Technical input and supervision: Kamil Żarnicki

Achieving efficient and effective full-text search capabilities can be a challenge when working with relational databases. Hibernate Search, a framework designed to bridge the gap between relational databases and NoSQL databases typically better suited for full-text search, can be a huge help here. From this article, you will learn what Hibernate Search is, how it works, and how it can enhance search capabilities within Java applications.

What is Hibernate Search?

Hibernate Search is a framework that integrates seamlessly with Hibernate Object-Relational Mapping (ORM) to provide full-text search capabilities in applications using relational databases.
Hibernate Search works as a bridge, mapping data between entities and indexes used for searching. It enables full-text search capabilities, traditionally associated with NoSQL databases.

Key Features of Hibernate Search

Data Mapping
Hibernate Search maps entity data to indexes which facilitate search operations. This mapping can be done with annotations in the entity classes or via the API. The primary annotation is @Indexed, which indicates that the entity will be indexed for search.

Index Building
Two main methods for building indexes are available:

1. Mass Indexing: Ideal for indexing large volumes of data, typically used when adding search functionality to an existing application with pre-existing data.
2. Automatic Indexing: Automatically indexes data as entities are modified through Hibernate sessions.

Full-Text Search
Full-text search involves breaking down documents into smaller chunks–we will call them tokens for simplicity and for the purposes of this text–and searching through these tokens using inverted indexes. It’s a mechanism where the key of the data is the token, and the value is the key of the entity that contains this data. An inverted index maps tokens to the keys of entities containing these tokens, making the search process efficient.

Annotation-Based Configuration
Annotations simplify the configuration of search functionality. Key annotations include:

• @Indexed: Marks an entity for indexing.
• @FullTextField: Prepares a string field for full-text search.
• @KeyWordField: Treats a string field as a single token for exact matches.
• @GenericField: Supports various data types for search.
• @IndexEmbedded: Facilitates nested indexing.

 

@Entity
@Indexed
public class Book {
@Id
@GeneratedValue
private Integer id;
@FullTextField
private String title;
@KeyWordField
private String isbn;
@GenericField
private int page Count;
@ManyToManu
@Indexed Embedded
private Set authors = new HashSet<>();

Text Analysis Process
Text analysis in Hibernate Search transforms text into searchable tokens through three main steps:

1. Character Filter:Optional; modifies or removes specific characters.
2. Tokenizer: Splits text into individual tokens, typically by whitespace.
3. Token Filter: Processes tokens, e.g., removing suffixes or filtering out common words.

Backend Options

Hibernate Search supports two backends for storing indexes: ElasticSearch and Apache Lucene. Each has its unique characteristics, and choosing the right one depends on your application’s needs.
ElasticSearch

• Pros: Scalable, suitable for microservices, handles large datasets.
• Cons: Slightly slower index updates, requires additional maintenance.

Apache Lucene

• Pros: Simplicity, high-speed updates.
• Cons: Difficult configuration, lacks horizontal scalability, not suitable for microservice-based applications.

Indexing Strategies

Automatic Indexing
Occurs automatically when entities are modified through Hibernate. However, direct changes and native queries bypass the Hibernate Search mechanism, and they are not visible in the index.
Mass Indexing
Used for initial setup or when rebuilding indexes after significant changes. It can be configured to run in multiple threads for efficiency.

Search Implementation Architectures

Hibernate Search recommends three main architectures for implementing search functionality:

1. Monolithic Applications with Lucene:
   • Pros: Simple, fast updates.
   • Cons: Limited scalability, potential index-data inconsistencies.
2. ElasticSearch with Single or Multiple Instances:
   • Pros: Scalability, suitable for microservices.
   • Cons: Maintenance overhead, slight delay in index updates.
3. ElasticSearch with Outbox Polling:
   • Pros: Ensures consistency between indexes and database data, scalable.
   • Cons: Additional complexity, requires extra database tables for updates.

Searching

Conclusion

Hibernate Search enables developers to integrate reliable full-text search capabilities into Java applications that use relational databases. By mapping entities to searchable indexes and providing a high-level API for search operations, Hibernate Search brings the efficiency and power of NoSQL-style search to the realm of relational databases. Whether you choose Lucene for simplicity or ElasticSearch for scalability, Hibernate Search offers a versatile solution to meet your application’s search needs.