Elasticsearch
Elasticsearch — search engine for full-text and vector search.
Stack Components
Why
Fast relevance-ranked search with faceting, highlighting, and typo tolerance. Meilisearch and Typesense emerged as open-source Algolia alternatives, 5-10x cheaper at scale.
Algolia handles 1.5B+ searches/month with sub-50ms latency via its MRR ranking algorithm.
When
Use for product search, log search, and RAG vector retrieval. Avoid for simple DB LIKE queries.
How
Example
Common Problems
- ⚠Elasticsearch index mapping changes require reindex — plan schema upfront
- ⚠High query latency — use {ne} replicas and tune shard count
- ⚠Configuration drift between environments — use infrastructure-as-code (Terraform/Pulumi) to keep dev/staging/prod identical
Best Practices
- ✓Use Elasticsearch analyzers appropriate for your language
- ✓Monitor {ne} index size and optimize mappings
- ✓Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit
FAQ
What is Elasticsearch and what projects is it for?
Elasticsearch — search engine for full-text and vector search. It pairs layered technologies for end-to-end type safety and out-of-the-box deploys, fitting everything from MVPs to production SaaS.
How do I spin up a working demo with Elasticsearch in 10 minutes?
Two steps: (1) Deploy Elasticsearch instance; (2) Create index/collection. Get the local dev server running, then wire one real data source to verify the end-to-end loop.
What is the most common pitfall with Elasticsearch and how do I avoid it?
The most common pitfall is: Elasticsearch index mapping changes require reindex — plan schema upfront. Avoid it by adding the matching checklist to your PR template and running type-check + lint + unit tests in CI — three gates before merge.
What are the best practices for Elasticsearch?
Best practices for Elasticsearch include: Use Elasticsearch analyzers appropriate for your language; Monitor {ne} index size and optimize mappings; Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit. The first one — "Use Elasticsearch analyzers appropriate for your language" — should be set up at project init to avoid costly rework later.
When should I NOT choose Elasticsearch?
Pick something else in three cases: (1) the team has zero experience with the core framework and the deadline is tight (learning-curve risk); (2) it's a pure content/marketing site (Astro is lighter); (3) you need very high throughput (>10k RPS) or strict-consistency microservices (Go/Rust). Elasticsearch shines for mid-to-high complexity full-stack and API work — don't over-engineer simple static pages with it.