MockServer is a highly sophisticated, multi-protocol HTTP(S) mock server and proxy for testing, written in Java with Netty. It supports HTTP/1.1, HTTP/2, HTTP/3 (QUIC), gRPC, WebSockets, DNS, TCP, and binary protocols on a single port. The core engine provides expectation-based request matching, response generation, forwarding, proxying, recording, verification, and chaos engineering (latency, connection drops, error injection). It also includes built-in LLM mocking (OpenAI, Anthropic, Gemini, etc.), OIDC/OAuth2/SAML identity providers, SCIM provisioning, WASM custom rules, and drift detection. The repository is a monorepo containing the Java server, multi-language client libraries (Java, Node, Python, Ruby, Go, .NET, Rust, PHP), a React dashboard UI, IDE plugins (JetBrains, VS Code), Helm charts, Docker images, and extensive CI/CD pipelines via Buildkite. The architecture is modular with clear separation of concerns: core model/matcher/serialization in `mockserver-core`, Netty transport in `mockserver-netty`, language-specific clients in `mockserver-client-*`, and a React UI in `mockserver-ui`. The codebase demonstrates strong engineering practices including DTO patterns with Jackson serializers, a matcher hierarchy with compiled-once validators, bounded caches, constant-time equality checks, and extensive test coverage with integration tests across multiple environments.
Apache Kudu is a distributed columnar storage system designed for fast analytics on fast data. It provides a horizontally scalable, fault-tolerant storage layer that integrates tightly with the Hadoop ecosystem (Spark, Impala, Hive, Flink). The architecture consists of master servers managing metadata and tablet servers storing data in columnar files (CFile) with Raft-based replication for consistency. The codebase includes production-grade C++ core, multi-language client libraries (Java, C++, Python, Spark), a comprehensive RPC framework, security features (Kerberos, TLS, Ranger), and extensive testing infrastructure. While primarily an application, it also contains benchmarking and experimental code (TPC-H, Jepsen, performance experiments) that give it a minor hybrid character. The repository is actively maintained with a large community and demonstrates strong engineering practices, though some vendored code and platform-specific workarounds indicate moderate technical drift.
This repository provides the official documentation, OpenAPI specification, and client libraries for the Congress.gov API (v3), a public REST API that exposes U.S. federal legislative data including bills, amendments, members, committees, hearings, nominations, treaties, and Congressional Record content. The repo is primarily a supporting asset for the API, containing detailed endpoint documentation in Markdown, a machine-readable OpenAPI spec (openapi.yaml/json), and example client implementations in Java and Python. The Python client (cdg_client.py) demonstrates a reusable pattern with dynamic HTTP method dispatch, session management, and optional keyring-based API key storage. The Java client (CDGClient.java) uses java.net.http.HttpClient and a config file reader. The repository does not contain the actual API server implementation; it is a companion repository for developers integrating with the Congress.gov API. The codebase is well-structured, actively maintained (as evidenced by the changelog), and the documentation is comprehensive, though some minor typos exist. The duplicate client directories (java/ and api_client/java/, python/ and api_client/python/) suggest some organizational drift, but the core components are solid.
This repository is the official Java client for Nebula Graph, a distributed graph database. It provides connectivity to Nebula Graph's graph, meta, and storage services through Thrift-based RPC. The client supports connection pooling, session management, SSL/TLS encryption, and HTTP/2 transport. Key features include load balancing across database nodes, automatic reconnection, and type-safe wrappers for Nebula's data types (values, vertices, edges, paths, geospatial). The architecture separates concerns into graph client (query execution), meta client (schema/cluster management), and storage client (direct data access). Examples demonstrate usage patterns for session pooling, SSL configuration, and storage scanning.