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.
This is the TigerGraph ecosystem repository, a large collection of tools, connectors, demos, benchmarks, and tutorials centered around the TigerGraph graph database platform. It is not a single application but an aggregation of community and in-house developed components. The dominant character is application: it includes production-grade ETL connectors (Spark DataSource V2, JDBC driver, Kafka Connect, Hadoop MapReduce), a multi-version GSQL CLI client, a Go-based REST API for remote GSQL management, a Kubernetes operator, and numerous demos showcasing graph analytics (PageRank, community detection, geospatial search, fraud detection, GraphRAG). There are also benchmarking suites (LDBC SNB) and sample data. The codebase exhibits high craftsmanship in the connector and client libraries, with robust retry logic, token management, streaming JSON parsing, and schema inference. However, it suffers from massive code duplication across GSQL client versions, incomplete JDBC driver implementations, hardcoded paths and credentials in demos, and outdated vendored libraries (Leaflet 0.7.7, moment-timezone 2016). Overall, it is a functional but uneven collection; the core connectors are reusable and well-engineered, while many demo scripts are fragile and require manual configuration.