Скачать или смотреть Golang Concurrency Patterns: Worker Pool & Rate Limiting

Master Golang concurrency for interview success with this deep dive into two fundamental patterns: the worker pool and rate limiting. Designed for Go learners and candidates preparing for software engineering interviews, this video walks you through practical implementations, interview-ready explanations, and key usage scenarios for scalable, reliable, and performant concurrent applications.

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Worker Pool Pattern in Go
The worker pool pattern efficiently processes a large number of tasks by using a fixed number of goroutines ("workers") to pull work from a shared channel queue. This prevents resource exhaustion, controls concurrency, and throttles throughput based on available workers—making it essential for scalable backends and systems with fluctuating workloads.

A worker pool is like a supermarket with a limited number of checkout cashiers; each “worker” goroutine picks up a new task from the queue as soon as it's free, ensuring fair distribution and preventing overload.

Implementation involves:

Creating a jobs channel as a task queue

Spawning a fixed number of worker goroutines

Sending jobs to the channel

Using sync.WaitGroup to ensure all jobs are completed before program exit

Always closing the jobs channel after dispatch to avoid deadlocks

This pattern helps control system resource usage, make concurrency predictable, and synchronize all work for clean program termination.

Rate Limiting in Go
Rate limiting is vital for controlling the frequency of operations like API requests, resource consumption, and preventing overload in concurrent environments.

Use cases: Protecting services, adhering to external API quotas, cost control, fairness among users, and preventing abuse.

Common strategies:

Token bucket: Refill tokens at a set rate and allow bursts up to bucket size

Leaky bucket: Smooths out request bursts

Fixed window counter: Simple counting within time windows

The golang.org/x/time/rate package provides a robust implementation using the token bucket algorithm:

rate.NewLimiter(rate, burst) creates a limiter to control how many tokens are replenished and the burst capacity.

limiter.Wait(ctx) blocks until allowed according to rate limits.

limiter.Allow() provides a non-blocking check to permit/deny requests instantly.

First few requests are allowed in bursts, then spaced out to maintain a consistent rate.

These patterns are frequently tested in interviews for Golang backend, cloud, and distributed systems roles. Knowing how and why to use them—including resource protection, fairness, and shutting down goroutines cleanly—is crucial for showing deep concurrency knowledge.

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