Articles

The Serverless Database You Really Want

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The dreaded part of every site reliability engineer’s (SRE) job eventually: capacity planning. You know, the dance between all the stakeholders when deploying your applications. Did engineering really simulate the right load and do we understand how the application scales? Did product managers accurately estimate the amount of usage? Did we make architectural decisions that will keep us from meeting our SLA goals? And then the question that everyone will have to answer eventually: how much is this going to cost? This forces SREs to assume the roles of engineer, accountant, and fortune teller.

The large cloud providers understood this a long time ago and so the term “cloud economics” was coined. Essentially this means: rent everything and only pay for what you need. I would say this message worked because we all love some cloud. It’s not a fad either. SREs can eliminate a lot of the downside when the initial infrastructure capacity discussion was maybe a little off. Being wrong is no longer devastating. Just add more of what you need and in the best cases, the services scale themselves — giving everyone a nice night’s sleep. All this without provisioning a server, which gave rise to the term “serverless.”

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How To Use Cluster Mesh for Multi-Region Kubernetes Pod Communication

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Thanks to services provided by AWS, GCP, and Azure it’s become relatively easy to develop applications that span multiple regions. This is great because slow apps kill businesses. There is one common problem with these applications: they are not supported by multi-region database architecture.

In this blog, I will provide a solution for the problem of getting Kubernetes pods to talk to each other in multi-region deployments.

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The Evolution of Configuration Management: IaC vs. GitOps

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Misconfigurations are the leading cause behind security incidents in Kubernetes-orchestrated or otherwise containerized environments. Without proper configuration in place, applications would run into problems ranging from noncompliance and inconsistencies to performance bottlenecks, security vulnerabilities, and functionality failure. Therefore, configuration management is a critical component in a software development lifecycle for maintaining systems in a desired, consistent state.

According to Red Hat’s State of Kubernetes Security report, misconfigurations were the leading cause behind security incidents in Kubernetes-orchestrated or otherwise containerized environments. Without proper configuration in place, applications would run into problems ranging from noncompliance and inconsistencies to performance bottlenecks, security vulnerabilities, and functionality failure. This would make cloud-native systems unstable and cause them to become a liability to businesses. For this reason, configuration management is a critical component in a software development lifecycle for maintaining systems in a desired, consistent state. However, the way configuration management is done has been evolving over the years. This post traces the history of configuration management, focusing on how GitOps handles this critical aspect of running cloud-native applications today.

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The Most Popular Kubernetes Alternatives and Competitors

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Kubernetes is an open-source container orchestration tool developed by Google and is also known as K8s. It is used in managing the complete lifecycle of containerized applications. Kubernetes provides high availability, scalability, and predictability to the containerized application. It automates the deployment, management, and scaling of containerized applications. Kubernetes also supports automated rollout and rollbacks,  service discovery, storage orchestration, scaling, batch execution, and more. Kubernetes provides the cluster where containerized applications can be deployed. Kubernetes is not the only container orchestration tool, but various “Kubernetes Alternatives” are available in the market.

Before we talk about the “Alternatives to Kubernetes,” let’s explore the key components of Kubernetes. The Kubernetes cluster consists of at least one worker node where containerized applications are deployed and one master node or control plane which manages the worker nodes. The Control plane or master node consists of Kube-API server, etcd, Kube-scheduler, and Kube-controller-manager, whereas the worker node consists of Kubelet, Kube-Proxy, and Container Runtime. 

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How to Use Shipa to Make Kubernetes Adoption Easier for Developers

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Kubernetes offers developers tremendous advantages… if they can overcome the platform’s inherent complexities. It can be a big « if. » Without additional tooling, developers aren’t able to simply develop their applications on Kubernetes, but must also become experts in writing complex YAML templates to define Kubernetes resources. A relatively new tool called Shipa provides an application management framework that largely relieves developers of this burden, enabling dev teams to ship applications with no Kubernetes expertise required.

Having recently put the tool to the test, this article will demonstrate how to install and utilize Shipa to simplify Kubernetes and ease some common developer frustrations.

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Next-Gen Data Pipes With Spark, Kafka, and K8s: Part 2

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Introduction 

In our previous article, we discussed two emerging options for building new-age data pipes using stream processing. One option leverages Apache Spark for stream processing and the other makes use of a Kafka-Kubernetes combination of any cloud platform for distributed computing. The first approach is reasonably popular, and a lot has already been written about it. However, the second option is catching up in the market as that is far less complex to set up and easier to maintain. Also, data-on-the-cloud is a natural outcome of the technological drivers that are prevailing in the market. So, this article will focus on the second approach to see how it can be implemented in different cloud environments.

Kafka-K8s Streaming Approach in Cloud

In this approach, if the number of partitions in the Kafka topic matches with the replication factor of the pods in the Kubernetes cluster, then the pods together form a consumer group and ensure all the advantages of distributed computing. It can be well depicted through the below equation:

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Azure Infrastructure Made Immutable With Locks

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After an application is deployed to production, developers should lock down its underlying infrastructure to prevent accidental changes. Some of the common accidents that can affect the availability of an application in production are: moving, renaming, or deleting the resource crucial to the function of the application. You can use locks that prevent anyone from performing a forbidden action to avoid such mishaps.

Creating Locks

Almost every resource in Azure supports locks, so you will find the lock option in the settings section of nearly all resources in the portal. For example, the following screenshot illustrates locks on resource groups:

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3 Steps To Streamline Kubernetes Multi-Cluster Management

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The footprint of Kubernetes is expanding rapidly in all industries. Many enterprises already operate multiple Kubernetes clusters in multiple regions to address the needs of global operations and reduce application latency for customers worldwide. You may already have a large number of Kubernetes clusters in on-premises data centers and a number of public cloud locations, possibly using several cloud providers to avoid lock-in.

Unfortunately, operating a distributed, multi-cluster, multi-cloud environment is not a simple task. Kubernetes is a relatively new technology. It’s hard to find staff with Kubernetes skills or to identify the best tools for multi-cloud Kubernetes management.

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Delete Multiple Resources and Resource Groups in Azure With Tags

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You might have noticed that resources comprising some Azure services such as Azure Kubernetes Service (AKS) span multiple resource groups by default. In some cases, you might intentionally want to segregate resources such as disks and network interfaces from VMs by placing them in different resource groups for better management. A common problem arising from the resource spread is that you might find it challenging to delete multiple resources and resource groups to entirely remove a service from a subscription.

We can solve the problem by using resource tags to associate resources and resource groups to a service. Tags are key-value pairs that can be applied to your Azure resources, resource groups, and subscriptions. Of course, you can use tags for many other purposes apart from resource management. The Azure docs website has a detailed guide on the various resource naming and tagging strategies and patterns.

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GitOps Takes DevOps Teams To Higher Levels of Maturity

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Stream-Aligned and Platform Teams

The 2021 State of DevOps report identifies two types of teams as the way high-maturity teams organize themselves: stream-aligned teams and platform teams. The idea of a « platform » or « internal developer platform » is fundamental to GitOps. While a GitOps pipeline begins with committing code to a Git repository, it is made possible by a platform that paves the way for that new code to move in an automated way from the repository to a production environment.

The platform is how the Ops team provisions resources such as cloud services, storage, service meshes, and security and monitoring tools in a ready-made template. These templates are created even before they are needed and made available to any developer within the organization. The big shift due to this is that developers need not raise a ticket for the resources they need: they can pick a ready-made template and deploy their code in a matter of minutes. From the Ops side, they have peace of mind knowing they have configured this template and that it follows security protocol by default. It does not require developers to separately configure security for deployments.

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