The pipeline pattern in Go is a powerful approach for processing data in a sequential and efficient manner. It involves breaking down a task into smaller, independent stages (or components) that are executed concurrently, with each stage responsible for a specific operation on the data.
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Data Processing: When you have a series of data processing steps that need to be executed sequentially, such as filtering, transforming, or aggregating data.
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Concurrency: When you want to leverage Go's concurrency features (goroutines and channels) to perform parallel processing of data while ensuring that the order of operations is maintained.
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Modularity: When you want to break down a complex data processing task into smaller, more manageable components, each responsible for a specific stage of processing.
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Scalability: When you need to scale your data processing pipeline to handle large volumes of data efficiently by distributing the workload across multiple stages and utilizing multiple CPU cores.
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Real-time Processing: When you need to process data in real-time or near real-time, such as processing streaming data from sensors, logs, or event streams.
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Testing and Maintenance: When you want to improve the testability and maintainability of your code by encapsulating each stage of processing into separate functions or modules.
- Install Go: If you haven't already, install Go from the official website.
- Clone the Repository: Clone this repository to your local machine.
- Navigate to the Repository: Open a terminal and navigate to the root directory of the cloned repository.
- Run the Code: Execute the Go code provided in the repository to see the pipeline pattern in action. You can modify the code to suit your specific use case.
Contributions are welcome! If you have ideas for improving this project or want to add new features, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.