The Supervisor/Restart pattern involves a supervisor goroutine that monitors one or more worker goroutines. If a worker fails (panics or exits unexpectedly), the supervisor restarts it. This pattern is essential for building resilient and fault-tolerant systems, automatically recovering from transient errors, and ensuring critical tasks are always running.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
The Producer-Consumer pattern decouples the production of data from its consumption using a shared buffer (channel). Multiple producers can generate data and send it to the buffer, while multiple consumers read from the buffer and process the data. This pattern is essential for buffering bursts of work, decoupling work rates between producers and consumers, and load leveling.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
The Worker Pools pattern maintains a fixed number of workers that process jobs from a shared queue. This pattern is essential for controlling resource usage, handling bursty workloads, and providing predictable performance in concurrent systems.
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The Fan-out/Fan-in pattern is a concurrency pattern that distributes work across multiple workers (fan-out) and then collects results from all workers (fan-in). This pattern is essential for parallel processing of independent tasks, load balancing, and improving throughput for CPU-intensive operations.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
The Pipeline pattern is a fundamental concurrency pattern that connects multiple stages of processing where each stage processes data and passes it to the next stage. This pattern is particularly useful for data transformation workflows, stream processing, and multi-step computations.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
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