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.
Dropbox runs hundreds of services, written in different languages, which exchange millions of requests per second. At the core of our Service Oriented Architecture is Courier, our gRPC-based Remote Procedure Call (RPC) framework. While developing Courier, we learned a lot about extending gRPC, optimizing performance for scale, and providing a bridge from our legacy RPC system.
Note: this post shows code generation examples in Python and Go. We also support Rust and Java.
The road to gRPC
Courier is not Dropbox’s first RPC framework. Even before we started to break our Python monolith into services in earnest,
We introduced Cape in a previous post. In a nutshell, Cape is a framework for enabling real-time asynchronous event processing at a large scale with strong guarantees. It has been over a year since the system was launched. Today Cape is a critical component for Dropbox infrastructure. It operates with both high performance and reliability at a very large scale. Here are a few key metrics, Cape is:
- running on thousands of servers across the continent
- subscribing to over 30 different event domains at a rate of 30K/s
- processing jobs of various sizes at rate of 150K/s
- delivering 95% of events under 1 second after they are created.
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.
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 has more than half a billion registered users who trust us with over an exabyte of data and petabytes of corresponding metadata.
In our previous blog posts, we talked about how we updated the Dropbox search engine to add intelligence into our users’ workflow, and how we built our optical character recognition (OCR) pipeline. One of the most impactful benefits that users will see from these changes is that users on Dropbox Professional and Dropbox Business Advanced and Enterprise plans can search for English text within images and PDFs using a system we’re describing as automatic image text recognition.
The potential benefit of automatically recognizing text in images (including PDFs containing images) is tremendous.