wanglijie95 commented on code in PR #546:
URL: https://github.com/apache/flink-web/pull/546#discussion_r896635172
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_posts/2022-06-06-adaptive-batch-scheduler.md:
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@@ -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:
Fixed
##########
_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:
Fixed
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