Fast and flexible observability with canonical log lines
Logging is one of the oldest and most ubiquitous patterns in computing. Key to gaining insight into problems ranging from basic failures in test environments to the most tangled problems in production, it’s common practice across all software stacks and all types of infrastructure, and has been for decades. Although logs are powerful and flexible, their sheer volume often makes it impractical to e
APIs as infrastructure: future-proofing Stripe with versioning
When it comes to APIs, change isn’t popular. While software developers are used to iterating quickly and often, API developers lose that flexibility as soon as even one user starts consuming their interface. Many of us are familiar with how the Unix operating system evolved. In 1994, The Unix-Haters Handbook was published containing a long list of missives about the software—everything from overly
Effectively using AWS Reserved Instances
Stripe uses Amazon Web Services to power our infrastructure. With AWS, we can dynamically scale our fleet of servers in real-time. This elasticity enables us to reliably serve a rapidly growing user base and scale along with their businesses. We use AWS Reserved Instances, which allow us to predictably forecast our cloud spend given a dynamic fleet with rapidly changing compute requirements. One o
Stripe: Radar 技術ガイド
このところの E-コマースの拡大、加速に伴って、オンライン決済の不正も増加しています。不正使用がビジネスに与える直接的な損失は世界全体で年間 200 億ドルに達しますが、不正使用による損失が発生するごとに運用コスト、ネットワーク手数料、顧客の解約が増加するため、実際の損失総額はこれよりずっと大きくなります。 不正使用には高額な費用が伴うだけでなく、巧妙な不正使用者が弱点を悪用する新しい方法を常に探しているため、不正使用対策が困難になっています。そのため、Stripe は、Stripe プラットフォームに完全に実装された機械学習ベースの不正防止ソリューション、Stripe Radar を構築しました。Radar の機械学習では、毎年、Stripe ネットワーク全体で処理される数千億ドルの決済から得られるデータを活用し、最新の傾向に迅速に適応することで、お客様が不正使用を増やすことなく成長を実
Learning to operate Kubernetes reliably
We recently built a distributed cron job scheduling system on top of Kubernetes, an exciting new platform for container orchestration. Kubernetes is very popular right now and makes a lot of exciting promises: one of the most exciting is that engineers don’t need to know or care what machines their applications run on. Distributed systems are really hard, and managing services on distributed syste
Stripe: 募集中の職種
当社は大規模に事業を行っていますが、機会の規模に比べると組織の規模はまだ十分ではありません。Stripe の採用情報にご興味のある方は、現在グローバルチームで募集中の職種をご覧ください。
Online migrations at scale
Engineering teams face a common challenge when building software: they eventually need to redesign the data models they use to support clean abstractions and more complex features. In production environments, this might mean migrating millions of active objects and refactoring thousands of lines of code. Stripe users expect availability and consistency from our API. This means that when we do migr
Open-sourcing tools for Hadoop
Stripe’s batch data infrastructure is built largely on top of Apache Hadoop. We use these systems for everything from fraud modeling to business analytics, and we’re open-sourcing a few pieces today: Timberlake Timberlake is a dashboard that gives you insight into the Hadoop jobs running on your cluster. Jeff built it as a replacement for the web interfaces currently provided by YARN’s ResourceMan
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