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SamSaffron avatar

SamSaffron/memory_profiler

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1,765 stars·90 forks·Ruby·MIT·19 views

Memory Profiler

Memory Profiler is a diagnostic library for Ruby applications designed to monitor runtime memory consumption and object lifecycles. It provides tools to track object allocations and memory usage, enabling the identification of performance bottlenecks and potential memory leaks that affect software stability.

The tool functions by observing memory behavior during program execution, allowing developers to distinguish between short-lived data and objects that persist beyond their intended lifecycle. It captures the execution context of allocations by walking the call stack, which helps attribute memory usage to specific code paths. Additionally, the library scans the heap to identify redundant string instances, providing insights into opportunities for reducing memory footprints through object reuse.

Beyond basic tracking, the library generates detailed reports that summarize allocated and retained objects, categorized by file and class. These reports assist in analyzing memory growth patterns and optimizing resource usage within Ruby applications. The project is distributed as a library that integrates into the development environment to facilitate ongoing memory analysis.

Features

  • Memory Profilers - Provides a diagnostic tool for tracking object allocations and memory usage to identify leaks and performance bottlenecks.
  • Memory Allocation Tracers - Tracks object creation and memory usage during program execution to pinpoint performance bottlenecks and memory leaks.
  • Ruby Memory Management - Analyzes and reduces the memory footprint of applications by identifying redundant objects and excessive allocations during runtime.
  • Memory Leak Detection - Identifies long-term object retention and memory growth patterns to ensure application stability under sustained workloads.
  • Performance Profilers - Tracks object creation and memory usage in real-time to pinpoint bottlenecks and leaks that degrade software stability.
  • Runtime Call-Stack Capturers - Captures the execution context of each allocation by walking the call stack to identify the specific code path responsible.
  • Memory Allocation Management - Creates detailed summaries of allocated and retained objects by tracking file-level attribution and class-specific data.
  • String Deduplication - Scans the heap for identical string content to identify redundant memory usage caused by multiple instances of the same value.
  • Heap String Duplication - Identifies redundant string objects in memory to reveal opportunities for reducing overall memory footprint.
  • Allocation Tracers - Hooks into the language runtime to intercept every object creation event and record metadata about the allocation source.
  • Memory Usage Analysis - Generates detailed reports on object retention and string duplication to optimize application memory footprint.
  • Heap Snapshot Comparison - Compares heap snapshots at different execution points to isolate objects that persist beyond their intended lifecycle.
  • Runtime Language Instrumentation - Injects monitoring logic into the language virtual machine to observe memory behavior without modifying application source code.
  • Runtime Memory Monitors - Observes memory consumption patterns and object lifecycles during the execution of code.

Star history

Star history chart for samsaffron/memory_profilerStar history chart for samsaffron/memory_profiler

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does samsaffron/memory_profiler do?

Memory Profiler is a diagnostic library for Ruby applications designed to monitor runtime memory consumption and object lifecycles. It provides tools to track object allocations and memory usage, enabling the identification of performance bottlenecks and potential memory leaks that affect software stability.

What are the main features of samsaffron/memory_profiler?

The main features of samsaffron/memory_profiler are: Memory Profilers, Memory Allocation Tracers, Ruby Memory Management, Memory Leak Detection, Performance Profilers, Runtime Call-Stack Capturers, Memory Allocation Management, String Deduplication.

Which projects share features with samsaffron/memory_profiler?

Projects with overlapping indexed features include: bloomberg/memray — Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++… facebook/memlab — Memlab is an automated browser memory profiler and JavaScript memory leak analyzer. It provides a toolkit for… tencent/matrix — Matrix is a suite of mobile application performance management and analysis tools. It provides a plugin-based… microsoft/perfview — PerfView is a set of integrated profiling and tracing tools built on Windows Event Tracing for Windows (ETW), designed… gperftools/gperftools — gperftools is a collection of specialized tools for profiling CPU usage, detecting memory errors, and providing… kde/heaptrack — Heaptrack is a heap memory profiler and diagnostic tool for applications running on Linux. It functions as a memory…

Projects sharing features with Memory Profiler

These projects share indexed features with Memory Profiler. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • bloomberg/memraybloomberg avatar

    bloomberg/memray

    14,885View on GitHub↗

    Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

    Pythonhacktoberfestmemorymemory-leak
    View on GitHub↗14,885
  • facebook/memlabfacebook avatar

    facebook/memlab

    4,981View on GitHub↗

    Memlab is an automated browser memory profiler and JavaScript memory leak analyzer. It provides a toolkit for detecting and analyzing memory leaks by inspecting and comparing heap snapshots to identify unbound object growth and detached DOM elements. The system distinguishes itself through an automated leak testing framework that executes end-to-end browser interaction sequences to programmatically isolate memory regressions. It utilizes heap snapshot diffing, retainer chain tracing, and heuristic-based filtering to determine why objects remain in memory and to map the shortest path from garb

    TypeScriptdetectore2efacebook
    View on GitHub↗4,981
  • tencent/matrixTencent avatar

    Tencent/matrix

    12,020View on GitHub↗

    Matrix is a suite of mobile application performance management and analysis tools. It provides a plugin-based monitoring system for capturing crashes, lags, and memory leaks, alongside a static binary auditor for reducing installation package size and a bytecode instrumentation tool for performance tracking. The project distinguishes itself through native memory debugging and a SQLite query linter that identifies inefficient database patterns. It employs native interception techniques to detect memory leaks and heap corruption without requiring source code recompilation, and uses a custom run

    Javaandroidapm-clientwechat
    View on GitHub↗12,020
  • microsoft/perfviewmicrosoft avatar

    microsoft/perfview

    4,633View on GitHub↗

    PerfView is a set of integrated profiling and tracing tools built on Windows Event Tracing for Windows (ETW), designed to diagnose CPU, memory, and ETW-based performance issues in .NET applications. It captures ETW events to analyze runtime behavior, CPU usage, and memory allocations, serving as a .NET performance profiler that measures both CPU time and garbage collection events. The tool distinguishes itself with a diff comparison engine that compares two performance traces side-by-side to highlight changes in method costs and event rates. It renders profiled call stacks as flame graphs whe

    C#dotnetdotnet-coreperformance
    View on GitHub↗4,633
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