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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Using machine learning to index text from billions of images

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.

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Validating performance and reliability of the new Dropbox search engine

In our previous post, we discussed the architecture of our new search engine, named Nautilus, and its use of machine intelligence to scale our search–ranking and content–understanding models. Along with best–in–class performance, scalability, and reliability, we also provided a foundation for implementing intelligent document ranking and retrieval features. This flexible system allows our engineers to easily customize the document–indexing and query–processing pipelines while maintaining strong safeguards to preserve the privacy of our users’ data. 

In this post, we will discuss the process that we undertook to ensure optimal performance and reliability.


Index format

Each of the hundreds of our search leaves runs our retrieval engine,

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Architecture of Nautilus, the new Dropbox search engine

Over the last few months, the Search Infrastructure engineering team at Dropbox has been busy releasing a new full-text search engine called Nautilus, as a replacement for our previous search engine.

Search presents a unique challenge when it comes to Dropbox due to our massive scale—with hundreds of billions of pieces of content—and also due to the need for providing a personalized search experience to each of our 500M+ registered users. It’s personalized in multiple ways: not only does each user have access to a different set of documents, but users also have different preferences and behaviors in how they search.

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Building better compression together with DivANS

Compressing your files is a good way to save space on your hard drive. At Dropbox’s scale, it’s not just a good idea; it is essential. Even a 1% improvement in compression efficiency can make a huge difference. That’s why we conduct research into lossless compression algorithms that are highly tuned for certain classes of files and storage, like Lepton for jpeg images, and Pied-Piper-esque lossless video encoding. For other file types, Dropbox currently uses the zlib compression format, which saves almost 8% of disk storage.

We introduce DivANS,

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