# Concurrency A computing concept in which a system or application handles more than one activity at a time. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Java 27 Reaches GA With The G1 Garbage Collector By Default Everywhere DevFeed: [Java 27 Reaches GA With The G1 Garbage Collector By Default Everywhere]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-15T13:39:00Z Content type: news Language: en Sources: [Phoronix]() Topics: [Java](), [openjdk](), [releases](), [Concurrency](), [Post-Quantum](), [TLS (Transport Layer Security)]() Tags: [availability](), [concurrency](), [desktop-linux](), [java](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [openjdk](), [phoronix](), [phoronix-test-suite](), [post-quantum](), [release](), [structured-concurrency](), [tls](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview OpenJDK 27 has reached general availability. Its G1 garbage collector is now enabled by default across environments, replacing Serial as the default in constrained environments after improvements in throughput, latency, and memory usage. The release also includes post-quantum hybrid key exchange for TLS 1.3, preview features for lazy constants and structured concurrency, compact object headers by default, and an incubating Vector API. ### Source excerpt Oracle christened Java 27 today with the OpenJDK 27 release reaching general availability (GA) status... ## How automated testing improves development of complex features DevFeed: [How automated testing improves development of complex features]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-15T13:16:49Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Testing](), [coding](), [Concurrency](), [Database]() Tags: [automated](), [concurrency](), [database](), [regression](), [testing]() ### AI overview The author contrasts manual testing with automated testing while developing job cancellation in a work queue. Automated tests help expose concurrency and regression risks, cover corner cases, and simulate mid-execution cancellation more reliably. ### Source excerpt It's hard for me to overstate how much my coding has improved since embracing automated testing. And yet, I used to think of it as a waste of time! This post is for my former self, demonstrating why I embrace the practice now. Allow me to demonstrate with ## Why your Redis is slow: it is single threaded and you sent it KEYS DevFeed: [Why your Redis is slow: it is single threaded and you sent it KEYS]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Redis](), [Latency](), [Concurrency]() Tags: [blocking](), [caching](), [command](), [latency](), [observability](), [performance](), [redis](), [single-threaded]() ### AI overview The article explains that Redis executes commands one at a time on a single thread, so expensive commands can block every client and cause latency spikes across all operations. It identifies KEYS as a key offender and also discusses large DEL operations, unbounded LRANGE, SMEMBERS on large sets, and FLUSHALL. Redis 6 threaded I/O helps with socket handling but does not change single-threaded command execution. ### Source excerpt Redis executes commands one at a time on a single thread, so any command that takes 200ms makes every other client wait 200ms regardless of how trivial their request was. KEYS scans the entire keyspace and is the obvious offender, but DEL on a large collection, unbounded LRANGE, SMEMBERS on a big set and FLUSHALL are all linear in the size of the data and block identically. The tell is a latency spike across all operations at once while CPU and network look normal. ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Java News Roundup: New OpenJDK JEPs, CDI 5.0, Spring, Open Liberty, RefactorFirst, ADK for Kotlin DevFeed: [Java News Roundup: New OpenJDK JEPs, CDI 5.0, Spring, Open Liberty, RefactorFirst, ADK for Kotlin]() Original publisher: [Read original article]() Author: Michael Redlich Published: 2026-09-14T20:15:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Java](), [Concurrency](), [JDK 27](), [releases](), [Jakarta EE](), [Gradle](), [Groovy]() Tags: [2026](), [architecture-design](), [concurrency](), [development](), [gradle](), [groovy](), [jakarta-ee](), [java](), [java-news-roundup-sep07-2026](), [jdk-27](), [jdk-28](), [kotlin](), [micronaut](), [news](), [open-jdk](), [open-liberty](), [refactorfirst](), [releases](), [spring-framework](), [tornadovm]() ### AI overview A Java news roundup for September 7, 2026, covers new OpenJDK proposals for ahead-of-time compilation and structured concurrency, Jakarta CDI 5.0 and ADK for Kotlin 1.0, Open Liberty, TornadoVM, RefactorFirst, Micronaut, Groovy 6.0, and Gradle 9.8. It also reports current JDK 27 and JDK 28 early-access builds and describes improvements in TornadoVM 6.1.0. ### Source excerpt This week's Java roundup for September 7th, 2026, features news highlighting: new JEPs for ahead-of-time compilation and structured concurrency; GA releases of Jakarta CDI 5.0 and ADK for Kotlin 1.0; the September 2026 edition of Open Liberty; point releases of TornadoVM and RefactorFirst; a maintenance release of Micronaut; and first releases candidates of Groovy 6.0 and Gradle 9.8. By Michael Redlich ## Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2 DevFeed: [Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:16Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [Amazon CloudWatch](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Database](), [Amazon EC2](), [Architecture & Design](), [Amazon Web Services](), [AWS CloudFormation]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [aws](), [aws-cloudformation](), [concurrency](), [monitoring](), [performance](), [postgresql](), [rds-for-postgresql](), [simulation](), [technical-how-to]() ### AI overview This tutorial explains how to diagnose and resolve lock contention in Amazon Aurora PostgreSQL using Amazon CloudWatch Database Insights. It demonstrates Lock Analysis and the Lock Tree visualization for identifying blocking sessions, then covers immediate fixes, configuration changes, optimistic concurrency control, asynchronous processing, SKIP LOCKED, and row splitting. ### Source excerpt Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput. ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## Python 3.14 free-threaded build is now available in RHEL DevFeed: [Python 3.14 free-threaded build is now available in RHEL]() Original publisher: [Read original article]() Author: Lumír Balhar Published: 2026-09-14T13:01:41Z Content type: release Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Python 3.14](), [Python](), [Concurrency](), [Linux](), [Concurrent Programming](), [cpu]() Tags: [cpu](), [developer-tools](), [developers](), [linux](), [performance](), [programming-languages-frameworks](), [python](), [python-3-14](), [red-hat](), [release](), [rhel](), [thread](), [upstream-release]() ### AI overview Red Hat Enterprise Linux 9.8 and 10.2 now provide a free-threaded Python 3.14 build through the Red Hat CodeReady Linux Builder repositories. With the GIL disabled, CPU-bound Python threads can execute in parallel across multiple CPU cores within a single process. The article explains expected performance benefits, installation, verification, and possible thread-safety issues in existing code. ### Source excerpt Developers on Red Hat Enterprise Linux 9.8 and 10.2 can now test full parallel CPU execution in Python using the new free-threaded Python 3.14 build. In addition to the regular Python 3.14 interpreter, we also provide the free-threaded variant in the Red Hat CodeReady Linux Builder repositories. Python 3.14 is the first upstream release where the free-threaded build is officially supported rather than experimental. The post Python 3.14 free-threaded build is now available in RHEL appeared first on Red Hat Developer. ## 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 ## How to Prevent Race Conditions in Django DevFeed: [How to Prevent Race Conditions in Django]() Original publisher: [Read original article]() Author: Mari Published: 2026-09-11T21:50:46Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Concurrency](), [Database](), [SQLite](), [Docker]() Tags: [backend](), [concurrency](), [database](), [django](), [how-to](), [postgresql](), [python](), [race-condition](), [sqlite](), [transactions]() ### AI overview A Django tutorial that demonstrates a credit-spending race condition and explains how database transactions and row locks prevent concurrent requests from using the same credit. ### Source excerpt Let's say you have enough credit left to generate one more image in an AI app. You submit a request in one browser tab, then submit another in a second tab before the first finishes. The app accepts b ## A Bootiful Podcast: Netflix's Paul Bakker DevFeed: [A Bootiful Podcast: Netflix's Paul Bakker]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Concurrency](), [Concurrent Programming](), [Tooling]() Tags: [ai](), [batch](), [boot](), [cloud](), [complexity](), [concurrency](), [event-driven](), [java](), [microservices](), [netflix](), [podcast](), [reactive](), [serverless](), [spring](), [tooling](), [web-applications]() ### AI overview A podcast interview with Netflix's Paul Bakker on scaling Java, adopting newer JDKs to reduce costs, and using virtual threads and structured concurrency instead of reactive complexity. It also previews AI-powered tooling and Project Valhalla's future. ### Source excerpt Hi, Spring fans! This week's interview is a must-watch if you care about where Java is headed next. This week, I talk to Netflix's Paull Bakker! We dig into Netflix's real-world playbook for scaling Java, cutting costs with newer JDKs, and replacing reactive complexity with virtual threads and structured concurrency. Plus, there's a sneak peek at the exciting future of Java, from AI-powered tooling to the upcoming Valhalla era. ## 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 ## Building async Python applications with Tortoise ORM and Amazon Aurora DSQL DevFeed: [Building async Python applications with Tortoise ORM and Amazon Aurora DSQL]() Original publisher: [Read original article]() Author: Lasita Bhattacharya Published: 2026-09-09T15:27:43Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [Concurrency](), [AWS IAM](), [Database](), [CRUD]() Tags: [advanced-300](), [amazon-aurora](), [concurrency](), [database](), [dsql](), [how-to](), [iam](), [orm](), [postgresql](), [python](), [rideshare](), [serverless](), [technical-how-to]() ### AI overview A tutorial for building a high-concurrency asynchronous Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. It covers UUID-based models, IAM-authenticated asyncpg connections, OCC retry handling, and asynchronous CRUD operations. ### Source excerpt Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic. ## Java Weekly, Issue 662 DevFeed: [Java Weekly, Issue 662]() Original publisher: [Read original article]() Author: baeldung Published: 2026-09-06T09:25:19Z Content type: article Language: en Sources: [Baeldung]() Topics: [IntelliJ IDEA](), [Concurrency](), [Tooling](), [Cryptography](), [elasticsearch]() Tags: [concurrency](), [cryptography](), [elasticsearch](), [intellij-idea](), [java](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [tooling](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview A Java ecosystem roundup led by a hands-on look at Project Loom in IntelliJ IDEA, covering virtual threads, scoped values, structured concurrency, and related tooling. It also links to Java and Spring articles, podcasts, release announcements, code-review commentary, and IntelliJ IDEA Conf 2026. ### Source excerpt The three Loom pillars and, more importantly, the tooling. Plus IntelliJ IDEA Conf 2026 getting close. The post Java Weekly, Issue 662 first appeared on Baeldung. ## How Databases Keep Their Sanity with Concurrency Control DevFeed: [How Databases Keep Their Sanity with Concurrency Control]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-03T15:31:17Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Databases](), [Concurrency]() Tags: [article](), [concurrency](), [concurrency-control](), [databases]() ### AI overview The article introduces how databases handle bugs related to concurrency control, but the supplied evidence does not provide the specific methods or findings discussed. ### Source excerpt So how do we handle such bugs? This is what we are going to try to answer in this article. ## Goroutine Leak Profiles DevFeed: [Goroutine Leak Profiles]() Original publisher: [Read original article]() Author: Vlad Saioc Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go Language](), [Concurrency](), [debugging](), [Tooling]() Tags: [bugs](), [concurrency](), [debugging](), [go](), [performance](), [production](), [testing](), [tools]() ### AI overview Go 1.27 introduces a goroutine leak profiler for detecting goroutines blocked permanently in running programs, including production systems. ### Source excerpt Go 1.27 includes new goroutine leak profiles. ## How LlamaIndex uses Temporal to scale reliable document orchestration DevFeed: [How LlamaIndex uses Temporal to scale reliable document orchestration]() Original publisher: [Read original article]() Author: Adrian Lyjak Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [llamaindex](), [Orchestration](), [RabbitMQ](), [Concurrency]() Tags: [api](), [concurrency](), [llamaindex](), [orchestration](), [rabbitmq](), [scale](), [temporal](), [temporal-voices]() ### AI overview This article explains how LlamaIndex moved document-processing workloads from RabbitMQ to Temporal. It describes the complexity of processing documents page by page and reports that Temporal helped the team improve durability and concurrency while processing tens of millions of pages per day for the Batch API. ### Source excerpt Learn how LlamaIndex moved from RabbitMQ to Temporal to make LlamaParse more durable, improve concurrency, and process tens of millions of pages per day. ## Hyperloop B: the coroutine engine behind Appwrite 2.0 DevFeed: [Hyperloop B: the coroutine engine behind Appwrite 2.0]() Original publisher: [Read original article]() Author: Luke B. Silver Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Coroutines](), [Concurrency](), [PHP](), [Benchmark]() Tags: [architectures](), [backend](), [benchmark](), [concurrency](), [coroutine](), [coroutines](), [cpu](), [memory](), [php](), [platform](), [process](), [redis]() ### AI overview The article explains Hyperloop B, a coroutine-based engine behind Appwrite 2.0. It describes how yielding during I/O lets one process serve many concurrent requests, reporting 7x higher requests per second and 6x lower memory use on an I/O-bound benchmark. ### Source excerpt Hyperloop B is the Swoole coroutine engine behind Appwrite 2.0. Here is what it is, the concurrency problem it solves, and the 7x I/O throughput it buys. ## A C++ Exception Handler Retries Without Releasing Deadlock Resources DevFeed: [A C++ Exception Handler Retries Without Releasing Deadlock Resources]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-08-26T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [C++](), [Deadlock](), [Exception](), [Concurrency]() Tags: [c-plus-plus](), [code](), [codesod](), [deadlock](), [exception]() ### AI overview The article examines a C++ exception handler that retries after detecting a deadlock. It explains that the retry mechanism appears to jump back to the start of the block without releasing resources, so it can leave the deadlock unresolved and potentially add more deadlocks. ### Source excerpt Kevin sends us an exception handler from C++. Let's see if we can spot what's going wrong: catch (Exception::Deadlock) { retry; } When we catch a deadlock happening, we retry. That's not a keyword in C++, and looking at how it's used, it has to be some kind of macro, and I suspect that the macro is hiding a goto underneath it. The real problem, though, is that we suspect we're in a deadlock situation. That means this thread is waiting on a resource held by another thread which is waiting for a resource held by this thread. Neither train may continue until the other has passed. So this retry only works if it releases the resource held by this thread (letting the deadlocking thread proceed). But does it? Not according ot Kevin. The code already had a pile of deadlocks in it, so they brought in a highly paid consultant to try and fix them by reordering access and tracing where mutexes were causing issues. This retry just jumps back up to the top of the block, without releasing any resources. It "seems the consultant wanted to add some deadlocks of their own," Kevin says. [Advertisement] BuildMaster allows you to create a self-service release management platform that allows different teams to manage their applications. Explore how! ## When code is abundant DevFeed: [When code is abundant]() Original publisher: [Read original article]() Author: Bill Staples Published: 2026-08-24T00:00:00Z Content type: opinion Language: en Sources: [GitLab]() Topics: [AI Development](), [Vibe coding](), [Software Engineering](), [GitLab](), [sdlc](), [anthropic](), [Concurrency](), [Graphs]() Tags: [ai](), [ai-agents](), [architecture](), [code](), [concurrency](), [devsecops-platform](), [enterprise](), [news](), [policy](), [software-development](), [workflow]() ### AI overview This opinion argues that AI agents and large language models are making code production cheaper and faster, shifting the main challenge for enterprise software development toward context, verification, governance, identity, policy, approval, and audit. It discusses how the software development lifecycle and its underlying architecture must adapt to machine-scale concurrency and automated workflows. ### Source excerpt I returned from the holiday break in January convinced that something fundamental had changed. Large language models had reached the point where they could produce useful code reliably enough, and cheaply enough, to change the economics of software development. Engineers everywhere seemed to be experimenting with the same thing: not just asking an AI assistant for suggestions, but giving agents real work and seeing how far they could take it. I started thinking about what happens if that continues. What changes when producing code is no longer the primary constraint in building software? I wrote those thoughts down in a board memo in January. In May, I published part of that thesis in GitLab's Act 2: the cost and time of producing software was collapsing, machines would increasingly build software under human direction, and the architecture underneath software development would have to change with it. In June, at GitLab Transcend, we showed the first pieces of that architecture: source control rebuilt for machine-scale concurrency, GitLab Orbit as a context graph spanning the software lifecycle, and governance around agent identity, policy, approval and audit. Then, on August 21, Anthropic published The AI-Native SDLC Playbook. It opens with a simple statement: "Code is no longer the bottleneck." I agree. Anthropic's playbook is a practical description of how the development lifecycle changes when agents can move implementation dramatically faster: planning becomes machine-readable, handoffs become automated, verification moves into the loop, and human judgment concentrates at the gates. What interests me is what happens one level beyond the workflow. If code is no longer the primary constraint, what becomes scarce? What architecture does an enterprise need when people, agents and multiple models are all acting across the software lifecycle at machine speed? And where does durable value move when generating the code itself becomes increasingly abundant? Over the pas ## 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 ... ## Connection pool sizing is a queueing theory problem, not a tuning knob DevFeed: [Connection pool sizing is a queueing theory problem, not a tuning knob]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-22T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [connection pool](), [queueing theory](), [Latency](), [Database](), [Concurrency]() Tags: [capacity-planning](), [concurrency](), [connection-pool](), [cpu](), [database](), [database-performance](), [hikaricp](), [inference](), [latency](), [network](), [postgresql](), [query](), [queueing-theory]() ### AI overview This article explains how to size a connection pool using Little's law: concurrency equals throughput multiplied by connection holding time. It argues that increasing the pool beyond the database's parallel execution capacity can move the queue, increase latency, and leave throughput flat. It also identifies excessive holding time from network round trips, N+1 queries, long transactions, and model inference calls as common causes of pool exhaustion. ### Source excerpt Pool size follows from Little's law: concurrency equals throughput multiplied by holding time, so 500 requests per second each holding a connection for 20ms needs ten connections rather than a round number someone picked. Past the point where the database can execute requests in parallel, adding connections moves the queue rather than removing it, and latency grows while throughput stays flat. The number that actually reaches the database is the pool size multiplied by the instance count, which is usually the number nobody has calculated. ## Low-latency, high-throughput SQS event processing with AWS Lambda provisioned mode DevFeed: [Low-latency, high-throughput SQS event processing with AWS Lambda provisioned mode]() Original publisher: [Read original article]() Author: Ben Freiberg Published: 2026-08-10T13:42:03Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Amazon Simple Queue Service (SQS)](), [AWS Lambda](), [Latency](), [Low Latency](), [Amazon Web Services](), [Concurrency](), [Kafka](), [Internet of things](), [telemetry]() Tags: [amazon-simple-queue-service-sqs](), [amazon-sqs](), [apache-kafka](), [aws](), [aws-lambda](), [concurrency](), [event-driven](), [flash](), [fraud](), [kafka](), [latency](), [low-latency](), [payment-processing](), [real-time](), [telemetry]() ### AI overview AWS explains how provisioned mode for Amazon SQS event source mappings with AWS Lambda provides explicit control over event pollers to support low-latency, high-throughput event processing. The article describes default and provisioned scaling, concurrency limits, throughput, and use cases including real-time payments, fraud detection, IoT telemetry, and flash-sale order fulfillment. ### Source excerpt Customers building event-driven applications on AWS rely on Amazon Simple Queue Service (Amazon SQS) and AWS Lambda event source mappings (ESMs) to process millions of events every day. The fully managed polling infrastructure of ESMs eliminates the need to write and maintain custom code. You can focus on business logic while Lambda handles scaling, batching, [...] ## Data Engineering Weekly #282 DevFeed: [Data Engineering Weekly #282]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-10T01:21:26Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [data-platforms](), [data observability](), [Graphs](), [semantic-layer](), [Large Language Model](), [Apache Iceberg](), [gRPC](), [Concurrency](), [Caching](), [Optimization](), [Streaming]() Tags: [caching](), [chaos](), [concurrency](), [data](), [data-engineering](), [data-platforms](), [data-quality](), [knowledge-graphs](), [llms](), [newsletter](), [observability](), [pipelines](), [semantic-layer](), [streaming]() ### AI overview Data Engineering Weekly #282 is a newsletter covering data platform fundamentals, semantic layers, ontology-backed knowledge graphs, converged databases, AI modernization, and Netflix's real-time distributed graph query architecture. It highlights composable architectures, data quality and observability, evolving schemas supported by LLM-assisted extraction, Iceberg full-text search, and optimization techniques including concurrency control, streaming filters, and caching. ### Source excerpt The Weekly Data Engineering Newsletter [Next page]()