Choosing Concurrency Abstractions in Haskell: STM, Async, and forkIO
A decision guide for Haskell developers choosing between forkIO, async, and STM for concurrency, featuring a comparison table and a concrete STM implementation.
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haskellMastering Clojure STM: Atomic Ref Updates with dosync, alter, and best practices
Clojure’s STM lets you manage shared mutable state safely with Refs, dosync, and alter. This guide shows how to atomically update refs, test concurrency, and avoid common pitfalls like side‑effects and long transactions.
clojureChoosing Haskell's STM for Safe Concurrent State Management
Learn how Haskell's Software Transactional Memory (STM) lets you manage shared state safely and composably, with a worked bank‑transfer example and practical adoption tips.
Building a Concurrent Counter with Haskell's Software Transactional Memory
Learn how to use Haskell's STM primitives to safely update a shared counter from multiple threads, compile with GHC, run with threading enabled, and verify the result.
MVar vs STM: A Practical Decision Guide for Haskell Concurrency
When building concurrent Haskell programs, choosing between MVar and STM is critical. This guide weighs constraints, compares options, and shows a hands‑on counter benchmark to help you decide.
Using Haskell STM for Safe Concurrent State: An Architecture Note
An architecture note on using Haskell's STM and TVar for composable, lock-free concurrent state: requirements, minimal design, trust boundaries, operational checks, failure modes, and when to switch approaches.
Using Haskell STM for Atomic Shared State: An Architecture Note
Explore how Haskell's STM provides a minimal, safe design for concurrent shared state, covering requirements, boundaries, checks, failure modes, and when to switch designs.
Which state management mechanism is optimal for high-contention multi-variable updates?
Clojure provides several primitives for managing shared mutable state, including Atoms, Refs (STM), and Agents. While Atoms are highly efficient for independent state changes using compare-and-swap (CAS) operations, they cannot coordinate updates across multiple variables atomically. The Software Transactional Memory (STM) system allows for coordinated chang
Spike in response time when multiple threads update a single Clojure ref via dosync
When a Clojure application processes concurrent requests that each perform a write to a shared ref inside a dosync block, the measured latency per request remains low in single‑threaded runs but rises sharply as the number of simultaneous threads increases. The goal is to determine whether this latency increase stems primarily from STM retry cycles, from the