![]() We at Sematext are running a huge Elasticsearch cluster on Kubernetes that handles millions of data points per minute from ingested logs, metrics, events, traces, etc. It’s great for storing and searching through large volumes of textual data, like logs, but can also be used to search many different kinds of documents. It’s also a real-time, distributed, and scalable search engine which allows for full-text and structured search, as well as analytics. ![]() What is Elasticsearch?Įlasticsearch is a datastore that stores data in indices. ![]() By the end of this tutorial, you will have a running Elasticsearch cluster on Kubernetes, learn best practices to leverage the platforms’ powers, and get some tips about memory requirements and storage. Elasticsearch handles storing and querying data, while Kubernetes handles the underlying infrastructure. However, running Elasticsearch on Kubernetes can save you a lot of trouble. The downside of that is that the more data you have the more of a headache it is to store, query, and make sense of. Data! Huge amounts of data that need to be managed. Even though they are hugely different from one another, they all have one thing in common. Bonus: Prebuilt Elasticsearch Helm chart with best practices in mindīig data, AI, machine learning, and numerous others are all buzzwords we seem to throw around lightly in recent years.Deploying a 7-Pod Elasticsearch cluster on Kubernetes with Helm.Deploying a 3-Pod Elasticsearch cluster on Kubernetes with Helm: Examples and Best Practices.How to Deploy Elasticsearch on Kubernetes.Deploying Elasticsearch on Kubernetes: Memory Requirements.Elasticsearch Deployment: Cluster Topology.Kubernetes Architecture: Basic Concepts.
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