Aem performance tuning profiling

AEM Performance Tuning & Profiling

Purpose

Identify, diagnose, and resolve performance bottlenecks in AEM using profiling tools, thread analysis, heap inspection, and systematic optimization of request processing pipelines.

When to Use (Triggers)

• User mentions "performance," "slow," "profiling," "thread dump," or "optimization"
• References to response time issues, high CPU, memory leaks, or GC pressure
• Questions about JVM tuning, thread analysis, or heap dump investigation
• Requests involving load testing, performance baselines, or capacity planning
• Discussion of AEM performance best practices or optimization strategies

Core Capabilities

• Capture and analyze thread dumps for deadlocks and contention
• Profile heap usage and identify memory leaks via MAT/VisualVM
• Tune JVM parameters (heap, GC algorithm, thread pools) for AEM workloads
• Optimize request processing pipeline (Sling resolution, filter chain, rendering)
• Establish performance baselines and regression detection

Domain Knowledge Required

Technical Foundation

• JVM internals (heap regions, GC algorithms: G1GC, ZGC, Shenandoah)
• Thread dump analysis (WAITING, BLOCKED, RUNNABLE states, lock contention)
• Memory profiling (dominator trees, retained heap, class histogram)
• HTTP request lifecycle and server thread pool management

AEM-Specific Context

• Sling request processing pipeline (resource resolution, servlet selection, filter chain)
• AEM request logging and timing (request.log analysis, TIMER patterns)
• Oak session management and its performance implications
• Bundle class loading and its impact on startup and runtime performance
• Tar segment store vs. document store performance characteristics

Implementation Approach

Step 1: Problem Identification

Define the performance issue precisely.

• Identify affected user journeys and their current response times
• Determine if issue is latency, throughput, or resource exhaustion
• Establish baseline metrics for comparison
• Categorize: author-side, publish-side, or dispatcher-side issue

Step 2: Data Collection

Gather diagnostic data for analysis.

• Capture thread dumps (3+ dumps, 10 seconds apart) during issue occurrence
• Collect request.log entries for slow requests (>5s threshold)
• Generate heap dump if memory-related (jmap or jcmd)
• Gather GC logs for garbage collection analysis
• Collect Oak query statistics for repository performance

Step 3: Analysis

Interpret collected data to identify root cause.

• Analyze thread dumps for blocked threads and lock contention
• Use MAT (Memory Analyzer Tool) for heap dump investigation
• Review request timing breakdown (resource resolution, model execution, HTL render)
• Identify N+1 query patterns or excessive session operations
• Check for resource leaks (unclosed sessions, connections)

Step 4: Optimization

Implement targeted fixes based on analysis.

• Tune JVM parameters (heap size, GC settings, thread pool sizes)
• Optimize identified code paths (caching, lazy loading, async processing)
• Restructure queries or content for faster resolution
• Implement connection pooling or resource pooling where applicable
• Add caching layers for expensive operations

Step 5: Validation

Confirm optimization effectiveness.

• Run load tests comparing before/after metrics
• Verify no regressions in other areas
• Monitor production metrics post-deployment
• Document optimization decisions and their measured impact

Quality Checklist

• Performance issue quantified with specific metrics (response time, throughput)
• Root cause identified with evidence (thread dumps, profiles, logs)
• Fix addresses root cause, not just symptoms
• No memory leaks introduced (validated with heap analysis)
• Load test confirms improvement under realistic traffic
• JVM settings documented and appropriate for instance size
• Performance regression detection in place for future changes
• Documentation updated with optimization rationale

Related Skills

• aem-query-optimization (JCR/Oak query performance)
• aem-caching-strategy (caching as optimization technique)
• aem-monitoring-alerting (performance metric tracking)

Example Use Cases

1. Author UI Slowness: Diagnose Sites console taking 30+ seconds to load for a site with 10,000+ pages by analyzing thread dumps, identifying Oak query traversals, and implementing index optimization.
2. Publish Throughput Degradation: Investigate publish instance degradation under load using thread dump analysis to find session contention, heap analysis for cache inefficiency, and request log timing breakdown.
3. Memory Leak Investigation: Track down a gradual memory increase over 72 hours using periodic heap dumps, MAT dominator tree analysis, and class histogram comparison to identify the leaking object graph.

Notes

• Always collect multiple thread dumps — a single dump is a snapshot, not a trend
• AEM Cloud Service provides predefined JVM settings — focus optimization on application code
• G1GC is the recommended garbage collector for AEM 6.5+ with heap > 6GB
• Request.log TIMER entries provide built-in performance profiling without additional tooling