Launching Krs – Kubetools Recommender System for DevOps and SRE

Launching Krs – Kubetools Recommender System for DevOps and SRE

The Problem Statement

  • 60% of DevOps engineers spend over 10 hours a week searching for optimal tools.
  • 40% have used the wrong tool for the job, leading to wasted time and resources.
  • Unoptimized Kubernetes clusters can cost companies $10,000+ per year.

Introducing Krs

What makes Krs unique?

Krs is a Kubernetes cluster health monitoring and tools recommendation service. The primary goal of KRS is to provide insights into the current state of a Kubernetes cluster, identify potential issues, and suggest relevant tools and resources to enhance the cluster’s efficiency and security.

The project is designed to work with a local or remote Kubernetes cluster, and it utilizes various data sources, such as CNCF tools, Kubernetes landscape, and LLM (Language Model) for contextual analysis. KRS aims to provide actionable recommendations based on the cluster’s current state and the latest trends in the Kubernetes ecosystem.

How does it works?

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To achieve this, KRS follows a multi-step process:

  • Scans the Kubernetes cluster for resource usage, configuration, and potential issues.
  • Fetches data from CNCF tools, Kubernetes landscape, and other relevant sources.
  • Utilizes LLM for contextual analysis and understanding of the cluster’s state.
  • Provides recommendations for improving the cluster’s efficiency, security, and resource utilization.
  • Reduced Time Spent Searching: Krs helps you find the right tools quickly and easily.
  • Improved Efficiency: Get matched with tools that perfectly align with your needs.
  • Cost Optimization: Reduce wasted resources and optimize your Kubernetes cluster performance.

We’re excited to share Krs with the developer community! We believe this open-source project has the potential to revolutionize the way DevOps and DevSecOps teams approach Kubernetes tooling.

Getting Started

Clone the repository

git clone https://github.com/kubetoolsca/krs.git

Install the Krs Tool

Change directory to /krs and run the following command to install krs locally on your system:

pip install .

Krs CLI

 krs --help

 Usage: krs [OPTIONS] COMMAND [ARGS]...

 krs: A command line interface to scan your Kubernetes Cluster, detect errors, provide resolutions using LLMs and recommend latest tools for your cluster

╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --install-completion          Install completion for the current shell.                                                                                       │
│ --show-completion             Show completion for the current shell, to copy it or customize the installation.                                                │
│ --help                        Show this message and exit.                                                                                                     │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ exit         Ends krs services safely and deletes all state files from system. Removes all cached data.                                                       │
│ export       Exports pod info with logs and events.                                                                                                           │
│ health       Starts an interactive terminal using an LLM of your choice to detect and fix issues with your cluster                                            │
│ init         Initializes the services and loads the scanner.                                                                                                  │
│ namespaces   Lists all the namespaces.                                                                                                                        │
│ pods         Lists all the pods with namespaces, or lists pods under a specified namespace.                                                                   │
│ recommend    Generates a table of recommended tools from our ranking database and their CNCF project status.                                                  │
│ scan         Scans the cluster and extracts a list of tools that are currently used.                                                                          │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Initialise and load the scanner

Run the following command to initialize the services and loads the scanner.

krs init

Scan your cluster

Run the following command to scan the cluster and extract a list of tools that are currently used.

krs scan

You will see the following results:


Scanning your cluster...

Cluster scanned successfully...

Extracted tools used in cluster...


The cluster is using the following tools:

+-------------+--------+------------+---------------+
| Tool Name   | Rank   | Category   | CNCF Status   |
+=============+========+============+===============+
+-------------+--------+------------+---------------+

Lists all the namespaces

krs namespaces
Namespaces in your cluster are:

1. default
2. kube-node-lease
3. kube-public
4. kube-system

Installing sample Kubernetes Tools

Assuming that you already have a bunch of Kubernetes tools running in your infrastructure.
If not, you can leverage samples/install-tools.sh script to install these sample tools.

cd samples
sh install-tools.sh

Use scanner

krs scan

Scanning your cluster...

Cluster scanned successfully...

Extracted tools used in cluster...


The cluster is using the following tools:

+-------------+--------+----------------------+---------------+
| Tool Name   |   Rank | Category             | CNCF Status   |
+=============+========+======================+===============+
| kubeshark   |      4 | Alert and Monitoring | unlisted      |
+-------------+--------+----------------------+---------------+
| portainer   |     39 | Cluster Management   | listed        |
+-------------+--------+----------------------+---------------+

Kubetools Recommender System

Generates a table of recommended tools from our ranking database and their CNCF project status.

krs recommend

Our recommended tools for this deployment are:

+----------------------+------------------+-------------+---------------+
| Category             | Recommendation   | Tool Name   | CNCF Status   |
+======================+==================+=============+===============+
| Alert and Monitoring | Recommended tool | grafana     | listed        |
+----------------------+------------------+-------------+---------------+
| Cluster Management   | Recommended tool | rancher     | unlisted      |
+----------------------+------------------+-------------+---------------+

Krs health

Assuming that there is a Nginx Pod under the namespace ns1

krs pods --namespace ns1

Pods in namespace 'ns1':

1. nginx-pod
krs health

Starting interactive terminal...


Choose the model provider for healthcheck:

[1] OpenAI
[2] Huggingface

>>

The user is prompted to choose a model provider for the health check.
The options provided are “OpenAI” and “Huggingface”. The selected option determines which LLM model will be used for the health check.

Let’s say you choose the option “1”, then it will install the necessary libraries.

Enter your OpenAI API key: sk-3iXXXXXTpTyyOq2mR

Enter the OpenAI model name: gpt-3.5-turbo
API key and model are valid.

Namespaces in the cluster:

1. default
2. kube-node-lease
3. kube-public
4. kube-system
5. ns1

Which namespace do you want to check the health for? Select a namespace by entering its number: >> 5

Pods in the namespace ns1:

1. nginx-pod

Which pod from ns1 do you want to check the health for? Select a pod by entering its number: >>
Checking status of the pod...

Extracting logs and events from the pod...

Logs and events from the pod extracted successfully!


Interactive session started. Type 'end chat' to exit from the session!

>>  The provided log entries are empty, as there is nothing between the curly braces {}. Therefore, everything looks good and there are no warnings or errors to report.

Let us pick up an example of Pod that throws an error:

krs health

Starting interactive terminal...


Do you want to continue fixing the previously selected pod ? (y/n): >> n

Loading LLM State..

Model:  gpt-3.5-turbo

Namespaces in the cluster:

1. default
2. kube-node-lease
3. kube-public
4. kube-system
5. portainer

Which namespace do you want to check the health for? Select a namespace by entering its number: >> 4

Pods in the namespace kube-system:

1. coredns-76f75df574-mdk6w
2. coredns-76f75df574-vg6z2
3. etcd-docker-desktop
4. kube-apiserver-docker-desktop
5. kube-controller-manager-docker-desktop
6. kube-proxy-p5hw4
7. kube-scheduler-docker-desktop
8. storage-provisioner
9. vpnkit-controller

Which pod from kube-system do you want to check the health for? Select a pod by entering its number: >> 4

Checking status of the pod...

Extracting logs and events from the pod...

Logs and events from the pod extracted successfully!


Interactive session started. Type 'end chat' to exit from the session!

>>  Warning/Error 1:
"Unable to authenticate the request" with err="[invalid bearer token, service account token has expired]"
This indicates that there was an issue with authenticating the request due to an invalid bearer token and an expired service account token. To resolve this issue, the bearer token needs to be updated or regenerated, and the service account token needs to be renewed.

Warning/Error 2:
"Failed to update lease" with err="StorageError: invalid object, Code: 4, Key: /registry/leases/kube-system/apiserver-457xdkumpkdqmkwikdt6khrbya"
This error indicates a storage error while trying to update a lease, possibly due to an invalid object or a conflict in UIDs. To resolve this issue, the lease object needs to be checked and possibly recreated with the correct UID.

Warning/Error 3:
"Error removing old endpoints from kubernetes service: no API server IP addresses were listed in storage, refusing to erase all endpoints for the kubernetes Service"
This warning indicates that there were no API server IP addresses listed in storage, causing the removal of old endpoints from the Kubernetes service to fail. To resolve this issue, the API server IP addresses need to be correctly listed in storage.

Warning/Error 4:
"Skipping API apps/v1beta1 because it has no resources."
This warning indicates that the API version 'apps/v1beta1' is being skipped because it has no associated resources. This might be an informational warning and may not necessarily require immediate action, depending on the use case.

Warning/Error 5:
"watch chan error: etcdserver: mvcc: required revision has been compacted"
This error indicates a watch channel error related to the revision being compacted in etcdserver. To resolve this issue, the etcd database revision or configuration may need to be checked and potentially adjusted.

Overall, it is important to investigate and address each warning and error to ensure the stability and reliability of the Kubernetes cluster. If there are no concerns in the log entries provided, the message 'Everything looks good!' can be returned.

>> can you help me in fixing this error message
>>  To help you fix the error messages mentioned in the log entries, here are some steps you can take:

1. For the "Unable to authenticate the request" error:
   - Generate a new bearer token and update it in the relevant configuration files.
   - Renew the service account token to prevent expiration issues.

2. For the "Failed to update lease" error:
   - Investigate the storage error and check for any invalid objects.
   - Recreate the lease object with the correct UID if needed.

3. For the "Error removing old endpoints from Kubernetes service" error:
   - Ensure that the API server IP addresses are correctly listed in the storage.
   - Verify the configuration of the Kubernetes service and update it if necessary.

4. For the "watch chan error: etcdserver: mvcc: required revision has been compacted" error:
   - Check the etcd server configuration and ensure that the revision is maintained correctly.
   - Monitor the etcd server for any potential issues causing revision compaction.

After performing these steps, restart relevant services or components as needed to apply the changes. Monitor the Kubernetes cluster for any further errors and ensure that the issues have been resolved successfully.

Feel free to provide more specific details or additional logs if you need further assistance with resolving the error messages.

Using Hugging Face

krs health

Starting interactive terminal...


Choose the model provider for healthcheck:

[1] OpenAI
[2] Huggingface

>> 2

Installing necessary libraries..........

transformers is already installed.

torch is already installed.
/opt/homebrew/lib/python3.11/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  torch.utils._pytree._register_pytree_node(

Enter the Huggingface model name: codellama/CodeLlama-13b-hf
tokenizer_config.json: 100%|█████████████████████████████████████████████| 749/749 [00:00<00:00, 768kB/s]
tokenizer.model: 100%|████████████████████████████████████████████████| 500k/500k [00:00<00:00, 1.94MB/s]
tokenizer.json: 100%|███████████████████████████████████████████████| 1.84M/1.84M [00:01<00:00, 1.78MB/s]
special_tokens_map.json: 100%|██████████████████████████████████████████| 411/411 [00:00<00:00, 1.49MB/s]
config.json: 100%|██████████████████████████████████████████████████████| 589/589 [00:00<00:00, 1.09MB/s]
model.safetensors.index.json: 100%|█████████████████████████████████| 31.4k/31.4k [00:00<00:00, 13.9MB/s]
...

Get Involved!

We welcome your contributions and feedback!
Let’s work together to build a smarter, more efficient future for Kubernetes!

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