Full-text search often looks easy at the start. You store text, run a query, and return results. However, once users expect fast responses, typo tolerance, and relevant ranking, search becomes a serious architectural decision.
In this article, you will compare PostgreSQL, Elasticsearch, and Algolia for full-text search. More importantly, you will learn when each option makes sense and why choosing the wrong one creates long-term pain.
What Full-Text Search Really Involves
Full-text search is not simple string matching. In practice, it usually includes:
- Tokenization and normalization
- Relevance scoring
- Language support
- Typo handling
- Fast response times at scale
Because of these requirements, search systems behave very differently from relational databases. Therefore, understanding their strengths early helps avoid costly migrations later.
PostgreSQL Full-Text Search
PostgreSQL includes native full-text search features. You can convert text into tsvector columns and query them using tsquery, while indexing them with GIN indexes.
When PostgreSQL Search Works Well
PostgreSQL full-text search fits best when:
- Your data already lives in PostgreSQL
- Search needs stay relatively simple
- Operational simplicity matters
- Search traffic remains moderate
For many applications, PostgreSQL search removes the need for extra infrastructure. It works especially well in systems that already focus on query tuning and indexing, as described in PostgreSQL Performance Tuning: Indexes, EXPLAIN, and Query Optimization.
Limits of PostgreSQL Search
PostgreSQL search also has limits:
- Ranking options remain basic
- Typo tolerance requires extensions or workarounds
- Search load competes with transactional queries
As datasets grow, search queries can slow down core application workflows. At that point, teams usually look for a dedicated search engine.
Elasticsearch: Built for Search at Scale
Elasticsearch is a distributed search engine designed specifically for full-text search and analytics. It uses inverted indexes and scales horizontally by design.
Where Elasticsearch Shines
Elasticsearch makes sense when:
- Search drives user experience
- Data volume grows quickly
- Advanced relevance and filtering matter
- Search must scale independently
Because Elasticsearch decouples search from the primary database, it fits well in event-driven systems. Patterns discussed in Event-Driven Microservices with Kafka often pair naturally with Elasticsearch indexing pipelines.
The Cost of Elasticsearch
Elasticsearch brings power, but it also brings complexity:
- Cluster setup and maintenance
- Memory and shard tuning
- Index lifecycle management
- Ongoing monitoring
Many teams underestimate the operational effort required to run Elasticsearch reliably.
Algolia: Search as a Managed Service
Algolia offers full-text search as a fully managed service. Instead of running infrastructure, you send data to Algolia and query it through APIs.
Why Teams Choose Algolia
Algolia works well when:
- You need excellent search quality quickly
- Frontend performance is critical
- You want minimal operational overhead
- Time to market matters
Algolia provides typo tolerance, ranking, and instant responses out of the box. As a result, teams can ship polished search experiences without managing clusters.
Algolia Trade-offs
Algolia also introduces trade-offs:
- Usage-based pricing
- Limited low-level control
- External data synchronization
As traffic grows, cost becomes an architectural concern. Therefore, Algolia fits best when search quality outweighs infrastructure control.
Indexing Differences Across Systems
Each solution indexes data differently.
PostgreSQL relies on GIN indexes over text vectors. Elasticsearch builds inverted indexes optimized for full-text queries. Algolia abstracts indexing entirely from the user.
Understanding these differences helps explain performance behavior. Concepts from Database Indexing Strategies: B-Tree, Hash, GIN, and GiST apply directly to PostgreSQL search and indirectly to search engines.
Data Synchronization Considerations
Search engines rarely own the source of truth. Instead, they index data from another system.
Common sync approaches include:
- Write-through updates
- Event-based pipelines
- Scheduled reindexing
Event-driven indexing often scales best and aligns with ideas from Real-Time APIs: WebSockets vs Server-Sent Events.
Search and Consistency
Consistency differs across search solutions.
PostgreSQL search stays transactional because it lives inside the database. Elasticsearch and Algolia provide eventual consistency instead.
This difference matters in systems that require strict guarantees, especially when combined with logic explained in Database Transactions and Isolation Levels Explained.
Choosing the Right Search Solution
A simple rule of thumb helps:
- Choose PostgreSQL when search stays simple and close to data
- Choose Elasticsearch when search becomes a core, scalable feature
- Choose Algolia when speed, UX, and low ops matter most
Most search failures happen when teams overengineer early or delay scaling decisions too long.
A Practical Example
Consider an e-commerce platform.
At first, PostgreSQL search handles product names and descriptions well. As the catalog grows and relevance becomes critical, Elasticsearch takes over. Later, a marketing-focused frontend adopts Algolia for instant autocomplete and suggestions.
Each transition matches product growth rather than hype.
Common Full-Text Search Mistakes
Teams often struggle when they:
- Add Elasticsearch too early
- Ignore operational cost
- Expect transactional guarantees from search engines
- Treat search as a secondary concern
Search deserves architectural attention from the beginning, even if implementation starts simple.
Full-Text Search in Modern Systems
Many modern architectures combine approaches:
- PostgreSQL for core data and simple search
- Elasticsearch for advanced relevance and analytics
- Algolia for frontend-heavy experiences
This layered approach mirrors ideas from API Gateway Patterns for SaaS Applications, where each component has a clear responsibility.
Conclusion
There is no universal best choice for full-text search. PostgreSQL, Elasticsearch, and Algolia each solve different problems at different scales.
A practical next step is to define what search means for your product today and how it may evolve. Choosing a solution that fits both current and future needs saves time, cost, and major rewrites later.
