Robusta KRR

Prometheus-based Kubernetes Resource Recommendations
Installation . Usage · How it works . Slack Integration
Report Bug · Request Feature · Support

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.

Supports: Prometheus, Thanos, Victoria Metrics, EKS, Azure, Coralogix and Grafana Cloud

Features

• No Agent Required: Run a CLI tool on your local machine for immediate results. (Or run in-cluster for weekly Slack reports.)
• Prometheus Integration: Get recommendations based on the data you already have
• Extensible Strategies: Easily create and use your own strategies for calculating resource recommendations.
• Free SaaS Platform: See why KRR recommends what it does, by using the free Robusta SaaS platform.
• 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

Requirements

KRR requires you to have Prometheus.

Additionally to that, kube-state-metrics needs to be running on your cluster, as KRR is dependant on those metrics:

• container_cpu_usage_seconds_total
• container_memory_working_set_bytes
• kube_replicaset_owner
• kube_pod_owner
• kube_pod_status_phase

Note: If one of last three metrics is absent KRR will still work, but it will only consider currently-running pods when calculating recommendations. Historic pods that no longer exist in the cluster will not be taken into consideration.

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

Other helpful flags:

• --cpu-min Sets the minimum recommended cpu value in millicores
• --mem-min Sets the minimum recommended memory value in MB
• --history_duration The duration of the prometheus history data to use (in hours)

More specific information on Strategy Settings can be found using

krr simple --help

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Optional: Free SaaS Platform

With the free Robusta SaaS platform you can:

• See why KRR recommends what it does
• Sort and filter recommendations by namespace, priority, and more
• Copy a YAML snippet to fix the problems KRR finds

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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.

Get a free breakdown of KRR recommendations in the Robusta SaaS.

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

Feature 🛠️	Robusta KRR 🚀	Kubernetes VPA 🌐
Resource Recommendations 💡	✅ CPU/Memory requests and limits	✅ CPU/Memory requests and limits
Installation Location 🌍	✅ Not required to be installed inside the cluster, can be used on your own device, connected to a cluster	❌ Must be installed inside the cluster
Workload Configuration 🔧	✅ No need to configure a VPA object for each workload	❌ Requires VPA object configuration for each workload
Immediate Results ⚡	✅ Gets results immediately (given Prometheus is running)	❌ Requires time to gather data and provide recommendations
Reporting 📊	✅ Detailed CLI Report, web UI in Robusta.dev	❌ Not supported
Extensibility 🔧	✅ Add your own strategies with few lines of Python	⚠️ Limited extensibility
Custom Metrics 📏	🔄 Support in future versions	❌ Not supported
Custom Resources 🎛️	🔄 Support in future versions (e.g., GPU)	❌ Not supported
Explainability 📖	🔄 Support in future versions (Robusta will send you additional graphs)	❌ Not supported
Autoscaling 🔀	🔄 Support in future versions	✅ Automatic application of recommendations

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"

See here about configuring labels for centralized prometheus

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EKS managed Prometheus

For EKS managed Prometheus you need to add your prometheus link and the flag --eks-managed-prom and krr will automatically use your aws credentials

python krr.py simple -p "https://aps-workspaces.REGION.amazonaws.com/workspaces/..." --eks-managed-prom

Additional optional parameters are:

--eks-profile-name PROFILE_NAME_HERE # to specify the profile to use from your config
--eks-access-key ACCESS_KEY # to specify your access key
--eks-secret-key SECRET_KEY # to specify your secret key
--eks-service-name SERVICE_NAME # to use a specific service name in the signature
--eks-managed-prom-region REGION_NAME # to specify the region the prometheus is in

See here about configuring labels for centralized prometheus

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Coralogix managed Prometheus

For Coralogix managed Prometheus you need to specify your prometheus link and add the flag coralogix_token with your Logs Query Key

python krr.py simple -p "https://prom-api.coralogix..." --coralogix_token

See here about configuring labels for centralized prometheus

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Grafana Cloud managed Prometheus

For Grafana Cloud managed Prometheus you need to specify prometheus link, prometheus user, and an access token of your Grafana Cloud stack. The Prometheus link and user for the stack can be found on the Grafana Cloud Portal. An access token with a metrics:read scope can also be created using Access Policies on the same portal.

Next, run the following command, after setting the values of PROM_URL, PROM_USER, and PROM_TOKEN variables with your Grafana Cloud stack's prometheus link, prometheus user, and access token.

python krr.py simple -p $PROM_URL --prometheus-auth-header "Bearer ${PROM_USER}:${PROM_TOKEN}" --prometheus-ssl-enabled

See here about configuring labels for centralized prometheus

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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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