Dexmaker is a set of libraries that enable runtime bytecode generation and mocking on Android, primarily to support Mockito test frameworks. It provides two mocking modes: a proxy-based approach (dexmaker-mockito) that generates subclasses via the Dalvik bytecode generation API, and an inline approach (dexmaker-mockito-inline) that uses a JVMTI agent to transform class bytecode at runtime, allowing mocking of final and static methods. The core module (dexmaker) offers a low-level, fluent API for constructing DEX files programmatically, including types, methods, fields, and bytecode instructions. This is complemented by a C++ library (slicer) that reads, writes, and instruments DEX files, used by the inline mock maker to inject hooks into methods. The project is deeply integrated with Android internals, using hidden APIs and native agents, and is actively maintained to keep up with Android platform changes. It serves as the primary mocking solution for Android instrumentation tests where Java bytecode-based mocks (like ByteBuddy) are not available.

shiv is a command-line utility for building fully self-contained Python zipapps (PEP 441) that include all dependencies. It uses pip to install packages into a temporary directory, then packages the site-packages directory along with a bootstrap script into a single executable .pyz file. At runtime, the bootstrap extracts the dependencies to a cache directory (default ~/.shiv) and adds them to sys.path before executing the specified entry point or dropping into an interactive interpreter. The architecture is a CLI tool built with Click, orchestrated by a main module that delegates to pip and a custom zip archive builder. Key components include a robust environment configuration system that supports build-time settings and runtime overrides via environment variables, a cross-platform file lock for safe extraction, and a clean bootstrap pipeline. The codebase is well-structured, with comprehensive tests and support for Python 3.8+. It is maintained by LinkedIn and is actively used for distributing Python applications.

Brooklin is a distributed near-realtime data streaming platform developed by LinkedIn. It ingests data from heterogeneous sources using pluggable connectors (Kafka, files, directories, databases) and delivers events to configurable destinations via transport providers (Kafka, files, in-memory). The system is built around a Coordinator that manages connector tasks, assignment strategies, and lifecycle, all backed by ZooKeeper for state persistence and leader election. A REST.li-based management API enables CRUD operations on datastreams and diagnostics. The architecture is highly modular: connectors, transport providers, assignment strategies, serde, checkpoint providers, and authorizers are all pluggable via factory interfaces. Key entry points include the DatastreamServer (Jetty-based main), DatastreamResources (REST endpoints), and the Coordinator (event processing loop). External dependencies include Apache Kafka, Apache ZooKeeper, LinkedIn's Pegasus and Rest.li frameworks, Jetty, Netty, and ParSeq for asynchronous operations. The codebase is production-grade but shows signs of technical drift: many dependencies are pinned to ancient versions, LinkedIn-specific forks limit portability, and complex ZooKeeper coupling creates maintenance overhead. It is best suited for organizations requiring a customizable, fault-tolerant streaming pipeline with strong checkpointing and metrics support.