are permutation of the same root cause. So the design issue of Spark
streaming framework is that it should do this serialization asynchronously.
Instead, it should either
1. do this serialization synchronously. This is preferred to eliminate the
issue completely. Or
2. Allow it to be configured per application whether to do this serialization
synchronously or asynchronously, depending on the nature of each application.
Also, Spark documentation should describe the conditions that trigger Spark to
do this type of serialization asynchronously, so the applications can work
around them until the fix is provided.
===
Vadim Semenov and Steve Loughran, per your inquiries in ticket
https://issues.apache.org/jira/browse/SPARK-21999, I am posting the reply here
because this issue involves Spark's design and not necessarily its code
implementation.
—
My application does not spin up its own thread. All the threads are controlled
by Spark.
Batch interval = 5 seconds
Batch #3
1. Driver - Spark Thread #1 - starts batch #3 and blocks until all slave
threads are done with this batch
2. Slave A - Spark Thread #2. Says it takes 10 seconds to complete
3. Slave B - Spark Thread #3. Says it takes 1 minutes to complete
4. Both thread #1 for the driver and thread 2# for Slave A do not jump ahead
and process batch #4. Instead, they wait for thread #3 until it is done. => So
there is already synchronization among the threads within the same batch. Also,
batch to batch is synchronous.
5. After Spark Thread #3 is done, the driver does other processing to finish
the current batch. In my case, it updates a list of objects.
The above steps repeat for the next batch #4 and subsequent batches.
Based on the exception stack trace, it looks like in step 5, Spark has another
thread #4 that serializes application objects asynchronously. So it causes
random occurrences of ConcurrentModificationException, because the list of
objects is being changed by Spark own thread #1 for the driver.
So the issue is not that my application "is modifying a collection
asynchronously w.r.t. Spark" as Sean kept claiming. Instead, it is Spark's
asynchronous operations among its own different threads within the same batch
that causes this issue.
I understand Spark needs to serializes objects for check point purposes.
However, since Spark controls all the threads and their synchronization, it is
a Spark design's issue for the lack of synchronization between threads #1 and
#4, that triggers ConcurrentModificationException. That is the root cause of
this issue.
Further, even if the application does not modify its list of objects, in step 5
the driver could be modifying multiple native objects say two integers. In
thread #1 the driver could have updated integer X and before it could update
integer Y, when Spark's thread #4 asynchronous serializes the application
objects. So the persisted serialized data does not match with the actual data.
This resulted in a permutation of this issue with a false positive condition
where the serialized checkpoint data has partially correct data.
One solution for both issues is to modify Spark's design and allow the
serialization of application objects by Spark's thread #4 to be configurable
per application to be either asynchronous or synchronous with Spark's thread
#1. That way, it is up to individual applications to decide based on the nature
of their business requirements and needed throughput.
was:
The application objects can contain List and can be modified dynamically as
well. However, Spark Streaming framework asynchronously serializes the
application's objects as the application runs. Therefore, it causes random
run-time exception on the List when Spark Streaming framework happens to
serializes the application's objects while the application modifies a List in
its own object.
In fact, there are multiple bugs reported about
Caused by: java.util.ConcurrentModificationException
at java.util.ArrayList.writeObject
that are permutation of the same root cause. So the design issue of Spark
streaming framework is that it should do this serialization asynchronously.
Instead, it should either
1. do this serialization synchronously. This is preferred to eliminate the
issue completely. Or
2. Allow it to be configured per application whether to do this serialization
synchronously or asynchronously, depending on the nature of each application.
Also, Spark documentation should describe the conditions that trigger Spark to
do this type of serialization asynchronously, so the applications can work
around them until the fix is provided.
===
Vadim Semenov and Steve Loughran, per your inquiries in ticket
https://issues.apache.org/jira/browse/SPARK-21999, I am posting the reply here
because this issue involves Spark's design and not necessarily its code
implementation.
—
My application does not spin up its own thread. All the threads are controlled
by Spark.
Batch interval = 5 seconds
Batch #3
1. Driver - Spark Thread #1 - starts batch #3 and blocks until all slave
threads are done with this batch
2. Slave A - Spark Thread #2. Says it takes 10 seconds to complete
3. Slave B - Spark Thread #3. Says it takes 1 minutes to complete
4. Both thread #1 for the driver and thread 2# for Slave A do not jump ahead
and process batch #4. Instead, they wait for thread #3 until it is done. => So
there is already synchronization among the threads within the same batch. Also,
batch to batch is synchronous.
5. After Spark Thread #3 is done, the driver does other processing to finish
the current batch. In my case, it updates a list of objects.
The above steps repeat for the next batch #4 and subsequent batches.
Based on the exception stack trace, it looks like in step 5, Spark has another
thread #4 that serializes application objects asynchronously. So it causes
random occurrences of ConcurrentModificationException, because the list of
objects is being changed by Spark own thread #1 for the driver.
So the issue is not that my application "is modifying a collection
asynchronously w.r.t. Spark" as Sean kept claiming. Instead, it is Spark's
asynchronous operations among its own different threads within the same batch
that causes this issue.
I understand Spark needs to serializes objects for check point purposes.
However, since Spark controls all the threads and their synchronization, it is
a Spark design's issue for the lack of synchronization between threads #1 and
#4, that triggers ConcurrentModificationException that is the root cause of
this issue.
Further, even if the application does not modify its list of objects, in step 5
the driver could be modifying multiple native objects say two integers. In
thread #1 the driver could have updated integer X and before it could update
integer Y, when Spark's thread #4 asynchronous serializes the application
objects. So the persisted serialized data does not match with the actual data.
This resulted in a permutation of this issue with a false positive condition
where the serialized checkpoint data has partially correct data.
One solution for both issues is to modify Spark's design and allow the
serialization of application objects by Spark's thread #4 to be configurable
per application to be either asynchronous or synchronous with Spark's thread
#1. That way, it is up to individual applications to decide based on the nature
of their business requirements and needed throughput.
> Design Issue of Spark Streaming that Causes Random Run-time Exception
> ---------------------------------------------------------------------
>
> Key: SPARK-22163
> URL: https://issues.apache.org/jira/browse/SPARK-22163
> Project: Spark
> Issue Type: Bug
> Components: DStreams, Structured Streaming
> Affects Versions: 2.2.0
> Environment: Spark Streaming
> Kafka
> Linux
> Reporter: Michael N
>
> The application objects can contain List and can be modified dynamically as
> well. However, Spark Streaming framework asynchronously serializes the
> application's objects as the application runs. Therefore, it causes random
> run-time exception on the List when Spark Streaming framework happens to
> serializes the application's objects while the application modifies a List in
> its own object.
> In fact, there are multiple bugs reported about
> Caused by: java.util.ConcurrentModificationException
> at java.util.ArrayList.writeObject
> that are permutation of the same root cause. So the design issue of Spark
> streaming framework is that it should do this serialization asynchronously.
> Instead, it should either
> 1. do this serialization synchronously. This is preferred to eliminate the
> issue completely. Or
> 2. Allow it to be configured per application whether to do this serialization
> synchronously or asynchronously, depending on the nature of each application.
> Also, Spark documentation should describe the conditions that trigger Spark
> to do this type of serialization asynchronously, so the applications can work
> around them until the fix is provided.
> ===
> Vadim Semenov and Steve Loughran, per your inquiries in ticket
> https://issues.apache.org/jira/browse/SPARK-21999, I am posting the reply
> here because this issue involves Spark's design and not necessarily its code
> implementation.
> —
> My application does not spin up its own thread. All the threads are
> controlled by Spark.
> Batch interval = 5 seconds
> Batch #3
> 1. Driver - Spark Thread #1 - starts batch #3 and blocks until all slave
> threads are done with this batch
> 2. Slave A - Spark Thread #2. Says it takes 10 seconds to complete
> 3. Slave B - Spark Thread #3. Says it takes 1 minutes to complete
> 4. Both thread #1 for the driver and thread 2# for Slave A do not jump ahead
> and process batch #4. Instead, they wait for thread #3 until it is done. =>
> So there is already synchronization among the threads within the same batch.
> Also, batch to batch is synchronous.
> 5. After Spark Thread #3 is done, the driver does other processing to finish
> the current batch. In my case, it updates a list of objects.
> The above steps repeat for the next batch #4 and subsequent batches.
> Based on the exception stack trace, it looks like in step 5, Spark has
> another thread #4 that serializes application objects asynchronously. So it
> causes random occurrences of ConcurrentModificationException, because the
> list of objects is being changed by Spark own thread #1 for the driver.
> So the issue is not that my application "is modifying a collection
> asynchronously w.r.t. Spark" as Sean kept claiming. Instead, it is Spark's
> asynchronous operations among its own different threads within the same batch
> that causes this issue.
> I understand Spark needs to serializes objects for check point purposes.
> However, since Spark controls all the threads and their synchronization, it
> is a Spark design's issue for the lack of synchronization between threads #1
> and #4, that triggers ConcurrentModificationException. That is the root
> cause of this issue.
> Further, even if the application does not modify its list of objects, in step
> 5 the driver could be modifying multiple native objects say two integers. In
> thread #1 the driver could have updated integer X and before it could update
> integer Y, when Spark's thread #4 asynchronous serializes the application
> objects. So the persisted serialized data does not match with the actual
> data. This resulted in a permutation of this issue with a false positive
> condition where the serialized checkpoint data has partially correct data.
> One solution for both issues is to modify Spark's design and allow the
> serialization of application objects by Spark's thread #4 to be configurable
> per application to be either asynchronous or synchronous with Spark's thread
> #1. That way, it is up to individual applications to decide based on the
> nature of their business requirements and needed throughput.
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