A lightweight and efficient stream processing library for Go, providing a declarative DSL to build data pipelines.
go-streams is a lightweight stream processing library for Go that provides a declarative domain-specific language (DSL) for building data pipelines. It solves the problem of complex data transformation and integration by offering composable sources, flows, and sinks, enabling efficient real-time and batch processing without external dependencies in its core.
Go developers building data-intensive applications, ETL pipelines, real-time stream processing systems, or integrations with message brokers and cloud storage services.
Developers choose go-streams for its simplicity, zero-dependency core, rich set of built-in flow operators, and extensible connectors to popular systems like Kafka and Redis, making it a versatile and efficient alternative to heavier stream processing frameworks.
A lightweight stream processing library for Go
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The framework offers a clear, composable syntax for defining pipelines with sources, flows, and sinks, as highlighted in the README's overview, making complex transformations readable and maintainable.
Includes a wide range of flow operations like Map, Filter, SlidingWindow, and AdaptiveThrottler, enabling sophisticated data manipulation without external dependencies in the core module.
Supports integrations with Kafka, Redis, and cloud storage through separate modules, allowing real-world use cases without bloating the lightweight core, as detailed in the connectors section.
The AdaptiveThrottler adjusts processing rates based on CPU and memory usage, providing efficient resource management for dynamic workloads, a feature emphasized in the key features.
Missing built-in mechanisms for state recovery and error handling in distributed environments, which are essential for production-grade stream processing and not addressed in the README.
Each external connector is a separate module with its own dependencies, requiring additional setup and potential version management overhead, as noted in the connectors list.
Developers must understand stream processing concepts like windowing and keyed operations, which might be challenging for those new to the domain, despite the examples provided.