Finding Kafka’s throughput limit in Dropbox infrastructure

Apache Kafka is a popular solution for distributed streaming and queuing for large amounts of data. It is widely adopted in the technology industry, and Dropbox is no exception. Kafka plays an important role in the data fabric of many of our critical distributed systems: data analytics, machine learning, monitoring, search, and stream processing (Cape), to name a few.

At Dropbox, Kafka clusters are managed by the Jetstream team, whose primary responsibility is to provide high quality Kafka services. Understanding Kafka’s throughput limit in Dropbox infrastructure is crucial in making proper provisioning decision for different use cases,

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Automating Datacenter Operations at Dropbox



As a company that manages our own infrastructure we need to be able to rapidly install new server capacity and ensure that the equipment entering our production environment is highly reliable. Prior to the creation and implementation of the Pirlo system, engineering personnel at Dropbox manually intervened in most aspects of server/switch provisioning and validation.

Pirlo was designed to eliminate and automate many of these manual processes. In this post we will describe Pirlo, a flexible system designed to validate and configure network switches and to ensure the reliability of servers before they enter production.

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Cross shard transactions at 10 million requests per second

Dropbox stores petabytes of metadata to support user-facing features and to power our production infrastructure. The primary system we use to store this metadata is named Edgestore and is described in a previous blog post, (Re)Introducing Edgestore. In simple terms, Edgestore is a service and abstraction over thousands of MySQL nodes that provides users with strongly consistent, transactional reads and writes at low latency.

Edgestore hides details of physical sharding from the application layer to allow developers to scale out their metadata storage needs without thinking about complexities of data placement and distribution.

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Dropbox traffic infrastructure: Edge network

In this post we will describe the Edge network part of Dropbox traffic infrastructure. This is an extended transcript of our NginxConf 2018 presentation. Around the same time last year we described low-level aspects of our infra in the Optimizing web servers for high throughput and low latency post. This time we’ll cover higher-level things like our points of presence around the world, GSLB, RUM DNS, L4 loadbalancers, nginx setup and its dynamic configuration, and a bit of gRPC proxying.

Dropbox scale

Dropbox has more than half a billion registered users who trust us with over an exabyte of data and petabytes of corresponding metadata.

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Migrating from Underscore to Lodash

Modernizing the front-end stack

The core Dropbox web application is 10 years old and used by millions of users per day. Hundreds of front-end engineers across multiple cities actively work on it. Unsurprisingly, our codebase is very large and somewhat irregular. Recently written parts have thorough test coverage, other parts haven’t been updated in years.

Over the past two years we’ve worked to modernize our front-end stack. We’ve successfully moved from CoffeeScript to TypeScript, from jQuery to React, and from a custom Flux implementation to Redux. Having completed these migrations we identified our utility library, Underscore, as one more candidate for migration.

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Improving Document Preview Performance


Ever open a file on, or click a shared link your coworker sent you? Chances are you didn’t need to download the file to see it—you saw it right in the browser. This is the work of the Previews team at Dropbox.

Previews are part of the core Dropbox experience. They allow architects to access their entire portfolios on while at the job site to show their work. Designers can send work-in-progress to clients without worrying about whether they have the correct software installed. Office managers can review, comment, and annotate new office design proposals,

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