# distributed-transaction Published articles for distributed-transaction. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## When the action succeeds and the audit write doesn't DevFeed: [When the action succeeds and the audit write doesn't]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-04T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Logging](), [systems](), [payload]() Tags: [audit](), [distributed-transaction](), [logging](), [metric](), [retry]() ### AI overview The article examines why audit logs can become unreliable when an application performs an action and writes its audit record as separate operations. It describes silent gaps, phantom events, and duplicate events caused by failures, ordering, and retries, emphasizing that audit records must remain trustworthy evidence. ### Source excerpt When the action succeeds and the log write fails, you get a gap that nothing alerts on. Here is the write path that closes it. ## Dropbox's Former Most Senior Eng: Building Great Systems and Advice for the AI Era | James Cowling DevFeed: [Dropbox's Former Most Senior Eng: Building Great Systems and Advice for the AI Era | James Cowling]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-05-25T10:00:57Z Content type: opinion Language: en Sources: [The Developing Dev]() Topics: [systems](), [distributed-systems](), [Concurrency](), [Transactions](), [Computer science](), [Replication](), [Protocol (disambiguation)]() Tags: [concurrency](), [consensus](), [distributed](), [distributed-system](), [distributed-transaction](), [protocol](), [replication](), [systems](), [technical](), [transactions]() ### AI overview A podcast interview with James Cowling, CTO of Convex and former senior engineer at Dropbox, about designing abstractions and large-scale transactional systems. Cowling discusses Granola, distributed transaction coordination, concurrency, Byzantine fault tolerance, and career advice in the context of AI. ### Source excerpt Transcript & Audio ## Distributed Transaction Design: Trade-offs Between Consistency and Scalability DevFeed: [Distributed Transaction Design: Trade-offs Between Consistency and Scalability]() Original publisher: [Read original article]() Author: System Design Classroom Published: 2025-09-25T11:43:41Z Content type: article Language: en Sources: [System Design Classroom]() Topics: [Scalability](), [Transactions](), [Architecture & Design]() Tags: [distributed-transaction](), [performance](), [scalability](), [transactions]() ### AI overview This article examines distributed transaction design through Two-Phase Commit, Saga with Orchestrator, and Saga with Choreography. It explains that these approaches protect distributed workflows but involve trade-offs between consistency, performance, locking, failure recovery, and scalability. ### Source excerpt Reality Doesn't Work That Way. ## Distributed transactions: What, why, and how to build a distributed transactional application DevFeed: [Distributed transactions: What, why, and how to build a distributed transactional application]() Original publisher: [Read original article]() Author: Charlie Custer Published: 2023-02-16T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [Transactions](), [Databases](), [distributed-systems](), [Scalability](), [Availability](), [Hardware](), [Network]() Tags: [availability](), [database](), [distributed](), [distributed-transaction](), [hardware](), [network](), [scalability](), [transactions]() ### AI overview This tutorial explains database transactions and distributed transactions, including their all-or-nothing behavior, how they span multiple database nodes, and why workloads are distributed for horizontal scalability, availability, and fault tolerance. ### Source excerpt Transactions make up an important part of the database workload for most modern applications. And when it comes time to scale up operations for a growing business, distributing those workloads across multiple hardware systems for horizontal scalability, high availability, and fault tolerance is often an important part of the plan. ## Varnish to Redis Migration DevFeed: [Varnish to Redis Migration]() Original publisher: [Read original article]() Author: Ravikumar Published: 2022-03-22T15:39:20Z Content type: article Language: en Sources: [Groupon Engineering -- Medium]() Topics: [migration](), [Caching](), [Amazon EKS](), [Redis](), [Amazon Web Services]() Tags: [aws](), [cache-invalidation](), [caching](), [cloud](), [distributed-transaction](), [haproxy](), [kafka](), [kubernetes](), [migration](), [nginx](), [proxy](), [redis](), [varnish]() ### AI overview Groupon describes migrating its Varnish caching infrastructure to Redis while moving application workloads to AWS EKS. The article focuses on a high-traffic place read service, the limitations of TTL-based cache invalidation, and the planned shift to event-based expiration. ### Source excerpt At Groupon, we are in the process of migrating our app workloads to AWS EKS. As part of the migration, we are also in the process of re-architecting our services to make them cloud & Kubernetes optimised. In this article, we will focus on the impact of cloud migration on Varnish caching at Groupon. For the uninitiated, Varnish is a high-performance reverse caching proxy. For simplicity, you can think of it as a general proxy such as HAProxy or Nginx with caching support so that it doesn't have to go to a backend service every time it receives a request. Internally, Varnish cache uses pthreads heavily(to the tune of 1000s) to serve the requests. In contrast, Redis is single-threaded. Varnish at Groupon & Place Read Service We have been using Varnish for many of our high-traffic services. These read-heavy services get traffic to the tune of millions of RPM. One such service is our place read service which is called on each & every deal page visit; it will be the focus of this article. Place read service stores all the locations a deal can be redeemed. Ex: Redemption location details of the deal https://www.groupon.com/deals/parent-big-littles-3 is highlighted below. Traffic pattern of place read service The service is called more than a billion times a day with peak traffic of ~2 million requests per minute. Current architecture (simplified) The traffic is served by a cluster of 4 Varnish servers (40GB RAM). The current Varnish hit rate is 95%. This is admittedly a low hit rate for a read-heavy service, the main reason being our TTL-based cache invalidation logic. Varnish cache invalidation We are using TTL-based cache invalidation. This usually leads to a problem where Varnish starts bombarding the backend service when most of the keys expire at the same time. Request coalescing is not very helpful when the unique keys count is in millions (which is the case with our service). As a workaround, we use randomised TTLs (between 30 to 60 mins) so that all the keys don't