In this article we will set up a simple and easy to follow example on how to use Elasticsearch and Kibana for basic business intelligence. In our demo we will be using real world Twitter data which we'll feed into Elasticsearch and then inspect and analyze it by using the Kibana dashboard.
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In this article we explore a workflow for exploring new data via mapping refinements. We index an example document, look at its default mapping and iteratively improve on it to get us one step closer to our goal.
Elasticsearch's Fuzzy query is a powerful tool for a multitude of situations. Username searches, misspellings, and other funky problems can oftentimes be solved with this unconventional query. In this article we clarify the sometimes confusing options for fuzzy searches, as well as dive into the internals of Lucene's FuzzyQuery.
Elasticsearch does not perform authentication or authorization, leaving that as an exercise for the developer. This article gives an overview of things to keep in mind when you configure the security settings for your Elasticsearch cluster, providing users with (limited) access to your cluster when you cannot necessarily (entirely) trust them.
One of the trickiest parts of integrating Elasticsearch into an existing app is figuring out how to manage the flow of data from an authoritative data source, such as an SQL database, into Elasticsearch. In most cases this means utilizing Elasticsearch's bulk API. It also implies designing an application to effectively make data available in an efficient, robust, on-time manner. This usually requires modifying an application's workflow to replicate data in batches. This article is a survey of various patterns for accomplishing this task.
This article introduces the networking part of Elasticsearch. We look at the network topology of an Elasticsearch cluster, which connections are established between which nodes and how the different Java clients works. Finally, we look a bit closer on the communication channels between two nodes.
Found provides hosted Elasticsearch as a service. Our goal is to make it effortless to provision and manage clusters, whether they are for production, staging or just experimenting. This article briefly describes how to spin up a cluster, as well as basic management.
This article gives an overview of the Elasticsearch internals. I will present a 10,000 foot view of the different modules that Elasticsearch is composed of and how we can extend or replace built-in functionality using plugins.
This article will give an introduction to the mapping feature of Elasticsearch. We'll define the key terms and take a closer look at what mapping is, when we specify it, how it is structured and how we can apply it to our data.
Elasticsearch easily lets you develop amazing things, and it has gone to great lengths to make Lucene's features readily available in a distributed setting. However, when it comes to running Elasticsearch in production, you still have a fairly complicated system on your hands: a system with high demands on network stability, a huge appetite for memory, and a system that assumes all users are trustworthy. These articles cover some of the lessons we've learned from securing and herding hundreds of Elasticsearch clusters.
In this article series, we look at Elasticsearch from a new perspective. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradually move upwards towards the user-visible layers, studying the various internal data structures and behaviours as we ascend.
Can Elasticsearch be used as a "NoSQL"-database? NoSQL means different things in different contexts, and interestingly it's not really about SQL. We will start out with a "Maybe!", and look into the various properties of Elasticsearch as well as those it has sacrificed, in order to become one of the most flexible, scalable and performant search and analytics engines yet.
Leader election is one of the most tricky things to do in distributed systems. At same time, understanding how a leader is elected and the responsibilities of the leader is key to understanding a distributed system.
Using plugins, it's possible to add new functionality to Elasticsearch without having to create a fork of Elasticsearch itself. In this article, we will go through the steps required to create a new Elasticsearch plugin from the ground up.
I am not a programmer, so I endeavoured to understand what Found's developers were talking about by starting to read up on the basics of search engine indexing. Finding documents that describe search engine indexing was easy. Understanding them at a beginner's level was anything but. I therefore decided to set off writing articles that explain search engine indexing from a beginner's perspective.