There are many APIs available for managing Elasticsearch. These APIs help us to manage cluster, indices, search, and so on. In this section, we will look at each of these APIs in detail.

We can use these APIs through Command Prompt, Console in Kibana, or any tool that can make calls to RESTful APIs.

Sense is a powerful plugin for Kibana that allows us to make calls to Elasticsearch APIs using a web interface. We will be learning about Sense in Chapter 8, Elasticsearch APIs. For this chapter, we will be using cURL, a Command Prompt utility that allows us to access HTTP requests to access the APIs.

A typical cURL request against ES contains a verb, URL, and message body:

$ curl -X{Verb} 'url' -d '{message-body}'

Verbs are GET, PUT, POST, DELETE, and HEAD...

In this manner, we need to provide a request body with the uri just like we have been using for Document APIs. We can rewrite our author search query as follows:

$ curl -XGET 'http://localhost:9200/library/book/_search?pretty' -d '{
    "query" : {
      "term" : {"author" : "gupta"}
    }
  }'

This query will return the same result. Whatever query parameters we defined using q=, we define them in term. To learn more about Query DSL, refer to https://www.elastic.co/guide/en/elasticsearch/reference/5.1/query-dsl.html.

This framework is a very important part of Elasticsearch. As the name suggests, this framework helps us to do aggregations and generate analytic information on result of a search query. Aggregations help us to get better insight of the data. For example, if we take our library index into account, we can get answers to: How many books in a specific year, which technology, average book per year, and many more.

These aggregations show their power when it comes to gaining insight of system data on a dashboard. Most often system dashboards have aggregated data in form of charts. We will also be using aggregations in later chapters and those aggregations will help Kibana to generate useful visualizations.

There are two types of core aggregations: metrics and buckets. We will learn about these in this section.

There are times when we use scripts, update data, scripted fields, and many more use cases. Prior to version 5.x, groovy was the default language for your scripts. We even did not specify which scripts we wanted to use back then. Since these scripts were executed remotely security was always a concern that Elastic Team had to address. This became the reason for designing Painless.

Painless is both secure and efficient when it comes to performance. It has a similar syntax as of Groovy so it is also easy to learn and use. For most of the cases, you don't need to make changes to your previously written scripts. All you need to add is a parameter called lang and specify the value as painless.

To define a variable in painless, simply use the following:

def myVar = 'my-value'; 

We don't need to specify any type. At runtime, the type of variable will be detected whatever suits appropriate. Painless supports all variable types defined by Java.

To define an array, use...

In this chapter, we learned about Elasticsearch architecture and the way Elasticsearch was born. Later we got familiar with Elasticsearch APIs - Search, Indices, and Document. With the help of these APIs we learned how to add documents to Elasticsearch, how to query those documents, managing the indices. At the end of the chapter, aggregations show how to effectively search documents. We will be practicing these concepts in the next chapters with more examples.

In the next chapter, we will learn about Logstash, and how to configure Logstash for complex data types, and Logstash plugins.