[GitHub] [flink-web] gaoyunhaii commented on a diff in pull request #546: Add blogs for FLIP-187 adaptive batch scheduler

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

gaoyunhaii commented on code in PR #546:
URL: https://github.com/apache/flink-web/pull/546#discussion_r894264806


##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 

Review Comment:
   `is not an easy work` ?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:

Review Comment:
   `will only automatically decide` -> `only automatically decides`?
   Add a space after `.` 



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.

Review Comment:
   Should here be `Enabled` ? Also for the following items.



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.
+- ***normalize(***x***)*** is a function that round ***x*** to the closest power of 2.
+
+then the parallelism of this job vertex ***P*** will be:
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/parallelism-formula.png" width="60%"/>
+</center>
+
+Note that we introduced two special treatment in the above formula :
+
+- [Limit the cap ratio of broadcast bytes](#limit-the-cap-ratio-of-broadcast-bytes)
+- [Normalize the parallelism to the closest power of 2](#normalize-the-parallelism-to-the-closest-power-of-2)
+
+However, the above formula cannot be used to decide the parallelism of the source vertices, because the source vertices have no input. To solve it, we introduced the configuration option `jobmanager.adaptive-batch-scheduler.default-source-parallelism` to allow users to manually configure the parallelism of source vertices. Note that not all data sources need this option, because some data sources can automatically infer parallelism (For example, HiveTableSource, see [HiveParallelismInference](https://github.com/apache/flink/blob/release-1.15/flink-connectors/flink-connector-hive/src/main/java/org/apache/flink/connectors/hive/HiveParallelismInference.java) for more detail). For these sources, it is recommended to decide parallelism by themselves.
+
+### Limit the cap ratio of broadcast bytes
+As you can see, we limit the cap ratio of broadcast bytes that affects the parallelism calculation to ***broadcastCapRatio***. That is, the non-broadcast bytes processed by each task is at least ***(1-broadcastCapRatio) * V***. If not so，when the total broadcast bytes is close to ***V***, even if the total non-broadcast bytes is very small, it may cause a large parallelism, which is unnecessary and may lead to resource waste and large task deployment overhead.
+
+Generally, the broadcast dataset is usually relatively small against the other co-processed datasets. Therefore, we set the cap ratio to 0.5 by default because we usually expect the broadcast bytes to be smaller than non-broadcast bytes. The value is hard coded in the first version, and we may make it configurable later.
+
+
+### Normalize the parallelism to the closest power of 2
+The normalize is to avoid introducing data skew. To better understand this section, we suggest you read the [Flexible subpartition mapping](#flexible-subpartition-mapping) section first.
+
+Taking Fig. 4 (b) as example, A1/A2 produces 4 subpartitions, and the decided parallelism of B is 3. In this case, B1 will consume 1 subpartition, B2 will consume 1 subpartition, and B3 will consume 2 subpartitions. We assume that subpartitions have the same amount of data, which means B3 will consume twice the data of other tasks, data skew is introduced due to the subpartition mapping.
+
+To solve this problem, we need to make the subpartitions evenly consumed by downstream tasks, which means the number of subpartitions should be a multiple of the number of downstream tasks. For simplicity, we require the user-specified max parallelism to be 2<sup>N</sup>, and then adjust the calculated parallelism to a closest 2<sup>M</sup> (M <= N), so that we can guarantee that subpartitions will be evenly consumed by downstream tasks.
+
+Note that this is a temporary solution, the ultimate solution would be the [Auto-rebalancing of workloads](#auto-rebalancing-of-workloads), which may come soon.
+
+## Build up execution graph dynamically
+Before Flink 1.15, the execution graph was fully built in a static way before starting scheduling. To allow parallelisms of job vertices to be decided lazily, the execution graph must be able to be built up dynamically.
+
+### Create execution vertices and execution edges lazily
+A dynamic execution graph means that a Flink job starts with an empty execution topology, and then gradually attaches vertices during job execution, as shown in Fig. 2.
+
+The execution topology consists of execution vertices and execution edges. The execution vertices will be created and attached to the execution topology only when:
+
+- The parallelism of the corresponding job vertex is decided.
+- All upstream execution vertices are already attached.
+
+A decided parallelism of the job vertex is needed so that Flink knows how many execution vertices should be created. Upstream execution vertices need to be attached first so that Flink can connect the newly created execution vertices to the upstream vertices with execution edges. 
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/2-dynamic-graph.png" width="90%"/>
+<br/>
+Fig. 2 - Build up execution graph dynamically
+</center>
+
+<br/>
+
+### Flexible subpartition mapping
+Before Flink 1.15, when deploying a task, Flink needs to know the parallelism of its consumer job vertex. This is because consumer vertex parallelism is used to decide the number of subpartitions produced by each upstream task. The reason behind that is, for one result partition, different subpartitions serve different consumer execution vertices. More specifically, one consumer execution vertex only consumes data from subpartition with the same index. 
+
+Taking Fig. 3 as example, parallelism of the consumer B is 2, so the result partition produced by A1/A2 should contain 2 subpartitions, the subpartition with index 0 serves B1, and the subpartition with index 1 serves B2.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/3-static-graph-subpartition-mapping.png" width="30%"/>
+<br/>
+Fig. 3 - How subpartitions serve consumer execution vertices with static execution graph
+</center>
+
+<br/>
+
+But obviously, this doesn't work for dynamic graphs, because when a job vertex is deployed, the parallelism of its consumer job vertices may not have been decided yet. To enable Flink to work in this case, we need a way to allow a job vertex to run without knowing the parallelism of its consumer job vertices(or rather, we need a way to allow execution vertices to run without knowing the number of their consumer execution vertices). 

Review Comment:
   Add space before `(`?
   `the number of their consumer execution vertices` -> `the parallelism of their consumer execution vertices`?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.
+- ***normalize(***x***)*** is a function that round ***x*** to the closest power of 2.
+
+then the parallelism of this job vertex ***P*** will be:
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/parallelism-formula.png" width="60%"/>
+</center>
+
+Note that we introduced two special treatment in the above formula :
+
+- [Limit the cap ratio of broadcast bytes](#limit-the-cap-ratio-of-broadcast-bytes)
+- [Normalize the parallelism to the closest power of 2](#normalize-the-parallelism-to-the-closest-power-of-2)
+
+However, the above formula cannot be used to decide the parallelism of the source vertices, because the source vertices have no input. To solve it, we introduced the configuration option `jobmanager.adaptive-batch-scheduler.default-source-parallelism` to allow users to manually configure the parallelism of source vertices. Note that not all data sources need this option, because some data sources can automatically infer parallelism (For example, HiveTableSource, see [HiveParallelismInference](https://github.com/apache/flink/blob/release-1.15/flink-connectors/flink-connector-hive/src/main/java/org/apache/flink/connectors/hive/HiveParallelismInference.java) for more detail). For these sources, it is recommended to decide parallelism by themselves.
+
+### Limit the cap ratio of broadcast bytes
+As you can see, we limit the cap ratio of broadcast bytes that affects the parallelism calculation to ***broadcastCapRatio***. That is, the non-broadcast bytes processed by each task is at least ***(1-broadcastCapRatio) * V***. If not so，when the total broadcast bytes is close to ***V***, even if the total non-broadcast bytes is very small, it may cause a large parallelism, which is unnecessary and may lead to resource waste and large task deployment overhead.
+
+Generally, the broadcast dataset is usually relatively small against the other co-processed datasets. Therefore, we set the cap ratio to 0.5 by default because we usually expect the broadcast bytes to be smaller than non-broadcast bytes. The value is hard coded in the first version, and we may make it configurable later.
+
+
+### Normalize the parallelism to the closest power of 2
+The normalize is to avoid introducing data skew. To better understand this section, we suggest you read the [Flexible subpartition mapping](#flexible-subpartition-mapping) section first.
+
+Taking Fig. 4 (b) as example, A1/A2 produces 4 subpartitions, and the decided parallelism of B is 3. In this case, B1 will consume 1 subpartition, B2 will consume 1 subpartition, and B3 will consume 2 subpartitions. We assume that subpartitions have the same amount of data, which means B3 will consume twice the data of other tasks, data skew is introduced due to the subpartition mapping.
+
+To solve this problem, we need to make the subpartitions evenly consumed by downstream tasks, which means the number of subpartitions should be a multiple of the number of downstream tasks. For simplicity, we require the user-specified max parallelism to be 2<sup>N</sup>, and then adjust the calculated parallelism to a closest 2<sup>M</sup> (M <= N), so that we can guarantee that subpartitions will be evenly consumed by downstream tasks.
+
+Note that this is a temporary solution, the ultimate solution would be the [Auto-rebalancing of workloads](#auto-rebalancing-of-workloads), which may come soon.
+
+## Build up execution graph dynamically
+Before Flink 1.15, the execution graph was fully built in a static way before starting scheduling. To allow parallelisms of job vertices to be decided lazily, the execution graph must be able to be built up dynamically.
+
+### Create execution vertices and execution edges lazily
+A dynamic execution graph means that a Flink job starts with an empty execution topology, and then gradually attaches vertices during job execution, as shown in Fig. 2.
+
+The execution topology consists of execution vertices and execution edges. The execution vertices will be created and attached to the execution topology only when:
+
+- The parallelism of the corresponding job vertex is decided.
+- All upstream execution vertices are already attached.
+
+A decided parallelism of the job vertex is needed so that Flink knows how many execution vertices should be created. Upstream execution vertices need to be attached first so that Flink can connect the newly created execution vertices to the upstream vertices with execution edges. 
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/2-dynamic-graph.png" width="90%"/>
+<br/>
+Fig. 2 - Build up execution graph dynamically
+</center>
+
+<br/>
+
+### Flexible subpartition mapping
+Before Flink 1.15, when deploying a task, Flink needs to know the parallelism of its consumer job vertex. This is because consumer vertex parallelism is used to decide the number of subpartitions produced by each upstream task. The reason behind that is, for one result partition, different subpartitions serve different consumer execution vertices. More specifically, one consumer execution vertex only consumes data from subpartition with the same index. 
+
+Taking Fig. 3 as example, parallelism of the consumer B is 2, so the result partition produced by A1/A2 should contain 2 subpartitions, the subpartition with index 0 serves B1, and the subpartition with index 1 serves B2.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/3-static-graph-subpartition-mapping.png" width="30%"/>
+<br/>
+Fig. 3 - How subpartitions serve consumer execution vertices with static execution graph
+</center>
+
+<br/>
+
+But obviously, this doesn't work for dynamic graphs, because when a job vertex is deployed, the parallelism of its consumer job vertices may not have been decided yet. To enable Flink to work in this case, we need a way to allow a job vertex to run without knowing the parallelism of its consumer job vertices(or rather, we need a way to allow execution vertices to run without knowing the number of their consumer execution vertices). 
+
+To achieve this goal, we can set the number of subpartitions to be the max parallelism of the consumer job vertex. Then when the consumer execution vertices are deployed, they should be assigned with a subpartition range to consume. Suppose N is the number of consumer execution vertices and P is the number of subpartitions. For the kth consumer execution vertex, the consumed subpartition range should be:
+
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/range-formula.png" width="55%"/>
+</center>
+
+Taking Fig. 4 as example, the max parallelism of B is 4, so A1/A2 have 4 subpartitions. And then if the decided parallelism of B is 2, then the subpartitions mapping will be Fig. 4 (a), if the decided parallelism of B is 3, then the subpartitions mapping will be  Fig. 4 (b).
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/4-dynamic-graph-subpartition-mapping.png" width="75%"/>
+<br/>
+Fig. 4 - How subpartitions serve consumer execution vertices with dynamic graph
+</center>
+
+<br/>
+
+## Update and schedule the dynamic execution graph
+The adaptive batch scheduler scheduling is similar to the default scheduler, the only difference is that an empty dynamic execution graph will be generated initially and vertices will be attached later. Before handling any scheduling event, the scheduler will try deciding the parallelisms for job vertices, and then initialize them to generate execution vertices, connecting execution edges, and update the execution graph.
+
+The scheduler will try to decide the parallelism for all job vertices before each scheduling, and the parallelism decision will be made for each job vertex in topological order:
+
+- For source vertices, the parallelism should have been decided before starting scheduling. 
+- For non-source vertices, the parallelism can be decided only when all its consumed results are fully finished.

Review Comment:
   nit: when all its precedent tasks are fully finished or when all its consumed results are fully produced?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`

Review Comment:
   Also, is this configuration required for both DataStream and SQL jobs or it only required for DataStream Jobs? 



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`

Review Comment:
   Add `.` ?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.
+- ***normalize(***x***)*** is a function that round ***x*** to the closest power of 2.
+
+then the parallelism of this job vertex ***P*** will be:
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/parallelism-formula.png" width="60%"/>
+</center>
+
+Note that we introduced two special treatment in the above formula :
+
+- [Limit the cap ratio of broadcast bytes](#limit-the-cap-ratio-of-broadcast-bytes)
+- [Normalize the parallelism to the closest power of 2](#normalize-the-parallelism-to-the-closest-power-of-2)
+
+However, the above formula cannot be used to decide the parallelism of the source vertices, because the source vertices have no input. To solve it, we introduced the configuration option `jobmanager.adaptive-batch-scheduler.default-source-parallelism` to allow users to manually configure the parallelism of source vertices. Note that not all data sources need this option, because some data sources can automatically infer parallelism (For example, HiveTableSource, see [HiveParallelismInference](https://github.com/apache/flink/blob/release-1.15/flink-connectors/flink-connector-hive/src/main/java/org/apache/flink/connectors/hive/HiveParallelismInference.java) for more detail). For these sources, it is recommended to decide parallelism by themselves.
+
+### Limit the cap ratio of broadcast bytes
+As you can see, we limit the cap ratio of broadcast bytes that affects the parallelism calculation to ***broadcastCapRatio***. That is, the non-broadcast bytes processed by each task is at least ***(1-broadcastCapRatio) * V***. If not so，when the total broadcast bytes is close to ***V***, even if the total non-broadcast bytes is very small, it may cause a large parallelism, which is unnecessary and may lead to resource waste and large task deployment overhead.
+
+Generally, the broadcast dataset is usually relatively small against the other co-processed datasets. Therefore, we set the cap ratio to 0.5 by default because we usually expect the broadcast bytes to be smaller than non-broadcast bytes. The value is hard coded in the first version, and we may make it configurable later.
+
+
+### Normalize the parallelism to the closest power of 2
+The normalize is to avoid introducing data skew. To better understand this section, we suggest you read the [Flexible subpartition mapping](#flexible-subpartition-mapping) section first.
+
+Taking Fig. 4 (b) as example, A1/A2 produces 4 subpartitions, and the decided parallelism of B is 3. In this case, B1 will consume 1 subpartition, B2 will consume 1 subpartition, and B3 will consume 2 subpartitions. We assume that subpartitions have the same amount of data, which means B3 will consume twice the data of other tasks, data skew is introduced due to the subpartition mapping.
+
+To solve this problem, we need to make the subpartitions evenly consumed by downstream tasks, which means the number of subpartitions should be a multiple of the number of downstream tasks. For simplicity, we require the user-specified max parallelism to be 2<sup>N</sup>, and then adjust the calculated parallelism to a closest 2<sup>M</sup> (M <= N), so that we can guarantee that subpartitions will be evenly consumed by downstream tasks.
+
+Note that this is a temporary solution, the ultimate solution would be the [Auto-rebalancing of workloads](#auto-rebalancing-of-workloads), which may come soon.
+
+## Build up execution graph dynamically
+Before Flink 1.15, the execution graph was fully built in a static way before starting scheduling. To allow parallelisms of job vertices to be decided lazily, the execution graph must be able to be built up dynamically.
+
+### Create execution vertices and execution edges lazily
+A dynamic execution graph means that a Flink job starts with an empty execution topology, and then gradually attaches vertices during job execution, as shown in Fig. 2.
+
+The execution topology consists of execution vertices and execution edges. The execution vertices will be created and attached to the execution topology only when:
+
+- The parallelism of the corresponding job vertex is decided.
+- All upstream execution vertices are already attached.
+
+A decided parallelism of the job vertex is needed so that Flink knows how many execution vertices should be created. Upstream execution vertices need to be attached first so that Flink can connect the newly created execution vertices to the upstream vertices with execution edges. 

Review Comment:
   The decided parallelism?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:

Review Comment:
   Perhaps simplified as 
   ```
   a new component `VertexParallelismDecider`
   ```



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.

Review Comment:
   Might have some explaination for `cap ratio`? 



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.

Review Comment:
   Perhaps simplified as
   ```
   a new component `VertexParallelismDecider`
   ```



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.
+- ***normalize(***x***)*** is a function that round ***x*** to the closest power of 2.
+
+then the parallelism of this job vertex ***P*** will be:
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/parallelism-formula.png" width="60%"/>
+</center>
+
+Note that we introduced two special treatment in the above formula :
+
+- [Limit the cap ratio of broadcast bytes](#limit-the-cap-ratio-of-broadcast-bytes)
+- [Normalize the parallelism to the closest power of 2](#normalize-the-parallelism-to-the-closest-power-of-2)
+
+However, the above formula cannot be used to decide the parallelism of the source vertices, because the source vertices have no input. To solve it, we introduced the configuration option `jobmanager.adaptive-batch-scheduler.default-source-parallelism` to allow users to manually configure the parallelism of source vertices. Note that not all data sources need this option, because some data sources can automatically infer parallelism (For example, HiveTableSource, see [HiveParallelismInference](https://github.com/apache/flink/blob/release-1.15/flink-connectors/flink-connector-hive/src/main/java/org/apache/flink/connectors/hive/HiveParallelismInference.java) for more detail). For these sources, it is recommended to decide parallelism by themselves.
+
+### Limit the cap ratio of broadcast bytes
+As you can see, we limit the cap ratio of broadcast bytes that affects the parallelism calculation to ***broadcastCapRatio***. That is, the non-broadcast bytes processed by each task is at least ***(1-broadcastCapRatio) * V***. If not so，when the total broadcast bytes is close to ***V***, even if the total non-broadcast bytes is very small, it may cause a large parallelism, which is unnecessary and may lead to resource waste and large task deployment overhead.
+
+Generally, the broadcast dataset is usually relatively small against the other co-processed datasets. Therefore, we set the cap ratio to 0.5 by default because we usually expect the broadcast bytes to be smaller than non-broadcast bytes. The value is hard coded in the first version, and we may make it configurable later.
+
+
+### Normalize the parallelism to the closest power of 2
+The normalize is to avoid introducing data skew. To better understand this section, we suggest you read the [Flexible subpartition mapping](#flexible-subpartition-mapping) section first.
+
+Taking Fig. 4 (b) as example, A1/A2 produces 4 subpartitions, and the decided parallelism of B is 3. In this case, B1 will consume 1 subpartition, B2 will consume 1 subpartition, and B3 will consume 2 subpartitions. We assume that subpartitions have the same amount of data, which means B3 will consume twice the data of other tasks, data skew is introduced due to the subpartition mapping.
+
+To solve this problem, we need to make the subpartitions evenly consumed by downstream tasks, which means the number of subpartitions should be a multiple of the number of downstream tasks. For simplicity, we require the user-specified max parallelism to be 2<sup>N</sup>, and then adjust the calculated parallelism to a closest 2<sup>M</sup> (M <= N), so that we can guarantee that subpartitions will be evenly consumed by downstream tasks.
+
+Note that this is a temporary solution, the ultimate solution would be the [Auto-rebalancing of workloads](#auto-rebalancing-of-workloads), which may come soon.
+
+## Build up execution graph dynamically
+Before Flink 1.15, the execution graph was fully built in a static way before starting scheduling. To allow parallelisms of job vertices to be decided lazily, the execution graph must be able to be built up dynamically.
+
+### Create execution vertices and execution edges lazily
+A dynamic execution graph means that a Flink job starts with an empty execution topology, and then gradually attaches vertices during job execution, as shown in Fig. 2.
+
+The execution topology consists of execution vertices and execution edges. The execution vertices will be created and attached to the execution topology only when:
+
+- The parallelism of the corresponding job vertex is decided.
+- All upstream execution vertices are already attached.
+
+A decided parallelism of the job vertex is needed so that Flink knows how many execution vertices should be created. Upstream execution vertices need to be attached first so that Flink can connect the newly created execution vertices to the upstream vertices with execution edges. 
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/2-dynamic-graph.png" width="90%"/>
+<br/>
+Fig. 2 - Build up execution graph dynamically
+</center>
+
+<br/>
+
+### Flexible subpartition mapping
+Before Flink 1.15, when deploying a task, Flink needs to know the parallelism of its consumer job vertex. This is because consumer vertex parallelism is used to decide the number of subpartitions produced by each upstream task. The reason behind that is, for one result partition, different subpartitions serve different consumer execution vertices. More specifically, one consumer execution vertex only consumes data from subpartition with the same index. 
+
+Taking Fig. 3 as example, parallelism of the consumer B is 2, so the result partition produced by A1/A2 should contain 2 subpartitions, the subpartition with index 0 serves B1, and the subpartition with index 1 serves B2.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/3-static-graph-subpartition-mapping.png" width="30%"/>
+<br/>
+Fig. 3 - How subpartitions serve consumer execution vertices with static execution graph
+</center>
+
+<br/>
+
+But obviously, this doesn't work for dynamic graphs, because when a job vertex is deployed, the parallelism of its consumer job vertices may not have been decided yet. To enable Flink to work in this case, we need a way to allow a job vertex to run without knowing the parallelism of its consumer job vertices(or rather, we need a way to allow execution vertices to run without knowing the number of their consumer execution vertices). 
+
+To achieve this goal, we can set the number of subpartitions to be the max parallelism of the consumer job vertex. Then when the consumer execution vertices are deployed, they should be assigned with a subpartition range to consume. Suppose N is the number of consumer execution vertices and P is the number of subpartitions. For the kth consumer execution vertex, the consumed subpartition range should be:
+
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/range-formula.png" width="55%"/>
+</center>
+
+Taking Fig. 4 as example, the max parallelism of B is 4, so A1/A2 have 4 subpartitions. And then if the decided parallelism of B is 2, then the subpartitions mapping will be Fig. 4 (a), if the decided parallelism of B is 3, then the subpartitions mapping will be  Fig. 4 (b).
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/4-dynamic-graph-subpartition-mapping.png" width="75%"/>
+<br/>
+Fig. 4 - How subpartitions serve consumer execution vertices with dynamic graph
+</center>
+
+<br/>
+
+## Update and schedule the dynamic execution graph
+The adaptive batch scheduler scheduling is similar to the default scheduler, the only difference is that an empty dynamic execution graph will be generated initially and vertices will be attached later. Before handling any scheduling event, the scheduler will try deciding the parallelisms for job vertices, and then initialize them to generate execution vertices, connecting execution edges, and update the execution graph.
+
+The scheduler will try to decide the parallelism for all job vertices before each scheduling, and the parallelism decision will be made for each job vertex in topological order:

Review Comment:
   `before each scheduling` -> `before scheduling`?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:
+
+- ***V*** is the bytes of data the user expects to be processed by each task.
+- ***totalBytes<sub>non-broadcast</sub>*** is the sum of the non-broadcast result sizes consumed by this job vertex.
+- ***totalBytes<sub>broadcast</sub>*** is the sum of the broadcast result sizes consumed by this job vertex.
+- ***broadcastCapRatio*** is the cap ratio of broadcast bytes that affects the parallelism calculation.
+- ***normalize(***x***)*** is a function that round ***x*** to the closest power of 2.
+
+then the parallelism of this job vertex ***P*** will be:
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/parallelism-formula.png" width="60%"/>
+</center>
+
+Note that we introduced two special treatment in the above formula :
+
+- [Limit the cap ratio of broadcast bytes](#limit-the-cap-ratio-of-broadcast-bytes)
+- [Normalize the parallelism to the closest power of 2](#normalize-the-parallelism-to-the-closest-power-of-2)
+
+However, the above formula cannot be used to decide the parallelism of the source vertices, because the source vertices have no input. To solve it, we introduced the configuration option `jobmanager.adaptive-batch-scheduler.default-source-parallelism` to allow users to manually configure the parallelism of source vertices. Note that not all data sources need this option, because some data sources can automatically infer parallelism (For example, HiveTableSource, see [HiveParallelismInference](https://github.com/apache/flink/blob/release-1.15/flink-connectors/flink-connector-hive/src/main/java/org/apache/flink/connectors/hive/HiveParallelismInference.java) for more detail). For these sources, it is recommended to decide parallelism by themselves.
+
+### Limit the cap ratio of broadcast bytes
+As you can see, we limit the cap ratio of broadcast bytes that affects the parallelism calculation to ***broadcastCapRatio***. That is, the non-broadcast bytes processed by each task is at least ***(1-broadcastCapRatio) * V***. If not so，when the total broadcast bytes is close to ***V***, even if the total non-broadcast bytes is very small, it may cause a large parallelism, which is unnecessary and may lead to resource waste and large task deployment overhead.
+
+Generally, the broadcast dataset is usually relatively small against the other co-processed datasets. Therefore, we set the cap ratio to 0.5 by default because we usually expect the broadcast bytes to be smaller than non-broadcast bytes. The value is hard coded in the first version, and we may make it configurable later.
+
+
+### Normalize the parallelism to the closest power of 2
+The normalize is to avoid introducing data skew. To better understand this section, we suggest you read the [Flexible subpartition mapping](#flexible-subpartition-mapping) section first.
+
+Taking Fig. 4 (b) as example, A1/A2 produces 4 subpartitions, and the decided parallelism of B is 3. In this case, B1 will consume 1 subpartition, B2 will consume 1 subpartition, and B3 will consume 2 subpartitions. We assume that subpartitions have the same amount of data, which means B3 will consume twice the data of other tasks, data skew is introduced due to the subpartition mapping.
+
+To solve this problem, we need to make the subpartitions evenly consumed by downstream tasks, which means the number of subpartitions should be a multiple of the number of downstream tasks. For simplicity, we require the user-specified max parallelism to be 2<sup>N</sup>, and then adjust the calculated parallelism to a closest 2<sup>M</sup> (M <= N), so that we can guarantee that subpartitions will be evenly consumed by downstream tasks.
+
+Note that this is a temporary solution, the ultimate solution would be the [Auto-rebalancing of workloads](#auto-rebalancing-of-workloads), which may come soon.
+
+## Build up execution graph dynamically
+Before Flink 1.15, the execution graph was fully built in a static way before starting scheduling. To allow parallelisms of job vertices to be decided lazily, the execution graph must be able to be built up dynamically.
+
+### Create execution vertices and execution edges lazily
+A dynamic execution graph means that a Flink job starts with an empty execution topology, and then gradually attaches vertices during job execution, as shown in Fig. 2.
+
+The execution topology consists of execution vertices and execution edges. The execution vertices will be created and attached to the execution topology only when:
+
+- The parallelism of the corresponding job vertex is decided.
+- All upstream execution vertices are already attached.
+
+A decided parallelism of the job vertex is needed so that Flink knows how many execution vertices should be created. Upstream execution vertices need to be attached first so that Flink can connect the newly created execution vertices to the upstream vertices with execution edges. 
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/2-dynamic-graph.png" width="90%"/>
+<br/>
+Fig. 2 - Build up execution graph dynamically
+</center>
+
+<br/>
+
+### Flexible subpartition mapping
+Before Flink 1.15, when deploying a task, Flink needs to know the parallelism of its consumer job vertex. This is because consumer vertex parallelism is used to decide the number of subpartitions produced by each upstream task. The reason behind that is, for one result partition, different subpartitions serve different consumer execution vertices. More specifically, one consumer execution vertex only consumes data from subpartition with the same index. 
+
+Taking Fig. 3 as example, parallelism of the consumer B is 2, so the result partition produced by A1/A2 should contain 2 subpartitions, the subpartition with index 0 serves B1, and the subpartition with index 1 serves B2.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/3-static-graph-subpartition-mapping.png" width="30%"/>
+<br/>
+Fig. 3 - How subpartitions serve consumer execution vertices with static execution graph
+</center>
+
+<br/>
+
+But obviously, this doesn't work for dynamic graphs, because when a job vertex is deployed, the parallelism of its consumer job vertices may not have been decided yet. To enable Flink to work in this case, we need a way to allow a job vertex to run without knowing the parallelism of its consumer job vertices(or rather, we need a way to allow execution vertices to run without knowing the number of their consumer execution vertices). 
+
+To achieve this goal, we can set the number of subpartitions to be the max parallelism of the consumer job vertex. Then when the consumer execution vertices are deployed, they should be assigned with a subpartition range to consume. Suppose N is the number of consumer execution vertices and P is the number of subpartitions. For the kth consumer execution vertex, the consumed subpartition range should be:
+
+<center>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/range-formula.png" width="55%"/>
+</center>
+
+Taking Fig. 4 as example, the max parallelism of B is 4, so A1/A2 have 4 subpartitions. And then if the decided parallelism of B is 2, then the subpartitions mapping will be Fig. 4 (a), if the decided parallelism of B is 3, then the subpartitions mapping will be  Fig. 4 (b).
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/4-dynamic-graph-subpartition-mapping.png" width="75%"/>
+<br/>
+Fig. 4 - How subpartitions serve consumer execution vertices with dynamic graph
+</center>
+
+<br/>
+
+## Update and schedule the dynamic execution graph
+The adaptive batch scheduler scheduling is similar to the default scheduler, the only difference is that an empty dynamic execution graph will be generated initially and vertices will be attached later. Before handling any scheduling event, the scheduler will try deciding the parallelisms for job vertices, and then initialize them to generate execution vertices, connecting execution edges, and update the execution graph.
+
+The scheduler will try to decide the parallelism for all job vertices before each scheduling, and the parallelism decision will be made for each job vertex in topological order:
+
+- For source vertices, the parallelism should have been decided before starting scheduling. 
+- For non-source vertices, the parallelism can be decided only when all its consumed results are fully finished.
+
+After trying to decide the parallelism for all job vertices, the scheduler will try to initialize the job vertices in topological order. A job vertex that can be initialized should meet the following conditions:

Review Comment:
   After decided the parallelism for ... ?
   will try to initialize  -> will initialize?



##########
_posts/2022-06-06-adaptive-batch-scheduler.md:
##########
@@ -0,0 +1,198 @@
+---
+layout: post
+title: "Automatically decide parallelism for Flink batch jobs"
+date: 2022-06-06T08:00:00.000Z
+authors:
+- Lijie Wang:
+  name: "Lijie Wang"
+- Zhu Zhu:
+  name: "Zhu Zhu"
+excerpt: To automatically decide parallelism for Flink batch jobs, we introduced adaptive batch scheduler in Flink 1.15. In this post, we'll take a close look at the design & implementation details.
+
+---
+
+{% toc %}
+
+# Introduction
+
+Deciding proper parallelisms for operators is not easy work for many users. For batch jobs, a small parallelism may result in long execution time and big failover regression. While an unnecessary large parallelism may result in resource waste and more overhead cost in task deployment and network shuffling. 
+
+To decide a proper parallelism, one needs to know how much data each operator needs to process. However, It can be hard to predict data volume to be processed by a job because it can be different everyday. And it can be harder or even impossible (due to complex operators or UDFs) to predict data volume to be processed by each operator.
+
+To solve this problem, we introduced the adaptive batch scheduler in Flink 1.15. The adaptive batch scheduler can automatically decide parallelism for an operator according to the size of its consumed datasets. Here are the benefits the adaptive batch scheduler can bring:
+
+1. Batch job users can be relieved from parallelism tuning.
+2. Parallelism tuning is fine grained considering different operators. This is particularly beneficial for SQL jobs which can only be set with a global parallelism previously.
+3. Parallelism tuning can better fit consumed datasets which have a varying volume size every day.
+
+# Get Started
+
+To automatically decide parallelism for operators, you need to:
+
+1. Configure to use adaptive batch scheduler.
+2. Set the parallelism of operators to -1.
+
+
+## Configure to use adaptive batch scheduler
+
+To use adaptive batch scheduler, you need to set configurations as below:
+
+- Set `jobmanager.scheduler: AdaptiveBatch`.
+- Leave the [execution.batch-shuffle-mode]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/config/#execution-batch-shuffle-mode) unset or explicitly set it to `ALL-EXCHANGES-BLOCKING` (default value). Currently, the adaptive batch scheduler only supports batch jobs whose shuffle mode is `ALL-EXCHANGES-BLOCKING`.
+
+In addition, there are several related configuration options to control the upper bounds and lower bounds of tuned parallelisms, to specify expected data volume to process by each operator, and to specify the default parallelism of sources. More details can be found in the [feature documentation page]({{site.DOCS_BASE_URL}}flink-docs-release-1.15/docs/deployment/elastic_scaling/#configure-to-use-adaptive-batch-scheduler).
+
+## Set the parallelism of operators to -1
+
+The adaptive batch scheduler will only automatically decide parallelism for operators whose parallelism is not set (which means the parallelism is -1).To leave parallelism unset, you should configure as follows:
+
+- Set `parallelism.default: -1`
+- Set `table.exec.resource.default-parallelism: -1` in SQL jobs.
+- Don't call `setParallelism()` for operators in DataStream/DataSet jobs.
+- Don't call `setParallelism()` on `StreamExecutionEnvironment/ExecutionEnvironment` in DataStream/DataSet jobs.
+
+
+# Implementation Details
+
+In this section, we will elaborate the details of the implementation. To automatically decide parallelism of operators, we introduced the following changes:
+
+1. Enables the scheduler to collect sizes of finished datasets.
+2. Introduces a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results.
+3. Enables to dynamically build up ExecutionGraph to allow the parallelisms of job vertices to be decided lazily. The execution graph starts with an empty execution topology and then gradually attaches the vertices during job execution.
+4. Introduces the adaptive batch scheduler to update and schedule the dynamic execution graph.
+
+The details will be introduced in the following sections.
+
+<center>
+<br/>
+<img src="{{site.baseurl}}/img/blog/2022-06-06-adaptive-batch-scheduler/1-overall-structure.png" width="60%"/>
+<br/>
+Fig. 1 - The overall structure of automatically deciding parallelism
+</center>
+
+<br/>
+
+## Collect sizes of consumed datasets
+
+The adaptive batch scheduler decides the parallelism of vertices by the size of input results, so the scheduler needs to know the sizes of result partitions produced by tasks. We introduced a numBytesProduced counter to record the size of each produced result partition, the accumulated result of the counter will be sent to the scheduler when tasks finish. 
+
+## Decide proper parallelisms for job vertices
+
+We introduced a new component(VertexParallelismDecider) to compute proper parallelisms for job vertices according to the sizes of their consumed results. The computation algorithm is as follows:
+Suppose:

Review Comment:
   Add an empty line before `Suppose: `.
   
   Also might not use two adjacent `:`



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