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new 7bdd27d add blog: Chaos Mesh X SkyWalking (#376)
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commit 7bdd27d0ab2180fa168fd6f974707da537666906
Author: FingerLeader <[email protected]>
AuthorDate: Tue Dec 21 15:59:14 2021 +0800
add blog: Chaos Mesh X SkyWalking (#376)
Signed-off-by: FingerLeader <[email protected]>
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+---
+slug: /better-observability-for-chaos-engineering
+title: 'Chaos Mesh + SkyWalking: Better Observability for Chaos Engineering'
+author: Ningxuan Wang
+author_title: Chaos Mesh Contributor
+author_url: https://github.com/FingerLeader
+author_image_url: https://avatars.githubusercontent.com/u/43462394?v=4
+image: /img/chaos-mesh-skywalking-banner.png
+tags: [Chaos Mesh, Chaos Engineering, Tutorial]
+---
+
+
+
+[Chaos Mesh](https://github.com/chaos-mesh/chaos-mesh) is an open-source
cloud-native [chaos
engineering](https://en.wikipedia.org/wiki/Chaos_engineering) platform. You can
use Chaos Mesh to conveniently inject failures and simulate abnormalities that
might occur in reality, so you can identify potential problems in your system.
Chaos Mesh also offers a Chaos Dashboard which allows you to monitor the status
of a chaos experiment. However, this dashboard cannot let you observe how the
fai [...]
+
+<!--truncate-->
+
+[Apache SkyWalking](https://github.com/apache/skywalking) is an open-source
application performance monitor (APM), specially designed to monitor, track,
and diagnose cloud native, container-based distributed systems. It collects
events that occur and then displays them on its dashboard, allowing you to
observe directly the type and number of events that have occurred in your
system and how different events impact the service performance.
+
+When you use SkyWalking and Chaos Mesh together during chaos experiments, you
can observe how different failures impact the service performance.
+
+This tutorial will show you how to configure SkyWalking and Chaos Mesh. You’ll
also learn how to leverage the two systems to monitor events and observe in
real time how chaos experiments impact applications’ service performance.
+
+## Preparation
+
+Before you start to use SkyWalking and Chaos Mesh, you have to:
+
+* Set up a SkyWalking cluster according to [the SkyWalking configuration
guide](https://github.com/apache/skywalking-kubernetes#install).
+* Deploy Chao Mesh [using
Helm](https://chaos-mesh.org/docs/production-installation-using-helm/).
+* Install [JMeter](https://jmeter.apache.org/index.html) or other Java testing
tools (to increase service loads).
+* Configure SkyWalking and Chaos Mesh according to [this
guide](https://github.com/chaos-mesh/chaos-mesh-on-skywalking) if you just want
to run a demo.
+
+Now, you are fully prepared, and we can cut to the chase.
+
+## Step 1: Access the SkyWalking cluster
+
+After you install the SkyWalking cluster, you can access its user interface
(UI). However, no service is running at this point, so before you start
monitoring, you have to add one and set the agents.
+
+In this tutorial, we take Spring Boot, a lightweight microservice framework,
as an example to build a simplified demo environment.
+
+1. Create a SkyWalking demo in Spring Boot by referring to [this
document](https://github.com/chaos-mesh/chaos-mesh-on-skywalking/blob/master/demo-deployment.yaml).
+2. Execute the command `kubectl apply -f demo-deployment.yaml -n skywalking`
to deploy the demo.
+
+After you finish deployment, you can observe the real-time monitoring results
at the SkyWalking UI.
+
+**Note:** Spring Boot and SkyWalking have the same default port number: 8080.
Be careful when you configure the port forwarding; otherise, you may have port
conflicts. For example, you can set Spring Boot’s port to 8079 by using a
command like `kubectl port-forward svc/spring-boot-skywalking-demo 8079:8080 -n
skywalking` to avoid conflicts.
+
+## Step 2: Deploy SkyWalking Kubernetes Event Exporter
+
+[SkyWalking Kubernetes Event
Exporter](https://github.com/apache/skywalking-kubernetes-event-exporter) is
able to watch, filter, and send Kubernetes events into the SkyWalking backend.
SkyWalking then associates the events with the system metrics and displays an
overview about when and how the metrics are affected by the events.
+
+If you want to deploy SkyWalking Kubernetes Event Explorer with one line of
commands, refer to [this
document](https://github.com/chaos-mesh/chaos-mesh-on-skywalking/blob/master/exporter-deployment.yaml)
to create configuration files in YAML format and then customize the parameters
in the filters and exporters. Now, you can use the command `kubectl apply` to
deploy SkyWalking Kubernetes Event Explorer.
+
+## Step 3: Use JMeter to increase service loads
+
+To better observe the change in service performance, you need to increase the
service loads on Spring Boot. In this tutorial, we use JMeter, a widely adopted
Java testing tool, to increase the service loads.
+
+Perform a stress test on `localhost:8079` using JMeter and add five threads to
continuously increase the service loads.
+
+
+
+
+
+
+Open the SkyWalking Dashboard. You can see that the access rate is 100%, and
that the service loads reach about 5,300 calls per minute (CPM).
+
+
+
+## Step 4: Inject failures via Chaos Mesh and observe results
+
+After you finish the three steps above, you can use the Chaos Dashboard to
simulate stress scenarios and observe the change in service performance during
chaos experiments.
+
+
+
+The following sections describe how service performance varies under the
stress of three chaos conditions:
+
+* CPU load: 10%; memory load: 128 MB
+
+ The first chaos experiment simulates low CPU usage. To display when a
chaos experiment starts and ends, click the switching button on the right side
of the dashboard. To learn whether the experiment is Applied to the system or
Recovered from the system, move your cursor onto the short, green line.
+
+ During the time period between the two short, green lines, the service
load decreases to 4,929 CPM, but returns to normal after the chaos experiment
ends.
+
+ 
+
+* CPU load: 50%; memory load: 128 MB
+
+ When the application’s CPU load increases to 50%, the service load
decreases to 4,307 CPM.
+
+ 
+
+* CPU load: 100%; memory load: 128 MB
+
+ When the CPU usage is at 100%, the service load decreases to only 40% of
what it would be if no chaos experiments were taking place.
+
+ 
+
+ Because the process scheduling under the Linux system does not allow a
process to occupy the CPU all the time, the deployed Spring Boot Demo can still
handle 40% of the access requests even in the extreme case of a full CPU load.
+
+## Summary
+
+By combining SkyWalking and Chaos Mesh, you can clearly observe when and to
what extent chaos experiments affect application service performance. This
combination of tools lets you observe the service performance in various
extreme conditions, thus boosting your confidence in your services.
+
+Chaos Mesh has grown a lot in 2021 thanks to the unremitting efforts of all
PingCAP engineers and community contributors. In order to continue to upgrade
our support for our wide variety of users and learn more about users’
experience in Chaos Engineering, we’d like to invite you to take[ this
survey](https://www.surveymonkey.com/r/X77BCNM) and give us your valuable
feedback.
+
+If you want to know more about Chaos Mesh, you’re welcome to join [the Chaos
Mesh community on GitHub](https://github.com/chaos-mesh) or our [Slack
discussions](https://slack.cncf.io/) (#project-chaos-mesh). If you find any
bugs or missing features when using Chaos Mesh, you can submit your pull
requests or issues to our [GitHub
repository](https://github.com/chaos-mesh/chaos-mesh).
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