# race-conditions Published articles for race-conditions. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Hardening Production Multi-Container Topologies with Docker Compose v2 DevFeed: [Hardening Production Multi-Container Topologies with Docker Compose v2]() Original publisher: [Read original article]() Author: Siva Sai Krishna Suryadevara Published: 2026-11-05T15:31:57Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Docker Compose](), [container-networking](), [container escape](), [CI/CD]() Tags: [agent](), [ai](), [ai-agent](), [architecture](), [backend](), [blueprint](), [compose](), [container](), [container-hardening](), [container-security](), [docker](), [docker-compose](), [docker-networking](), [docker-secrets](), [docker-security](), [hackernoon-top-story](), [hardening](), [health](), [least-privilege](), [network](), [production](), [race-conditions](), [startup](), [v2](), [yaml-configuration]() ### AI overview The excerpt's production Compose blueprint makes startup readiness, network access, secrets, and resource limits explicit. Long-form `depends_on` gates the API on a TCP health check for Postgres, then on a one-shot migration's successful exit; this avoids startup races and concurrent migration runs when scaling the app. Separate `dmz` and internal backend networks leave only Nginx's port published and isolate the data stores. File-sourced Compose secrets stay out of environment variables and `docker inspect`, though outside Swarm their host file permissions control container access. The blueprint also applies CPU, memory, and process limits, non-root users, read-only filesystems, and dropped capabilities. It notes named-volume compatibility with image ownership, bind-mount ownership pitfalls, Redis cache headroom, and rootless Docker's restriction on publishing ports 80/443. It recommends validating the rendered configuration, service health, migration exit status, network isolation, and applied limits. ### Source excerpt Eliminate silent startup failures, race conditions, and network exposure in multi-container stacks using Docker Compose v2 health gates and least privilege. ## Why Your Python Cache Does the Same Work Twice (P.S: GIL Won't Save You) DevFeed: [Why Your Python Cache Does the Same Work Twice (P.S: GIL Won't Save You)]() Original publisher: [Read original article]() Author: Sohel Alam Published: 2026-11-01T15:18:56Z Content type: tutorial Language: en Sources: [HackerNoon]() Topics: [Python](), [Cache](), [scheduling](), [cache-invalidation]() Tags: [cache](), [gil](), [gotcha](), [hackernoon-top-story](), [locks](), [lru](), [python](), [python-concurrency](), [python-gil](), [python-programming](), [python-threading](), [race-conditions](), [thread-safety]() ### AI overview A cache can return correct values yet run expensive work repeatedly because its check-then-act sequence is not atomic, even on standard CPython with the GIL. A single lock around both steps prevents duplicate computations but makes threads wait even when they request different keys. Per-key locks let different keys proceed independently; recheck the cache after acquiring the key lock. The example's fast path assumes entries are not removed or replaced, its lock map grows with the key set, and thread locks coordinate only within one process. `functools.lru_cache` keeps its internal state consistent but may call the wrapped function more than once during concurrent misses; `cached_property` can also run its getter repeatedly. For side effects, the article recommends idempotency keys and persistent deduplication because in-process locking does not guarantee exactly-once execution. The author tested on CPython 3.12.3 and says they did not test the examples on a free-threaded build. ### Source excerpt Your Python cache can run the same work twice, even with the GIL. See the check-then-act race, the lru_cache gotcha, and how locks fix it. ## An Intro to Threading in Python DevFeed: [An Intro to Threading in Python]() Original publisher: [Read original article]() Author: Jim Anderson Published: 2026-10-24T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [multithreading](), [Python](), [race-condition](), [shared mutable state](), [thread safe data sharing](), [scheduling]() Tags: [concurrency](), [concurrent](), [concurrently](), [deadlocks](), [locks](), [python](), [python-threading](), [queues](), [race-conditions](), [threading](), [threads]() ### AI overview Python threading can simplify concurrent program design and is most useful for tasks that spend time waiting on external events. In standard CPython builds, the GIL generally prevents Python threads from running CPU-bound code in parallel; the optional free-threaded build introduced in Python 3.13 is an exception. The tutorial shows how to start threads and wait with `join()`, and how `ThreadPoolExecutor` manages a group of workers. It demonstrates a lost update when threads share mutable data, then uses `Lock` to protect the read-modify-write section. A `Lock` used as a context manager is released when the block exits; `RLock` supports repeated acquisition by the same thread. For producer-consumer work, a single-slot lock-based pipeline cannot absorb bursts, while thread-safe `queue.Queue` can buffer messages and block at a configured maximum size; an `Event` signals shutdown, and the consumer drains queued messages before exiting. The article also surveys `Semaphore` for limiting access to finite resources, `Timer` for delayed calls, and `Barrier` for synchronizing a fixed number of threads. ### Source excerpt Learn how to use Python threading to run tasks concurrently, synchronize threads with locks and queues, and avoid race conditions and deadlocks. ## React useEffect Cleanup Patterns for Timers, Listeners, and Fetch Races DevFeed: [React useEffect Cleanup Patterns for Timers, Listeners, and Fetch Races]() Original publisher: [Read original article]() Author: SitePoint Team Published: 2026-10-19T15:42:38Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [React](), [fetch](), [Code review]() Tags: [code](), [code-review](), [fetch](), [javascript](), [programming](), [race-conditions](), [react](), [web]() ### AI overview This tutorial explains React useEffect cleanup for timers, event listeners, and asynchronous fetches. It describes how cleanup runs before an effect reruns or when a component unmounts, how to prevent stale responses with AbortController, and how to review effects for missing cleanup, dependency, and error-handling issues. ### Source excerpt Learn how React's useEffect cleanup function handles fetch race conditions, intervals, and event listeners. Includes code patterns, lifecycle explanations, and a PR review checklist. Continue reading React useEffect Cleanup Patterns for Timers, Listeners, and Fetch Races on SitePoint. ## Concurrency vs Multithreading: What's the Difference? DevFeed: [Concurrency vs Multithreading: What's the Difference?]() Original publisher: [Read original article]() Author: Kashif Khan Published: 2026-10-15T02:42:26Z Content type: tutorial Language: en Sources: [Talent500]() Topics: [Deadlock](), [synchronization](), [Declarative programming](), [Test-driven development]() Tags: [asynchronous-programming](), [backend](), [concurrency](), [concurrency-vs-multithreading](), [deadlocks](), [multithreading](), [parallelism](), [python-threading](), [race-conditions](), [software-development](), [thread-pools](), [thread-safety]() ### AI overview This tutorial explains concurrency as the broader way to manage tasks that make progress during overlapping periods, while multithreading is one implementation technique. It contrasts threads with asynchronous programming and parallel execution, provides Python examples, and discusses I/O-bound and CPU-bound workloads, synchronization, thread pools, race conditions, and deadlocks. ### Source excerpt Understand Concurrency vs Multithreading, including how they differ in task execution, resource sharing, performance, and parallelism. Explore key concepts, practical examples, advantages, limitations, and use cases to determine which approach best suits different software development needs. The post Concurrency vs Multithreading: What's the Difference? appeared first on Talent500 blog. ## Racing Trains DevFeed: [Racing Trains]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-10-08T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [Concurrency](), [Deadlock]() Tags: [feature-articles](), [race-condition](), [race-conditions]() ### AI overview A train reservation app apparently confirmed two passengers for the same seat. The article attributes the duplicate booking to a race condition in the reservation system, where concurrent checks and reservations were not atomic, and speculates that the bug may have gone unfixed because it was not prioritized. ### Source excerpt Robert G sends us a WTF from the outside, but I think we can come up with some hypotheses about what actually happened. Robert was traveling by train. For complicated train reasons, the car he was seated on was not actually scheduled to continue with the train he was booked to ride, the replacement can missed its scheduled arrival, and the result was that Robert was left at a station needing to rebook his travel. Now, given that this is in a region of the world with a fairly robust train network, this represented a hiccup of thirty minutes in a many hour trip. Not a particularly big deal. But Robert didn't want to get stranded any longer than he had to, so he took out his phone, fired up the train company's app, and made sure he had a reservation on the next train. He got the confirmation, and grabbed a coffee while he waited. Not quite thirty minutes later, Robert boarded the train and went towards his reserved seat. Another passenger approached from the opposite direction, and they met at the seat. "Excuse me," Robert said, "but I reserved this one." "No, I reserved this one." Being reasonable people, they both pulled out their phones and pulled up their apps. Sure enough, each one of them was confirmed for that seat. Since the email confirmation had more details than the app provided, they both pulled up the email. Same seat. Both confirmed. The timestamp of the reservation? The same, down to the second. "I don't think the conductor will let us sit in each other's laps," the other passenger said. As it turned out, there was enough room on the train that no lap sitting was required. Everyone got a seat, even if it wasn't the one they reserved. But the story stuck with Robert. A major transit provider had a reservation system that clearly had a race condition. It seemed like the most basic example of race conditions too, the kind you're shown in college to explain the basic concept. "Check if the seat is reserved, and if it's not, reserve it." Because that sequence ## Cursor writes all my code now DevFeed: [Cursor writes all my code now]() Original publisher: [Read original article]() Author: Nick Hodges Published: 2026-10-07T09:00:00Z Content type: opinion Language: en Sources: [InfoWorld]() Topics: [AI-assisted coding](), [Code review](), [Pull Request](), [native-client](), [Parallel reasoning]() Tags: [ai-coding-agents](), [artificial-intelligence](), [artificial-intelligence-careers-developer-generative-ai-roles-software-development](), [careers](), [claude-code](), [codex](), [cursor](), [developer](), [generative-ai](), [multithreading](), [pull-request](), [race-conditions](), [roles](), [software-development](), [writing-code]() ### AI overview The author describes using Cursor in a pull request review workflow to identify and fix more than 20 potential race condition scenarios in code. The experience prompts a broader opinion that AI coding agents are changing software development, reducing manual coding and potentially enabling native apps across devices. ### Source excerpt I was at the gym yesterday, talking with a relatively new CrossFitter named John, and naturally he asked me what I do for a living. I said I write software all day. But it hit me--I don't actually write software anymore. I told him that, nowadays, Cursor writes all my code--I just tell Cursor what to do. I've written about how code isn't important anymore and that we won't be writing code much longer, but somehow that truth finally hit me. And of course, when a truth hits me, someone else has already spoken that truth. Ruby creator David Heinemeier Hansson just a few weeks ago told us to "put down our pencils" at Rails World 2026. The simple fact of the matter is that Cursor, Claude Code, Codex, etc., are better coders than we are. This dawned on me over the past week at my day job where Cursor found a startlingly large number of threading issues in some code that I had written. I'm not ashamed--except for a handful of savants, we all have a very hard time reasoning clearly about complex multithreading. We just don't have enough RAM in our heads to hold all of the possibilities in our brains. Leave it to Cursor But you know who does? Cursor. We use Cursor at work, and we have it wired into our pull request process to examine the code we are reviewing and see if it can find any problems. And boy, does Cursor find problems. In that code I wrote, Cursor found more than 20 different scenarios where race conditions could occur. And then, Cursor fixed them. In minutes. Although I felt a little dumb as a result, I was pleased that our agent found these issues before our customers did. I think we all know that multithreading bugs are all but impossible to track down and fix when a customer reports them. Cursor? It found them and fixed them no problem. It was humbling, to say the least. This has happened before. If you have seen or read Hidden Figures, you know how important a group of Black women were to the early American space program. They were called "computers" because th ## What TLA+ can and can't check DevFeed: [What TLA+ can and can't check]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-09-30T13:27:03Z Content type: article Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [AutoCorrode library](), [Test-driven development](), [Functional programming]() Tags: [boris-cherny](), [claude](), [claude-code](), [formal-methods](), [formal-verification](), [race-conditions](), [tla-plus]() ### AI overview The article explains that TLA+ can model complex concurrent systems and check properties such as invariants, while emphasizing that formal methods cannot guarantee correct code or verify properties that have not been specified. It cautions against treating formal verification as a complete solution for agentic software development. ### Source excerpt Last week Boris Cherny, the inventor of Claude Code, mentioned that Opus was able to use TLA+1 to find race conditions in code. And now everybody on the internet is talking about formal verification. As a long-time educator (1 2) and advocate of TLA+, this is really exciting! TLA+ is great at designing complex concurrent systems and making sure they're bug-free.2 As a long-time advocate of level-headedness, this new euphoria worries me. I read a lot of people saying that formal methods will solve the problem of agentic software development once and for all, and that's nonsense. Enough words have been spilled about the weaknesses of TLA+ in terms of what it can guarantee, like how correct designs don't automatically translate into correct code. So I'd like to focus on a different limitation for this newsletter: to verify a property, we need to have a property to verify! So what are the properties that TLA+ can't even express? (This assumes some basic knowledge of TLA+. If you're a total beginner, check out those [1] [2] things above or read here.) What TLA+ can check TLA+ divides the system into a set of behaviors. Each behavior is a sequence of states, like "light one is green, then yellow, then red". In each state we can express a regular boolean expressions like "Light four is green" or "All lights are red." We can also modify expressions with three "temporal" logical operators: []P ("always P") is true if P is true in the current state and every future state. Example: [](at_most_one_green) is true if every state going forward has no more than one green light. P' ("P prime") is true if P is true in the next state. Example: light="green" && light'="red" is true if the light changes from green to red. <>P ("eventually P") is true if P is true in the current state or in at least one future state. Example: <>(light4 = "yellow") is true if light4 is yellow or is yellow in a future state. When we say that P is a property of the system, we mean it is true in the initial ## Free-Threaded Python Post-Era (Python Language Summit 2026) DevFeed: [Free-Threaded Python Post-Era (Python Language Summit 2026)]() Original publisher: [Read original article]() Author: Seth Larson Published: 2026-09-30T12:00:08Z Content type: article Language: en Sources: [Python Insider]() Topics: [Python](), [Erlang](), [Common Lisp]() Tags: [concurrency](), [deadlocks](), [erlang](), [language-summit](), [language-summit-2026](), [parallelism](), [performance](), [programming](), [programming-languages](), [race-conditions]() ### AI overview At the 2026 Python Language Summit, Tobias Wrigstad, Fridtjof Stoldt, and Donghee Na discussed high-level concurrency primitives for free-threaded Python. The article frames design choices around speed, simplicity, and safety, with examples from C, Erlang, Rust, and Go. ### Source excerpt Tobias Wrigstad, Fridtjof Stoldt, and Donghee Na propose a safe and performant, high-level concurrency model for free-threaded Python ## Claude Opus 5.5 vs. Fable 5.1: Coding accuracy, benchmark results, and cost DevFeed: [Claude Opus 5.5 vs. Fable 5.1: Coding accuracy, benchmark results, and cost]() Original publisher: [Read original article]() Author: Jessica Wachtel Published: 2026-09-29T15:00:00Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Claude](), [Frontier AI](), [Test-driven development](), [Concurrency](), [Deadlock](), [data migrations](), [Deep neural networks]() Tags: [ai-agents](), [ai-models](), [anthropic](), [claude](), [claude-fable-5](), [claude-fable-5-1](), [claude-opus](), [claude-opus-5](), [claude-opus-5-5](), [concurrency](), [concurrency-bugs](), [cursorbench](), [fable](), [fable-5-1](), [opus-5-5](), [python](), [race-conditions](), [software-testing]() ### AI overview The author compares Claude Opus 5.5 and Claude Fable 5.1 on coding tasks using identical API prompts, adaptive thinking, and maximum effort. Across repeated tests, the evaluation tracks hidden-test accuracy, token use, cost, and run time across bug fixing, dependency resolution, and concurrency bugs. The excerpt describes the test setup but does not include the results. ### Source excerpt Anthropic released Claude Opus 5.5 on September 22, saying it performs at the level of Claude Fable 5.1 on most The post Claude Opus 5.5 vs. Fable 5.1: One overthinks, the other cuts corners appeared first on The New Stack. ## Cache invalidation is a distributed systems problem DevFeed: [Cache invalidation is a distributed systems problem]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-13T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [distributed-systems]() Tags: [cache](), [cache-invalidation](), [caching](), [consistency](), [distributed](), [distributed-systems](), [ordering](), [race-conditions](), [redis](), [ttl]() ### AI overview Cache invalidation coordinates updates between a database and a cache without a shared transaction. The article explains how operation ordering can let a concurrent reader repopulate stale data and recommends writing to the database before invalidating the cache, followed by a second invalidation to close the remaining race. ### Source excerpt A cache and a database are two stores that must agree, which makes every invalidation a distributed transaction without a coordinator. The ordering matters more than the mechanism: invalidating before the database write leaves a window where a concurrent reader repopulates the cache with the old value and it stays wrong until the TTL expires. Deleting the key rather than writing the new value removes a whole class of ordering bug, because two concurrent deletes commute and two concurrent writes do not. ## Testing race conditions with memory access tracing and stack-based delay injection DevFeed: [Testing race conditions with memory access tracing and stack-based delay injection]() Original publisher: [Read original article]() Author: Jann Horn Published: 2026-09-08T07:00:00Z Content type: article Language: en Sources: [Project Zero]() Topics: [race-condition](), [Test automation](), [Memory Safety](), [Static code analysis]() Tags: [delay](), [fuzzing](), [memory](), [race-conditions](), [stack](), [testing](), [tracing]() ### AI overview MAccConc explores Linux kernel race conditions by tracing memory accesses and injecting delays at selected points to force thread interleavings. It uses outline ASAN hooks to record accesses through KCOV, then identifies potentially interacting accesses across threads. Count-augmented stack traces identify access points across runs without relying on changing data addresses. Users can explore interleavings with terminal and GUI tools using happens-before constraints; an automatic tester tries A-B-A interleavings. The approach can expose outcomes such as concurrent dup() and close() behavior, but has limits: ASAN may miss direct stack accesses, upstream Linux patches are not yet available, and the prototype's fuzzing support and handling of background work are incomplete. The command-line tool supports two threads; the GUI supports additional execution contexts. ### Source excerpt Many security bugs are race conditions, where multi-threaded execution has to occur with the right interleaving for a negative effect to appear. This creates challenges for several use cases: Confirming bug candidates that have been discovered manually or through static analysis. Regression tests: After fixing a race condition bug, there is often no good way to write a regression test that reliably triggers the bug as part of a test suite. Automatic bug discovery, such as fuzzing: It is hard for a fuzzer to exercise all interesting interleavings of concurrent operations, or reach code paths that are only exercised when operations are racing. ## Microsoft Plugs Nearly 400 Security Holes DevFeed: [Microsoft Plugs Nearly 400 Security Holes]() Original publisher: [Read original article]() Author: BrianKrebs Published: 2026-08-11T21:28:35Z Content type: news Language: en Sources: [Krebs on Security]() Topics: [Microsoft](), [Vulnerabilities](), [Security](), [Windows](), [Software]() Tags: [1password](), [2026](), [afd-sys](), [automox](), [cve-2026-62832](), [cve-2026-68820](), [cve-2026-72971](), [ed-skoudis](), [exploited](), [landon-miles](), [latest-warnings](), [microsoft](), [microsoft-patch-tuesday-august-2026](), [nightmare-eclipse](), [operating-systems](), [patch-tuesday](), [privilege-escalation](), [race-conditions](), [sans-technology-institute](), [security](), [security-tools](), [security-vulnerabilities](), [software](), [time-to-patch](), [windows]() ### AI overview Microsoft released updates addressing at least 398 security vulnerabilities in Windows and supported software. One flaw was being actively exploited, while two others had been publicly disclosed. The release included 42 vulnerabilities rated critical. ### Source excerpt Microsoft today released updates to remedy at least 398 security vulnerabilities in its Windows operating systems and supported software, including one weakness that is already being actively exploited and two others that were publicly detailed prior to today. ## ksmbd - Exploiting CVE-2025-37947 (3/3) DevFeed: [ksmbd - Exploiting CVE-2025-37947 (3/3)]() Original publisher: [Read original article]() Published: 2025-10-07T22:00:00Z Content type: article Language: en Sources: [Doyensec]() Topics: [Memory Safety](), [exploit chaining](), [race-condition]() Tags: [2025](), [auditing](), [automation](), [bug](), [bugs](), [bypass](), [cache](), [cve](), [doyensec](), [doyensecurity](), [environment](), [exploit](), [exploitation](), [exploits](), [function](), [hacking](), [hardening](), [infosec](), [kaslr](), [kernel](), [locking](), [offensive](), [pentest](), [pentester](), [proof-of-concept](), [race-conditions](), [remote](), [reverse-engineering](), [security](), [struct](), [techniques](), [timing](), [vulnerability]() ### AI overview The article demonstrates local root escalation on Ubuntu 22.04.5 using CVE-2025-37947 in ksmbd. When `stream_xattr` is enabled on a writable share, `ksmbd_vfs_stream_write()` truncates the allocation size at `XATTR_SIZE_MAX` but can copy data beyond the allocated buffer if the write position exceeds that limit. The authors shaped kernel page allocations so the overflow could corrupt adjacent `msg_msg` objects, then used message and socket sprays, forged message and pipe structures, a kernel address leak, and a ROP chain to bypass mitigations. The exploit worked on kernel 5.15.0-153-generic but required several attempts; the authors report occasional failure and possible kernel panic. They focused on local escalation: remote exploitation would additionally need an information leak and reliable heap grooming. ### Source excerpt Introduction This is the last of our posts about ksmbd. For the previous posts, see part1 and part2. Considering all discovered bugs and proof-of-concept exploits we reported, we had to select some suitable candidates for exploitation. In particular, we wanted to use something reported more recently to avoid downgrading our working environment. We first experimented with several use-after-free (UAF) bugs, since this class of bugs has a reputation for almost always being exploitable, as proven in numerous articles. However, many of them required race conditions and specific timing, so we postponed them in favor of bugs with more reliable or deterministic exploitation paths. Then there were bugs that depended on factors outside user control, or that had peculiar behavior. Let's first look at CVE-2025-22041, which we initially intended to use. Due to missing locking, it's possible to invoke the ksmbd_free_user function twice: void ksmbd_free_user(struct ksmbd_user *user) { ksmbd_ipc_logout_request(user->name, user->flags); kfree(user->name); kfree(user->passkey); kfree(user); } In this double-free scenario, an attacker has to replace user->name with another object, so it can be freed the second time. The problem is that the kmalloc cache size depends on the size of the username. If it is slightly longer than 8 characters, it will fit into kmalloc-16 instead of kmalloc-8, which means different exploitation techniques are required, depending on the username length. Hence we decided to take a look at CVE-2025-37947, which seemed promising from the start. We considered remote exploitation by combining the bug with an infoleak, but we lacked a primitive such as a writeleak, and we were not aware of any such bug having been reported in the last year. Even so, as mentioned, we restricted ourselves to bugs we had discovered. This bug alone appeared to offer the capabilities we needed to bypass common mitigations (e.g., KASLR, SMAP, SMEP, and several Ubuntu kernel hardening opt ## Concurrent Optimistic Updates in React Query DevFeed: [Concurrent Optimistic Updates in React Query]() Original publisher: [Read original article]() Published: 2025-04-28T00:00:00Z Content type: tutorial Language: en Sources: [TkDodo]() Topics: [React](), [User Interfaces](), [Code]() Tags: [concurrent](), [how-to](), [race-conditions](), [react](), [ui]() ### AI overview A tutorial on implementing optimistic UI updates in React Query when multiple mutations affect the same entity concurrently. It explains how client-side predictions can diverge from server results and how filtering and category changes create edge cases. ### Source excerpt How to build optimistic UI that is resilient to race conditions, even when multiple mutations update the same entity concurrently. ## @apollo/client-integration-nextjs Officially Released DevFeed: [@apollo/client-integration-nextjs Officially Released]() Original publisher: [Read original article]() Author: Lenz Weber-Tronic Published: 2025-04-07T11:26:16Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [apollo-client](), [Server-side rendering]() Tags: [alpha](), [apis](), [apollo](), [apollo-client](), [application](), [cache](), [colocation](), [hybrid](), [integration](), [introduced](), [nextjs](), [queries](), [race-conditions](), [stable]() ### AI overview The Apollo Client integration for Next.js is described as stable after nearly two years of testing and production use, with the earlier hydration race-condition concerns not reported in the wild. The package name has changed from an experimental name to a stable one, so existing users need to update imports; the names themselves are omitted from the supplied excerpt. A new implementation supports multipart responses, enabling query preloading in RSC and cache rehydration. The described approach combines fragment colocation and streaming SSR to render available results early, then handles later requests on the client through the normalized cache. Setup guidance is referenced for RSC and SSR, but its details are not included here. ### Source excerpt Exciting News: Officially Released! After almost two years of testing, gathering user feedback, and refining our approach, we're thrilled to announce that has been released as a stable package with the new name . This milestone marks a significant achievement for the Apollo Client community 🎉 A Brief History of Apollo Client + Next.js When we first started working on this integration, we were concerned about potential race conditions during hydration with how Next.js works. ## How do I store inventory data in my Stripe application DevFeed: [How do I store inventory data in my Stripe application]() Original publisher: [Read original article]() Author: Ben Smith Published: 2024-12-16T00:00:00Z Content type: tutorial Language: en Sources: [Stripe Dot Dev Blog]() Topics: [event driven](), [Serverless](), [Event-Streaming](), [Firestore](), [Back end](), [Internet of things]() Tags: [amazon-dynamodb](), [aws-lambda](), [dynamodb](), [event-driven](), [event-driven-architecture](), [inventory-management](), [iot](), [race-conditions](), [real-time](), [stripe]() ### AI overview This tutorial describes an event-driven, serverless inventory system that synchronizes stock with Stripe payment events. Using a DevRel Swag Store example, it discusses AWS services including DynamoDB, Lambda, and IoT Core, and explains inventory challenges such as race conditions, dynamic stock checks, and choices between Payment Links and custom payment processes. ### Source excerpt Explore the critical importance of managing real-time inventory updates for both online marketplaces and physical stores. This blog post details the development of an event-driven architecture designed to synchronize inventory levels with Stripe payment events using AWS cloud services. The demonstration centers around the DevRel Swag Store, showcasing a practical application used at the GOTO Chicago event, which integrates serverless technologies like Amazon DynamoDB, AWS Lambda, and IoT Core for efficient inventory management. Learn about the challenges of using Stripe metadata for inventory, and discover robust solutions for handling race conditions and implementing dynamic inventory checks. Additionally, understand the trade-offs between using Stripe Payment Links and custom payment processes for real-time stock validation. ## Fearless Concurrency Ep.4: Understanding Mutexes and Thread Safety in Rust DevFeed: [Fearless Concurrency Ep.4: Understanding Mutexes and Thread Safety in Rust]() Original publisher: [Read original article]() Published: 2024-10-25T00:00:00Z Content type: article Language: en Sources: [William Kennedy]() Topics: [Rust](), [thread safe data sharing](), [locking](), [Memory Safety](), [synchronization](), [race-condition](), [Distributed Systems & Parallel Computing](), [undefined\_behavior](), [typesafety]() Tags: [avoiding-race-conditions-in-rust](), [concurrency](), [concurrency-and-memory-management-in-rust](), [concurrency-in-rust](), [managing-shared-data-in-rust](), [mutex](), [mutex-and-drop-trait-in-rust](), [mutexes-in-rust](), [race-conditions](), [rust](), [rust-concurrency-best-practices](), [rust-concurrency-guide](), [rust-drop-trait-in-concurrency](), [rust-multithreading-tutorial](), [rust-mutex-example](), [rust-mutex-vs-lock](), [rust-ownership-and-borrowing-in-concurrency](), [rust-thread-safety](), [rust-thread-safety-without-garbage-collection](), [safety](), [thread-safety](), [thread-synchronization-in-rust](), [understanding-poisoned-mutexes-in-rust]() ### AI overview The excerpt explains how Rust's ownership and borrowing rules govern variables shared across threads, including exclusive access and ensuring values outlive the threads that use them. It describes mutexes as enforcing mutual exclusion and says Rust's scope-based resource cleanup releases locks automatically. It also notes that the episode covers poisoned mutexes, which can result when a thread panics while holding a lock. The excerpt does not identify the specific Rust types or features by name. ### Source excerpt Introduction: Welcome to Episode 4 of Fearless Concurrency in Rust! In this episode, we explore how Rust's ownership and borrowing rules impact multithreaded programming, focusing on the complexities of managing memory in a concurrent environment. We'll break down how Rust's strict borrowing and ownership model ensures safety and prevents common pitfalls like data races and undefined behavior, making it an ideal choice for building reliable multithreaded applications. Thread Safety and Ownership: Managing variables across threads using Rust's ownership rules. ## Safe and scalable signal handling in Temporal Workflows DevFeed: [Safe and scalable signal handling in Temporal Workflows]() Original publisher: [Read original article]() Author: Drew Hoskins Published: 2024-10-15T16:00:00Z Content type: article Language: en Sources: [Temporal]() Topics: [Concurrency](), [Developer experience](), [SDK](), [Go Language](), [Python](), [AI Chat]() Tags: [announce](), [concurrency](), [concurrency-control](), [developer-experience](), [go](), [handler](), [how-to](), [python](), [race-conditions](), [safety]() ### AI overview This article explains how Signal and Update handlers let callers interact with Temporal Workflows, focusing on concurrency problems, message-processing styles, and improvements intended to make handlers safer and easier to use. ### Source excerpt Avoid race conditions in Temporal Workflows. Learn how new Signal and Update handler tools improve safety, initialization, and concurrency control. ## Fearless Concurrency Ep.2: Managing Threaded Programs and Data Races in Rust DevFeed: [Fearless Concurrency Ep.2: Managing Threaded Programs and Data Races in Rust]() Original publisher: [Read original article]() Published: 2024-09-26T00:00:00Z Content type: article Language: en Sources: [William Kennedy]() Topics: [Rust](), [thread safe data sharing](), [Concurrency](), [race-condition](), [Distributed Systems & Parallel Computing](), [shared mutable state]() Tags: [atomics](), [concurrency](), [concurrency-in-rust](), [creating-threads-in-rust](), [managing-threads-in-rust](), [operating-system](), [ownership](), [preventing-race-conditions-in-rust](), [race-conditions](), [rust](), [rust-atomic-operations](), [rust-borrow-checker-for-threads](), [rust-concurrency-tutorial](), [rust-concurrent-programming](), [rust-data-race-prevention](), [rust-join-handles](), [rust-multithreading](), [rust-ownership-system](), [rust-thread-safety](), [rust-threading-basics](), [safety](), [std-thread-spawn-in-rust](), [thread-lifecycle-in-rust](), [threading]() ### AI overview The excerpt explains how Rust's std::thread::spawn starts operating-system threads and how joining their handles ensures they finish before the main program exits; handles can also return values. Rust's ownership and borrowing rules restrict shared access to multiple immutable references or one mutable reference, allowing the borrow checker to prevent unsafe data races. The episode also covers atomics for modifying shared state safely. The standard-library threading interface is presented as straightforward, but without platform-specific features such as thread priority or CPU affinity. The supplied text ends mid-sentence, so this overview reflects only the available excerpt. ### Source excerpt Introduction: Welcome to Episode 2 of the Fearless Concurrency in Rust series! In this episode, Herbert Wolverson dives into the fundamentals of threading in Rust, demonstrating how to use Rust's threading capabilities effectively while maintaining safety. The focus is on using std::thread to spawn threads, preventing data races, and ensuring efficient, reliable multithreaded execution. Creating Threads in Rust: How to spawn and manage operating system threads using std::thread::spawn. Handling Data Races: Rust's borrow checker and ownership system preventing unsafe data access across threads. ## Req API Client Testing DevFeed: [Req API Client Testing]() Original publisher: [Read original article]() Author: Wojtek Mach Published: 2024-04-02T00:00:00Z Content type: tutorial Language: en Sources: [Dashbit Blog]() Topics: [Software Testing](), [Elixir](), [HTTP](), [client](), [implementation](), [reliability-engineering](), [Feathers]() Tags: [api](), [boilerplate](), [client](), [coupling](), [elixir](), [http](), [http-client](), [mock](), [race-conditions](), [req](), [testing](), [tests]() ### AI overview A guide to testing HTTP API clients built with Req in Elixir. It explains the drawbacks of testing external network requests with mocks and stubs, then discusses explicit contracts, configurable implementations, and race conditions caused by global application state. ### Source excerpt A guide through different testing approaches with Req-based API clients. ## Testing Zero Touch Production Platforms and Safe Proxies DevFeed: [Testing Zero Touch Production Platforms and Safe Proxies]() Original publisher: [Read original article]() Published: 2023-05-03T22:00:00Z Content type: article Language: en Sources: [Doyensec]() Topics: [Production Engineering](), [DevSecOps](), [DevOps](), [rate-limiting](), [Logging](), [ssh](), [virtual machines]() Tags: [auditing](), [automate](), [automation](), [command](), [devsecops](), [doyensec](), [doyensecurity](), [exploit](), [hacking](), [hooks](), [infosec](), [logging](), [offensive](), [pattern](), [pentest](), [pentester](), [pitfalls](), [platforms](), [production](), [proxy](), [race-conditions](), [rate-limiting](), [reverse-engineering](), [security](), [sre](), [ssh](), [testing](), [traceability](), [vulnerability]() ### AI overview Zero Touch Production (ZTP) reduces production risk by automating most operations and routing necessary manual changes through audited proxies. A safe proxy checks role-based ACLs for permitted RPCs, sends approved requests to target systems, and logs commands. The excerpt warns that ZTP platforms can themselves become high-value attack paths. Review web frontends and approval webhooks; protect centralized safety checks and their network communications; use secure default policy templates; control log retention and rate limits; keep permission ownership outside user-controlled bypass paths; validate command arguments; and require a reason, traceability, and fine-grained data scopes. Compare RPC and CLI authorization behavior, ensure service rules cannot weaken global rules, and prevent races with queued operations. Emergency break-glass access should trigger alerts and heavy logging. The excerpt describes these as common testing concerns, not a comprehensive implementation standard. ### Source excerpt As more companies develop in-house services and tools to moderate access to production environments, the importance of understanding and testing these Zero Touch Production (ZTP) platforms grows 1 2. This blog post aims to provide an overview of ZTP tools and services, explore their security role in DevSecOps, and outline common pitfalls to watch out for when testing them. SRE? ZTP? Safe Proxies In Production The safety & security roles of Safe Proxies What does ZTP look like today What to look for when auditing ZTP tools/services A. Web Attack Surface B. Hooks C. Safe Centralization D. Insecure Default Templates E. Logging F. Rate-limiting G. ACL Ownership H. Command Safeguards I. Traceability and Scoping J. Scoped Access K. Different Interfaces, Different Requirements L. Service vs Global rules M. Command Parsing N. Race Conditions O. Break-glass Conclusions References SRE? ZTP? "Every change in production must be either made by automation, prevalidated by software or made via audited break-glass mechanism." - Seth Hettich, Former Production TL, Google This terminology was popularized by Google's DevOps teams and is the golden standard to this day. According to this picture, there are SREs, a selected group of engineers that can exclusively use their SSH production access to act when something breaks. But that access introduces reliability and security risks if they make a mistake or their accounts are compromised. To balance this risk, companies should automate the majority of the production operations while providing routes for manual changes when necessary. This is the basic reasoning behind what was introduced by the "Zero Touch Production" pattern. Safe Proxies In Production The "Safe Proxy" model refers to the tools that allow authorized persons to access or modify the state of physical servers, virtual machines, or particular applications. From the original definition: At Google, we enforce this behavior by restricting the target system to accept only calls ## GitHub CLI 2.22.1 DevFeed: [GitHub CLI 2.22.1]() Original publisher: [Read original article]() Published: 2023-01-27T17:42:53Z Content type: release Language: en Sources: [GitHub CLI]() Topics: [GitHub](), [GitHub CLI extension](), [GitHub Issues]() Tags: [github](), [github-cli](), [github-enterprise-server](), [issue](), [pr](), [race-conditions](), [test]() ### AI overview GitHub CLI 2.22.1 fixes editing issues and pull requests on GitHub Enterprise Server, adds completion for the `--reviewer` flag, renames `--confirm` to `--yes` for destructive commands, and fixes race conditions in a test. The release also includes a first contribution from benjlevesque. ### Source excerpt ## What's Changed * Fix `gh issue edit` and `gh pr edit` for GitHub Enterprise Server by @mislav in https://github.com/cli/cli/pull/6921 * Add `--reviewer` flag completion by @benjlevesque in https://github.com/cli/cli/pull/6873 * Rename `--confirm` flag to `--yes` for various destructive commands by @damiensedgwick in https://github.com/cli/cli/pull/6915 * Fix race conditions in invoker_test by @cmbrose in https://github.com/cli/cli/pull/6905 ## New Contributors * @benjlevesque made their first contribution in https://github.com/cli/cli/pull/6873 **Full Changelog**: https://github.com/cli/cli/compare/v2.22.0...v2.22.1 ## FLIP-147: Support Checkpoints After Tasks Finished - Part Two DevFeed: [FLIP-147: Support Checkpoints After Tasks Finished - Part Two]() Original publisher: [Read original article]() Published: 2022-07-11T00:00:00Z Content type: article Language: en Sources: [Apache Flink]() Topics: [implementation](), [execution](), [Stateful](), [Graphs](), [Remote Procedure Call (RPC)](), [Parallelism](), [container]() Tags: [checkpoint](), [container](), [execution](), [format](), [graph](), [implementation](), [parallel](), [protocol](), [race-conditions](), [recovery](), [rpc](), [snapshots](), [stateful]() ### AI overview This article explains Apache Flink's implementation for taking checkpoints after some tasks have finished. It describes selecting new checkpoint source tasks, handling races during triggering, extending the checkpoint format to record finished operators, and skipping those operators during recovery. ### Source excerpt In the first part of this blog, we have briefly introduced the work to support checkpoints after tasks get finished and revised the process of finishing. In this part we will present more details on the implementation, including how we support checkpoints with finished tasks and the revised protocol of the finish process. Implementation of support Checkpointing with Finished Tasks # As described in part one, to support checkpoints after some tasks are finished, the core idea is to mark the finished operators in checkpoints and skip executing these operators after recovery. [Next page]()