About The Project

Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance.

Features

• No Agent Required: Robusta KRR is a CLI tool that runs on your local machine. It does not require running Pods in your cluster. (But it can optionally be run in-cluster for weekly Slack reports.)
• Prometheus Integration: Gather resource usage data using built-in Prometheus queries, with support for custom queries coming soon.
• Extensible Strategies: Easily create and use your own strategies for calculating resource recommendations.
• Future Support: Upcoming versions will support custom resources (e.g. GPUs) and custom metrics.

Resource Allocation Statistics

According to a recent Sysdig study, on average, Kubernetes clusters have:

• 69% unused CPU
• 18% unused memory

By right-sizing your containers with KRR, you can save an average of 69% on cloud costs.

Read more about how KRR works and KRR vs Kubernetes VPA

Installation

With brew (MacOS/Linux):

1. Add our tap:

brew tap robusta-dev/homebrew-krr

2. Install KRR:

brew install krr

3. Check that installation was successfull (First launch might take a little longer):

krr --help

On Windows:

You can install using brew (see above) on WSL2, or install manually.

Manual Installation

1. Make sure you have Python 3.9 (or greater) installed
2. Clone the repo:

git clone https://github.com/robusta-dev/krr

3. Navigate to the project root directory (cd ./krr)
4. Install requirements:

pip install -r requirements.txt

5. Run the tool:

python krr.py --help

Notice that using source code requires you to run as a python script, when installing with brew allows to run krr. All above examples show running command as krr ..., replace it with python krr.py ... if you are using a manual installation.

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Other Configuration Methods

• View KRR Reports in a Web UI
• Get a Weekly Message in Slack with KRR Recommendations
• Setup KRR on Google Cloud Managed Prometheus
• Setup KRR for Azure managed Prometheus

Usage

Straightforward usage, to run the simple strategy:

krr simple

If you want only specific namespaces (default and ingress-nginx):

krr simple -n default -n ingress-nginx

Filtering by labels (more info here):

python krr.py simple --selector 'app.kubernetes.io/instance in (robusta, ingress-nginx)'

By default krr will run in the current context. If you want to run it in a different context:

krr simple -c my-cluster-1 -c my-cluster-2

If you want to get the output in JSON format (--logtostderr is required so no logs go to the result file):

krr simple --logtostderr -f json > result.json

If you want to get the output in YAML format:

krr simple --logtostderr -f yaml > result.yaml

If you want to see additional debug logs:

krr simple -v

More specific information on Strategy Settings can be found using

krr simple --help

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How it works

Metrics Gathering

Robusta KRR uses the following Prometheus queries to gather usage data:

• CPU Usage:

  sum(irate(container_cpu_usage_seconds_total{{namespace="{object.namespace}", pod="{pod}", container="{object.container}"}}[{step}]))
  

• Memory Usage:

  sum(container_memory_working_set_bytes{job="kubelet", metrics_path="/metrics/cadvisor", image!="", namespace="{object.namespace}", pod="{pod}", container="{object.container}"})
  

Need to customize the metrics? Tell us and we'll add support.

Algorithm

By default, we use a simple strategy to calculate resource recommendations. It is calculated as follows (The exact numbers can be customized in CLI arguments):

• For CPU, we set a request at the 99th percentile with no limit. Meaning, in 99% of the cases, your CPU request will be sufficient. For the remaining 1%, we set no limit. This means your pod can burst and use any CPU available on the node - e.g. CPU that other pods requested but aren’t using right now.

• For memory, we take the maximum value over the past week and add a 5% buffer.

Prometheus connection

Find about how KRR tries to find the default prometheus to connect here.

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Difference with Kubernetes VPA

Robusta UI integration

If you are using Robusta SaaS, then KRR is integrated starting from v0.10.15. You can view all your recommendations (previous ones also), filter and sort them by either cluster, namespace or name.

More features (like seeing graphs, based on which recommendations were made) coming soon. Tell us what you need the most!

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Slack integration

Put cost savings on autopilot. Get notified in Slack about recommendations above X%. Send a weekly global report, or one report per team.

Prerequisites

• A Slack workspace

Setup

1. Install Robusta with Helm to your cluster and configure slack
2. Create your KRR slack playbook by adding the following to generated_values.yaml:

customPlaybooks:
# Runs a weekly krr scan on the namespace devs-namespace and sends it to the configured slack channel
customPlaybooks:
- triggers:
  - on_schedule:
      fixed_delay_repeat:
        repeat: 1 # number of times to run or -1 to run forever
        seconds_delay: 604800 # 1 week
  actions:
  - krr_scan:
      args: "--namespace devs-namespace" ## KRR args here
  sinks:
      - "main_slack_sink" # slack sink you want to send the report to here

3. Do a Helm upgrade to apply the new values: helm upgrade robusta robusta/robusta --values=generated_values.yaml --set clusterName=<YOUR_CLUSTER_NAME>

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Prometheus, Victoria Metrics and Thanos auto-discovery

By default, KRR will try to auto-discover the running Prometheus Victoria Metrics and Thanos. For discovering prometheus it scan services for those labels:

"app=kube-prometheus-stack-prometheus"
"app=prometheus,component=server"
"app=prometheus-server"
"app=prometheus-operator-prometheus"
"app=prometheus-msteams"
"app=rancher-monitoring-prometheus"
"app=prometheus-prometheus"

For Thanos its these labels:

"app.kubernetes.io/component=query,app.kubernetes.io/name=thanos",
"app.kubernetes.io/name=thanos-query",
"app=thanos-query",
"app=thanos-querier",

And for Victoria Metrics its the following labels:

"app.kubernetes.io/name=vmsingle",
"app.kubernetes.io/name=victoria-metrics-single",
"app.kubernetes.io/name=vmselect",
"app=vmselect",

If none of those labels result in finding Prometheus, Victoria Metrics or Thanos, you will get an error and will have to pass the working url explicitly (using the -p flag).

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Example of using port-forward for Prometheus

If your prometheus is not auto-connecting, you can use kubectl port-forward for manually forwarding Prometheus.

For example, if you have a Prometheus Pod called kube-prometheus-st-prometheus-0, then run this command to port-forward it:

kubectl port-forward pod/kube-prometheus-st-prometheus-0 9090

Then, open another terminal and run krr in it, giving an explicit prometheus url:

krr simple -p http://127.0.0.1:9090

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Scanning with a centralized Prometheus

If your Prometheus monitors multiple clusters we require the label you defined for your cluster in Prometheus.

For example, if your cluster has the Prometheus label cluster: "my-cluster-name" and your prometheus is at url http://my-centralized-prometheus:9090, then run this command:

krr.py simple -p http://my-centralized-prometheus:9090 --prometheus-label cluster -l my-cluster-name

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Azure managed Prometheus

For Azure managed Prometheus you need to generate an access token, which can be done by running the following command:

# If you are not logged in to Azure, uncomment out the following line
# az login
AZURE_BEARER=$(az account get-access-token --resource=https://prometheus.monitor.azure.com  --query accessToken --output tsv); echo $AZURE_BEARER

Than run the following command with PROMETHEUS_URL substituted for your Azure Managed Prometheus URL:

python krr.py simple --namespace default -p PROMETHEUS_URL --prometheus-auth-header "Bearer $AZURE_BEARER"

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Available formatters

Currently KRR ships with a few formatters to represent the scan data:

• table - a pretty CLI table used by default, powered by Rich library
• json
• yaml
• pprint - data representation from python's pprint library

To run a strategy with a selected formatter, add a -f flag:

krr simple -f json

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Creating a Custom Strategy/Formatter

Look into the examples directory for examples on how to create a custom strategy/formatter.

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Testing

We use pytest to run tests.

1. Install the project manually (see above)
2. Navigate to the project root directory
3. Install poetry (https://python-poetry.org/docs/#installing-with-the-official-installer)
4. Install dev dependencies:

poetry install --group dev

5. Install robusta_krr as editable dependency:

pip install -e .

6. Run the tests:

poetry run pytest

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Support

If you have any questions, feel free to contact [email protected] or message us on robustacommunity.slack.com

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