Let's Do a Search!

Data searching is a very important aspect when we want to retrieve relevant information from a dataset. Sometimes, it is also important to understand the context before applying the actual search because if the context is incorrect, then we can return wrong and irrelevant data. Elasticsearch provides us with a variety of ways to tweak the search so that we can return the most relevant search results to the end users.

In this chapter, we will data search using Elasticsearch by delving into the following topics:

• Introduction to data search
• Search API

Introduction to data search

Elasticsearch provides a search option for queries, strings, and even bodies of text. Most search APIs are multi-index, meaning that they can be applied over multiple indices; the exception to multi-index will be explained in this chapter.

To illustrate searching properly, let's look at a specific example throughout this chapter—searching for a Facebook user. When performing a search, the routing parameter is used to point to the location of the shards in the indices that are to be searched. The following example will route to the user called cactus_flower:

POST facebook/_doc?routing=cactus_flower
{
  "user": "cactus_flower",
  "postDate": "2017-05-19T13:10:02",
  "message": "Just looking for the user Cactus Flower"
}

This search is known as a round-robin between replicas, where each...

Search API

The search API performs a search using a query string, a parameter, or a request body as the search criteria, and then it returns exact matches. The multi-index syntax is used by most search APIs to search over multiple indices, as follows:

• Users can search all of the documents in a specific index using the cactus_flower user:

GET facebook/_search?q=user:cactus_flower

• This can also be applied using a tag:

GET facebook/_search?q=tag:wow

• To search all the indices, use the _all tag instead of the index name:

GET _all/_search?q=tag:wow

In the preceding query, we can query through all the available Elasticsearch indices for the tag using wow. Now, let's see how we can perform a URI-based search in Elasticsearch.

URI search

...

Summary

In this chapter, we have learned about data search in Elasticsearch. We covered URI search and body search so that we could explain how we can perform these searches. Then we covered query examples using term, from/size, sort, and source filtering. We also covered highlighting, rescoring, search type, and named queries. After that, we covered the APIs for the search shard, multi search, count, validate, explain, Profile, and field capabilities.

In the next chapter, we will cover data sparsity and how to improve the performance of Elasticsearch.