The Evolution of Kubernetes Query Language

TechOps Examples

Hey — It's Govardhana MK 👋

Along with a use case deep dive, we identify the top news, tools, videos, and articles in the TechOps industry.

Before we begin... a big thank you to today's sponsor WRITER

• It’s late, and you need to integrate generative AI into your cloud workflows.

• Try WRITER — Human taste. Human tweaks. AI speed.

• 70% faster, with over 1,000 processes optimized already.

IN TODAY'S EDITION

🧠 Use Case

• The Evolution of Kubernetes Query Language

🚀 Top News

• AWS CodePipeline Introduces Automatic Retry On Stage Failures

📽️ Videos

• Practical Anomaly Detection at Scale With PromQL in Prometheus

• Linux Basics You Need To Know: Who Am I? With And Without 'su'

📚️ Resources

• How to Set Up Grafana on EC2

• Optimizing Docker Image Sizes: Advanced Techniques and Tools

• A Collection Of Awesome Tools And Scripts For Automating Routine Tasks On AWS Cloud

🛠️ TOOL OF THE DAY

mapotf - A meta programming tool designed to work with Terraform.

• Match Terraform blocks using data blocks and define transform blocks to mutate the original code.

• Update, insert, or remove parts of the Terraform block as needed.

🧠 USE CASE

The Evolution of Kubernetes Query Language

To begin with, why at all do we need to query Kubernetes?

Let me put up some scenarios that would make sense:

• Tracking down a specific pod in a massive cluster

• Identifying misconfigured resources causing errors

• Pinpointing services that aren't adhering to scaling limits

• Auditing access and security policies across environments

This opens up the discussion on what options we have — enter Kube-Query, KubeQL, and Cyphernetes.

Cyphernetes is my favorite – you'll likely agree by the end.

These tools may not solve all use cases but offer better visibility into what's running under the hood.

1. Kube-Query:

Kube-Query is an extension for osquery, a simple yet powerful tool that allows you to query Kubernetes resources using SQL-like syntax.

It streamlines operations by letting you quickly filter, search, and analyze resources like pods, services, and nodes without complex kubectl commands.

Note: When running Kube-Query, you should always pass the -kubeconfig flag, specifying the path to your kubeconfig file.

How Does It work?

Osquery provides extensibility, and Kube-Query bridges the gap between osquery and Kubernetes. It communicates with the Kubernetes API to gather, parse, and present data for osquery to consume.

A simple query looks like this:

2. KubeQL:

KubeQL translates SQL-like queries into Kubernetes API calls, allowing users to filter and join data from various resources like pods, nodes, and services in a structured, readable way for easier handling of complex datasets.

It simplifies querying and enhances visibility across your cluster by combining Kubernetes resources under one query interface.

Kube-Query integrates with osquery for real-time cluster monitoring, while KubeQL focuses on efficient data aggregation and deeper analysis across multiple Kubernetes resources in a single query.

A sample query looks like this:

3. Cyphernetes:

Cyphernetes is a Cypher-inspired query language for Kubernetes. It blends ASCII-art, SQL, and JSON, allowing us to express Kubernetes operations efficiently, with a fun and creative approach.

There are multiple ways to run Cyphernetes queries:

• Use the web client by running cyphernetes web and visiting http://localhost:8080

• Access the interactive shell with cyphernetes shell

• Run single queries from the command line with cyphernetes query "your query" — perfect for scripting and CI/CD pipelines

• Create DynamicOperators with cyphernetes-operator for on-the-fly Kubernetes workflows

• Integrate the Cyphernetes API in your own Go programs

A sample query looks like this, with a visualization element:

Cyphernetes' superpower is understanding relationships between Kubernetes resources, allowing connected operations without handling the Kubernetes API directly.

Hope these tools make your Kubernetes life easier and more fun!

Writer RAG tool: build production-ready RAG apps in minutes

RAG in just a few lines of code? We’ve launched a predefined RAG tool on our developer platform, making it easy to bring your data into a Knowledge Graph and interact with it with AI. With a single API call, writer LLMs will intelligently call the RAG tool to chat with your data.

Integrated into Writer’s full-stack platform, it eliminates the need for complex vendor RAG setups, making it quick to build scalable, highly accurate AI workflows just by passing a graph ID of your data as a parameter to your RAG tool.

Learn more about our production ready RAG tooling here.

If you're enjoying TechOps Examples please forward this email to a colleague.

It helps us keep this content free.

Looking to promote your company, product, service, or event to 15,000+ TechOps Professionals? Let's work together.