Elasticsearch Server (2nd Edition) by Marek Rogoziński, Rafał Kuć

By Marek Rogoziński, Rafał Kuć

This e-book starts off via introducing the main prevalent Elasticsearch server functionalities, from growing your personal index constitution, via querying, faceting, and aggregations, and ends with cluster tracking and challenge analysis. As you move in the course of the ebook, you are going to disguise issues akin to beginning Elasticsearch, making a new index, and designing its right constitution. After that, you'll examine the question API that Elasticsearch exposes, in addition to approximately filtering functions, aggregations, and faceting. final yet now not least, you'll get to grasp how to define comparable files through the use of related functionalities and the way to enforce software indicators by utilizing the possible seek performance referred to as percolator. a few complicated subject matters akin to shard allocation regulate, gateway configuration, and the way to exploit the invention module can be mentioned. This booklet also will express you the probabilities of cluster country and overall healthiness tracking in addition to find out how to use third-party instruments.

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Extra info for Elasticsearch Server (2nd Edition)

Example text

If you come from the relational database world, you can think of an index like a table. However, the index structure is prepared for fast and efficient full-text searching, and in particular, does not store original values. If you know MongoDB, you can think of the Elasticsearch index as a collection in MongoDB. If you are familiar with CouchDB, you can think about an index as you would about the CouchDB database. Elasticsearch can hold many indices located on one machine or spread over many servers.

Index Index is the logical place where Elasticsearch stores logical data, so that it can be divided into smaller pieces. If you come from the relational database world, you can think of an index like a table. However, the index structure is prepared for fast and efficient full-text searching, and in particular, does not store original values. If you know MongoDB, you can think of the Elasticsearch index as a collection in MongoDB. If you are familiar with CouchDB, you can think about an index as you would about the CouchDB database.

It is not difficult to guess that the process to remove a document is similar; we need to send a proper HTTP request using the DELETE request type. For example, to delete our example document, we will run the following command: curl -XDELETE http://localhost:9200/blog/article/1 The response from Elasticsearch will be as follows: {"found":true,"_index":"blog","_type":"article","_id":"1","_version":3} This means that our document was found and it was deleted. Now we can use the CRUD operations. This lets us create applications using Elasticsearch as a simple key-value store.

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