Kubernetes is a popular container orchestration and container management platform for automating deployment, scheduling, and update of your containerized workloads in distributed compute environments. It goes without saying that managing multiple nodes and applications in Kubernetes requires an efficient monitoring system. You need to have a real-time picture of events happening in your cluster to get actionable insights for optimization and improving performance. 

Kubernetes ships with some default monitoring and metrics solutions like Heapster and Kubernetes Metrics Server. However, in order to apply analytics, do data discovery, and visualize metrics data flowing from your cluster, you'll be better off using solutions designed specifically for such type of tasks. One popular option for log and metrics monitoring and analysis is the ELK stack (Elasticsearch, Logstash, Kibana) used in pair with Elastic Beats log shippers. 

In this article, we introduce you to monitoring Kubernetes with ELK and Elastic Beats. In particular, we'll show how to send Kubernetes metrics to Elasticsearch indexes using Metricbeat and access them in your Kibana dashboard for subsequent processing. Let's get started!

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In this guide, we explore Refresh and Flush operations in Elasticsearch. This guide will bring resolution to the differences between the two in an effective manner. We also cover the underlying basics of Lucene functionalities, like reopen and commits, which helps in understanding refresh and flush operations.

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In this tutorial, we cover a few common issues related to shard management in Elasticsearch, their solutions, and several best practices. In some use cases, we incorporate special tricks to get things done. 

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Slow Logs in Elasticsearch

Posted by Vineeth Mohan January 16, 2018

In this blog post, we explore slow logs in Elasticsearch, which are immensely helpful both in production and debugging environments. We show how slow logs generated by Elasticsearch can be a critical information provider regarding numerous events and issues happening in your Elasticsearch cluster.

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In this blog post, we show how the Suggest API in Elasticsearch can handle misspelled words using the terms suggester. We also explore various implementations of the term suggester API in Elasticsearch > 6.0

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Phrase suggester is an advanced version of the term suggester. The additional functionality, which phrase suggester employs, is the selection of entire corrected phrases instead of individual words. This is based on the ngram-language modeling, and phrase suggesters can make better choices of tokens based on both frequency and concurrency.

In this tutorial, we'll show you how to use the phrase suggester to correct spellings in phrases, which offers  "did you mean" search functionality in Elasticsearch.

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