Profile Every Query Without Changing Code.
Captured at the kernel level
Automatic SQL capture and analysis via eBPF — see exactly which queries slow down your services, identify missing indexes, and detect N+1 patterns
See inside your database.
Without touching your code
From automatic SQL capture to query plan analysis — deep database visibility from eBPF
💾 Automatic SQL Capture
Every database query your services execute is captured automatically at the kernel level via eBPF. No agents to install, no connection string changes, no code modifications.
📊 Query Performance Profiling
See p50, p95, and p99 latency for every query. Identify which queries are responsible for the most total database time and where optimization will have the biggest impact.
⚡ N+1 Query Detection
Automatically identifies N+1 query patterns by correlating request paths with query executions. See exactly which API endpoints generate excessive database calls.
🔗 Connection Pool Monitoring
Track connection pool utilization across all services. Detect pool exhaustion before it causes timeouts, and right-size your connection limits.
🔍 Query Plan Analysis
Understand why queries are slow. See execution plans, identify missing indexes, and get specific recommendations for schema improvements.
📋 Historical Query Trends
Track query performance over time. Detect gradual degradation, correlate changes with deployments, and validate that optimizations actually worked.
Use ApexData for…
🔍 Optimizing ORM-Generated Queries
See the actual SQL your ORM generates and how it performs. Identify where eager loading, query batching, or raw SQL would improve performance.
⚡ Identifying N+1 Patterns
Automatically detect N+1 query patterns across your entire application. See which endpoints generate hundreds of queries per request.
🔗 Connection Pool Tuning
Right-size connection pools based on actual usage data. Stop guessing and start configuring pools based on real traffic patterns.
🚀 Migration Performance Validation
Compare query performance before and after schema migrations. Verify that new indexes actually improve the queries they target.
⏱ Debugging Timeout Errors
Trace timeout errors to the specific slow queries that cause them. See the full chain from HTTP request to database query.
📊 Capacity Planning for Databases
Understand query growth trends and predict when you will need to scale your database. Plan upgrades based on data, not guesswork.
One platform vs. a stack of tools
Replace the patchwork of monitoring, logging, tracing, and incident management tools with a single AI-powered platform
| Traditional Stack | ApexData | |
|---|---|---|
| Setup | Install agents on every service | Zero setup — eBPF captures queries automatically |
| Query capture | Requires code changes or proxy | Kernel-level capture with no code modifications |
| N+1 detection | Manual code review or profiling | Automatic detection across all endpoints |
| Connection pools | Manual monitoring scripts | Real-time tracking with right-sizing recommendations |
| Missing indexes | Run EXPLAIN manually on suspected queries | Auto-detected with CREATE INDEX suggestions |
| Historical data | Point-in-time snapshots at best | Continuous trends with deployment correlation |
| Coverage | Only instrumented services | Every service that talks to a database |
| Overhead | Agent CPU and memory on every pod | Near-zero overhead — runs in kernel space |
Why engineers choose ApexData
🧠 Kernel-Level Visibility
eBPF captures queries at the kernel level — below your application code. No agents, no proxies, no code changes. See every query from every service.
⚡ Automatic Analysis
Not just query logs. AI analyzes patterns, detects N+1 queries, identifies missing indexes, and recommends specific optimizations.
🔒 Zero Application Impact
No SDK, no agent, no sidecar. Query capture happens in kernel space with near-zero overhead on your application performance.
✅ Actionable Results
Every finding includes specific recommendations. Missing index? Get the CREATE INDEX statement. N+1 pattern? See the exact endpoint and ORM call.
