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Kubernetes Monitoring


UPGRADE YOUR K8S MONITORING EXPERIENCE


OUR LATEST KUBERNETES INTEGRATION OFFERS MORE CONFIGURATION OPTIONS, ENHANCED
LOGS, AND A REDUCED MEMORY FOOTPRINT.

Published May 18, 2022 • 7 min read

By Paolo Gallina

As Kubernetes environments continue to scale, they also get more complex, making
it harder to monitor their performance and health. Our updated Kubernetes
integration (v3) significantly reduces CPU and memory, adds support for external
control planes such as Rancher, and includes improvements to log messages to
help identify issues. For instance, in big clusters, our new integration can be
configured to request 80% less memory than before. That’s a huge improvement.

In this post, I’ll cover the benefits of our new integration as well as some of
the challenges we faced with our upgrade to v3.


WHY SHOULD YOU UPGRADE YOUR KUBERNETES INTEGRATION?

If you’re using an older version of our Kubernetes integration (or even if
you’re new to monitoring Kubernetes with New Relic), you’ll get a lot of
benefits by using v3.

 * Reduced memory footprint: Get up to 80% reduction in big clusters, thanks to
   an improved kube-state-metrics (KSM) scraping component.
 * Improved troubleshooting: Triage bugs and fix issues easier with enhanced
   logs and process cycle.
 * More configurable: Use three individually configurable components, including
   config files that provide more granular settings for each data source.
 * Scrape external control planes: Scrape metrics from components outside your
   clusters.
 * More flexible scraping intervals: Dial up or dial down data ingest to suit
   your needs.


HOW TO UPGRADE YOUR KUBERNETES INTEGRATION

If you’re using v2 and ready to upgrade now, you just need to run this command:

helm upgrade --install my-installation newrelic/nri-bundle

Copy

You don’t need to update your configuration, dashboards, or alerts. For details,
see the migration guide. 


IMPROVING OUR KUBERNETES INTEGRATION BEHIND THE SCENES

When we were updating the integration, there were a lot of factors we had to
keep in mind. We wanted to make the new integration compatible for our customers
using v2, deliver valuable enhancements, and make the upgrade process as easy as
possible. We also needed to continuously test the new implementation while the
integration’s architecture was changing.

With the upgrade to v3, we decided to fully leverage the Kubernetes deployment
model and the capabilities that we hadn’t been using yet. To explain what this
means, I’m going to get a bit technical.

To fully unlock Kubernetes capabilities, we made these improvements:

 * Moved scraping tasks to separate components to allow the integration to make
   smarter decisions at scheduling and deployment time, not at runtime.
 * Switched to a sidecar pattern to move integration and agent processes to
   different containers, ensuring that the integration follows Kubernetes best
   practices.
 * Added functionality to scrape external control planes to add support for
   solutions such as Rancher and managed apiServers.
 * Reduced complexity in the cache system by leveraging Kubernetes informers.
 * Added support for complex data structures in integration configurations such
   as lists and maps.
 * Removed hardcoded scraping interval for control planes so you can manually
   configure the interval to fit your applications’ needs.

The next sections dive into the details of each of these improvements.


DECOUPLED COMPONENT CONFIGURATION

To avoid data duplication while scraping KSM and control plane components from a
DaemonSet, we performed locality-based leader election.

In version 2, the infra DaemonSet changed its behavior depending on which pods
were running in the same node.

When designing the new architecture, we divided the different integration tasks
into different components to be scheduled as different workloads. Each component
has its own separate configuration, resources, annotations, logs, and so on.
Moreover, the behavior of the components is no longer changing depending on pods
running in the same node. This allows the integration to make smarter decisions
at scheduling and deployment time, rather than at runtime, as described in the
next section.

V3 includes a DaemonSet scraping the Kubelet process running on the node. It
also includes separate deployments for scraping kube-state-metrics and Control
Plane components /metrics. Note that, depending on your configuration, the
Control Plane component could be deployed as well as a DaemonSet.

In version 3, increased complexity in the architecture allowed us to reduce the
codebase size and improve the user experience.

Ultimately, optimizing the integration’s architecture allowed us to reduce the
codebase size, which improves the Kubernetes integration experience in New
Relic.


USING A SIDECAR PATTERN TO REMOVE UNNECESSARY PROCESSES

In v2, there were multiple processes per container: The New Relic infrastructure
agent triggered the execution of the nri-kubernetes integration as a different
process. This approach meant that only failures in the main process of a
container surface and trigger a pod restart.

In v2, the integration was not the container’s main process, so in the case of a
configuration or execution error, the failure didn't surface. There was no
automatic pod restart, making it difficult to detect issues. 

The new implementation in v3 follows a sidecar pattern having the nri-kubernetes
scraper container next to an agent running in a separate sidecar. Having a
single process per container provides Kubernetes control over the lifecycle of
the integration process, ensuring that when an error causes the integration to
fail, it sends out a notification, and the Pod is restarted.


ADDED FUNCTIONALITY TO SCRAPE EXTERNAL CONTROL PLANES

With v2, scraping external control planes wasn’t possible, even if you specified
a URL to scrape.

With our updated Kubernetes integration v3, you can specify a static endpoint,
and if you need to avoid data duplication, deploy the control plane component as
a deployment and not as a DaemonSet.

Scraping the AWS API server control plane in version 3.

You can also specify different authentication methods for each component,
including bearer tokens and Mutual Transport Layer Security (MTLS).

And thanks to autodiscovery and other configurations added in the defaults, the
default configuration supports several Kubernetes flavors, including Kubeadm,
kind, and minikube. For flavors that the configuration doesn’t cover, you can
configure the integration to use any selector, URL, or auth method.


ENVIRONMENT VARIABLES-BASED CONFIGURATION

In v2, if you wanted to configure the integration, you could only use
environment variables. This approach made it tedious to support complex
scenarios because you were limited to key=value configs. For example, since the
configuration option was a flat list, we needed to add prefixes to show you
which group variables belonged to. That created confusion whenever we needed to
add a new parameter in nested configuration groups, so we needed environment
variables with different prefixes: DiscoveryCacheTTL, APIServerCacheTTL,
APIServerCacheK8SVersionTTL, APIServerCacheK8SVersionTTLJitter.

The new integration leverages a YAML config to support more complex data
structures such as lists and maps, and it allows customers to configure most of
the integration’s parameters. At first glance, the new YAML configuration of the
Helm chart seems more complex than the previous configuration. However, the new
values.yaml provides additional syntax that is often used in Helm charts.

Finally, on configuration changes, the instances are automatically restarted to
reload the config file if Helm is used as an installation method.


REMOVED HARDCODED SCRAPING INTERVAL CONFIG

In v2, the scraping interval config option was hardcoded to 15s, and you might
have wished that it was configurable.

Now it is! We’ve exposed this parameter so you can set the interval manually
between 10 and 40 seconds. By default, the interval is 15s. Selecting
lowDataMode automatically changes the interval to 30s, lowering the data
fetched. 


HOW WE CONTINUOUSLY TESTED THE NEW ARCHITECTURE

For v3, we added a set of integration tests that leverage our improved discovery
and modular approach to feed static, pre-generated data to each component. 

We improved end-to-end tests with a new approach decoupled from the
implementation itself. The tests install a chart in a Kubernetes environment and
check to see if all entities are created and all metrics contained in the
Kubernetes spec files are reported. These tests are not reliant on a specific
architecture, and they test the whole pipeline, so we can run them with any
version or implementation of the integration. We were able to run these tests
throughout our update from v2 to v3, which led us to discover (and fix) some
existing bugs.

E2E tests don't rely on a specific architecture. They test the whole pipeline as
a black box.


HOW WE REDUCED BREAKING CHANGES

To make it easier for you to upgrade to v3, we used a compatibility layer in the
Helm chart to map old values to the new ones. This way, you receive all the
benefits of the latest version of nri-kubernetes without any of the headaches of
breaking or configuration changes.

A compatibility layer is in place to support version 2 values.


NEXT STEPS

Get started and monitor your Kubernetes environment by signing up for a free New
Relic account. Your free account includes 100 GB/month of free data ingest, one
free full-access user, and unlimited free basic users.

Learn more about our Kubernetes integration by reading Introduction to the
Kubernetes integration.

Install the integration with these steps. If you already have nri-kubernetes and
would like to upgrade to v3, you just need to run this command:

helm upgrade --install my-installation newrelic/nri-bundle


RELATED TOPICS

Kubernetes Monitoring Observability

--------------------------------------------------------------------------------

By Paolo Gallina

Paolo Gallina is a Software Engineer at New Relic. He is, together with Coreint
team, the maintainer of onHost and Kubernetes integrations. Prior to joining New
Relic, he was a Cloud Native Engineer at Harbur Cloud Solutions in Barcelona,
Spain.

The views expressed on this blog are those of the author and do not necessarily
reflect the views of New Relic. Any solutions offered by the author are
environment-specific and not part of the commercial solutions or support offered
by New Relic. Please join us exclusively at the Explorers Hub
(discuss.newrelic.com) for questions and support related to this blog post. This
blog may contain links to content on third-party sites. By providing such links,
New Relic does not adopt, guarantee, approve or endorse the information, views
or products available on such sites.

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In this article
 * Why should you upgrade your Kubernetes integration?
 * How to upgrade your Kubernetes integration
 * Improving our Kubernetes integration behind the scenes
 * Decoupled component configuration
 * Using a sidecar pattern to remove unnecessary processes
 * Added functionality to scrape external control planes
 * Environment variables-based configuration
 * Removed hardcoded scraping interval config
 * How we continuously tested the new architecture
 * How we reduced breaking changes
 * Next steps


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