Heaptrack

Overview

Heaptrack is a heap memory profiler for Linux that traces all memory allocations and annotates these events with stack traces. Developed by the KDE project, it aims to be faster than Valgrind's massif while providing more comprehensive data for memory analysis.

Key Characteristics:

Heap memory profiler for Linux
50-100% overhead (1.5-2x slowdown)
Tracks allocations, peaks, and leaks
GUI for analysis and visualization
Multi-threaded application support without serialization

Performance Characteristics

Overhead: 50-100% performance impact (approximately 2x slowdown)
Accuracy: High - tracks individual allocation/deallocation events
False Positives: Low - direct tracking reduces false leak detection
Production Ready: No - overhead too high for production use
Platform: Linux only
Memory Usage: Upfront extra 180MB of RAM for the profiler process

Performance Comparison

vs Valgrind: ~12x faster execution, no thread serialization
vs Massif: Significantly lower overhead in both time and memory
CPU Impact: Only affects memory allocation calls, not CPU-intensive calculations

How It Works

Heaptrack uses several mechanisms to track memory allocations:

LD_PRELOAD Interception: Uses LD_PRELOAD to intercept calls to core memory allocation functions (malloc, free, etc.)
Backtrace Collection: Obtains and logs stack traces for each allocation event
Event Logging: Records each individual malloc/free call with function arguments
Compressed Data Storage: Minimizes data files by avoiding repeated backtrace information
No Aggregation: Unlike Massif, doesn't aggregate data until analysis phase

The profiler logs allocation events to compressed files (typically /tmp/heaptrack.APP.PID.gz) for later analysis.

System-Agent Implementation Plan

Recommended Usage Scope

Development/staging use only - Never deploy in production
Short debugging sessions - Limited time windows due to overhead
Not for continuous monitoring - Use sampling-based tools instead
Emergency debugging tool - When other methods fail to identify leaks

Integration Strategy

On-demand activation: Trigger profiling for specific troubleshooting sessions
Container isolation: Run in separate debugging containers
Resource monitoring: Ensure adequate RAM (extra 180MB minimum)
Time-bounded sessions: Limit profiling duration to minimize impact

Features

Core Capabilities

Memory Consumption Tracking: Monitor heap usage over time
Leak Detection: Identify memory that's allocated but never freed
Allocation Hotspots: Find code locations with frequent allocations
Flame Graphs: Visual representation of call stacks leading to allocations
Peak Memory Analysis: Identify maximum memory usage points
Temporary Allocation Detection: Find allocations immediately followed by deallocations

Advanced Features

Process Attachment: Can attach to already running processes
Multi-threaded Support: No thread serialization unlike Valgrind
Real-time Monitoring: Track heap while program is running
Timeline Analysis: View memory usage patterns over time
Export Capabilities: Convert to Massif format or flamegraph data

Installation & Setup

Package Installation

Ubuntu/Debian:

sudo apt install heaptrack heaptrack-gui

Fedora/RHEL:

sudo dnf install heaptrack heaptrack-gui

Building from Source

# Clone repository
git clone https://github.com/KDE/heaptrack.git
cd heaptrack

# Create build directory
mkdir build && cd build

# Configure with CMake
cmake -DCMAKE_BUILD_TYPE=Release ..

# Build
make -j$(nproc)

# Install
sudo make install

GUI Setup

Dependencies:

Qt 5
KDE Frameworks 5 (KF5)
CMake
libunwind
zlib

For minimal installation (embedded/older systems): Build only the data collector on the target system, then analyze data on a machine with GUI dependencies.

Dependencies

Runtime Dependencies:

libunwind (for backtraces)
zlib (for compression)
gdb (for runtime process attachment)

Build Dependencies:

CMake
Qt5 development packages
KF5 development packages
C++ compiler with C++11 support

Usage

Basic Usage

Profile from application start:

heaptrack <your_application> [application_args]

Attach to running process:

heaptrack --pid <process_id>

Profile with custom output location:

heaptrack --output /path/to/output.gz <application>

Analyzing Results

GUI Analysis (Recommended):

heaptrack_gui /tmp/heaptrack.APP.PID.gz

Command-line Analysis:

heaptrack_print /tmp/heaptrack.APP.PID.gz

Auto-analysis after profiling:

heaptrack --analyze /tmp/heaptrack.APP.PID.gz

Report Generation

Generate ASCII report:

heaptrack_print --print-ascii /path/to/data.gz

Convert to Massif format:

heaptrack_print --print-massif /path/to/data.gz > massif.out

Generate flamegraph data:

heaptrack_print --print-flamegraph /path/to/data.gz > flamegraph.txt

Code Examples

Basic Profiling Script

#!/bin/bash
# profile-app.sh - Basic heaptrack profiling script

APP_NAME="$1"
OUTPUT_DIR="/tmp/heaptrack-results"

if [ -z "$APP_NAME" ]; then
    echo "Usage: $0 <application_path> [args...]"
    exit 1
fi

# Create output directory
mkdir -p "$OUTPUT_DIR"

# Run heaptrack
echo "Starting heaptrack profiling..."
heaptrack --output "$OUTPUT_DIR/$(basename $APP_NAME).$(date +%Y%m%d_%H%M%S).gz" "$@"

echo "Profiling complete. Results in: $OUTPUT_DIR"
echo "Analyze with: heaptrack_gui $OUTPUT_DIR/*.gz"

Automated Analysis Script

#!/bin/bash
# analyze-heap.sh - Automated heaptrack analysis

DATA_FILE="$1"
REPORT_DIR="$(dirname "$DATA_FILE")/reports"

if [ ! -f "$DATA_FILE" ]; then
    echo "Data file not found: $DATA_FILE"
    exit 1
fi

mkdir -p "$REPORT_DIR"

echo "Generating ASCII report..."
heaptrack_print --print-ascii "$DATA_FILE" > "$REPORT_DIR/summary.txt"

echo "Generating flamegraph data..."
heaptrack_print --print-flamegraph "$DATA_FILE" > "$REPORT_DIR/flamegraph.txt"

echo "Converting to Massif format..."
heaptrack_print --print-massif "$DATA_FILE" > "$REPORT_DIR/massif.out"

echo "Analysis complete. Reports in: $REPORT_DIR"

Integration with Testing

#!/bin/bash
# test-with-profiling.sh - Run tests with memory profiling

TEST_BINARY="./my_test_suite"
OUTPUT_BASE="/tmp/test-profiling"
TIMESTAMP=$(date +%Y%m%d_%H%M%S)

# Profile test execution
heaptrack --output "$OUTPUT_BASE/test_run_$TIMESTAMP.gz" "$TEST_BINARY"

# Generate summary report
heaptrack_print --print-ascii "$OUTPUT_BASE/test_run_$TIMESTAMP.gz" | head -50

# Check for memory leaks
LEAKS=$(heaptrack_print "$OUTPUT_BASE/test_run_$TIMESTAMP.gz" | grep -c "leaked")
if [ "$LEAKS" -gt 0 ]; then
    echo "WARNING: Memory leaks detected!"
    exit 1
fi

echo "Memory profiling completed successfully"

Report Parsing Script

#!/usr/bin/env python3
# parse_heaptrack.py - Parse heaptrack output for CI integration

import re
import sys
import subprocess

def parse_heaptrack_output(data_file):
    """Parse heaptrack data and extract key metrics"""
    
    # Run heaptrack_print to get text output
    result = subprocess.run(['heaptrack_print', data_file], 
                          capture_output=True, text=True)
    
    if result.returncode != 0:
        print(f"Error running heaptrack_print: {result.stderr}")
        return None
    
    output = result.stdout
    metrics = {}
    
    # Extract peak memory usage
    peak_match = re.search(r'peak heap memory consumption:\s+(\d+(?:\.\d+)?)\s*([KMGT]?B)', output)
    if peak_match:
        value, unit = peak_match.groups()
        metrics['peak_memory'] = f"{value}{unit}"
    
    # Extract total allocations
    alloc_match = re.search(r'total heap allocations:\s+(\d+)', output)
    if alloc_match:
        metrics['total_allocations'] = int(alloc_match.group(1))
    
    # Extract leaked memory
    leak_match = re.search(r'leaked heap memory:\s+(\d+(?:\.\d+)?)\s*([KMGT]?B)', output)
    if leak_match:
        value, unit = leak_match.groups()
        metrics['leaked_memory'] = f"{value}{unit}"
    
    return metrics

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python3 parse_heaptrack.py <heaptrack_data_file>")
        sys.exit(1)
    
    metrics = parse_heaptrack_output(sys.argv[1])
    if metrics:
        print("Heaptrack Analysis Results:")
        for key, value in metrics.items():
            print(f"  {key}: {value}")
    else:
        print("Failed to parse heaptrack data")
        sys.exit(1)

GUI Features

Main Interface Components

Summary View:

Peak heap memory consumption
Total number of allocations
Memory leaked at program termination
Temporary allocations statistics

Charts and Visualizations:

Memory Timeline: Heap consumption over time
Allocation Timeline: Number of allocations over time
Temporary Allocations: Short-lived allocation patterns
Flame Graph: Interactive call stack visualization

Tree Views:

Bottom-up: Shows functions that allocate the most memory
Top-down: Shows call paths leading to allocations
Caller/Callee: Aggregated caller and callee relationships

Navigation Features

Double-click Integration:

Double-click locations in GUI to open source code in editor
Requires running GUI from project root for relative path resolution

Filtering and Search:

Filter by allocation size thresholds
Search for specific function names
Focus on temporary vs persistent allocations

Data Export:

Export filtered data to various formats
Generate reports for specific time ranges
Save analysis sessions

Interactive Analysis

Drill-down Capabilities:

Click on flame graph sections to focus on specific call paths
Expand/collapse tree view nodes
Filter by allocation frequency or size

Timeline Scrubbing:

Navigate through program execution timeline
Correlate memory usage with program phases
Identify memory usage patterns

Comparison with Alternatives

vs Valgrind Massif

Heaptrack Advantages:

12x faster execution time
No thread serialization (better for multi-threaded apps)
More detailed data (individual allocations vs aggregated)
Better temporary allocation detection
Real-time monitoring capability

Massif Advantages:

Part of mature Valgrind suite
Can profile stack memory (heaptrack cannot)
More widely supported and documented
Integration with other Valgrind tools

Performance Comparison:

Heaptrack: ~50-100% overhead
Massif: 10-50x slower, serializes threads

vs ByteHound

Similarities:

Both designed for lower overhead than Valgrind
Focus on heap profiling
Provide detailed allocation tracking

Differences:

ByteHound: ~33% less overhead than heaptrack
Heaptrack: More mature, better GUI
ByteHound: Better Rust integration
Heaptrack: Better KDE/Qt integration

vs jemalloc Profiling

Heaptrack vs jemalloc:

Scope: Heaptrack works with any allocator, jemalloc only with jemalloc
Overhead: Similar performance impact
Detail: Heaptrack provides more detailed stack traces
Integration: jemalloc profiling requires linking/configuration changes

When to Use Each:

Heaptrack: When you need detailed analysis with minimal setup
jemalloc: When already using jemalloc and want integrated profiling
Complete vs Statistical: Heaptrack tracks all allocations vs jemalloc's sampling

Output Analysis

Understanding Reports

Key Metrics to Monitor:

peak heap memory consumption: 128.3MB
total heap allocations: 1,234,567
total heap deallocations: 1,234,500
leaked heap memory: 67KB (remaining allocations: 67)

Interpreting Results:

Peak Memory: Maximum heap usage during execution
Allocation Count: Total number of malloc calls
Leaked Memory: Allocations without corresponding frees
Temporary Allocations: Allocations immediately followed by frees

Identifying Leaks

True Memory Leaks:

Allocations with no corresponding deallocation
Growing memory usage over time
Memory not reachable at program termination

False Positives:

Static allocations that persist until program end
Allocations managed by cleanup handlers
Memory pools that defer deallocation

Analysis Strategy:

Focus on largest leaked allocations first
Check for patterns in allocation call stacks
Correlate with application lifecycle events
Verify with longer program runs

Finding Hotspots

Allocation Hotspots:

Functions with highest allocation frequency
Call paths leading to large allocations
Code locations with repeated temporary allocations

Optimization Opportunities:

Reduce allocation frequency in hot paths
Pool allocations for frequently used objects
Eliminate unnecessary temporary allocations
Use stack allocation where possible

Performance Bottlenecks:

Functions appearing frequently in flame graphs
Deep call stacks leading to allocations
Allocation patterns that fragment memory

Optimization Opportunities

Memory Usage Reduction:

Identify largest memory consumers
Find opportunities for object reuse
Eliminate duplicate allocations
Optimize data structure sizes

Performance Improvements:

Reduce allocation frequency in critical paths
Batch allocations where possible
Use memory pools for fixed-size objects
Eliminate temporary allocations in loops

Real-world Example: After fixing code identified by heaptrack, peak memory use dropped from 400MB to <100MB, leveling out at about 60MB instead of 380MB.

Use Cases

Development Profiling

Local Development:

Profile applications during development cycles
Identify memory inefficiencies before code review
Validate memory optimization changes
Debug memory-related crashes

Integration Testing:

Profile complete application workflows
Test memory usage under realistic loads
Verify memory cleanup in long-running tests
Catch regressions in memory usage

Pre-production Testing

Staging Environment Profiling:

Validate memory usage with production-like data
Test memory behavior under load
Identify scaling bottlenecks
Verify fixes before production deployment

Performance Testing:

Baseline memory usage for performance tests
Compare memory usage across application versions
Identify memory usage patterns under different loads
Validate memory limits and thresholds

Performance Optimization

Memory Footprint Reduction:

Identify largest memory consumers in applications
Find opportunities to reduce allocation frequency
Optimize data structures for memory efficiency
Eliminate memory waste in critical code paths

Application Tuning:

Profile memory usage in different application modes
Optimize memory usage for specific use cases
Balance memory usage vs performance trade-offs
Validate memory optimizations

Leak Hunting

Debugging Memory Leaks:

Identify sources of memory leaks in development
Track down hard-to-find leaks with detailed stack traces
Validate leak fixes with before/after profiling
Analyze leak patterns to prevent future issues

Regression Detection:

Catch new memory leaks in continuous integration
Compare memory usage across code versions
Identify changes that introduce memory problems
Maintain memory usage baselines

Limitations

Platform and Environment

Linux-only Support:

Cannot profile applications on Windows or macOS
Limited to GNU/Linux distributions
Requires specific system libraries (libunwind, etc.)

Heap Memory Focus:

Cannot profile stack memory consumption (use Massif for stack profiling)
Limited insight into kernel memory usage
No analysis of memory-mapped files
Cannot track memory usage of child processes automatically

Performance and Production Use

High Overhead:

50-100% performance impact prevents production use
Significant memory overhead (180MB+ for profiler process)
May cause container OOM kills if insufficient memory
Can timeout applications with strict runtime limits

Application Restart Required:

Cannot profile already running applications without attachment
Requires LD_PRELOAD or explicit attachment
May interfere with applications that manipulate LD_PRELOAD
Cannot profile applications with specific runtime requirements

Language and Runtime Support

Limited Language Support:

Primarily designed for C/C++ applications
Limited Swift support (may not capture Swift-specific memory management)
Cannot profile garbage-collected languages effectively
May miss language-specific allocation patterns