This is an automated email from the ASF dual-hosted git repository.
mxm pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/flink-kubernetes-operator.git
The following commit(s) were added to refs/heads/main by this push:
new 2204b3d4 [docs] Add a README to the flink-autoscaler module (#694)
2204b3d4 is described below
commit 2204b3d4ebaf6fbccef9d4a63c5021c5c0342490
Author: Maximilian Michels <[email protected]>
AuthorDate: Thu Oct 26 16:38:31 2023 +0200
[docs] Add a README to the flink-autoscaler module (#694)
---
flink-autoscaler/README.md | 27 +++++++++++++++++++++++++++
1 file changed, 27 insertions(+)
diff --git a/flink-autoscaler/README.md b/flink-autoscaler/README.md
new file mode 100644
index 00000000..1cca6445
--- /dev/null
+++ b/flink-autoscaler/README.md
@@ -0,0 +1,27 @@
+# Flink Autoscaler
+
+This module contains the implementation for autoscaling Flink jobs.
+Please see
[FLIP-271](https://cwiki.apache.org/confluence/display/FLINK/FLIP-271%3A+Autoscaling)
+for an overview of how autoscaling works. Also see the [docs](../docs).
+
+## How To Use
+
+If you want to use this code as part of the Flink Kubernetes Operator, you
don't need to
+do anything. The code here is already bundled with the Flink Kubernetes
operator.
+If you are looking to use autoscaling with a different orchestration
framework, read on!
+
+## Interfacing with the autoscaler
+
+While earlier implementations were dependent on other modules of this
repository,
+thus tightly coupling the autoscaler implementation with Kubernetes, this no
+longer is the case. This module is not dependent on any either modules in this
repository.
+It lives here for historical reasons but also to ease the release process for
its main
+consumer, the Flink Kubernetes operator. Regardless, any downstream project is
welcome
+to consume the `flink-autoscaler` artifact and use autoscaling with its own
orchestration
+framework.
+
+Under the hood, the autoscaler exposes a set of interfaces for storing
autoscaler state,
+handling autoscaling events, and executing scaling decisions. How these are
implemented
+is specific to the orchestration framework used (e.g. Kubernetes), but the
interfaces are
+designed to be as generic as possible. Currently, there are several other
implementations
+in the making, e.g. YARN or a standalone implementation.