An array or multi-value fields are very common in data models (such as multiple phone numbers, addresses, names, aliases, and so on), but not natively supported in traditional SQL solutions.

In SQL, multi-value fields require the creation of accessory tables that must be joined to gather all the values, leading to poor performance when the cardinality of records is huge.

Elasticsearch, which works natively in JSON, provides support for multi-value fields transparently.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client can be used, such as curl (https://curl...

An object is a base structure (analogous to a record in SQL). Elasticsearch extends the traditional use of objects, thus allowing for recursive embedded objects.

You need an up-and-running Elasticsearch installation as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute the commands, any HTTP client can be used such as curl (https://curl.haxx.se/), postman (https://www.getpostman.com/), or similar. Again, I suggest using Kibana console, which provides code completion and better character escaping for Elasticsearch.

The document is also referred to as the root object. This has special parameters that control its behavior, which are mainly used internally to do special processing, such as routing or time-to-live of documents.

In this recipe, we'll take a look at these special fields and learn how to use them.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, every HTTP client can be used, such as curl (https://curl.haxx.se/), postman (https://www.getpostman.com/), or similar. I suggest using the Kibana console, which provides code completion and better character escaping...

In the Using explicit mapping creation recipe, we have seen how Elasticsearch is able to guess the field type using reflection. In this recipe, we'll see how we can help it improve its guessing capabilities via dynamic templates.

The dynamic template feature is very useful. For example, it may be useful in situations in which you need to create several indices with similar types because it allows you to move the need to define mappings from coded initial routines to automatic index-document creation. A typical usage is to define types for Logstash log indices.

You need an up-and-running Elasticsearch installation as we described in the Downloading and installing...

There is a special type of embedded object: the nested one. This resolves a problem related to Lucene indexing architecture, in which all the fields of embedded objects are viewed as a single object. During search, in Lucene, it is not possible to distinguish between values and different embedded objects in the same multi-valued array.

If we consider the previous order example, it's not possible to distinguish an item name and its quantity with the same query, as Lucene puts them in the same Lucene document object. We need to index them in different documents and then join them. This entire trip is managed by nested objects and nested queries.

You need an up-and-running Elasticsearch installation...

In the previous recipe, we have seen how it's possible to manage relations between objects with the nested object type. The disadvantage of nested objects is their dependence from their parent. If you need to change a value of a nested object, you need to reindex the parent (this brings a potential performance overhead if the nested objects change too quickly). To solve this problem, Elasticsearch allows you to define child documents.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client can be used, such as curl...

Often a field must be processed with several core types or in different ways. For example, a string field must be processed as tokenized for search and not-tokenized for sorting. To do this, we need to define a fields multifield special property.

The fields property is a very powerful feature of mappings because it allows you to use the same field in different ways.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client can be used such as curl (https://curl.haxx.se/), postman (https://www.getpostman.com/), or similar...

Elasticsearch natively supports the use of geolocation types—special types that allow you to localize your document in geographic coordinates (latitude and longitude) around the world.

There are two main types that are used in the geographic world: the point and the shape. In this recipe, we'll look at GeoPoint—the base element of geo location.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client can be used such as curl (https://curl.haxx.se/), postman (https://www.getpostman.com/), or similar. I suggest...

An extension to the concept of point is the shape. Elasticsearch provides a type that facilitates the management of arbitrary polygons in GeoShape.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To be able to use advanced shape management, Elasticsearch requires two JAR libraries in its classpath (usually the lib directory), as follows:

• Spatial4J (v0.3)
• JTS (v1.13)

To map a geo_shape type, a user must explicitly provide some...

Elasticsearch is used in a lot of systems to collect and search logs, such as Kibana (https://www.elastic.co/products/kibana) and LogStash (https://www.elastic.co/products/logstash). To improve search when using IP addresses, Elasticsearch provides the IPv4 and IPv6 type that can be used to store IP addresses in an optimized way.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

You need to define the type of the field that contains...

It is very common to have a lot of different types in several indices. Because Elasticsearch makes it possible to search in many indices, you should filter for common fields at the same time.

In the real world, these fields are not always called in the same way in all mappings (generally because they are derived from different entities), it's very common to have a mix of added_date, timestamp, @timestamp, and date_add fields that are referring to the same date concept.

The alias fields allow you to define an alias name to be resolved, as well as a query time to simplify the call of all fields with the same meaning.

You need an up-and-running Elasticsearch installation...

The Percolator is a special type field that makes it possible to store an Elasticsearch query inside the field and use it in percolator query.

The Percolator can be used to detect all queries that match a document.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client can be used such as curl (https://curl.haxx.se/), postman (https://www.getpostman.com/), or similar. I suggest using Kibana console, which provides code completion and better character escaping for Elasticsearch.

It's common to have the requirement to score a document dynamically, depending on the context. For example, scoring more particular documents that are inside a category—the classic scenario is to boost (increase low scored) documents that are based on value such as page rank, hits, or categories.

Elasticsearch 7.x provides two new ways to boost your scores based on values: one is the feature fields and the other is its extension to a vector of values.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

To execute these commands, any HTTP client...

Sometimes, when we are working with our mapping, it is required to store some additional data to be used for display purposes, ORM facilities, permissions, or simply to track them in the mapping.

Elasticsearch allows you to store every kind of JSON data you want in the mapping with the special _meta field.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

The _meta mapping field can be populated with any data we want. Consider...

In the previous recipes, we have looked at how to map different fields and objects in Elasticsearch, and we have described how it's easy to change the standard analyzer with the analyzer and search_analyzer properties.

In this recipe, we will loot at several analyzers and learn how to use them to improve indexing and searching quality.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

Every core type field allows...

To be able to provide search functionalities for our user, one of the most common requirements is to provide a text suggestion for our query.

Elasticsearch provides a helper for archiving this functionality using a special type mapping called completion.

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

The definition of a completion field is similar to the previous core type fields. For example, to provide a suggestion...