1. Mount a Memory Storage Directory on the Host Create a directory to mount 1 mkdir /mnt/memory_storage Mount the tmpfs filesystem 1 mount -t tmpfs -o size=800G tmpfs /mnt/memory_storage Storage is allocated on demand, so using 100G of storage only consumes 100G of memory. The host node has 2T of memory, and here 800G is allocated to store Elasticsearch data.

This article gives a brief introduction to the ELK stack and provides a Docker compose orchestration configuration. After reading it, you can get ELK running locally with Docker. A later article will cover deploying ELK on servers and adding the related configuration. 1. Introduction to the ELK Stack ELK is not actually a single piece of software but a whole solution — an acronym formed from the initials of three software products: Elasticsearch, Logstash, and Kibana.

A quick word on the project requirements: the team needed to publish documentation externally. The documents are written in Markdown and need to be published as HTML. At first we used an Nginx + Jekyll solution. As the documentation grew, the document system developed a strong need for search. I

Introduction: Jekyll is a static HTML generation tool. Our documentation system is deployed with Jekyll, which ultimately converts Markdown into HTML files for output. The search requirement is: given a keyword, match it against a document’s title or content and list the matching documents. 1. Option One: Simple-Jekyll-Search Reference: https://github.com/christian-fei/Simple-Jekyll-Search