A while back we got the requirement for working on Apache Druid. By working on Apache Druid, We mean setup, management, and monitoring. Since it was a new topic for us we started evaluating it and we actually find it has a lot of amazing features.
So for the people who don’t have an idea about Druid and just starting with Druid. Let me give a quick walk-through of it.
As we know that CI/CD (Continuous Integration/Continuous Deployment) is inevitable process in our DevOps culture , we should always look for a better .. more efficient solution to implement the same.
CI/CD gives us the capability to continuously integrate code changes, test it , deploy it and having continuous feedback which helps us to accelerate our development speed , off-course it reduces time in testing perspective and it helps you to make your releases streamline.
So you dont have to worry about anything except CODING as CI/CD will take care of everything for you. 🙂
Logging is a critical part of monitoring and there are a lot of tools for logs monitoring like Splunk, Sumologic, and Elasticsearch, etc. Since Kubernetes is becoming so much popular now, and running multiple applications and services on a Kubernetes cluster requires a centralized, cluster-level stack to analyze the logs created by pods. One of the well-liked centralized logging solutions is the combination of multiple opensource tools i.e. Elasticsearch, Fluentd, and Kibana. In this blog, we will talk about setting up the logging stack on the Kubernetes cluster with our newly developed operator named “Logging Operator”.
Generally, organizations move towards AWS to furnish their foundation with the capacity to develop and extend their abilities and because they can only pay for the resources they use. An unfortunate side effect of this methodology is that little costs regularly go unnoticed and can add up over time, prompting high monthly bills. The monetary effect of the current pandemic is forcing the world to adjust spending within their organizations. Everybody is turning over every rock to discover approaches to cut waste without impacting business. One way to get fast cost savings is to eliminate wasted spend on cloud services.
We all have faced the problem when the system gets too slow. Have you wondered why this problem occurs? Well, there could be several reasons but one of them is application performance. In today’s time, the term application has become very large and complex. Users using these applications often choose different mediums, as they have separate goals and requirements as per their needs.
This diversity in consumption medium brings complexity in a configuration which is only increasing in today’s time. Application performance means how available your application is for this real-world which brings us to APM. In this blog, first of all, we will discuss, what is APM, why it is needed, and what are the APM tools, which can help us obtain the information and health of the system.
Redis is a popular and opensource in-memory database that supports multiple data structures like strings, hashes, lists, and sets. But similar to other tools, we can scale standalone redis to a particular extent and not beyond that. That’s why we have a cluster mode setup in which we can scale Redis nodes horizontally and then distribute data among those nodes.
Since Kubernetes is becoming buzz technology and people are using it to manage their applications, databases, and middlewares at a single place. So in this blog, we will see how we can deploy the Redis cluster in production mode in the Kubernetes cluster and test failover.
When I set forth with my journey of containerization with docker, I have gone through a misconception that Overlay networking in docker can’t be set up without any orchestrator like Docker swarm, Kubernetes. But after spending some time with containers I realized that I was wrong, Orchestrators leverage the functionality of overlay networking but it is not true that we cannot use overlay networks without any swarm or Kubernetes.
Right off the bat, I want to say that, this blog does not cover installing and configuring ElastAlert in the usual sense, i.e. working with pre-existing rules. It helps, I hope, in understanding the requirements for adding one’s own rule. Continue reading “Make Your Own Rules, ElastAlert Style”
Source code quality analysis is a basic piece of the Continuous Integration process. Along with automated tests, it is the key component to deliver reliable software without numerous bugs, security vulnerabilities, or performance spills.
There are many open source as well as commercial tools available in the market for static code analysis such as LGTM, PMD,Graudit, reshift, Codacy, and many more. One of the best static code analyzer you can find on the market is SonarQube.