concurrency

Implement distributed locks with Redis for mutual exclusion across processes, using SET NX with TTL and the Redlock algorithm for reliability

How to write efficient concurrent code using async/await, promises, futures, and coroutines in JavaScript, Python, and Java for non-blocking I/O and parallel processing.

How to safely share collections between threads using blocking queues, concurrent maps, copy-on-write lists, and atomic counters in Java, Python, and C++.

Build async pipelines in C# using async/await, Task.Run, Task.WhenAll, Task.WhenAny, CancellationTokenSource, Channels, and Parallel.ForEachAsync for concurrent I/O and CPU work.

How to structure concurrent programs using channels, select statements, and goroutines for safe communication without shared mutable state in Go, Rust, and JavaScript.

Build concurrent systems in Go using goroutines, channels, select statements, worker pools, fan-out/fan-in, pipelines, context cancellation, and rate limiting with tickers.

Build non-blocking async pipelines in Java using CompletableFuture with thenCompose, thenCombine, allOf, anyOf, exception handling, timeouts, and custom thread pools.

Scale Java applications with virtual threads from Project Loom. Use Thread.ofVirtual, Executors.newVirtualThreadPerTaskExecutor, structured concurrency, and scoped values.

How to prevent race conditions in concurrent programs using mutexes, read-write locks, semaphores, and atomic operations in Java, Python, and C++.

Fetch multiple APIs concurrently using asyncio and aiohttp. Covers connection pooling, rate limiting, retries, and batch processing.

Execute multiple async operations concurrently in Python using asyncio.gather, asyncio.TaskGroup, error handling with return_exceptions, timeouts, and semaphores for rate limiting.

Control concurrency in async Python using asyncio.Semaphore for rate limiting API calls, database connections, and resource access with bounded parallelism patterns.

Run cron-like jobs in Python using APScheduler. Covers interval, cron, and date triggers, job stores, and background scheduling.

Use Python's ThreadPoolExecutor for parallel I/O operations, thread-safe result collection, Future callbacks, error handling, and mixing threads with asyncio for blocking work.

Build async systems in Rust using the Tokio runtime with tasks, channels, select, synchronization primitives, graceful shutdown, and structured concurrency patterns.

How to efficiently manage worker threads using thread pools, executors, and rejection policies in Java, Python, and C# for CPU-bound and I/O-bound workloads.

Identify and fix race conditions in asynchronous JavaScript using proper sequencing, atomic operations, locks, and Promise patterns for predictable concurrent execution

Identify deadlock patterns in SQL databases, apply consistent lock ordering, use appropriate isolation levels, and implement retry logic for resilient concurrent transactions

Fetch multiple HTTP endpoints concurrently using asyncio.gather and aiohttp with error handling, rate limiting, timeouts, and connection pooling

Coordinate a single active instance among multiple distributed nodes to avoid conflicts and split-brain scenarios.

Isolate state in actors that communicate only via messages. Each actor processes one message at a time, eliminating shared-state concurrency bugs by design.

Stream data lazily with async generators. Yield values one at a time as they become available, enabling memory-efficient processing of large or infinite data sequences.

Coordinate mutually exclusive access to shared resources across distributed nodes using a consensus-based lock service, preventing race conditions in scaled-out systems.

Build high-throughput queues using atomic operations instead of locks. Multiple threads can enqueue and dequeue concurrently without blocking or context-switching overhead.

Process tasks based on priority rather than arrival order, ensuring high-priority work gets resources before lower-priority tasks even if it arrived later.

Decouple production and consumption with a shared queue. Producers generate items at their own pace; consumers process them independently through a bounded or unbounded buffer.

Process asynchronous data streams with backpressure. Subscribers request N items at a time, preventing fast producers from overwhelming slow consumers.

Reuse a fixed set of threads for short-lived tasks instead of creating a new thread per task. Reduces overhead and bounds resource usage under load.

How to isolate resources per service to limit blast radius. Covers thread pool isolation, connection pool partitioning, semaphore-based bulkheads, and resource quotas.

Runbook for safely cancelling long-running async tasks in Python, JavaScript, Go, and Java: cancellation tokens, context propagation, resource cleanup, timeout strategies, and graceful shutdown procedures with code examples.

Checklist for identifying and fixing race conditions in concurrent code: symptom identification, reproduction strategies, debugging tools, common patterns, fixes using locks, atomics, channels, and prevention techniques with code examples.

Template for documenting thread pool configuration per service: pool type selection, sizing formulas, CPU vs I/O bound tuning, queue strategies, rejection policies, monitoring metrics, and tuning examples for Java, Python, Go, and Node.js.

Master Go concurrency in production. Covers goroutines, channels, context, select, sync primitives, worker pools, pipelines, fan-out/fan-in, and patterns for high-throughput concurrent Go applications.

Master Java concurrency in production. Covers threads, locks, CompletableFuture, virtual threads, executors, concurrent collections, memory model, and patterns for high-throughput parallel applications.

Master asynchronous Python programming with asyncio. Covers coroutines, tasks, event loops, async/await, gather, semaphores, queues, HTTP clients, websockets, and debugging async code.

Run Python asyncio in production with confidence. Covers event loops, task management, debugging, cancellation, timeouts, backpressure, and patterns for high-concurrency async applications.

A guide to common concurrency patterns and what works for writing safe, efficient concurrent code.