Why Your Indexes Are Actually Slowing You Down – And How AI Fixes That
By A. Purushotham Reddy | | ~6200 words
Indexes are not free. Every INSERT, UPDATE, or DELETE must maintain every index on the table. When you add indexes without monitoring usage, write latency spikes and storage explodes. AI‑driven index management observes real query patterns, automatically drops unused indexes, and creates only those that accelerate your actual workload. This guide, based on the ebook Database Management Using AI by A. Purushotham Reddy, shows how to reclaim performance and stop guessing.
You added an index to speed up a slow query last month. Now your nightly batch jobs take twice as long, and your disk is filling up. You’ve fallen into the index trap. Traditional wisdom says “add indexes for every WHERE, JOIN, and ORDER BY.” But in modern databases, each index adds overhead to every write operation. With ten indexes on a table, one INSERT becomes eleven writes. If half those indexes are never used, you’re burning CPU, memory, and I/O for nothing.
The problem is worse than you think. A 2025 study of 500 production PostgreSQL databases found that over 40% of indexes were never used – but still maintained on every write. That’s millions of wasted operations per day. And the query planner still evaluates every index, even the unused ones, adding milliseconds of planning time to every query. The solution is not to stop indexing – it’s to index intelligently, with AI that learns your workload.
Why Traditional Indexing Advice Fails in 2026
The classic advice – “index foreign keys, index columns in WHERE, don’t over‑index” – is too vague. In practice, developers add indexes reactively, often without measuring. A slow query appears, someone adds an index, and the team moves on. Six months later, no one remembers why that index exists. And the database suffers.
Modern workloads are dynamic. A B‑tree index that is perfect for transactional queries may be useless for analytical scans. Partial indexes, covering indexes, and expression‑based indexes can be powerful – but only if used correctly. AI index management brings observability and automation together. It learns which index types benefit your specific queries and which are dead weight.
“The database that never drops unused indexes is like a hoarder who never throws anything away. AI gives you the courage to delete.” – A. Purushotham Reddy
Real‑World Example: Removing 32 Unused Indexes
A logistics company’s shipments table had 47 indexes. Write throughput was 80% lower than expected. After deploying an AI index advisor (based on the techniques in Chapter 6 of the ebook), the system analysed one week of workload and found that 32 indexes were never used. They were remnants of old reporting queries that no longer existed. After dropping them, write latency dropped by 65%, and read queries became faster because the planner had fewer options to evaluate. The company saved $4,000 per month in storage and compute costs.
The AI didn’t just drop indexes – it also suggested three new composite indexes that improved the most frequent slow queries by 90%. That’s the power of workload‑aware AI tuning.
How AI Monitors and Manages Indexes Continuously
AI index management works as a background agent that collects telemetry from your database: index usage statistics (e.g., pg_stat_user_indexes for PostgreSQL), query execution plans, and wait events. It builds a model of which indexes are hot, which are cold, and which are harmful. The model then takes actions:
- Recommendation mode – sends email or Slack alerts suggesting indexes to drop or create.
- Auto‑execute mode – with safety checks, automatically drops indexes that have been unused for a configurable period (e.g., 30 days).
- Hypothetical index testing – creates invisible indexes or uses
pg_hint_planto test the impact of new indexes without actually building them.
The AI also detects index fragmentation. As B‑trees are updated, pages become partially empty, leading to inefficient scans. AI schedules index rebuilds or reindexing during low‑load windows, but only when fragmentation exceeds a threshold (e.g., 20%). This prevents unnecessary rebuilds that can cause contention.
Index Creation with Reinforcement Learning
Advanced AI index tuning uses reinforcement learning to explore index configurations. The agent starts with a baseline (e.g., no indexes). It experiments by adding one index at a time, measuring the impact on query latency and write throughput. The reward function balances read performance against write overhead and storage cost. Over a few days, the agent discovers a near‑optimal set of indexes – often outperforming human DBAs.
The ebook’s Chapter 8 provides a complete implementation using pg_stat_statements and a lightweight Python RL library. You can train the agent on your own workload in under an hour.
Practical Steps to De‑Bloat Your Indexes Today
Before implementing full AI automation, you can start with these manual steps (which the ebook automates for you):
- Find unused indexes – In PostgreSQL:
SELECT schemaname, indexname, idx_scan, idx_tup_read, idx_tup_fetch FROM pg_stat_user_indexes WHERE idx_scan = 0;In MySQL:SELECT * FROM sys.schema_unused_indexes; - Find duplicate indexes – Use
pg_indexesand compare column lists. Tools likedexter(open source) can automate detection. - Measure write amplification – Compare the number of index updates to table inserts/updates. A high ratio indicates over‑indexing.
- Use index usage dashboards – Tools like pgDash or Prometheus + Grafana can visualise index hit rates.
Once you’ve identified bloat, drop indexes one at a time during low‑load periods and monitor query performance. The AI approach does this safely, automatically, and continuously.
📘 Stop Letting Unused Indexes Kill Your Performance
The techniques in this article are just the beginning. The Database Management Using AI: A Comprehensive Guide eBook contains 400+ pages covering AI index management, automatic index defragmentation, learned index structures, and 30+ other AI-powered database optimisations. Includes production‑ready Python scripts and step‑by‑step deployment guides.
Explore the detailed Table of Contents on Open Library →
Case Study: E‑Commerce Giant Saves 40% on Storage
A major online retailer had over 200 tables with an average of 12 indexes each. Some tables had 30+ indexes. After implementing AI index management (as described in the ebook’s case study chapter), the system identified 40% of indexes as completely unused. Dropping them reduced storage consumption by 1.2TB and improved write throughput by 55%. Additionally, the AI recommended 8 new composite indexes that replaced 15 single‑column indexes, further reducing bloat.
Total project time: two weeks, with zero application downtime. The company now runs AI index tuning monthly, automatically, and has eliminated manual index reviews.
Advanced AI Indexing Techniques
Beyond simple usage monitoring, the ebook covers:
- Learned index structures – replacing B‑trees with neural network‑based indexes (e.g., learned index) for read‑heavy workloads.
- Partial index recommendation – AI detects that most queries filter on a specific value (e.g.,
status = 'active') and suggests a partial index. - Expression index suggestion – AI finds common functions like
LOWER(email)orDATE(created_at)and recommends expression indexes. - Index merging – identifies when a multi‑column index makes several single‑column indexes redundant.
These techniques are already used by cloud databases like Amazon Aurora and Google AlloyDB. The ebook shows you how to implement them in open‑source databases using extensions and custom agents.
Safety Mechanisms: Never Drop an Important Index
AI index management includes safety features. Before dropping any index, it verifies that:
- The index has had zero reads for at least 30 days (configurable).
- Dropping the index does not break any foreign key or unique constraint.
- A shadow test with hypothetical index removal shows no increase in query latency for the top 100 queries.
If any check fails, the AI sends an alert and waits for human approval. This makes AI index tuning safe for production.
Conclusion: Stop Guessing – Let AI Tune Your Indexes Automatically
Indexes are the most powerful performance tool in a database – but they are also the most abused. Every unused index is a tax on every write, a drag on the query planner, and a waste of precious storage. Traditional manual management cannot keep up with dynamic workloads and evolving schemas. AI index management changes the game: it observes, learns, recommends, and acts – continuously and safely.
Whether you start with simple unused index detection or deploy a full reinforcement learning agent, the techniques in the Database Management Using AI ebook will help you reclaim performance, reduce costs, and free your team from index firefighting.
Stop letting unused indexes kill your performance. Let AI handle the tedious work – so you can focus on what matters.
Ready to Stop Wasting Resources on Unused Indexes?
Get the complete Database Management Using AI eBook – 400+ pages covering AI index management, learned index structures, automated defragmentation, and every technique you need to build a self‑tuning database system. Includes production‑ready Python code, SQL scripts, and step‑by‑step guides.
📚 Further Reading — AI Database Management Series
- AI Database Postmortem – The AI That Learns from Failure
- AI Service Discovery – Stop Hardcoding Database Connections
- Autonomous Tuning – AI That Tunes Your Database
- Time Series – Why Your Database Needs AI
- AI Changelog – The AI That Writes Your Database Changelog
- AI Database Management – Core Concepts
- Database Management Using AI – Overview
- Schema Evolution – The Death of Manual Migrations
- AI Log Mining – Extract Insights from Logs
- AI Relationship Discovery – Hidden Data Connections
- AI Stored Procedures – Intelligent Query Execution
- AI Workload Forecasting – Predict Database Load
- AI Join Optimisation – Smarter Query Plans
- AI Data Corruption Detection
- Stop Guessing Buffer Pool Size – AI Tunes It
- AI Deadlock Prevention – Proactive Lock Management
- AI Memory Layer – Beyond Vector Databases
- Adaptive Encryption – AI-Driven Data Security
- Conversational AI for Database Queries
- AI Data Masking – Privacy Protection
- AI Backup & Recovery – Intelligent Data Protection
- AI Automated Maintenance – Self-Healing Databases
- Approximate Query Processing with AI
- Adaptive Work Memory – AI Memory Management
- SELECT * FROM Customers Is Killing Your DB
- The $100K Mistake – Cloud Database Costs
- Active Replicas – AI-Driven Replication
- Temporal Queries – AI Time-Series Optimisation
- AI Negotiation – The AI That Negotiates Schema Changes
- Developer to DBA – How AI Bridges the Gap
- Data Lifecycle – AI-Managed Information Governance
- Auto Sharding – Stop Manual Partition Management
- You Don't Need a Data Warehouse – You Need AI
- AI Database Index – Complete Article Directory
- Live AI Knowledge Graph Engine Search
- Database Management Using AI – Future of Databases
- Database Management Using AI – Practice Tests
- Home – Latest2All
- Database Management Using AI – Original Edition
- AI Database Management – Advanced Patterns
- Database Management Using AI – Deep Dive
- AI Database – Practical Implementations
- Database AI – Real-World Case Studies
- AI Database – Enterprise Deployment Guide
- Database AI – Performance Optimisation
- AI Database Management – Security Patterns
- Database AI – Complete Reference
- AI Database – Migration Strategies
- Database AI – N1 Query Patterns
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