[airflow] branch master updated: Enhanced the Kubernetes Executor doc (#10433)

[ka...@apache.org]

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kaxilnaik pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/airflow.git


The following commit(s) were added to refs/heads/master by this push:
     new 46ac09d  Enhanced the Kubernetes Executor doc  (#10433)
46ac09d is described below

commit 46ac09d5c9b9f6e36cce0a1d3812f483ed7201eb
Author: Vikram Koka <vi...@astronomer.io>
AuthorDate: Wed Aug 26 10:42:35 2020 -0700

    Enhanced the Kubernetes Executor doc  (#10433)
    
    A simple architecture diagram to show the Airflow setup when used with the Kubernetes executor
---
 docs/executor/kubernetes.rst       |  16 +++++++++++++---
 docs/img/arch-diag-kubernetes.png  | Bin 0 -> 21804 bytes
 docs/img/arch-diag-kubernetes2.png | Bin 0 -> 86384 bytes
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/docs/executor/kubernetes.rst b/docs/executor/kubernetes.rst
index 3c64c16..d3664b9 100644
--- a/docs/executor/kubernetes.rst
+++ b/docs/executor/kubernetes.rst
@@ -44,15 +44,25 @@ KubernetesExecutor Architecture
 The KubernetesExecutor runs as a process in the Scheduler that only requires access to the Kubernetes API (it does *not* need to run inside of a Kubernetes cluster). The KubernetesExecutor requires a non-sqlite database in the backend, but there are no external brokers or persistent workers needed.
 For these reasons, we recommend the KubernetesExecutor for deployments have long periods of dormancy between DAG execution.
 
+When a DAG submits a task, the KubernetesExecutor requests a worker pod from the Kubernetes API. The worker pod then runs the task, reports the result, and terminates.
 
-.. image:: ../img/k8s-0-worker.jpeg
 
+.. image:: ../img/arch-diag-kubernetes.png
 
-When a DAG submits a task, the KubernetesExecutor requests a worker pod from the Kubernetes API. The worker pod then runs the task, reports the result, and terminates.
 
+In contrast to the Celery Executor, the Kubernetes Executor does not require additional components such as Redis and Flower, but does require the Kubernetes infrastructure.
+
+One example of an Airflow deployment running on a distributed set of five nodes in a Kubernetes cluster is shown below. 
+
+.. image:: ../img/arch-diag-kubernetes2.png
+
+The Kubernetes Executor has an advantage over the Celery Executor in that Pods are only spun up when required for task execution compared to the Celery Executor where the workers are statically configured and are running all the time, regardless of workloads. However, this could be a disadvantage depending on the latency needs, since a task takes longer to start using the Kubernetes Executor, since it now includes the Pod startup time.
+
+Consistent with the regular Airflow architecture, the Workers need access to the DAG files to execute the tasks within those DAGs and interact with the Metadata repository. Also, configuration information specific to the Kubernetes Executor, such as the worker namespace and image information, needs to be specified in the Airflow Configuration file.
+
+Additionally, the Kubernetes Executor enables specification of additional features on a per-task basis using the Executor config.
 
 
-.. image:: ../img/k8s-3-worker.jpeg
 
 .. @startuml
 .. Airflow_Scheduler -> Kubernetes: Request a new pod with command "airflow run..."
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