Java on Tech Blog

Mastering Concurrency In Java - Part 4: Deep Dives and Modern Patterns

Wed, 08 Apr 2026 20:04:08 +0530

In Part 1, Part 2, and Part 3, we covered hazards, primitives, and execution models. In this final part, we will focus on structured concurrency, fan-out/fan-in, fail-fast cancellations, timeout propagation, resource scoping, and observability with thread dumps and Java Flight Recorder (JFR), emphasizing when to choose which pattern.

Mastering Concurrency In Java - Part 3: Execution Models

Tue, 07 Apr 2026 20:04:08 +0530

In Part 1 and Part 2, we covered the fundamentals and building blocks of concurrency. In this part, we will discuss the execution models of concurrency - classic thread pools, task queues, Future/Callable, CompletableFuture, and, from Java 21+, virtual threads and virtual-thread-per-task executors. Choosing among them is ultimately about latency, throughput, and operational simplicity, not about syntax.

Mastering Concurrency In Java - Part 2: The Fundamentals

Mon, 06 Apr 2026 20:04:08 +0530

In Part 1, we discussed the core concurrency hazards and control concepts in Java: race conditions, visibility, atomicity, deadlocks, starvation, livelock, contention, backpressure, interruption, and cancellation.

In this part, we will discuss the coordination primitives - synchronized, volatile, Atomics, Locks, Semaphores, Blocking Queues, and Concurrent Collections.

Mastering Concurrency In Java - Part 1: The Fundamentals

Sun, 05 Apr 2026 11:26:08 +0530

Introduction
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Here, we will discuss the core concurrency hazards and control concepts in Java: race conditions, visibility, atomicity, deadlocks, starvation, livelock, contention, backpressure, interruption, and cancellation.

Race Conditions
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A race condition occurs when the correctness of a task depends on the relative timing or interleaving of threads accessing shared mutable state. The bug is not “thread A ran before B”, it is “if they interleave in this specific way, invariants break.”

How to Cache?

Sat, 28 Mar 2026 08:42:23 +0530

Modern APIs frequently access databases, or complex business logic that introduce significant latency and consume CPU and I/O resources. Without caching, every request pays the full cost of database queries, network calls, and computation. This can lead to slow response times and poor scalability as traffic increases.