In our previous blog posts on Dropbox’s document scanner (Part 1, Part 2 and Part 3), we focused on the algorithms that powered the scanner and on the optimizations that made them speedy. However, speed is not the only thing that matters in a mobile environment: what about memory? Bounding both peak memory usage and memory spikes is important, since the operating system may terminate the app outright when under memory pressure. In this blog post, we will discuss some tweaks we made to lower the memory usage of our iOS document scanner.
Imagine you’re an engineer working on a new product feature that is going to have a high impact on the end user, like the Dropbox Badge. You want to get quick validation on the functionality and utility of the feature. Each individual change you make might be relatively simple, like a tweak to the CSS changing the size of a font, or more substantial, like enabling the Badge on a new file type. You could set up user studies, but these are relatively expensive and slow, and are a statistically small sample size. Ideally,
Dropbox employs traditional cross-site attack defenses, but we also employ same-site cookies as a defense in depth on newer browsers. In this post, we describe how we rolled out same-site cookie based defenses on Dropbox, and offer some guidelines on how you can do the same on your website.
Recently, the IETF released a new RFC introducing same-site cookies.
Keeping users on the latest version of the Dropbox desktop app is critical. It allows our developers to rapidly innovate, showcase new features to our users, maintain compatibility with server endpoints, and mitigate risk of incompatibilities that may creep in with platform/OS changes.
Our auto-update system, as originally designed, was written as a feature of the desktop client. Basically, as part of regular file syncing, the server can send down an entry in the metadata that says, “Please update to version X with checksum Y.” The client would then download the file, verify the checksum, open the payload,
A SaaS company like Dropbox needs to update our systems constantly, at all levels of the stack. When it comes time to tune some piece of infrastructure, roll out a new feature, or set up an A/B test, it’s important that we can make changes and have them hit production fast.
Making a change to our code and then “simply” pushing it is not an option: completing a push to our web servers can take hours, and shipping a new mobile or desktop platform release takes even longer. In any case, a full code deployment can be dangerous because it could introduce new bugs: what we really want is a way to put some configurable “knobs” into our products,
Security incidents happen. And when they do, they need to be dealt with—quickly. That’s where detection comes into play. The faster incidents are detected, the faster they can be handed off to the security team and resolved. To make detection as fast as possible, teams are usually aided by monitoring infrastructure that fires off an alert any time something even slightly questionable occurs. These alerts can lead to a deluge of information, making it difficult for engineers to sift through. Even worse, a large number of these alerts are false positives, caused by engineers arbitrarily running