# threads Published articles for threads. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Back to the Basics. Threads. DevFeed: [Back to the Basics. Threads.]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-14T12:19:11Z Content type: tutorial Language: en Sources: [Data Engineering Central]() Topics: [Concurrency](), [Rust](), [Concurrent Programming](), [Programming](), [Python]() Tags: [concurrency](), [programming](), [python](), [rust](), [threads]() ### AI overview An introductory, applied discussion of threads and concurrency, focusing on Rust and briefly recalling Python ProcessPools for parallel data and file processing. ### Source excerpt staying grounded in an agentic world ## A quick overview of atomics in C DevFeed: [A quick overview of atomics in C]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-09T20:41:53Z Content type: tutorial Language: en Sources: [Daniel Lemire]() Topics: [C](), [Concurrency](), [Concurrent Programming](), [Programming]() Tags: [atomics](), [c](), [join](), [ordering](), [posix](), [programming-language](), [systems](), [thread](), [threads]() ### AI overview This tutorial introduces atomics in C, explaining data races on shared non-atomic variables, atomic accesses, instruction reordering, and memory-ordering models including relaxed, release, and acquire semantics. It also discusses C11 threads and platform support limitations. ### Source excerpt If you write in C, by default, you use a single thread. Extra cores do not help until you create more threads. However, if you include the header , you can pass a function to thrd_create, and wait for it with thrd_join. #include #include int worker(void *arg) { printf("hello from thread %d\n", *(int ... Continue reading A quick overview of atomics in C ## Putting an agent in a shared thread makes it a colleague DevFeed: [Putting an agent in a shared thread makes it a colleague]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T15:16:58Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Slack]() Tags: [agent](), [agents](), [ai](), [context](), [slack](), [threads]() ### AI overview The article argues that placing AI agents in shared Slack threads makes them function like colleagues, creating social and operational questions around addressing, assignment, permissions, presence, and audience. Drawing on the experience of running more than 80 team agents, it presents mention-only responses and a distinction between room membership and assignment as practical rules. ### Source excerpt Internal AI moved from dashboards into Slack threads. That changes addressing, mandate, presence, and audience -- questions that used to apply only to people. ## Concurrent Servers: Part 8 - Go DevFeed: [Concurrent Servers: Part 8 - Go]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-22T14:52:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Go Language](), [Concurrency](), [Server](), [Concurrent Programming](), [Network]() Tags: [capacity](), [concurrency](), [concurrent](), [go](), [goroutines](), [misc](), [network-programming](), [servers](), [threads]() ### AI overview Part 8 of a series on concurrent network servers explains how Go implements sequential and concurrent servers. It demonstrates serving each client with a lightweight goroutine and discusses why concurrency may still need to be limited, including when tasks compete for finite CPU capacity. ### Source excerpt This is part 8 in a series of posts on writing concurrent network servers. In this part, we'll switch to Go and see how it tackles the challenges described earlier in the series. All posts in the series: Part 1 - Introduction Part 2 - Threads Part 3 - Event-driven Part 4 - libuv ... ## C++29 -- начало. Встреча ISO C++ в Брно DevFeed: [C++29 -- начало. Встреча ISO C++ в Брно]() Original publisher: [Read original article]() Author: antoshkka (Яндекс) Published: 2026-08-17T07:01:31Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [C++](), [floating-point](), [implementation]() Tags: [c-plus-plus](), [c-plus-plus-29](), [floating-point](), [fmt](), [format](), [implementation](), [iso](), [standard](), [standard-library](), [tagged-pointers](), [thread](), [threads](), [undefined-behavior](), [undefined-behaviour]() ### AI overview A report on the ISO C++ committee meeting in Brno, where work on C++29 began. It describes plans to organize and clarify documented undefined behavior and ill-formed-no-diagnostic-required cases, along with changes involving constexpr floating-point evaluation and other language rules. ### Source excerpt Привет! На связи Антон Полухин из Техплатформы Городских сервисов Яндекса. Недавно в Брно состоялась встреча международного комитета по стандартизации языка программирования C++, в которой я принимал активное участие. В этот раз началась работа над C++29 и как раз о новинках и хочется рассказать. Читать далее ## Concurrent Servers: Part 7 - Rust DevFeed: [Concurrent Servers: Part 7 - Rust]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-15T16:41:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Rust](), [Concurrent Programming](), [Network](), [Server](), [Programming]() Tags: [concurrency](), [concurrent](), [misc](), [network](), [network-programming](), [programming](), [programming-language](), [rust](), [servers](), [threads]() ### AI overview Part 7 of a series on concurrent network servers explains how to implement the series' state-machine protocol in Rust. It covers a sequential server, one thread per client, and a fixed thread pool. ### Source excerpt This is part 7 in a series of posts on writing concurrent network servers. In this part, we discuss how the challenges described in earlier parts are tackled in the Rust programming language. All posts in the series: Part 1 - Introduction Part 2 - Threads Part 3 - Event-driven Part 4 - libuv ... ## Test & Set DevFeed: [Test & Set]() Original publisher: [Read original article]() Author: Jesse Wilson Published: 2026-07-13T22:22:27Z Content type: tutorial Language: en Sources: [Public Object]() Topics: [Concurrency](), [Concurrent Programming](), [Code](), [function](), [business logic]() Tags: [business-logic](), [code](), [concurrency](), [flaky-tests](), [function](), [programming](), [tests](), [threads]() ### AI overview An article about managing concurrency in threaded programs. It discusses the limits of simple and stress tests, describes a trusted concurrency pattern, and presents a helper function that separates business logic from concurrency logic while retrying after a lost race. ### Source excerpt Working with threads always feels hazardous to me. Non-determinism limits how much we can exercise with simple tests. Stress tests can shake out some bugs, but they're slow and potentially flaky. I don't like flaky tests. So I have a few concurrency patterns that I trust, and I ## Releasing Execution Contexts DevFeed: [Releasing Execution Contexts]() Original publisher: [Read original article]() Author: Julien Portalier Published: 2026-07-12T00:00:00Z Content type: release Language: en Sources: [Crystal]() Topics: [Crystal](), [Concurrency](), [Concurrent Programming](), [context](), [cpu]() Tags: [concurrent](), [cpu](), [kotlin](), [main-thread](), [multithreading](), [parallelism](), [threads]() ### AI overview Crystal is introducing Execution Contexts, an interface for orchestrating fibers across one or more threads. The article describes concurrent and parallel contexts, configurable parallelism, and execution across CPU cores while preserving a single-threaded default. ### Source excerpt Two and a half years ago, with the invaluable support from 84codes, we re-examined the multithreading model inherited from Crystal 0.28 (preview MT). ## strace-ui, Bonsai\_term, and the TUI renaissance DevFeed: [strace-ui, Bonsai\_term, and the TUI renaissance]() Original publisher: [Read original article]() Author: James Somers Published: 2026-05-26T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Text-based user interface](), [Terminal](), [debug](), [tracing](), [OCaml](), [reactive](), [Bonsai](), [Library]() Tags: [debugging](), [framework](), [functional](), [library](), [ocaml](), [reactive](), [terminal](), [threads](), [trace](), [ui]() ### AI overview The article introduces strace-ui, an interactive terminal UI that makes strace output easier to inspect by formatting data, assigning short identifiers to processes and threads, and supporting interactive filtering and navigation. It also discusses how OCaml and the Bonsai framework have made interactive terminal UI development more practical. ### Source excerpt We've always found strace useful but somewhat hard to work with. Its output is often inscrutable, it's hard to follow subprocesses or threads, and if you want to filter syscalls you have to rerun the trace with a flag for each one. What you want in debugging is a tool for exploring, refining, etc., but strace can make this difficult. ## Introducing Parallel Agents in Zed DevFeed: [Introducing Parallel Agents in Zed]() Original publisher: [Read original article]() Author: Mikayla Maki, Richard Feldman Published: 2026-04-22T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [Artificial Intelligence](), [Tool](), [Code](), [Software](), [Open Source](), [Git]() Tags: [agent](), [agents](), [ai](), [ai-tools](), [code](), [git](), [layout](), [open-source](), [parallel](), [projects](), [systems](), [threads]() ### AI overview Zed introduces parallel agents that can run in the same window. Its Threads Sidebar lets users manage agent access to folders and repositories, monitor threads, choose agents per thread, work across projects, and isolate worktrees. The release also describes a revised panel layout and connects the feature to Zed's approach of combining human software craftsmanship with AI tools. ### Source excerpt Run multiple agents at once, in the same window. ## Thread-per-Core Architecture: How Context Switching and Cache Thrashing Can Reduce Throughput DevFeed: [Thread-per-Core Architecture: How Context Switching and Cache Thrashing Can Reduce Throughput]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-21T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Concurrency](), [Concurrent Programming](), [cpu](), [Kernel](), [Hardware](), [Latency]() Tags: [architecture](), [cache](), [concurrency](), [context-switch](), [cpu](), [cpu-affinity](), [kernel](), [latency](), [performance](), [systems](), [threading](), [threads]() ### AI overview The article explains why adding threads beyond the number of physical CPU cores can reduce throughput and increase latency. It attributes the decline to scheduler context switching and cache thrashing, and recommends sizing worker pools to physical cores, using CPU affinity, and relying on non-blocking I/O. ### Source excerpt A machine with 16 cores runs 16 streams of execution regardless of how many threads the runtime creates. Past that point the scheduler timeslices, and each context switch costs roughly 1 to 2 microseconds of bookkeeping plus the far larger hidden cost of cache thrashing as each thread evicts the previous one's working set. Size worker pools to physical cores, pin them, and use non-blocking I/O so a thread never parks a core waiting on the network. ## How a Learning Project Became Our Modern Mobile Test Framework DevFeed: [How a Learning Project Became Our Modern Mobile Test Framework]() Original publisher: [Read original article]() Author: Raymond Saba I'm an SDET who enjoys building test automation; Finding Ways Published: 2026-02-18T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Mobile](), [Automation](), [webDriver](), [Concurrency](), [Java]() Tags: [appium](), [automation](), [engineering](), [java](), [migration](), [mobile](), [modularity](), [overhead](), [quality-assurance](), [standards](), [threads](), [webdriver]() ### AI overview This engineering article describes how trivago's aging homegrown mobile automation framework evolved into a shared iOS and Android test framework. It covers the transition from Appium 1 to Appium 2 and later Appium 3, including a rewrite based on W3C WebDriver standards, platform-specific drivers, modularity, and parallel execution with threads in one JVM. ### Source excerpt About six years ago, our mobile automation setup was showing its age. It was a small, homegrown framework that had worked "well enough" for a long time, until we tried to upgrade Java and a few ... ## Don't Block Suspend Functions DevFeed: [Don't Block Suspend Functions]() Original publisher: [Read original article]() Author: Jesse Wilson Published: 2026-01-22T04:32:49Z Content type: tutorial Language: en Sources: [Public Object]() Topics: [Coroutines](), [Kotlin](), [Concurrent Programming](), [Concurrency](), [async](), [Job](), [IO]() Tags: [async](), [await](), [blocking](), [blog-post](), [concurrency](), [coroutines](), [dispatcher](), [kotlin](), [thread](), [threads]() ### AI overview This Kotlin tutorial explains why blocking calls inside suspend functions can prevent other coroutines from running. It contrasts preemptive thread concurrency with cooperative coroutine concurrency and recommends avoiding blocking functions in suspending code, using the I/O dispatcher when necessary, and avoiding runBlocking. ### Source excerpt Here's a program that launches 3 jobs. The first runs forever and the other two exchange a value. @Test fun test() = runTest { val channel = Channel() val deferredA = async { while (isActive) { delay(1_000) } } val deferredB = async { channel.send("hello") } val deferredC = async { channel.receive() } deferredB.await( ## Process Memory Sharing DevFeed: [Process Memory Sharing]() Original publisher: [Read original article]() Published: 2025-11-09T20:39:05Z Content type: article Language: en Sources: [Signs of Triviality]() Topics: [Processes](), [Process](), [Concurrent Programming](), [Concurrency]() Tags: [memory](), [process](), [processes](), [threads]() ### AI overview An overview of which parts of one another's memory spaces threads and related processes can access. ### Source excerpt A look at what parts of each others memory space threads and related processes can access. ## Understanding Thread Stacks in Memory and ASLR DevFeed: [Understanding Thread Stacks in Memory and ASLR]() Original publisher: [Read original article]() Published: 2025-10-30T00:54:51Z Content type: tutorial Language: en Sources: [Signs of Triviality]() Topics: [Concurrency](), [Concurrent Programming]() Tags: [context](), [thread](), [threads]() ### AI overview The article examines thread stacks in memory, particularly in the context of Address Space Layout Randomization (ASLR). ### Source excerpt An attempt to clarify my understanding of thread stacks in memory, particularly within the context of Address Space Layout Randomization (ASLR). ## Intel N100 Mini PC for R&D and Self-Hosting Compared with Raspberry Pi 5 DevFeed: [Intel N100 Mini PC for R&D and Self-Hosting Compared with Raspberry Pi 5]() Original publisher: [Read original article]() Author: Alex Ellis Published: 2025-08-18T08:09:48Z Content type: opinion Language: en Sources: [Alex Ellis' Blog]() Topics: [pc](), [intel](), [cpu](), [Self-hosted](), [Raspberry Pi](), [NVMe](), [Firecracker](), [Kubernetes](), [Linux](), [k3s](), [OpenFaaS](), [ddr5]() Tags: [ddr5](), [firecracker](), [github-actions](), [intel](), [k3s](), [kubernetes](), [linux](), [m-2-nvme](), [mini-pc](), [pc](), [processor](), [self-hosting](), [storage](), [threads](), [work]() ### AI overview The article evaluates the low-power Intel N100 Mini PC for R&D and self-hosting, comparing it with the Raspberry Pi 5. It discusses CPU, RAM, NVMe storage, Ethernet, virtualization, Kubernetes, Firecracker, and cost considerations. ### Source excerpt Exploring the capabilities of the Intel N100 Mini PC for work and self-hosting as an alternative to public cloud. ## Converting Future to CompletableFuture With Java Virtual Threads DevFeed: [Converting Future to CompletableFuture With Java Virtual Threads]() Original publisher: [Read original article]() Published: 2025-07-17T08:25:00Z Content type: tutorial Language: en Sources: [Gunnar Morling]() Topics: [Java](), [Concurrency](), [Concurrent Programming]() Tags: [api](), [asynchronous](), [blocking](), [java](), [java-8](), [threads]() ### AI overview This tutorial explains how Java 21+ virtual threads can help convert legacy Future objects into CompletableFuture instances. It contrasts blocking, polling, and asynchronous approaches, noting that virtual threads make blocking inexpensive by unmounting blocked threads from their underlying platform threads. ### Source excerpt This post explores how virtual threads in Java 21+ provide an elegant solution for converting legacy Future objects into CompletableFuture instances. Since Java 8, the CompletableFuture API provides a convenient way for performing asynchronous operations in a functional, composable way. This makes it very simple to call some long-running methods--for instance involving external I/O--asynchronously and process each result as soon as it is available, without blocking on any threads: ## Scheduling In Go : Part II - Go Scheduler DevFeed: [Scheduling In Go : Part II - Go Scheduler]() Original publisher: [Read original article]() Published: 2025-04-14T00:00:00Z Content type: article Language: en Sources: [William Kennedy]() Topics: [Go Language](), [Concurrency](), [Optimization](), [systems]() Tags: [ardan-labs](), [blog](), [concurrency](), [concurrent](), [context](), [go](), [go-programming](), [golang](), [optimization](), [os](), [parallel](), [programming](), [scheduler](), [switching](), [systems](), [threads]() ### AI overview The second article in a three-part series explains the Go scheduler at a semantic level. It covers logical processors, global and local run queues, context switching, work stealing, and synchronous and asynchronous system calls. ### Source excerpt This blogpost is the second installment in a three-part series exploring the mechanics and semantics of the Go scheduler. Despite being published in 2018, the content remains relevant today, as the Go scheduler's design continues to influence the development of efficient and scalable concurrent systems. In this post, we will go into the inner workings of the Go scheduler, discussing its components, such as the Global Run Queue (GRQ) and Local Run Queue (LRQ), and its behavior, including context switching, work stealing, and the handling of synchronous and asynchronous system calls. By understanding these concepts, developers can make informed decisions about concurrency and optimization in their Go applications. ## Understanding Dispatchers: Main and Main.immediate DevFeed: [Understanding Dispatchers: Main and Main.immediate]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2025-04-02T12:22:06Z Content type: tutorial Language: en Sources: [Shreyas Patil's Blog]() Topics: [Coroutines](), [kotlin-coroutines](), [Kotlin](), [Android](), [Programming]() Tags: [android](), [android-app-development](), [android-apps](), [android-studio](), [coroutine](), [coroutines](), [coroutines-flow](), [kotlin](), [kotlin-beginner](), [kotlin-coroutines](), [kotlin-multiplatform](), [main-thread](), [multithreading](), [reactive-programming](), [threadpools](), [threads]() ### AI overview A deep dive into Kotlin coroutine dispatchers on Android, explaining the difference between Dispatchers.Main and Dispatchers.Main.immediate. It describes how HandlerContext dispatches work and when execution is posted to the main thread or performed synchronously on the current thread. ### Source excerpt A deep dive into Kotlin Coroutine Dispatchers. Understand the subtle but important difference between Dispatchers.Main and Dispatchers.Main.immediate in Android. ## The case of the vanishing CPU: A Linux kernel debugging story DevFeed: [The case of the vanishing CPU: A Linux kernel debugging story]() Original publisher: [Read original article]() Author: Sergei Trifonov Published: 2025-03-05T12:18:20Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Kernel](), [Linux](), [debugging](), [Google Cloud Platform (GCP)](), [Monitoring](), [incident]() Tags: [clickhouse](), [debugging](), [gcp](), [gdb](), [incident](), [kernel](), [linux](), [linux-kernel](), [memory-management](), [monitoring](), [performance](), [process](), [threads]() ### AI overview A mysterious CPU spike affecting ClickHouse Cloud on GCP led engineers through tracing and debugging into Linux kernel memory-management internals. The investigation uncovered a hidden livelock, produced a reproducible test case and fix, and was followed by the discovery of another kernel bug. ### Source excerpt Read about how a Linux kernel memory bug led to instability in ClickHouse Cloud on GCP, and the challenges of diagnosing and resolving it. ## LLM Inference Machine for $300 DevFeed: [LLM Inference Machine for $300]() Original publisher: [Read original article]() Published: 2024-12-28T04:17:14Z Content type: article Language: en Sources: [Ariya Hidayat]() Topics: [Inference](), [Large Language Model](), [GPU](), [datacenter](), [data]() Tags: [3d-printed](), [amd](), [cost](), [ddr4](), [gpu](), [inference](), [llama](), [llm](), [memory](), [models](), [nvidia](), [performance](), [qwen](), [ssd](), [threads](), [vision]() ### AI overview A $300 used-hardware build runs quantized LLMs including Qwen-2.5 32B, Llama-3.1 8B, and Llama-3.2 Vision 11B. It uses an NVIDIA Tesla M40 with 24GB of VRAM and achieves model-dependent speeds from 7 to 47 tokens per second. The article compares its performance, cost, and upgrade options with newer GPUs and Apple Silicon. ### Source excerpt You can absolutely run Qwen-2.5 32B. And of course, Llama-3.1 8B and Llama-3.2 Vision 11B are no problem at all. ## Demystifying Java Object Sizes: Compact Headers, Compressed Oops, and Beyond DevFeed: [Demystifying Java Object Sizes: Compact Headers, Compressed Oops, and Beyond]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-10T16:51:00Z Content type: article Language: en Sources: [Vanilla Java]() Topics: [Java](), [systems](), [Code](), [Concurrency]() Tags: [code](), [gc](), [info](), [java](), [jvm](), [low-latency](), [memory](), [object](), [opinion](), [performance](), [threads]() ### AI overview An article explaining how Java object sizes are affected by Compressed Oops and Compact Object Headers. It describes estimating object sizes with JVM memory measurements, while accounting for TLAB allocation, garbage collection, and concurrent allocations. It also summarizes JEP 450's proposed reduction of object headers to 64 bits on supported 64-bit platforms. ### Source excerpt Introduction Measuring an object's size in Java is not straightforward. The platform encourages you to consider references and abstractions rather than raw memory usage. Still, understanding how objects fit into memory can yield significant benefits, especially for high-performance, low-latency systems. Over time, the JVM has introduced optimisations like Compressed Ordinary Object Pointers (Compressed Oops) and, more recently, Compact Object Headers. Each of these can influence how large or small your objects appear. Understanding these factors helps you reason about memory usage more concretely. Measuring Object Sizes In principle, you can estimate an object's size by creating instances and observing changes in the JVM's free memory. However, you must neutralise certain factors to get consistent results. For example, turning off TLAB allocation (-XX:-UseTLAB) makes memory usage more directly observable. Repeated measurements and median calculations can reduce the impact of GC and concurrent allocations. A GC can occur while you are creating your object. This will result in more free memory at the end than when you started. I ignore any negative sizes in this test ;) Other threads in the system could use memory at the same time. I perform multiple test and take the median, which removes any outliers. Below is a rough approach: long before = usedMemory(); Object obj = createYourObject(); long after = usedMemory(); long approximateSize = after - before; This test SizeofTest.java is a simple test which creates a number of objects and measures the memory used to create each object. This is usually the same as the amount of memory the object retains for simple objects. Approximate layout of an object Memory Region Description Size (Bytes) Mark Word Header information including identity hash code, lock state, and GC metadata 8 bytes (on 64-bit JVMs) Class Pointer (Klass Pointer) Reference to the object's class metadata, used internally by the JVM Typically 4 bytes with C ## Introduction to Zephyr OS Tracing and Profiling DevFeed: [Introduction to Zephyr OS Tracing and Profiling]() Original publisher: [Read original article]() Author: John Lee Published: 2024-11-15T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Zephyr RTOS](), [Embedded Software Dev](), [tracing](), [Embedded Systems](), [debugging](), [ESP32]() Tags: [architecture](), [blog](), [cpu](), [debugging](), [embedded](), [embedded-systems](), [esp32](), [esp32-c6](), [introduction](), [jtag](), [logging](), [performance](), [profiling](), [python](), [techniques](), [threads](), [tracing](), [usb](), [zephyr](), [zephyr-os]() ### AI overview This article introduces tracing and profiling for embedded systems using Zephyr RTOS. It explains how tracing records execution data from functions, threads, and interrupts, while profiling measures metrics such as execution time, CPU usage, and dynamic memory usage. It also demonstrates Zephyr's native tracing workflow on an ESP32-C6 and introduces Percepio's Tracealyzer as a third-party analysis tool. ### Source excerpt This is an introduction to the critical embedded system debugging techniques of tracing and profiling within the Zephyr RTOS. After reading this article, you can use Zephyr's native tracing tools to gain a visual, in-depth understanding of system dynamics, helping to evaluate performance, spot timing issues, and diagnose complex problems like memory leaks or thread interaction glitches. ## Choosing a Coroutine Dispatcher for Spring Boot Backend Request Handlers DevFeed: [Choosing a Coroutine Dispatcher for Spring Boot Backend Request Handlers]() Original publisher: [Read original article]() Published: 2024-11-13T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [kotlin-coroutines](), [Spring Boot](), [backends](), [Kotlin](), [Ktor]() Tags: [kotlin](), [kotlin-coroutines](), [network](), [spring-boot](), [threads](), [workshop-learning-programming]() ### AI overview This article compares coroutine dispatchers for Spring Boot backend request handlers. It explains how dispatcher choice affects thread usage, blocking operations, coroutine resumption, and request throughput, including examples involving DefaultExecutor, Dispatchers.IO, and Ktor Client. ### Source excerpt Let's explore different dispatchers and find the best one for a backend request handlers. [Next page]()