L1 Field Note
Databases When Search Becomes a Product: OpenSearch and Elasticsearch Architecture
Why marketplace, travel, retail, local commerce, and support search often need a dedicated search platform instead of only pgvector.
Elasticsearch / OpenSearch
Shard and heap troubleshooting, hybrid search, kNN architecture.
14 posts
L2 Deep Dive
Databases OpenSearch Hybrid Search for Product Catalogs: BM25, Vectors, Filters, and Rerankers
A production architecture for product catalog hybrid search with OpenSearch, combining BM25, vector retrieval, filters, shard design, reranking, and relevance debugging.
L2 Deep Dive
Databases OpenSearch as a Vector Database: When Search Infrastructure Beats a Dedicated Vector DB
When OpenSearch is the right vector-search platform because keyword search, hybrid retrieval, relevance debugging, and search operations already matter.
L2 Deep Dive
Databases pgvector vs OpenSearch vs Qdrant vs Weaviate: A Database Architect's Decision Matrix
A production-oriented decision matrix for choosing pgvector, OpenSearch, Qdrant, or Weaviate by workload shape, filters, hybrid search, operations, cost, tenancy, and recovery.
L2 Deep Dive
Databases Migrating from Elasticsearch/OpenSearch Keyword Search to Semantic Search
A safe migration path from keyword search to semantic or hybrid OpenSearch retrieval using dual indexing, embeddings, backfill, relevance evaluation, A/B testing, fallback, rollback, and cutover.
L2 Deep Dive
Databases When OpenSearch Vector Search Fails: Shard Sizing, Recall Loss, and Hot Nodes
OpenSearch vector search failure modes for operators, including shard count, hot shards, tenant skew, memory pressure, recall degradation, slow merges, filters, and recovery.
L2 Deep Dive
Databases Building Hybrid Search in OpenSearch: BM25, k-NN, Filters, and Reranking
A production guide to OpenSearch hybrid retrieval with BM25, vector k-NN, metadata filters, score fusion, reranking, relevance debugging, and observability.
L2 Deep Dive
Databases OpenSearch Vector Search on AWS: Managed Convenience, Hidden Cost
The tradeoffs of Amazon OpenSearch Service for vector search, including managed operations, scaling, instance choice, storage, memory, transfer, snapshots, and index design cost.
L2 Deep Dive
Databases OpenSearch k-NN Architecture: Shards, Segments, HNSW, and Memory
An infrastructure view of OpenSearch vector search, covering k-NN fields, HNSW, shards, segments, refresh, merges, memory, node sizing, and operational gotchas.
L2 Deep Dive
Databases Why Is This Elasticsearch Query Slow? Search Profiling and LLM Analysis
A deep dive into diagnosing slow Elasticsearch queries: using the Search Profile API to separate query, fetch, and aggregation phases from thread queue delays and network transit.
L2 Deep Dive
AI Engineering LLM-Assisted Elasticsearch Performance Troubleshooting: Heap, Shards, CPU and Search
A cluster-level diagnostic guide for Elasticsearch: isolating JVM garbage collection stalls, thread pool rejections, over-sharding bloat, disk watermarks, and circuit breakers.
L2 Deep Dive
System Design Catalog Sync Workflow: Database, Search Index, CDN, and Cache Invalidation
Propagating a catalog update from database commit through Elasticsearch, CDN edge cache, and application cache without stranding stale reads downstream.
L2 Deep Dive
System Design Search Indexes in Commerce: Why Elasticsearch Is Not the Source of Truth
Elasticsearch is a read index, not a record system — routing writes through it creates catalog drift that surfaces only after orders are placed.
L2 Deep Dive
System Design AWS Database Cost Triage: RDS, Aurora, DynamoDB, ElastiCache, and OpenSearch
Database bills grow when ownership, workload shape, and control loops drift apart — a structured triage approach for RDS, Aurora, DynamoDB, ElastiCache, and OpenSearch spend before it becomes an emergency.