Engineering

We love the web, and care deeply about beautiful code, APIs, and documentation. This belief is as true today as it was when we first wrote this line on our website when we were ten people. Our engineering blog captures how, what, and why we code at Stripe.

Engineering

Connect: behind the front-end experience

We recently released a new and improved version of Connect, our suite of tools designed for platforms and marketplaces. Stripe’s design team works hard to create unique landing pages that tell a story for our major products. For this release, we designed Connect’s landing page to reflect its intricate, cutting-edge capabilities while keeping things light and simple on the surface.

In this blog post, we’ll describe how we used several next-generation web technologies to bring Connect to life, and walk through some of the finer technical details (and excitement!) on our front-end journey.

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Engineering

Scaling your API with rate limiters

Paul Tarjan Engineering

Availability and reliability are paramount for all web applications and APIs. If you’re providing an API, chances are you’ve already experienced sudden increases in traffic that affect the quality of your service, potentially even leading to a service outage for all your users.

The first few times this happens, it’s reasonable to just add more capacity to your infrastructure to accommodate user growth. However, when you’re running a production API, not only do you have to make it robust with techniques like idempotency, you also need to build for scale and ensure that one bad actor can’t accidentally or deliberately affect its availability.

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Engineering

Designing robust and predictable APIs with idempotency

Brandur Leach API Experience

The networks connecting our servers are, on average, more reliable than consumer-level last miles like cellular or home ISPs, but given enough information moving across the wire, they’re still going to fail in exotic ways. Outages, routing problems, and other intermittent failures may be statistically unusual on the whole, but still bound to be happening all the time at some ambient background rate.

To overcome this sort of inherently unreliable environment, it’s important to design APIs and clients that will be robust in the event of failure, and will predictably bring a complex integration to a consistent state despite them. Let’s take a look at a few ways to do that.

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Engineering

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 migrations, we need to be extra careful: objects stored in our systems need to have accurate values, and Stripe’s services need to remain available at all times.

In this post, we’ll explain how we safely did one large migration of our hundreds of millions of Subscriptions objects.

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Engineering

Service discovery at Stripe

Julia Evans Engineering

With so many new technologies coming out every year (like Kubernetes or Habitat), it’s easy to become so entangled in our excitement about the future that we forget to pay homage to the tools that have been quietly supporting our production environments. One such tool we've been using at Stripe for several years now is Consul. Consul helps discover services (that is, it helps us navigate the thousands of servers we run with various services running on them and tells us which ones are up and available for use). This effective and practical architectural choice wasn't flashy or entirely novel, but has served us dutifully in our continued mission to provide reliable service to our users around the world.

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Engineering

A primer on machine learning for fraud detection

Michael Manapat Engineering

Stripe Radar is a collection of tools to help businesses detect and prevent fraud. At Radar’s core is a machine learning engine that scans every card payment across Stripe’s 100,000+ businesses, aggregates information from those payments into behavioral signals that are predictive of fraud, and blocks payments that have a high probability of being fraudulent. Here's how we use machine learning to detect and prevent fraud.

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Engineering

Introducing Veneur: high performance and global aggregation for Datadog

Cory Watson Reliability

When a company writes about their observability stack, they often focus on sweet visualizations, advanced anomaly detection or innovative data stores. Those are well and good, but today we’d like to talk about the tip of the spear when it comes to observing your systems: metrics pipelines! Metrics pipelines are how we get metrics from where they happen—our hosts and services—to storage quickly and efficiently so they can be queried, all without interrupting the host service.

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Engineering

Open-Source Retreat 2016 grantees

Michelle Bu Payments

Like many developers, we often contribute to open-source software in bits and pieces over long periods of time. So we started the Open-Source Retreat to help open-source developers make concentrated progress on features and releases with the potential for significant impact. For 2016’s Retreat, we’re inviting three developers to work on their projects from Stripe’s office in SF.

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Engineering

Open-Source Retreat 2016

Kyle Conroy Engineering

We increasingly rely on (and contribute back to!) a lot of open-source software to build Stripe, and we’d like to give back and get more people working on open-source.

Last year, we invited four developers to the Stripe office as part of our first Open-Source Retreat. Our grantees made significant progress on their projects in a relatively short time. Starting January, we’re hosting another Open-Source Retreat at Stripe.

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Engineering

Exploring Python Using GDB

People tend to have a narrow view of the problems they can solve using GDB. Many think that GDB is just for debugging segfaults or that it's only useful with C or C++ programs. In reality, GDB is an impressively general and powerful tool. When you know how to use it, you can debug just about anything, including Python, Ruby, and other dynamic languages. It's not just for inspection either—GDB can also be used to modify a program's behavior while it's running.

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