In production environments, network security is ineluctable. When elasticsearch is deployed, there are many ways to secure the environment. You can use Ngnix, commercial products like Shield, open source products, or easily selectable plugins via Qbox. However, you can also create your own security plugins and have more control over security. This article is intended to give readers a running start on how to write their own in-house security plugin.

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How do you easily integrate Elasticsearch to your application? Elasticsearch gives us two ways, REST APIs and Native clients.

Which is the better solution? Like everything, there are pros and cons to both. For the REST APIs provided by Elasticsearch, you have to use third party libraries like JAX-RS to carry out the interaction. Although native clients are an easy option that come in many languages like Java, Python, Ruby, problems occur whenever there is a major version upgrade of Elasticsearch. You have to upgrade your native client, and many deem this as an unnecessary maintenance effort.

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Not yet enjoying the benefits of a hosted ELK-stack enterprise search on Qbox? Discover how easy it is to manage and scale your Elasticsearch environment.

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Lately, “ChatOps” has become a buzzword in places that are aiming for continuous delivery. It is based on chat clients like Slack and Hipchat, and is plugged in with chatbots for real-time communication and task execution among members of development and IT operations teams.

Chat has become an integral part of the “better” delivery models. With huge amounts of data flowing within the system, wouldn’t it be nice if we could put it into an analysis tool and churn out some results that might improve the business?

In this article, we explore how to integrate Slack with Elasticsearch, and perform basic data analyses for examples.

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In this tutorial, we discuss stopwords. We explain what they are, why they are needed, and the various types of stopwords. We also show how to use them correctly, how to delete them, and how to create your own. 

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We have already considered many concepts from Elasticsearch and studied various filters and search nuances. In this article, we discuss sorting and relevance of documents.

Let's see how elasticsearch scoring is calculated. To begin, let’s find out what happens during a search. First, Elasticsearch finds all the appropriate documents. This means it receives a Boolean response of 0 if the document is not suitable, and 1 if the document is suitable. Next, for all documents with the response equal to 1, the scoring will be calculated and they will be sorted by this value.

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Having the ability to deploy Elasticsearch, Logstash and Kibana (ELK) from a single command is a wonderous thing. Together, in this post, we shall build the Ansible playbook to do just that.

There are some prerequisites. This Ansible playbook is made for Ubuntu Server and executed on Ubuntu Server 16.04. A basic system of 2 CPU cores and 4GB of RAM will be enough. The specs of the machine are entirely up to the situation and the volume of data.

This blog post is an alternative to using the ELK stack on Qbox. To easily deploy and run your own ELK setup on Qbox, simply sign up or launch your cluster here, and refer to the tutorial "Provisioning a Qbox Elasticsearch Cluster."

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