Elasticsearch in Action by Matthew Lee Hinman, Radu Gheorghe, Roy Russo

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By Matthew Lee Hinman, Radu Gheorghe, Roy Russo

Elasticsearch in motion teaches you the way to construct scalable seek purposes utilizing Elasticsearch. You’ll ramp up quickly, with an informative assessment and an interesting introductory instance. in the first few chapters, you’ll choose up the center thoughts you must enforce simple searches and effective indexing. With the basics good in hand, you’ll cross directly to achieve an equipped view of the way to optimize your layout. ideal for builders and directors development and handling search-oriented functions.

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Extra info for Elasticsearch in Action

Sample text

Com> 11 Exploring typical Elasticsearch use cases section, there’s a strong community building tools for Elasticsearch, and sometimes you don’t have to build any custom component. 3 Using Elasticsearch with existing tools In some use cases, you don’t have to write a single line of code to get a job done with Elasticsearch. Many tools are available that work with Elasticsearch, so you don’t have to write yours from scratch. For example, say you want to deploy a large-scale logging framework to store, search, and analyze a large number of events.

Even though Elasticsearch is written in Java, there’s more than a Java API that lets you work with it. It also exposes a REST API, which any application can access, no matter the programming language it was written in. What’s more, the REST requests and replies are typically in JSON (JavaScript Object Notation) format. Typically, a REST request has its payload in JSON, and replies are also a JSON document. com> 12 CHAPTER 1 Introducing Elasticsearch JSON and YAML JSON is a format for expressing data structures.

For now, let’s take a closer look at why Elasticsearch is document-oriented and how it groups documents in types and indices. 6 Structuring your data in Elasticsearch Unlike a relational database, which stores data in records or rows, Elasticsearch stores data in documents. Yet, to some extent, the two concepts are similar. With rows in a table, you have columns, and for each column, each row has a value. With a document you have keys and values, in much the same way. The difference is that a document is more flexible than a row, mainly because—in Elasticsearch, at least—a document can be hierarchical.

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