Elasticsearch: Why and When Do We Need It If Databases Already Offer Full-Text Search Capabilities?

A lot of applications requires to have search functionality, and PostgreSQL practitioners already use Full Text Search. However, as requirements change or traffic drastically increases we also have to ask when is the time to use a search specific platform. This guide aims to delve into when to use Full Text Search, Elastic Search, or both.

An Introduction to Elasticsearch and PostgreSQL Full-Text Search

Elasticsearch, a robust real-time, distributed search and analytics engine, is lauded for its scalability, resilience, and comprehensive search functionalities. Conversely, PostgreSQL is a standard relational database equipped with reliable full-text search capabilities. While PostgreSQL may not match Elasticsearch's breadth of search features, it provides an efficient solution for less intricate use-cases.

Relying on PostgreSQL Full-Text Search for Simplicity and Integration

Though PostgreSQL isn't explicitly designed for complex search scenarios, it does possess full-text search capabilities. These are suitable for smaller datasets or less complex search needs. PostgreSQL can handle text searches across various fields, offering features such as stemming and ranking. It proposes an integrated solution without the necessity of an additional service like Elasticsearch.

Harnessing Elasticsearch for Advanced Search Needs

Elasticsearch is the optimal choice for circumstances demanding real-time indexing and advanced search capabilities across large datasets. Rooted in Lucene, Elasticsearch thrives in log and event data analysis, as well as text searches. It processes textual data effectively, offering relevance scoring, tokenization, and synonym handling. Its ability to provide near-real-time search and indexing, even with vast data volumes, sets it apart from traditional databases.

Crucial Factors for Decision-Making

When navigating the decision between Elasticsearch, PostgreSQL Full-Text Search, or a combination of both, consider these key elements:

1. Data Volume: Elasticsearch excels in handling large volumes of data, making it the ideal choice for big data situations. Despite its robustness, PostgreSQL may struggle with voluminous datasets.
2. Search Complexity: Elasticsearch is superior for complex search needs, including advanced text analysis and real-time search. PostgreSQL, meanwhile, can efficiently address simpler search requirements.
3. Maintenance and Overhead: Elasticsearch, although powerful, demands expertise for setup and ongoing maintenance. In contrast, PostgreSQL is broadly used and generally simpler to manage.
4. Integration: If PostgreSQL is already in use, leveraging its in-built full-text search capabilities can reduce complexity and overhead, provided it satisfies your search needs.

Sample Application of Each

Scenario 1

You have a small to medium-sized e-commerce store. The website has thousands of product listings that need to be searchable by potential customers. However, your product database isn't huge, and your search requirements are quite simple - customers should be able to search for products using names or descriptions. In this scenario, PostgreSQL's built-in full-text search could be an efficient solution. It can handle text search across a variety of fields and offers capabilities such as stemming and ranking. Since the search requirements are not overly complex, and the data size is not huge, using PostgreSQL full-text search will minimize overheads of managing an additional service like Elasticsearch.

Scenario 2

Now, let's say your e-commerce business grows rapidly, and now you have millions of products listed on your website. Moreover, you want to provide more sophisticated search features to your customers - you want them to be able to use filters, sort products by relevance, and get search suggestions as they type. In this case, Elasticsearch would be a better fit. Its advanced search capabilities and scalability would allow you to handle the increased data volume and provide a rich search experience. The ability to search in near real-time would also ensure that newly added products are immediately available for users to find.

The Hybrid Approach: Merging the Best of Both Worlds

One innovative solution is the hybrid approach, utilizing both PostgreSQL and Elasticsearch. This strategy is effective when only certain sections of your application require advanced search capabilities, while the remaining involve transactional operations and simpler queries.

Consider a real-world e-commerce application above: product details and customer reviews could reside in both PostgreSQL and Elasticsearch. This setup allows for advanced search capabilities and real-time indexing with Elasticsearch, while transactional data such as user information and order details remain in PostgreSQL.

However, this hybrid solution necessitates maintenance of data synchronization between PostgreSQL and Elasticsearch. You can ensure this synchronization using methods like database polling for changes, or implementing database triggers or middleware. to push updates to Elasticsearch.

For synchronization: django teams can leverage the signal dispatched, while node typeorm teams can utilize entity listeners or even subscribers

Conclusion

The choice between Elasticsearch, PostgreSQL Full-Text Search, or a blend of both is influenced by several key considerations. Understanding your data volume, search complexity, maintenance capacity, and current system integration is vital. For smaller, simpler applications, PostgreSQL could suffice. For larger, more intricate scenarios, Elasticsearch generally performs better. Yet, a well-managed hybrid approach could offer a strong, scalable system adept at handling a broad range of data operations efficiently. Your decision should address your project's specific needs and constraints, enabling you to harness the strengths of each technology.