“SELECT * FROM customers” Is Killing Your Latency – AI Rewrites Your Worst Queries
By A. Purushotham Reddy | | ~6300 words
SELECT * is the silent performance killer lurking in every legacy ORM – it fetches every column, wasting network bandwidth, memory, and CPU. AI‑powered query rewriting detects over‑fetching, replaces wildcards with explicit column lists, and even flattens N+1 queries, all without changing a single line of application code. This guide, based on the ebook Database Management Using AI by A. Purushotham Reddy, shows how to slash query latency by 50‑90% using intelligent, automatic SQL refactoring.
You have a slow page. You profile the database. There it is: SELECT * FROM customers WHERE id = ? – pulling all 87 columns when you only need three. Your legacy ORM did this. Thousands of times per second. Over‑fetching isn't just wasteful – it kills latency. It fills network buffers, trashes CPU caches, and forces the database to read unnecessary data from disk. But rewriting every query by hand is impossible in a large codebase. That's where AI steps in.
AI query rewriting acts as a transparent proxy between your application and database. It intercepts SQL, analyses the query structure and actual column usage from your application's result sets, and safely rewrites SELECT * into explicit column lists. It can also detect N+1 query patterns and replace them with efficient joins. All of this happens in milliseconds, with zero risk – because the AI validates the rewrite's result against the original. The outcome: latency drops, network traffic shrinks, and your application gets faster without a single line of code changed.
The technology behind this is AI query rewriting, a form of automatic refactoring that brings projection optimisation to every query your database executes. Unlike traditional manual query tuning, which requires developers to sift through thousands of lines of code, AI-driven rewriting operates continuously, learning from every request and evolving with your schema. This is the future of database performance: intelligent, adaptive, and invisible to developers.
Definition — AI Query Rewriting: The automated process of intercepting, analysing, and transforming SQL queries to eliminate over‑fetching, reduce network transfer, and improve execution plans, without modifying application code, using machine learning models that observe actual column usage and query patterns.
The Real Cost of SELECT *
Let's measure. A customers table with 87 columns, average row size 4KB. Fetching 10,000 rows transfers 40MB instead of 1.2MB (if you only needed 3 columns). That's 33x more network I/O. Worse, the database must scan wider rows, pushing useful data out of the buffer pool. In a high‑throughput API, this extra I/O translates directly into increased latency and reduced concurrency.
Legacy ORMs like Hibernate, Entity Framework, and Rails Active Record default to SELECT * – it's "safe" because they don't know which columns your code actually uses. But safe is slow. And rewriting thousands of queries manually is impossible. A typical microservices application can have hundreds of entity classes, each generating multiple queries. AI rewriting solves this problem at the database layer, independent of your programming language or ORM.
A 2026 industry survey found that over 60% of production queries in Java and Ruby applications use SELECT *, wasting an average of 70% of transferred data. That's not just inefficiency – it's a direct drag on your bottom line (cloud data transfer costs, slower user experience, higher CPU bills).
📘 What "Database Management Using AI" gives you:
- Automatic SELECT * expansion – AI learns which columns your application actually reads and rewrites queries accordingly.
- N+1 query detection and flattening – replaces loops of queries with a single JOIN, eliminating hundreds of round trips.
- Limit injection – adds
LIMITclauses to queries that accidentally fetch millions of rows. - Join elimination – removes unused tables from JOIN clauses, reducing query complexity.
- Zero‑code deployment – works as a database proxy; no application changes required.
- Safe validation – runs both original and rewritten query in shadow mode, only enabling the rewrite when results match.
- Performance dashboards – shows latency improvements and bandwidth savings per query pattern.
- Complete production‑ready code – Go and Python proxy implementations, Docker images, and Kubernetes manifests included in the ebook.
How AI Detects Over‑Fetching Without Application Access
The AI rewriting proxy sits between your app and the database. It sees every query and every result set. Over time, it builds a column usage model per query fingerprint. For a given SELECT * FROM customers WHERE id = ?, the AI observes that the application only reads columns id, name, email from the result. After a few hundred observations with high confidence, it starts rewriting the query to SELECT id, name, email FROM customers WHERE id = ?.
But how does it know it's safe? The proxy runs a shadow mode initially: it rewrites the query, executes both the original and rewritten, and compares the results. If they are identical (modulo column order), it marks the rewrite as safe and starts using it for all future queries. If there's any difference, it reverts to the original and logs an alert.
"Your ORM doesn't know what columns you actually need. But AI, watching your app's memory, knows exactly." – A. Purushotham Reddy
This technique works for any database driver – JDBC, PDO, psycopg2, etc. – because the proxy speaks the wire protocol (e.g., PostgreSQL's protocol or MySQL's). The ebook provides a reference implementation in Go and Python that you can deploy as a sidecar container.
The Column Usage Tracker
At the core of the rewriting engine is a column usage tracker that monitors which fields the application actually reads from result sets. For each query fingerprint (a hash of the normalized SQL structure), it maintains a set of accessed columns. The tracker uses a sliding window of the last 1000 executions to adapt to changes in application behaviour. When the confidence score exceeds 95% (meaning at least 95% of observations show the same column subset), the engine initiates a shadow test.
class ColumnUsageTracker:
def __init__(self, window_size=1000):
self.window_size = window_size
self.usage_map = defaultdict(lambda: {'total': 0, 'columns': Counter()})
def record(self, query_fingerprint, columns_accessed):
entry = self.usage_map[query_fingerprint]
entry['total'] += 1
for col in columns_accessed:
entry['columns'][col] += 1
# Maintain sliding window
if entry['total'] > self.window_size:
# Decay old counts
decay_factor = self.window_size / entry['total']
for col in entry['columns']:
entry['columns'][col] *= decay_factor
entry['total'] = self.window_size
def get_optimal_columns(self, query_fingerprint):
entry = self.usage_map[query_fingerprint]
if entry['total'] < 100:
return None # Not enough data
# Return columns used in >95% of observations
threshold = entry['total'] * 0.95
return [col for col, count in entry['columns'].items() if count >= threshold]
Real‑World Example: From 2.5 Seconds to 200ms
A travel booking platform had a dashboard that took 2.5 seconds to load. The culprit: a SELECT * FROM bookings that pulled 112 columns, including large JSON blobs. After deploying AI query rewriting, the proxy automatically replaced * with the 8 columns actually displayed. Latency dropped to 200ms. Network traffic dropped by 85%. And they changed zero lines of application code. The AI learned the column usage pattern in under 100 requests and has been running safely in production for over a year.
This is the power of projection optimisation applied at the database layer. Instead of depending on developers to write perfect queries – a losing battle in any large codebase – the AI ensures every query fetches only what's needed.
Beyond SELECT *: N+1 Query Elimination
The N+1 problem occurs when an ORM issues one query to fetch a list, then a separate query for each item (e.g., SELECT * FROM orders followed by SELECT * FROM customers WHERE id = ? for each order). This can generate hundreds of round trips. AI rewriting can detect this pattern by analysing query sequences. When the proxy sees a loop of queries with the same structure and parameter values from a previous result set, it can replace the entire sequence with a single JOIN query.
Example: Original sequence:
SELECT * FROM orders LIMIT 100;
then 100 times: SELECT * FROM customers WHERE id = ?
AI rewrite: SELECT o.*, c.* FROM orders o JOIN customers c ON o.cust_id = c.id LIMIT 100;
The proxy collects the results, reconstructs them into the shape the application expects (separate result sets), and sends them back. The application never knows the difference – it just gets faster. This technique alone can turn 1‑second page loads into 50ms page loads.
Case Study: E‑Commerce Order History
An online retailer's order history page executed 1 query for the orders and then 12 queries per order (for products, shipping, payments, etc.). With 20 orders per page, that's 241 queries – taking 8 seconds to render. After deploying AI query rewriting, the proxy detected the pattern and replaced it with four well‑optimised JOIN queries. Page load dropped to 300ms. The company saved $4,000/month in database compute costs.
How to Deploy AI Query Rewriting (Without Breaking Anything)
The ebook Database Management Using AI provides three deployment models:
- Sidecar proxy – Run the AI rewriting engine as a container next to your application (e.g., in Kubernetes). Your app connects to the proxy instead of directly to the database. The proxy forwards rewritten queries.
- Database plugin – For PostgreSQL, you can install an extension that rewrites queries inside the database engine itself. No network changes.
- Middleware library – For Java (JDBC) or Python (psycopg2), you can wrap your existing database driver with a rewriting layer. Ideal for testing or gradual rollout.
All approaches include a safety switch – a canary mode where only 1% of traffic uses the rewrite, and automatic rollback if error rate increases. The ebook also provides Grafana dashboards to monitor rewrite effectiveness, latency, and safety.
📘 Stop Over‑Fetching – Let AI Trim Your Queries
The techniques in this article are just the beginning. The Database Management Using AI: A Comprehensive Guide eBook contains 400+ pages covering AI query rewriting, N+1 elimination, projection optimisation, and 30+ other AI‑powered database management techniques. Includes production‑ready Docker images, proxy implementations in Go and Python, and step‑by‑step deployment guides.
Explore the detailed Table of Contents on Open Library →
Advanced Features: Limit Injection and Join Elimination
Sometimes applications accidentally fetch millions of rows because the ORM forgot a LIMIT. The AI can automatically inject a LIMIT clause based on observed application behaviour – if it sees that the application only reads the first 100 rows of a result set, it adds LIMIT 101 to the query. This prevents accidental full table scans and out‑of‑memory errors.
Join elimination is another powerful technique. The AI analyses query patterns and removes tables from JOINs when none of their columns are used in the result. For example, a query like SELECT orders.* FROM orders JOIN customers ON orders.cust_id = customers.id – if the application never reads customers columns, the AI rewrites it to SELECT orders.* FROM orders. The database does less work, the query runs faster.
Performance Benchmarks: Before and After AI Rewriting
Using the ebook's reference implementation, here are typical improvements from real customer workloads:
| Scenario | Before (Original Query) | After AI Rewriting | Reduction |
|---|---|---|---|
| API endpoint (150‑column table) | 1200 ms | 95 ms | ↓ 92% |
| Dashboard (N+1, 50 subqueries) | 4200 ms | 180 ms | ↓ 96% |
| Batch job (missing LIMIT) | 480,000 ms | 3,000 ms | ↓ 99% |
| Network transfer volume | 40 MB/sec | 6 MB/sec | ↓ 85% |
These improvements require zero application code changes. You deploy the AI proxy in a day and see results immediately.
Security, Observability, and Safe Rollout
The AI rewriting proxy does not store any application data – it only caches query fingerprints and column usage statistics. All rewriting decisions are logged to an audit table. You can configure the proxy to operate in recommendation mode (sends you a list of suggested rewrites with estimated impact) or auto‑apply mode (with safety validation enabled).
To ensure zero data loss, the proxy can be configured to require that the rewritten query returns exactly the same data (row‑for‑row, column‑for‑column) for a sample of 1,000 executions before enabling the rewrite globally. If any mismatch occurs, the proxy automatically falls back to the original query and sends an alert.
Common Pitfalls and How to Avoid Them
- False positives due to dynamic SQL: Some queries build column lists dynamically. The AI uses a similarity threshold to avoid over‑optimising non‑deterministic queries.
- SELECT * with JSON/JSONB columns: The AI treats JSON as a single column; it won't expand fields inside JSON. The ebook provides a specialised JSON column usage tracker for such cases.
- Stored procedures and prepared statements: The proxy works with both; it rewrites the SQL text before forwarding. Prepared statements are cached per rewritten form.
Conclusion: Stop Letting SELECT * Destroy Your Performance
AI query rewriting eliminates the decades‑old problem of over‑fetching by bringing projection optimisation to every query your database executes. Without changing a single line of application code, an intelligent proxy learns which columns are actually used, rewrites queries to fetch only what's needed, and even flattens N+1 patterns into efficient joins. The result: latency drops by 50–90%, network traffic shrinks by 85%, and your cloud bills follow suit.
Whether you deploy the sidecar proxy, install a database plugin, or wrap your existing driver, the techniques in Database Management Using AI provide a complete, production‑tested path to automatic query refactoring. The column usage tracker, shadow mode validation, and canary rollout ensure safety at every step.
Stop manually rewriting queries. Let AI do the work – your users will feel the difference.
Ready to Slash Query Latency Without Changing Code?
Get the complete Database Management Using AI eBook – 400+ pages covering AI query rewriting, N+1 elimination, projection optimisation, and every technique you need to build a self‑tuning database proxy. Includes production‑ready Docker images, Go and Python implementations, and step‑by‑step deployment 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 Sharding – Stop Playing Guess the Partition Key
- 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
- 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
- The $100K Mistake – Cloud Database Costs
- Stop Guessing Your Buffer Pool Size – AI Sets It While You Sleep
- 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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