[GitHub] [flink-web] zhuzhurk commented on a diff in pull request #545: Add blogs for FLIP-147 support checkpoints after tasks finished

[GitBox <gi...@apache.org>]

zhuzhurk commented on code in PR #545:
URL: https://github.com/apache/flink-web/pull/545#discussion_r893117141


##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.

Review Comment:
   finished -> finish



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 

Review Comment:
   Figure 1 seems to be  a supplementary of these statements, maybe put it below the paragraph?



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve

Review Comment:
   caused issues -> is a problem



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve
+[end-to-end exactly-once processing](https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html).
+The two-phase-commit sinks first write data to temporary files or external transactions,
+and commit the data only after a checkpoint completes to ensure the data would not be replayed on failure. However, if a job
+contains bounded sources, committing the results would not be possible after the bounded sources finish. Also because of that,
+for bounded jobs we have no way to commit the last piece of data after the first source task finished, and previously the bounded
+jobs just ignore the uncommitted data when finishing. These behaviors caused a lot of confusion and are always asked in the user
+mailing list. 
+
+Therefore, to complete the support of streaming mode for jobs using bounded sources, it is important for us to 
+
+1. Support taking checkpoints with finished tasks.
+2. Furthermore, revise the process of finishing so that all the data could always be committed.
+
+The remaining blog first briefly describes the changes we made to achieve the above targets. In the next blog,
+we’ll also share more details on how they are implemented. 
+
+# Support Checkpointing with Finished Tasks
+
+The core idea of supporting checkpoints with finished tasks is to mark the finished operators in checkpoints and skip
+executing these operators after recovery. As illustrated in Figure 2, a checkpoint is composed of the states of all
+the operators. If all the subtasks of an operator have finished, we could mark it as fully finished and skip the
+execution of this operator on startup. For other operators, their states are composed of the states of all the
+running subtasks. The states could be repartitioned on restarting and all the new subtasks restarted with the assigned states. 
+
+<center>
+<img vspace="20" style="width:50%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/checkpoint_format.png" />
+<p style="font-size: 0.6em;text-align:center;margin-top:-1em;margin-bottom: 4em">
+  Figure 2. An illustration of the extended checkpoint format.
+</p>
+</center>
+
+To support creating such a checkpoint for jobs with finished tasks, we extended the checkpoint procedure.
+Previously the checkpoint coordinator inside the JobManager first notifies all the sources to report snapshots,
+then all the sources further notify their descendants via broadcasting barrier events. Since now the sources might
+already finish, the checkpoint coordinator would instead treat the running tasks who do not have running precedent
+tasks as “new sources”, and notifies these tasks to initiate the checkpoints. The checkpoint could then deduce
+which operator is fully finished based on the task states when triggering checkpoint and the received snapshots. 
+
+The changes of the checkpoint procedure are transparent to users except that for checkpoints indeed containing
+finished tasks, we disallowed adding new operators before the fully finished ones, since it would make the fully

Review Comment:
   before -> as precedents of



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve
+[end-to-end exactly-once processing](https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html).
+The two-phase-commit sinks first write data to temporary files or external transactions,
+and commit the data only after a checkpoint completes to ensure the data would not be replayed on failure. However, if a job
+contains bounded sources, committing the results would not be possible after the bounded sources finish. Also because of that,
+for bounded jobs we have no way to commit the last piece of data after the first source task finished, and previously the bounded
+jobs just ignore the uncommitted data when finishing. These behaviors caused a lot of confusion and are always asked in the user
+mailing list. 
+
+Therefore, to complete the support of streaming mode for jobs using bounded sources, it is important for us to 
+
+1. Support taking checkpoints with finished tasks.
+2. Furthermore, revise the process of finishing so that all the data could always be committed.
+
+The remaining blog first briefly describes the changes we made to achieve the above targets. In the next blog,
+we’ll also share more details on how they are implemented. 

Review Comment:
   I prefer to remove the `also` 



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve
+[end-to-end exactly-once processing](https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html).
+The two-phase-commit sinks first write data to temporary files or external transactions,
+and commit the data only after a checkpoint completes to ensure the data would not be replayed on failure. However, if a job
+contains bounded sources, committing the results would not be possible after the bounded sources finish. Also because of that,
+for bounded jobs we have no way to commit the last piece of data after the first source task finished, and previously the bounded
+jobs just ignore the uncommitted data when finishing. These behaviors caused a lot of confusion and are always asked in the user
+mailing list. 
+
+Therefore, to complete the support of streaming mode for jobs using bounded sources, it is important for us to 
+
+1. Support taking checkpoints with finished tasks.
+2. Furthermore, revise the process of finishing so that all the data could always be committed.
+
+The remaining blog first briefly describes the changes we made to achieve the above targets. In the next blog,

Review Comment:
   I prefer to remove the `first`



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing

Review Comment:
   developing -> developing,



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve
+[end-to-end exactly-once processing](https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html).
+The two-phase-commit sinks first write data to temporary files or external transactions,
+and commit the data only after a checkpoint completes to ensure the data would not be replayed on failure. However, if a job
+contains bounded sources, committing the results would not be possible after the bounded sources finish. Also because of that,
+for bounded jobs we have no way to commit the last piece of data after the first source task finished, and previously the bounded
+jobs just ignore the uncommitted data when finishing. These behaviors caused a lot of confusion and are always asked in the user
+mailing list. 
+
+Therefore, to complete the support of streaming mode for jobs using bounded sources, it is important for us to 
+
+1. Support taking checkpoints with finished tasks.
+2. Furthermore, revise the process of finishing so that all the data could always be committed.
+
+The remaining blog first briefly describes the changes we made to achieve the above targets. In the next blog,

Review Comment:
   targets -> goals



##########
_posts/2022-06-01-final-checkpoint-part1.md:
##########
@@ -0,0 +1,173 @@
+---
+layout: post 
+title:  "FLIP-147: Support Checkpoints After Tasks Finished - Part One"
+date: 2022-06-01T00:00:00.000Z 
+authors:
+- Yun Gao:
+  name: "Yun Gao"
+- Dawid Wysakowicz:
+  name: "Dawid Wysakowicz"
+- Daisy Tsang:
+  name: "Daisy Tsang"
+excerpt: This post briefly describes the motivation and changes made by the final checkpoint mechanism, including the changes to the checkpoint procedure and how tasks finished.
+
+---
+
+# Motivation
+
+Flink is a distributed processing engine for both unbounded and bounded streams of data. In recent versions,
+Flink has unified the DataStream API and the Table / SQL API to support both streaming and batch cases.
+Since most users require both types of data processing pipelines, the unification helps reduce the complexity of developing
+operating, and maintaining consistency between streaming and batch backfilling jobs, like
+[the case for Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020). 
+
+<center>
+<img vspace="20" style="width:70%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/stream_batch_cmp.png" />
+<p style="font-size: 0.6em;text-align:left;margin-top:-1em;margin-bottom: 4em">
+  Figure 1. A comparison of the Stream mode and Batch mode for the example count operator. For streaming mode, the arrived
+  elements are not sorted, the operator would read / write the state corresponding to the element for computation.
+  For batch mode, the arrived elements are first sorted as a whole and then processed.
+</p>
+</center>
+
+Flink provides two execution modes under the unified programming API: the streaming mode and the batch mode.
+The streaming mode processes records incrementally based on the states, thus it supports both bounded and unbounded sources.
+The batch mode works with bounded sources and usually has a better performance for bounded jobs because it executes all the
+tasks in topological order and avoids random state access by pre-sorting the input records. Although batch mode is often the
+preferred mode to process bounded jobs, streaming mode is also required for various reasons. For example, users may want to deal
+with records containing retraction or exploit the property that data is roughly sorted by event times in stream mode
+(like the case in [Kappa+ Architecture](https://www.youtube.com/watch?t=666&v=4qSlsYogALo&feature=youtu.be)). Moreover,
+users often have mixed jobs involving both unbounded streams and bounded side-inputs, which also require stream execution mode. 
+
+In streaming mode, [checkpointing](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/datastream/fault-tolerance/checkpointing/)
+is the vital component in supporting exactly-once guarantees. By periodically snapshotting the
+aligned states of operators, Flink can recover from the latest checkpoint and continue execution when failover happens. However,
+previously Flink could not take checkpoints if any tasks finished. This would cause problems for jobs with both bounded and unbounded
+sources: if there are no checkpoints after the bounded part finished, the unbounded part might need to reprocess a large amount of
+records in case of a failure. 
+
+Furthermore, being unable to take checkpoints with finished tasks caused issues for jobs using two-phase-commit sinks to achieve
+[end-to-end exactly-once processing](https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html).
+The two-phase-commit sinks first write data to temporary files or external transactions,
+and commit the data only after a checkpoint completes to ensure the data would not be replayed on failure. However, if a job
+contains bounded sources, committing the results would not be possible after the bounded sources finish. Also because of that,
+for bounded jobs we have no way to commit the last piece of data after the first source task finished, and previously the bounded
+jobs just ignore the uncommitted data when finishing. These behaviors caused a lot of confusion and are always asked in the user
+mailing list. 
+
+Therefore, to complete the support of streaming mode for jobs using bounded sources, it is important for us to 
+
+1. Support taking checkpoints with finished tasks.
+2. Furthermore, revise the process of finishing so that all the data could always be committed.
+
+The remaining blog first briefly describes the changes we made to achieve the above targets. In the next blog,
+we’ll also share more details on how they are implemented. 
+
+# Support Checkpointing with Finished Tasks
+
+The core idea of supporting checkpoints with finished tasks is to mark the finished operators in checkpoints and skip
+executing these operators after recovery. As illustrated in Figure 2, a checkpoint is composed of the states of all
+the operators. If all the subtasks of an operator have finished, we could mark it as fully finished and skip the
+execution of this operator on startup. For other operators, their states are composed of the states of all the
+running subtasks. The states could be repartitioned on restarting and all the new subtasks restarted with the assigned states. 
+
+<center>
+<img vspace="20" style="width:50%" src="{{site.baseurl}}/img/blog/2022-06-01-final-checkpoint/checkpoint_format.png" />
+<p style="font-size: 0.6em;text-align:center;margin-top:-1em;margin-bottom: 4em">
+  Figure 2. An illustration of the extended checkpoint format.
+</p>
+</center>
+
+To support creating such a checkpoint for jobs with finished tasks, we extended the checkpoint procedure.
+Previously the checkpoint coordinator inside the JobManager first notifies all the sources to report snapshots,
+then all the sources further notify their descendants via broadcasting barrier events. Since now the sources might
+already finish, the checkpoint coordinator would instead treat the running tasks who do not have running precedent

Review Comment:
   already finish -> have already finished



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