MirahLabs Engineering Blog
Technical insights, tutorials, and architectures written by our design and backend engineers.
Scaling Python Flask Applications with PostgreSQL
Best practices for configuring database pools, write indexes, caching, and utilizing Docker deployment structures for Flask backends.
Vector Search at Scale: Hierarchical Navigable Small World (HNSW) Indexes
Similarity searches on millions of high-dimensional vectors are computationally expensive. Learn how HNSW graph indexes make nearest-neighbor search sub-millisecond.
Vector Databases Compared: Pinecone vs Weaviate vs pgvector
A detailed comparison of three leading vector database solutions—Pinecone, Weaviate, and pgvector—covering performance, scalability, cost, and best-fit use cases.
PostgreSQL Full-Text Search: FTS vs pgvector vs Elasticsearch
Full-text search can be implemented several ways in a PostgreSQL-based stack. Compare native FTS, vector semantic search with pgvector, and Elasticsearch for your search requirements.
SQLAlchemy 2.0: The Complete Migration Guide
SQLAlchemy 2.0 introduces a new style API, async-first design, and significant performance improvements. Learn what changed and how to migrate your Flask applications.
PostgreSQL Performance Tuning: Indexes, Query Plans, and Connection Pooling
PostgreSQL is incredibly powerful but requires thoughtful configuration for high-traffic production workloads. This guide covers index strategies, query plan analysis with EXPLAIN, and PgBouncer connection pooling.