PostgreSQL & Redis Optimization. Slow Query Elimination. Partial Indexing. Two-Tier Caching Architecture. - Aleksandar Kuzmanovic offers many high-quality services. When applications begin scaling, the database is almost always the first catastrophic bottleneck. Slow sequential scans, connection pool exhaustion, N+1 query storms from ORMs, and transaction deadlocks degrade user experience and spike cloud infrastructure costs. Key stats: >85% Cache hit rate, <5ms Enqueue latency, 0 Deadlocks Concurrency safety, 10x Throughput gain. Technologies: PostgreSQL, Redis, BullMQ, Prisma, PgBouncer, AWS RDS & Aurora, Docker, SQL CTEs, TypeScript, Node.js. Contact: aleksandarkuzmanovic02@gmail.com.
PostgreSQL & Redis Optimization.
Slow Query Elimination. Partial Indexing. Two-Tier Caching Architecture.
When applications begin scaling, the database is almost always the first catastrophic bottleneck. Slow sequential scans, connection pool exhaustion, N+1 query storms from ORMs, and transaction deadlocks degrade user experience and spike cloud infrastructure costs.
I diagnose, re-architect, and optimize PostgreSQL and Redis data layers to handle extreme concurrent traffic with sub-10ms query speeds. By replacing unindexed full-table scans with composite and partial indexes, tuning connection poolers (PgBouncer), and implementing intelligent two-tier Redis caching, I turn sluggish database clusters into resilient, high-throughput engines.
As engineered across our production AI systems and eCommerce platforms, these database optimizations deliver instant cache hits at cost 0, prevent database downtime under sudden traffic surges, and eliminate concurrency race conditions using strict row-level isolation and partial unique index concurrency locks.
Whether your application is struggling with high CPU spikes on AWS RDS, slow ORM queries in Prisma, or complex analytical JOIN latency, I deliver measurable database performance gains.
When applications begin scaling, the database is almost always the first catastrophic bottleneck. Slow sequential scans, connection pool exhaustion, N+1 query storms from ORMs, and transaction deadlocks degrade user experience and spike cloud infrastructure costs.
Services & deliverables.
End-to-end.
Database Performance & Slow Query Audit
Comprehensive report profiling pg_stat_statements, missing indexes, buffer cache hit ratios, and lock contention.
Non-Destructive Index Migration Scripts
Production-ready SQL migration files applying concurrent indexes, composite keys, and partial filters.
Redis Caching & Invalidation Architecture
Implementation of smart cache keys with TTLs, tag-based invalidation, and two-tier worker lookup logic.
PgBouncer & Connection Pool Tuning
Production configuration of connection poolers and PostgreSQL server configuration parameters (work_mem, maintenance_work_mem).
Worker Queue Concurrency Hardening
Implementation of SKIP LOCKED consumer patterns and idempotency keys that eliminate race conditions in distributed workers.
Why choose my database optimization services?
Deep EXPLAIN ANALYZE Diagnostics
I do not guess performance bottlenecks. I use PostgreSQL EXPLAIN (ANALYZE, BUFFERS) to inspect actual query execution plans, identifying sequential scans, hash joins, and memory spillage.
Composite & Partial Indexing
Strategic index design: creating partial unique indexes that enforce concurrency locks and composite B-tree indexes that cover high-frequency WHERE and ORDER BY filters with minimal disk overhead.
Two-Tier Redis Caching Strategy
Tier-1 enqueue-time caching delivers instant HTTP 202 responses for identical inputs; Tier-2 worker-time caching refunds reserved tokens on cache hits, reducing database load to near-zero.
Connection Pool Sizing & PgBouncer
Tuning max_connections, shared_buffers, work_mem, and deploying PgBouncer in transaction pooling mode to support thousands of concurrent client connections without memory exhaustion.
ORM Query Sanitization (Prisma / TypeORM)
Refactoring inefficient ORM abstractions that generate hidden N+1 queries, replacing them with optimized raw SQL CTEs and batch lookups.
Row-Level Concurrency & Deadlock Prevention
Designing safe pessimistic locking patterns (SELECT FOR UPDATE SKIP LOCKED) for asynchronous worker queues and financial transactions.
My process.
Simple. Transparent.
Telemetry & Profiling Collection
We enable pg_stat_statements and inspect server telemetry to identify the top 5 most expensive queries by total execution time.
Execution Plan Analysis
Running EXPLAIN ANALYZE on target queries to map disk reads, memory allocations, and join bottlenecks.
Index & Query Refactoring
Authoring composite indexes, rewriting slow CTEs, and eliminating ORM N+1 query chains.
Redis Caching Integration
Implementing multi-tier in-memory caching and background worker queue synchronization.
Load Testing & Verification
Executing concurrent load tests to verify sub-10ms response times under peak simulated user traffic.
Technology Stack
Mastering the database engine.
Software developers often treat relational databases as abstract storage appliances, relying blindly on ORMs to generate SQL. In production, however, the laws of storage I/O and memory pages are unforgiving: a query that scans 1,000,000 rows to return 10 records will quickly starve your CPU cache and saturate your disk bandwidth.
On our distributed AI platforms, handling concurrent webhook events requires rigorous concurrency control. Naive unique constraints under heavy load trigger database deadlocks. By utilizing PostgreSQL partial unique indexes alongside Redis queue serialization, we achieve zero concurrency locks while maintaining 100% transactional idempotency.
Furthermore, two-tier caching transforms system economics: instead of hitting database replicas for duplicate analytical calculations, commit SHA hashing verifies byte-level identity instantly in Redis, returning valid results in under 5 milliseconds.
Optimizing your data layer not only eliminates user-facing latency — it allows your business to handle 10x traffic on existing hardware without expensive database upgrades.
Common questions.
Clear answers.
- Why is our database CPU at 100% even with moderate user traffic?
- In 90% of cases, 100% database CPU is caused by a handful of unindexed queries performing full table sequential scans, or ORMs generating runaway N+1 query loops that execute hundreds of queries per page render. Proper composite indexing and batching resolves this immediately.
- Can you optimize our database without causing production downtime?
- Yes. All index creations are executed using CREATE INDEX CONCURRENTLY, which builds indexes in the background without locking tables against writes or reads.
- When should we use Redis caching versus optimizing PostgreSQL queries directly?
- Always optimize the relational query first. Caching slow, broken queries in Redis masks underlying architectural flaws. Once relational queries are cleanly indexed and performant, Redis is layered on top for frequently read, byte-identical datasets to absorb peak traffic spikes.
- How does PgBouncer improve connection handling?
- PostgreSQL forks a process for each connection, consuming ~10MB RAM per client. PgBouncer maintains a small, fixed pool of database connections and multiplexes thousands of incoming application requests through them, preventing connection exhaustion.
Ready to start?
Send a message and I'll respond within 24 hours with a clear plan for your project.