RocketMVPRocketMVP
Important for many MVPs

Search Implementation for MVPs

Full-text search, filtering, and faceted search for product catalogs, content, and user data.

3-7 days
Typical Timeline
$1,000 - $3,000
Typical Cost

When to Include

  • Large content libraries or catalogs
  • E-commerce product search
  • User-generated content discovery
  • Multi-criteria filtering needed

When to Skip

  • Small datasets (<100 items)
  • Simple list filtering is sufficient
  • No discovery use case

Technology Options

TechnologyProsCons
PostgreSQL Full-Text Search
Built-in search capabilities in PostgreSQL
  • No extra service
  • Good for simple search
  • Transactional consistency
  • Limited relevance tuning
  • Slower at scale
  • Basic features
Algolia
Hosted search-as-a-service
  • Instant search
  • Excellent relevance
  • Great UI libraries
  • Easy setup
  • Expensive at scale
  • Data sync required
  • Vendor lock-in
Meilisearch
Open-source, fast search engine
  • Self-hostable
  • Very fast
  • Good typo tolerance
  • Simple API
  • Smaller ecosystem
  • Manual hosting/scaling
Elasticsearch
Enterprise-grade search engine
  • Extremely powerful
  • Scalable
  • Rich analytics
  • Complex to operate
  • Resource-intensive
  • Overkill for MVPs

Implementation Steps

1
Define search requirements (speed, relevance, facets)
2
Choose search technology based on scale
3
Design search index schema
4
Implement data sync/indexing pipeline
5
Build search API endpoint
6
Create search UI with instant results
7
Add filters and faceted navigation
8
Implement search analytics

Common Mistakes to Avoid

  • Not handling empty/no results states
  • Missing typo tolerance
  • Poor relevance ranking
  • Not syncing index with database
  • Missing search suggestions
  • Ignoring search analytics

Frequently Asked Questions

When should I upgrade from PostgreSQL search?

When you need instant search (<100ms), typo tolerance, faceted filtering, or have 10k+ searchable items. Start with PostgreSQL, migrate when needed.

How do I keep search index in sync?

Use database webhooks/triggers or event-driven updates. For MVPs, near-real-time sync (few seconds delay) is usually acceptable.

Should I build autocomplete?

Yes for discovery-focused products. Autocomplete improves UX significantly. Use search-as-you-type with debouncing (200-300ms).

Need Help Implementing Search Functionality?

We'll build it right the first time. Search Functionality is included in our $3,999 MVP package.

Get Started