Full-text search technology is crucial for extracting precise information from vast amounts of data. Especially for companies dealing with large volumes of text data, this technology offers a better search experience for users. However, many companies face a dilemma when choosing a search engine. Among the most well-known options is Elasticsearch. Yet, this powerful tool may not always be the best choice in every scenario.
Advantages and Disadvantages of Elasticsearch
Elasticsearch offers remarkable performance and a powerful feature set, making it particularly useful when handling large datasets. Its fast search speed and diverse search capabilities ensure precise results. Moreover, it can be utilized not just as a search engine but also for data analytics, security, and as an observability platform, providing versatility in its applications.
However, despite these advantages, Elasticsearch comes with some significant drawbacks. Firstly, it can be unstable as a reliable data store, leading to potential data inconsistencies or losses. Additionally, it requires an ETL pipeline, which adds operational overhead. Lastly, the complexity of managing and operating clusters can lead to soaring costs, causing many companies to struggle with Elasticsearch cluster management.
Postgres FTS: Simplicity and Real-Time Search Benefits
In contrast, Postgres FTS (Full Text Search) might be more suitable for companies with simple search requirements. Postgres FTS does not require additional infrastructure and can be used directly within an existing Postgres database. This makes it much easier to operate and manage, and its real-time search capability is highly advantageous for applications where search latency is critical, such as e-commerce sites or fintech companies.
However, Postgres FTS offers a limited feature set compared to Elasticsearch and may suffer performance degradation with large datasets. Additionally, if advanced search features are required, Postgres FTS has clear limitations.
Alternative Search Engines: Algolia, Meilisearch, and ParadeDB
Recently, modern search engines like Algolia, Meilisearch, and ParadeDB have emerged, offering developers new choices. These engines focus on building user-centric search experiences and exhibit diverse features with unique characteristics.
Notably, ParadeDB is an intriguing alternative specifically built for Postgres, operating similarly to Postgres FTS while offering advanced features akin to Elasticsearch. This engine integrates Tantivy, a Rust-based Lucene alternative, enabling advanced full-text search without additional infrastructure.
Conclusion: What Should You Choose?
The choice of search engine depends on a company’s specific requirements. Elasticsearch is a powerful tool for complex and large-scale data searches but requires careful consideration of management complexity and costs. On the other hand, Postgres FTS is suitable for simpler search needs and is easy to operate. Meanwhile, the latest search engine alternatives, particularly ParadeDB, represent efforts to overcome existing limitations.
Reference: paradedb, “Full Text Search over Postgres: Elasticsearch vs. Alternatives”