Full-text search that scales beyond what Postgres can handle.
Elasticsearch for full-text search, faceted filtering, and log analytics in applications where Postgres full-text search hits its limits. We integrate Elasticsearch (or Elastic Cloud) for applications with complex search requirements, multi-field relevance, and faceted navigation.
Application with search requirements that have outgrown Postgres full-text search — slow queries on large datasets, complex relevance tuning needs, or faceted filtering across many fields
Postgres full-text search (tsvector, tsquery, pg_trgm) works well for simple search on small-to-medium datasets. It breaks down when:
Large datasets. Postgres full-text search on millions of records with complex queries degrades significantly. Elasticsearch's inverted index architecture handles large-scale search efficiently.
Relevance tuning. Postgres full-text has limited relevance customization. Elasticsearch's BM25 scoring, custom field boosts, function scoring, and decay functions give fine-grained control over result ranking.
Faceted aggregations. "Filter by category, then see counts for each sub-category" — faceted navigation requires aggregation queries that are expensive in Postgres but native to Elasticsearch.
Real-time analytics. Log aggregation, time-series dashboards, and usage analytics. Elasticsearch + Kibana (the ELK stack) is the standard for log analytics.
The alternatives comparison:
Algolia. Managed search with excellent developer experience. Best for applications that want search-as-a-service without managing Elasticsearch. Higher cost per operation at scale; limited customizability.
Typesense. Open-source Algolia alternative. Self-hostable. Better cost model at scale; less mature than Elasticsearch.
Elasticsearch. Most powerful and flexible. Significant operational overhead if self-hosted. Elastic Cloud (managed) reduces this.
Elasticsearch integration with index design, relevance tuning, faceted aggregations, and the query DSL that delivers sub-100ms search across large datasets
Index design
Index mapping for field types (`keyword`, `text`, `date`, `nested`). Custom analyzers for language processing. Multi-field mapping for relevance and filtering.
Sync pipeline
Database trigger or webhook to sync Postgres records to Elasticsearch. Bulk indexing for initial data load. Partial update for field changes.
Query DSL
`bool` queries combining `must`, `should`, `filter`, and `must_not` clauses. Multi-match for cross-field search. Highlighting for matched terms in results.
Faceted navigation
`terms` aggregations for category filters. `range` aggregations for price and date filters. Post-filter for applying filters after aggregations.
Elastic Cloud or Elasticsearch cluster
Managed Elastic Cloud configuration or Docker-based Elasticsearch for self-hosted deployments.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Elasticsearch integration with index design, relevance tuning, faceted aggregations, and the query DSL that delivers sub-100ms search across large datasets
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Search implementation scope is defined by the index design and the query requirements. Fixed price.
Questions, answered.
Algolia for: teams that want managed search with minimal operational overhead, excellent ranking out of the box, and are willing to pay for convenience. Elasticsearch for: teams that need maximum control, have complex aggregation needs, or are handling log/analytics use cases alongside search.
The sync pattern: Debezium or custom triggers on Postgres changes, published to a queue (SQS, Redis), consumed by an indexing worker that updates Elasticsearch. This provides near-real-time sync with good reliability.
Search as part of the application build. Full application from $25k. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.