Praeco Alerting for ElasticSearch (Part -1)

Praeco is an open-source tool for alerting Elasticsearch. It can build the alert for elasticsearch in an interactive query builder. A preview of the result will be shown in charts. This tool has an easy installation & configuration process which we will learn further in this blog. We can receive alerts on commonly used channels like slack, email, and many more.

This will have two parts- first, we install & configure the Praeco; in the second part, we learn – how to create an alert?

Why do we use this over others?

In terms of open-source tools which are used for alerting in elasticsearch the most popular option is elastalert. In this creating an alert is a very hectic process because one has to write YAML which can be sometimes frustrating for those who don’t know the syntax.

Now we have to search for other options, which leads us to Praeco. This provides an interactive GUI to create the alert condition and hassle-free integration with alert channels.

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Google Python API: The easy way

When life gives you APIs, just automate it. 🙂

As a developer or tech geek, when technology is part of your lifestyle or work, we definitely look forward to exploring all developer things. APIs & libraries are one of the important things we generally look for.

Why is it so? Because, when we use that specific technology on a daily basis, we definitely want to automate most of the things. For that, we try to explore its functional part just to make our work easy. We can use that functional part [ API/Library ], to make an automation script or application.

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When not to think of Canary Deployment?

Read here to know more about the ideal scenarios when enterprises and DevOps teams should not consider Canary Deployment for their product release.

Canary deployments support the incremental release of the software components, it allows features to be updated/rolled out in phases. It is a technique to reduce the risk of bringing in an update in production by slowly rolling out the changes to a small subset of users before allowing users to access it.

Here, in this blog, we’ve discussed the various scenarios when teams should not consider implementing Canary release deployment. But, before we dive into this, let’s discuss what all does the DevOps teams require for executing a Canary deployment strategy.

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A Savior – Imperative in K8s

There are two basic ways to deploy to Kubernetes: Imperative acts as a command which is active and immediate, whereas declarative is passive, by writing manifest file and using kubectl apply.

Why Imperative?

The imperative command is the first mode of managing objects, to use CLI for CUD (Create, Update, Delete) objects on Kubernetes cluster without specifying on manifest file ahead of time. They are a blessing for Kubernetes application developers and administrators because they are very easy to remember and handy. According to K8s, it’s like a ‘Swiss Army Knife” of container orchestration and management.

Imperative commands can help in getting tasks done quickly, as well as generating definition file templates easily. It saves a considerable amount of time and prevents human errors.

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Canary vs Blue-Green Deployment- Which one should you choose?

Read here to know which process should your team choose for a smooth and quick product release.

What is Canary Deployment?

Canary deployment is a technique to reduce the risk of updating software or introducing new changes in the production environment by slowly rolling out the change to a small subset of users before making the software functional for everyone.

Pros

Canary deployments provide the following benefits to businesses.

  • Allows enterprises to test in production with real users and use cases.
  • Enables comparison of different service versions side by side.
  • Cheaper than blue-green deployments because it does not require two production environments.
  • DevOps team can rapidly and safely trigger a rollback to a previous version of an application.

Cons

  • Scripting a canary release can be complex as manual verification or testing can sometimes become time-consuming.
  • Monitoring and instrumentation for testing in production may involve exhaustive research and additional skills & knowledge.

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