Concurrency is difficult in every JVM language because the hard part is not syntax—it is coordination. Once multiple threads can observe and change the same “thing,” you get failure modes that are rare in single-threaded code:
- race conditions (“sometimes it’s wrong”)
- visibility issues (“one thread didn’t see the update”)
- deadlocks and lock ordering problems
- lost updates and broken invariants
Clojure’s default of immutable data removes a huge class of problems: values can be shared freely across threads because they do not change. But you still need state somewhere, and state must be managed explicitly.
This chapter teaches how Clojure models state with a small set of tools (atoms, refs/STM, agents, and channels), and how to choose the one that matches the kind of coordination you actually need.
In this section
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Concurrency in Modern Applications: Understanding and Managing Challenges
Explore the significance of concurrency in modern software development, its benefits, and the challenges it presents. Learn how Clojure's concurrency model offers solutions to common problems faced by Java developers.
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Understanding Issues with Shared Mutable State in Concurrency
Explore the challenges of shared mutable state in concurrent programming, focusing on Java and Clojure. Learn how Clojure's immutable data structures offer a solution to concurrency issues.
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Traditional Concurrency Mechanisms in Java: Threads, Locks, and Synchronization
Explore Java's traditional concurrency mechanisms, including threads, locks, synchronized blocks, and concurrent collections. Understand the complexities and challenges these tools present, such as explicit synchronization and potential deadlocks, and set the stage for Clojure's more effective concurrency management.