$ show square/leakcanary
LeakCanary is a sophisticated memory leak detection library for Android and Java, developed by Square. In production, it integrates into Android applications to automatically monitor object allocations, trigger heap dumps upon detecting suspicious memory growth, and analyze these dumps to identify leaking objects. It provides detailed leak traces showing the reference path from GC roots to the leaking object, helping developers pinpoint the root cause. The project also includes Shark Dive, a powerful desktop application for interactive heap dump exploration using treemap, radial, and stack visualizations, and a CLI tool for batch analysis. The architecture is a multi-module monorepo with a clean separation of concerns. The core analysis engine (shark) is platform-independent, handling HPROF parsing, heap graph construction, dominator tree computation (Lengauer-Tarjan), and shortest path finding. Android-specific modules (leakcanary-android-*) provide lifecycle integration, UI, and notification handling. The desktop app (shark-dive-app) is built with Kotlin Compose UI and features an agent protocol for AI-assisted analysis. The codebase is primarily Kotlin, leveraging Okio for I/O and Gradle for building. The repository exhibits exceptionally high craftsmanship. It implements complex graph algorithms (dominator trees, multi-strength BFS reachability, squarified treemap layout) with clear, well-tested code. It solves hard engineering problems like sorting data larger than 2GB (PagedByteArrayTimSort) and building memory-efficient indexes. While actively maintained, the codebase shows some technical drift with deprecated APIs and TODOs, but the core algorithms and architecture are timeless and robust. It is a prime example of a well-engineered, domain-specific analysis tool.
The LLM identified 5 files worth highlighting for reuse:
Provides a transparent paged storage mechanism for byte arrays exceeding the JVM's single array size limit (~2GB). It abstracts over multiple byte[] pages, enabling the analysis of heap dumps larger than 2GB without running out of memory. This is a fundamental, reusable building block for any Java application dealing with large binary data, and is completely self-contained.
A clean, generic implementation of the squarified treemap layout algorithm. It uses Double arithmetic to avoid overflow with large retained sizes. This component is highly reusable for any visualization project requiring a treemap view, demonstrating a non-obvious algorithm implemented with precision and no internal project dependencies.
Implements a radial (sunburst) layout algorithm for visualizing tree structures. It is generic over node types and handles adaptive depth, cell budgets, and hit testing. This component is self-contained and can be extracted into any project needing a sunburst chart layout.
Strips the content of primitive arrays from HPROF heap dump files for privacy purposes, while perfectly preserving the object graph structure and metadata. This solves a specific and difficult data privacy problem in a complex binary format. The pattern of 'strip content, preserve structure' is applicable to many other binary data formats.
An adaptation of the TimSort algorithm for sorting paged byte arrays, enabling efficient sorting of data that exceeds the JVM's single array limit. This demonstrates deep expertise in both sorting algorithms and memory-efficient data structures. While it depends on PagedByteArray, the algorithm implementation itself is a masterpiece of engineering for its niche.