A Year Without a Byte

One of the largest cost drivers in running a service like Flickr is storage. We’ve described multiple techniques to get this cost down over the years: use of COS, creating sizes dynamically on GPUs and perceptual compression. These projects have been very successful, but our storage cost is still significant.
At the beginning of 2016, we challenged ourselves to go further — to go a full year without needing new storage hardware. Using multiple techniques, we got there.

The Cost Story

A little back-of-the-envelope math shows storage costs are a real concern. On a very high-traffic day, Flickr users upload as many as twenty-five million photos. These photos require an average of 3.25 megabytes of storage each, totalling over 80 terabytes of data. Stored naively in a cloud service similar to S3, this day’s worth of data would cost over $30,000 per year, and continue to incur costs every year.

And a very large service will have over two hundred million active users. At a thousand images each, storage in a service similar to S3 would cost over $250 million per year (or $1.25 / user-year) plus network and other expenses. This compounds as new users sign up and existing users continue to take photos at an accelerating rate. Thankfully, our costs, and every large service’s costs, are different than storing naively at S3, but remain significant.



Cost per byte have decreased, but bytes per image from iPhone-type platforms have increased. Cost per image hasn’t changed significantly.

Storage costs do drop over time. For example, S3 costs dropped from $0.15 per gigabyte month in 2009 to $0.03 per gigabyte-month in 2014, and cloud storage vendors have added low-cost options for data that is infrequently accessed. NAS vendors have also delivered large price reductions.

Unfortunately, these lower costs per byte are counteracted by other forces. On iPhones, increasing camera resolution, burst mode and the addition of short animations (Live Photos) have increased bytes-per-image rapidly enough to keep storage cost per image roughly constant. And iPhone images are far from the largest.

In response to these costs, photo storage services have pursued a variety of product options. To name a few: storing lower quality images or re-compressing, charging users for their data usage, incorporating advertising, selling associated products such as prints, and tying storage to purchases of handsets.

There are also a number of engineering approaches to controlling storage costs. We sketched out a few and cover three that we implemented below: adjusting thresholds on our storage systems, rolling out existing savings approaches to more images, and deploying lossless JPG compression.

Adjusting Storage Thresholds

As we dug into the problem, we looked at our storage systems in detail. We discovered that our settings were based on assumptions about high write and delete loads that didn’t hold. Our storage is pretty static. Users only rarely delete or change images once uploaded. We also had two distinct areas of just-in-case space. 5% of our storage was reserved space for snapshots, useful for undoing accidental deletes or writes, and 8.5% was held free in reserve. This resulted in about 13% of our storage going unused. Trade lore states that disks should remain 10% free to avoid performance degradation, but we found 5% to be sufficient for our workload. So we combined our our two just-in-case areas into one and reduced our free space threshold to that level. This was our simplest approach to the problem (by far), but it resulted in a large gain. With a couple simple configuration changes, we freed up more than 8% of our storage.



Adjusting storage thresholds

Extending Existing Approaches

In our earlier posts, we have described dynamic generation of thumbnail sizes and perceptual compression. Combining the two approaches decreased thumbnail storage requirements by 65%, though we hadn’t applied these techniques to many of our images uploaded prior to 2014. One big reason for this: large-scale changes to older files are inherently risky, and require significant time and engineering work to do safely.

Because we were concerned that further rollout of dynamic thumbnail generation would place a heavy load on our resizing infrastructure, we targeted only thumbnails from less-popular images for deletes. Using this approach, we were able to handle our complete resize load with just four GPUs. The process put a heavy load on our storage systems; to minimize the impact we randomized our operations across volumes. The entire process took about four months, resulting in even more significant gains than our storage threshold adjustments.



Decreasing the number of thumbnail sizes

Lossless JPG Compression

Flickr has had a long-standing commitment to keeping uploaded images byte-for-byte intact. This has placed a floor on how much storage reduction we can do, but there are tools that can losslessly compress JPG images. Two well-known options are PackJPG and Lepton, from Dropbox. These tools work by decoding the JPG, then very carefully compressing it using a more efficient approach. This typically shrinks a JPG by about 22%. At Flickr’s scale, this is significant. The downside is that these re-compressors use a lot of CPU. PackJPG compresses at about 2MB/s on a single core, or about fifteen core-years for a single petabyte worth of JPGs. Lepton uses multiple cores and, at 15MB/s, is much faster than packJPG, but uses roughly the same amount of CPU time.

This CPU requirement also complicated on-demand serving. If we recompressed all the images on Flickr, we would need potentially thousands of cores to handle our decompress load. We considered putting some restrictions on access to compressed images, such as requiring users to login to access original images, but ultimately found that if we targeted only rarely accessed private images, decompressions would occur only infrequently. Additionally, restricting the maximum size of images we compressed limited our CPU time per decompress. We rolled this out as a component of our existing serving stack without requiring any additional CPUs, and with only minor impact to user experience.

Running our users’ original photos through lossless compression was probably our highest-risk approach. We can recreate thumbnails easily, but a corrupted source image cannot be recovered. Key to our approach was a re-compress-decompress-verify strategy: every recompressed image was decompressed and compared to its source before removing the uncompressed source image.

This is still a work-in-progress. We have compressed many images but to do our entire corpus is a lengthy process, and we had reached our zero-new-storage-gear goal by mid-year.

On The Drawing Board

We have several other ideas which we’ve investigated but haven’t implemented yet.

In our current storage model, we have originals and thumbnails available for every image, each stored in two datacenters. This model assumes that the images need to be viewable relatively quickly at any point in time. But private images belonging to accounts that have been inactive for more than a few months are unlikely to be accessed. We could “freeze” these images, dropping their thumbnails and recreate them when the dormant user returns. This “thaw” process would take under thirty seconds for a typical account. Additionally, for photos that are private (but not dormant), we could go to a single uncompressed copy of each thumbnail, storing a compressed copy in a second datacenter that would be decompressed as needed.

We might not even need two copies of each dormant original image available on disk. We’ve pencilled out a model where we place one copy on a slower, but underutilized, tape-based system while leaving the other on disk. This would decrease availability during an outage, but as these images belong to dormant users, the effect would be minimal and users would still see their thumbnails. The delicate piece here is the placement of data, as seeks on tape systems are prohibitively slow. Depending on the details of what constitutes a “dormant” photo these techniques could comfortably reduce storage used by over 25%.

We’ve also looked into de-duplication, but we found our duplicate rate is in the 3% range. Users do have many duplicates of their own images on their devices, but these are excluded by our upload tools.  We’ve also looked into using alternate image formats for our thumbnail storage.    WebP can be much more compact than ordinary JPG but our use of perceptual compression gets us close to WebP byte size and permits much faster resize.  The BPG project proposes a dramatically smaller, H.265 based encoding but has IP and other issues.

There are several similar optimizations available for videos. Although Flickr is primarily image-focused, videos are typically much larger than images and consume considerably more storage.

Conclusion



Optimization over several releases

Since 2013 we’ve optimized our usage of storage by nearly 50%.  Our latest efforts helped us get through 2016 without purchasing any additional storage,  and we still have a few more options available.

Peter Norby, Teja Komma, Shijo Joy and Bei Wu formed the core team for our zero-storage-budget project. Many others assisted the effort.

We Want You… and Your Teammates

14493569810_7ac064e3c4_oWe’re hiring here at Flickr and we got pretty excited the other week when we saw Stripe’s post: BYOT (Bring Your Own Team). The sum of the parts is greater than the whole and all that. Genius <big hat tip to them>.

In case you didn’t read Stripe’s post, here’s the gist: you’re a team player, you like to make an impact, focus on a tough problem, set a challenging goal, and see the fruits of your labor after blood, sweat, and tears (or, maybe just brainpower). But you’ve got the itch to collaborate, to talk an idea through, break it down, and parallelize tasks or simply to be around your mates through work and play. Turns out you already have your go-to group of colleagues, roommates, siblings, or buddies that push, inspire, and get the best out of you. Well, in that case we may want to hire all of you!

Like Stripe, we understand the importance of team dynamics. So if you’ve already got something good going on, we want in on it too. We love Stripe and are stoked for this initiative of theirs, but if Flickr tickles your fancy (and it does ours :) consider bringing that team of yours this way too, especially if you’ve got a penchant for mobile development. We’d love to chat!

Email us: jobs at flickr.com

Team crop

Photos by: @Chris Martin and @Captain Eric Willis

Configuration management for distributed systems (using GitHub and cfg4j)

Norbert Potocki, Software Engineer @ Yahoo Inc.

Warm up: Why configuration management?

When working with large-scale software systems, configuration management becomes crucial; supporting non-uniform environments gets greatly simplified if you decouple code from configuration. While building complex software/products such as Flickr, we had to come up with a simple, yet powerful, way to manage configuration. Popular approaches to solving this problem include using configuration files or having a dedicated configuration service. Our new solution combines the extremely popular GitHub and cfg4j library, giving you a very flexible approach that will work with applications of any size.

Why should I decouple configuration from the code?

  • Faster configuration changes (e.g. flipping feature toggles): Configuration can simply be injected without requiring parts of your code to be reloaded and re-executed. Config-only updates tend to be faster than code deployment.
  • Different configuration for different environments: Running your app on a laptop or in a test environment requires a different set of settings than production instance.
  • Keeping credentials private: If you don’t have a dedicated credential store, it may be convenient to keep credentials as part of configuration. They usually aren’t supposed to be “public,” but the code still may be. Be a good sport and don’t keep credentials in a public GitHub repo. :)

Meet the Gang: Overview of configuration management players

Let’s see what configuration-specific components we’ll be working with today:

image

Figure 1 –  Overview of configuration management components

Configuration repository and editor: Where your configuration lives. We’re using Git for storing configuration files and GitHub as an ad hoc editor.

Push cache : Intermediary store that we use to improve fetch speed and to ease load on GitHub servers.

CD pipeline: Continuous deployment pipeline pushing changes from repository to push cache, and validating config correctness.

Configuration library: Fetches configs from push cache and exposing them to your business logic.

Bootstrap configuration : Initial configuration specifying where your push cache is (so that library knows where to get configuration from).

All these players work as a team to provide an end-to-end configuration management solution.

The Coach: Configuration repository and editor

The first thing you might expect from the configuration repository and editor is ease of use. Let’s enumerate what that means:

  • Configuration should be easy to read and write.
  • It should be straightforward to add a new configuration set.
  • You most certainly want to be able to review changes if your team is bigger than one person.
  • It’s nice to see a history of changes, especially when you’re trying to fix a bug in the middle of the night.
  • Support from popular IDEs – freedom of choice is priceless.
  • Multi-tenancy support (optional) is often pragmatic.

So what options are out there that may satisfy those requirements? The three very popular formats for storing configuration are YAML, Java Property files, and XML files. We use YAML – it is widely supported by multiple programming languages and IDEs, and it’s very readable and easy to understand, even by a non-engineer.

We could use a dedicated configuration store; however, the great thing about files is that they can be easily versioned by version control tools like Git, which we decided to use as it’s widely known and proven.

Git provides us with a history of changes and an easy way to branch off configuration. It also has great support in the form of GitHub which we use both as an editor (built-in support for YAML files) and collaboration tool (pull requests, forks, review tool). Both are nicely glued together by following the Git flow branching model. Here’s an example of a configuration file that we use:

Figure 2 –  configuration file preview

One of the goals was to make managing multiple configuration sets (execution environments) a breeze. We need the ability to add and remove environments quickly. If you look at the screenshot below, you’ll notice a “prod-us-east” directory in the path. For every environment, we store a separate directory with config files in Git. All of them have the exact same structure and only differ in YAML file contents.

This solution makes working with environments simple and comes in very handy during local development or new production fleet rollout (see use cases at the end of this article). Here’s a sample config repo for a project that has only one “feature”:

Figure 3 –  support for multiple environments

Some of the products that we work with at Yahoo have a very granular architecture with hundreds of micro-services working together. For scenarios like this, it’s convenient to store configurations for all services in a single repository. It greatly reduces the overhead of maintaining multiple repositories. We support this use case by having multiple top-level directories, each holding configurations for one service only.

The sprinter: Push cache

The main role of push cache is to decrease the load put on the GitHub server and improve configuration fetch time. Since speed is the only concern here, we decided to keep the push cache simple: it’s just a key-value store. Consul was our choice, in part because it’s fully distributed.

You can install Consul clients on the edge nodes and they will keep being synchronized across the fleet. This greatly improves both the reliability and the performance of the system. If performance is not a concern, any key-value store will do. You can skip using push cache altogether and connect directly to Github, which comes in handy during development (see use cases to learn more about this).

The Manager: CD Pipeline

When the configuration repository is updated, a CD pipeline kicks in. This fetches configuration, converts it into a more optimized format, and pushes it to cache. Additionally, the CD pipeline validates the configuration (once at pull-request stage and again after being merged to master) and controls multi-phase deployment by deploying config change to only 20% of production hosts at one time.

The Mascot: Bootstrap configuration

Before we can connect to the push cache to fetch configuration, we need to know where it is. That’s where bootstrap configuration comes into play. It’s very simple. The config contains the hostname, port to connect to, and the name of the environment to use. You need to put this config with your code or as part of the CD pipeline. This simple yaml file binding Spring profiles to different Consul hosts suffices for our needs:

image

Figure 4 –  bootstrap configuration

The Cool Guy: Configuration library

image

The configuration library takes care of fetching the configuration from push cache and exposing it to your business logic. We use the library called cfg4j (“configuration for java”). This library re-loads configurations from the push cache every few seconds and injects them into configuration objects that our code uses. It also takes care of local caching, merging properties from different repositories, and falling back to user-provided defaults when necessary (read more at http://www.cfg4j.org/).

Briefly summarizing how we use cfg4j’s features:

  • Configuration auto-reloading: Each service reloads configuration every ~30 seconds and auto re-configures itself.
  • Multi-environment support: for our multiple environments (beta, performance, canary, production-us-west, production-us-east, etc.).
  • Local caching: Remedies service interruption when the push cache or configuration repository is down and also improves the performance for obtaining configs.
  • Fallback and merge strategies: Simplifies local development and provides support for multiple configuration repositories.
  • Integration with Dependency Injection containers – because we love DI!

If you want to play with this library yourself, there’s plenty of examples both in its documentation and cfg4j-sample-apps Github repository.

The Heavy Lifter: Configurable code

The most important piece is business logic. To best make use of a configuration service, the business logic has to be able to re-configure itself in runtime. Here are a few rules of thumb and code samples:

  • Use dependency injection for injecting configuration. This is how we do it using Spring Framework (see the bootstrap configuration above for host/port values):

  • Use configuration objects to inject configuration instead of providing configuration directly – here’s where the difference is:

Direct configuration injection (won’t reload as config changes)

Configuration injection via “interface binding” (will reload as config changes):

The exercise: Common use-cases (applying our simple solution)

Configuration during development (local overrides)

When you develop a feature, a main concern is the ability to evolve your code quickly.  A full configuration-management pipeline is not conducive to this. We use the following approaches when doing local development:

  • Add a temporary configuration file to the project and use cfg4j’s MergeConfigurationSource for reading config both from the configuration store and your file. By making your local file a primary configuration source, you provide an override mechanism. If the property is found in your file, it will be used. If not, cfg4j will fall back to using values from configuration store. Here’s an example (reference examples above to get a complete code):

  • Fork the configuration repository, make changes to the fork and use cfg4j’s GitConfigurationSource to access it directly (no push
    cache required):

  • Set up your private push cache, point your service to the cache, and edit values in it directly.

Configuration defaults

When you work with multiple environments, some of them may share a configuration. That’s when using configuration defaults may be convenient. You can do this by creating a “default” environment and using cfg4j’s MergeConfigurationSource for reading config first from the original environment and then (as a fallback) from “default” environment.

Dealing with outages

Configuration repository, push cache, and configuration CD pipeline can experience outages. To minimize the impact of such events, it’s good practice to cache configuration locally (in-memory) after each fetch. cfg4j does that automatically.

Responding to incidents – ultra fast configuration updates (skipping configuration CD pipeline)

Tests can’t always detect all problems. Bugs leak to the production environment and at times it’s important to make a config change as fast as possible to stop the fire. If you’re using push cache, the fastest way to modify config values is to make changes directly within the cache. Consul offers a rich REST API and web ui for updating configuration in the key-value store.

Keeping code and configuration in sync

Verifying that code and configuration are kept in sync happens at the configuration CD pipeline level. One part of the continuous deployment process deploys the code into a temporary execution environment, and points it to the branch that contains the configuration changes. Once the service is up, we execute a batch of functional tests to verify configuration correctness.

The cool down: Summary

The presented solution is the result of work that we put into building huge-scale photo-serving services. We needed a simple, yet flexible, configuration management system. Combining Git, Github, Consul and cfg4j provided a very satisfactory solution that we encourage you to try.

I want to thank the following people for reviewing this article: Bhautik Joshi, Elanna Belanger, Archie Russell.

PS. You can also follow me on Twitter, GitHub, LinkedIn or my private blog.

Flickr’s experience with iOS 9

In the last couple of months, Apple has released new features as part of iOS 9 that allow a deeper integration between apps and the operating system. Among those features are Spotlight Search integration, Universal Links, and 3D Touch for iPhone 6S and iPhone 6S Plus.

Here at Flickr, we have added support for these new features and we have learned a few lessons that we would love to share.

Spotlight Search

There are two different kinds of content that can be searched through Spotlight: the kind that you explicitly index, and the kind that gets indexed based on the state your app is in. To explicitly index content, you use Core Spotlight, which lets you index multiple items at once. To index content related to your app’s current state, you use NSUserActivity: when a piece of content becomes visible, you start an activity to make iOS aware of this fact. iOS can then determine which pieces of content are more frequently visited, and thus more relevant to the user. NSUserActivity also allows us to mark certain items as public, which means that they might be shown to other iOS users as well.

For a better user experience, we index as much useful information as we can right off the bat. We prefetch all the user’s albums, groups, and people they follow, and add them to the search index using Core Spotlight. Indexing an item looks like this:

// Create the attribute set, which encapsulates the metadata of the item we're indexing
CSSearchableItemAttributeSet *attributeSet = [[CSSearchableItemAttributeSet alloc] initWithItemContentType:(NSString *)kUTTypeImage];
attributeSet.title = photo.title;
attributeSet.contentDescription = photo.searchableDescription;
attributeSet.keywords = photo.keywords;
attributeSet.thumbnailData = UIImageJPEGRepresentation(photo.thumbnail, 0.98);

// Create the searchable item and index it.
CSSearchableItem *searchableItem = [[CSSearchableItem alloc] initWithUniqueIdentifier:[NSString stringWithFormat:@"%@/%@", photo.identifier, photo.searchContentType] domainIdentifier:@"FLKCurrentUserSearchDomain" attributeSet:attributeSet];
[[CSSearchableIndex defaultSearchableIndex] indexSearchableItems:@[ searchableItem ] completionHandler:^(NSError * _Nullable error) {
                       if (error) {
                           // Handle failures.
                       }
              }];

Since we have multiple kinds of data – photos, albums, and groups – we had to create an identifier that is a combination of its type and its actual model ID.

Many users will have a large amount of data to be fetched, so it’s important that we take measures to make sure that the app still performs well. Since searching is unlikely to happen right after the user opens the app (that’s when we start prefetching this data, if needed), all this work is performed by a low-priority NSOperationQueue. If we ever need to fetch images to be used as thumbnails, we request it with low-priority NSURLSessionDownloadTask. These kinds of measures ensure that we don’t affect the performance of any operation or network request triggered by user actions, such as fetching new images and pages when scrolling through content.

Flickr provides a huge amount of public content, including many amazing photos. If anybody searches for “Northern Lights” in Spotlight, shouldn’t we show them our best Aurora Borealis photos? For this public content – photos, public groups, tags and so on – we leverage NSUserActivity, with its new search APIs, to make it all searchable when viewed. Here’s an example:

CSSearchableItemAttributeSet *attributeSet = [[CSSearchableItemAttributeSet alloc] initWithItemContentType:(NSString *) kUTTypeImage];
// Setup attributeSet the same way we did before...
// Set the related unique identifier, so it matches to any existing item indexed with Core Spotlight.     
attributeSet.relatedUniqueIdentifier = [NSString stringWithFormat:@"%@/%@", photo.identifier, photo.searchContentType];
        
self.userActivity = [[NSUserActivity alloc] initWithActivityType:@"FLKSearchableUserActivityType"];
self.userActivity.title = photo.title;
self.userActivity.keywords = [NSSet setWithArray:photo.keywords];
self.userActivity.webpageURL = photo.photoPageURL;
self.userActivity.contentAttributeSet = attributeSet;
self.userActivity.eligibleForSearch = YES;
self.userActivity.eligibleForPublicIndexing = photo.isPublic;
self.userActivity.requiredUserInfoKeys = [NSSet setWithArray:self.userActivity.userInfo.allKeys];
        
[self.userActivity becomeCurrent];

Every time a user opens a photo, public group, location page, etc., we create a new NSUserActivity and make it current. The more often a specific activity is made current, the more relevant iOS considers it. In fact, the more often an activity is made current by any number of different users, the more relevant Apple considers it globally, and the more likely it will show up for other iOS users as well (provided it’s public).

Until now we’ve only seen half the picture. We’ve seen how to index things for Spotlight search; when a user finally does search and taps on a result, how do we take them to the right place in our app? We’ll get to this a bit later, but for now suffice it to say that you’ll get a call to the method application:continueUserActivity:restorationHandler: to our application delegate.

It’s important to note that if we wanted to make use of the userInfo in the NSUserActivity, iOS won’t give it back to you for free in this method. To get it, we have to make sure that we assigned an NSSet to the requiredUserInfoKeys property of our NSUserActivity when we created it. In their documentation, Apple also tells us that if you set the webpageURL property when eligibleForSearch is YES, you need to make sure that you’re pointing to the right web URL corresponding to your content, otherwise you might end up with duplicate results in Spotlight (Apple crawls your site for content to surface in Spotlight, and if it finds the same content at a different URL it’ll think it’s a different piece of content).

Universal Links

In order to support Universal Links, Apple requires that every domain supported by the app host an “apple-app-site-association” file at its root. This is a JSON file that describes which relative paths in your domains can be handled by the app. When a user taps a link from another app in iOS, if your app is able to handle that domain for a specific path, it will open your app and call application:continueUserActivity:restorationHandler:. Otherwise your application won’t be opened – Safari will handle the URL instead.

{
    "applinks": {
        "apps": [],
        "details": {
            "XXXXXXXXXX.com.some.flickr.domain": {
                "paths": [
                    "/",
                    "/photos/*",
                    "/people/*",
                    "/groups/*"
                ]
            }
        }
    }
}

This file has to be hosted on HTTPS with a valid certificate. Its MIME type needs to be “application/pkcs7-mime.” No redirects are allowed when requesting the file. If the only intent is to support Universal Links, no further steps are required. But if you’re also using this file to support Handoffs (introduced in iOS 8), then your file has to be CMS signed by a valid TLS certificate.

In Flickr, we have a few different domains. That means that each one of flickr.com, http://www.flickr.com, m.flickr.com and flic.kr must provide its own JSON association file, whether or not they differ. In our case, the flic.kr domain actually does support different paths, since it’s only used for short URLs; hence, its “apple-app-site-association” is different than the others.

On the client side, only a few steps are required to support Universal Links. First, “Associated Domains” must be enabled under the Capabilities tab of the app’s target settings. For each supported domain, an entry “applinks:” entry must be added. Here is how it looks for Flickr:

Screen Shot 2015-10-28 at 2.00.59 PM

That is it. Now if someone receives a text message with a Flickr link, she will jump right to the Flickr app when she taps on it.

Deep linking into the app

Great! We have Flickr photos showing up as search results and Flickr URLs opening directly in our app. Now we just have to get the user to the proper place within the app. There are different entry points into our app, and we need to make the implementation consistent and avoid code duplication.

iOS has been supporting deep linking for a while already and so has Flickr. To support deep linking, apps could register to handle custom URLs (meaning a custom scheme, such as myscheme://mydata/123). The website corresponding to the app could then publish links directly to the app. For every custom URL published on the Flickr website, our app translates it into a representation of the data to be shown. This representation looks like this:

@interface FLKRoute : NSObject

@property (nonatomic) FLKRouteType type;
@property (nonatomic, copy) NSString *identifier;

@end

It describes the type of data to present, and a unique identifier for that type of data.

- (void)navigateToRoute:(FLKRoute *)route
{
    switch (route.type) {
        case FLKRouteTypePhoto:
            // Navigate to photo screen
            break;
        case FLKRouteTypeAlbum:
           // Navigate to album screen
            break;
        case FLKRouteTypeGroup:
            // Navigate to group screen
            break;
        // ...
        default:
            break;
    }
}

Now, all we have to do is to make sure we are able to translate both NSURLs and NSUserActivity objects into FLKRoute instances. For NSURLs, this translation is straightforward. Our custom URLs follow the same pattern as the corresponding website URLs; their paths correspond exactly. So translating both website URLs and custom URLs is a matter of using NSURLComponents to extract the necessary information to create the FLKRoute object.

As for NSUserActivity objects passed into application:continueUserActivity:restorationHandler:, there are two cases. One arises when the NSUserActivity instance was used to index a public item in the app. Remember that when we created the NSUserActivity object we also assigned its webpageURL? This is really handy because it not only uniquely identifies the data we want to present, but also gives us a NSURL object, which we can handle the same way we handle deep links or Universal Links.

The other case is when the NSUserActivity originated from a CSSearchableItem; we have some more work to do in this case. We need to parse the identifier we created for the item and translate it into a FLKRoute. Remember that our item’s identifier is a combination of its type and the model ID. We can decompose it and then create our route object. Its simplified implementation looks like this:

FLKRoute * FLKRouteFromSearchableItemIdentifier(NSString *searchableItemIdentifier)
{
    NSArray *routeComponents = [searchableItemIdentifier componentsSeparatedByString:@"/"];
    if ([routeComponents count] != 2) { // type + id
        return nil;
    }
    
    // Handle the route type
    NSString *searchableItemContentType = [routeComponents firstObject];
    FLKRouteType type = FLKRouteTypeFromSearchableItemContentType(searchableItemContentType);
    
    // Get the item identifier
    NSString *itemIdentifier = [routeComponents lastObject];
    
    // Build the route object
    FLKRoute *route = [FLKRoute new];
    route.type = type;
    route.parameter = itemIdentifier;
    
    return route;
}

Now we have all our bases covered and we’re sure that we’ll drop the user in the right place when she lands in our app. The final application delegate method looks like this:

- (BOOL)application:(nonnull UIApplication *)application continueUserActivity:(nonnull NSUserActivity *)userActivity restorationHandler:(nonnull void (^)(NSArray * __nullable))restorationHandler
{
    FLKRoute *route;
    NSString *activityType = [userActivity activityType];
    NSURL *url;
    
    if ([activityType isEqualToString:CSSearchableItemActionType]) {
        // Searchable item from Core Spotlight
        NSString *itemIdentifier = [userActivity.userInfo objectForKey:CSSearchableItemActivityIdentifier];
        route = FLKRouteFromSearchableItemIdentifier(itemIdentifier);
        
    } else if ([activityType isEqualToString:@"FLKSearchableUserActivityType"] ||
               [activityType isEqualToString:NSUserActivityTypeBrowsingWeb]) {
        // Searchable item from NSUserActivity or Universal Link
        url = userActivity.webpageURL;
        route = [url flk_route];
        
    }
    
    if (route) {
        [self.router navigateToRoute:route];
        return YES;
    } else if (url) {
        [[UIApplication sharedApplication] openURL:url]; // Fail gracefully
        return YES;
    } else {
        return NO;
    }
}

3D Touch

With the release of iPhone 6S and iPhone 6S Plus, Apple introduced a new gesture that can be used with your iOS app: 3D Touch. One of the coolest features it has brought is the ability to preview content before pushing it onto the navigation stack. This is also known as “peek and pop.”

You can easily see how this feature is implemented in the native Mail app. But you won’t always have a simple UIView hierarchy like Mail’s UITableView, where a tap anywhere on a cell opens a UIViewController. Take Flickr’s notifications screen, for example:

4.0-04-core-five-notifications

If the user taps on a photo in one of these cells, it will open the photo view. But if the user taps on another user’s name, it will open that user’s profile view. Previews of these UIViewControllers should be shown accordingly. But the “peek and pop” mechanism requires you to register a delegate on your UIViewController with registerForPreviewingWithDelegate:sourceView:, which means that you’re working in a much higher layer. Your UIViewController’s view might not even know about its subviews’ structures.

To solve this problem, we used UIView’s method hitTest:withEvent:. As the documentation describes, it will give us the “farthest descendant of the receiver in the view hierarchy.” But not every hitTest will necessarily return the UIView that we want. So we defined a protocol, FLKPeekAndPopTargetView, that must be implemented by any UIView subclass that wants to support peeking and popping from it. That view is then responsible for returning the model used to populate the UIViewController that the user is trying to preview. If the view doesn’t implement this protocol, we query its superview. We keep checking for it until a UIView is found or there aren’t any more superviews available. This is how this logic looks:

+ (id)modelAtLocation:(CGPoint)location inSourceView:(UIView*)sourceView
    
    // Walk up hit-test tree until we find a peek-pop target.
    UIView *testView = [sourceView hitTest:location withEvent:nil];
    id model = nil;
    while(testView && !model) {
      
        // Check if the current testView conforms to the protocol.
        if([testView conformsToProtocol:@protocol(FLKPeekAndPopTargetView)]) {
            
            // Translate location to view coordinates.
            CGPoint locationInView = [testView convertPoint:location fromView:sourceView];
            
            // Get model from peek and pop target.
            model = [((id<FLKPeekAndPopTargetView>)testView) flk_peekAndPopModelAtLocation:locationInView];
            
        } else {
            //Move up view tree to next view
            testView = testView.superview;
        }
    }
    
    return model;
}

With this code in place, all we have to do is to implement UIViewControllerPreviewingDelegate methods in our delegate, perform the hitTest and take the model out of the FLKPeekAndPopTargetView‘s implementor. Here’s is the final implementation:

- (UIViewController *)previewingContext:(id<UIViewControllerPreviewing>)previewingContext
              viewControllerForLocation:(CGPoint)location {
    
    id model = [[self class] modelAtLocation:location inSourceView:previewingContext.sourceView];
    UIViewController *viewController = nil;
    if ([model isKindOfClass:[FLKPhoto class]]) {
        viewController = // ... UIViewController that displays a photo.
    } else if ([model isKindOfClass:[FLKAlbum class]]) {
        viewController = // ... UIViewController that displays an album.
    } else if ([model isKindOfClass:[FLKGroup class]]) {
        viewController = // ... UIViewController that displays a group.
    } // ...
    return viewController;
    
}

- (void)previewingContext:(id<UIViewControllerPreviewing>)previewingContext
     commitViewController:(UIViewController *)viewControllerToCommit {
    
    [self.navigationController pushViewController:viewControllerToCommit animated:YES];
    
}

Last but not least, we added support for Quick Actions. Now the user has the ability to quickly jump into a specific section of the app just by pressing down on the app icon. Defining these Quick Actions statically in the Info.plist file is an easy way to implement this feature, but we decided to go one step further and define these options dynamically. One of the options we provide is “Upload Photo,” which takes the user to the asset picker screen. But if the user has Auto Uploadr turned on, this option isn’t that relevant, so instead we provide a different app icon menu option in its place.

Here’s how you can create Quick Actions:

NSMutableArray<UIApplicationShortcutItem *> *items = [NSMutableArray array];
    
[items addObject:[[UIApplicationShortcutItem alloc] initWithType:@"FLKShortcutItemFeed"
                                                  localizedTitle:NSLocalizedString(@"Feed", nil)]];
    
[items addObject:[[UIApplicationShortcutItem alloc] initWithType:@"FLKShortcutItemTakePhoto"
                                                  localizedTitle:NSLocalizedString(@"Upload Photo", nil)] ];

[items addObject:[[UIApplicationShortcutItem alloc] initWithType:@"FLKShortcutItemNotifications"
                                                  localizedTitle:NSLocalizedString(@"Notifications", nil)]];
    
[items addObject:[[UIApplicationShortcutItem alloc] initWithType:@"FLKShortcutItemSearch"
                                                  localizedTitle:NSLocalizedString(@"Search", nil)]];
    
[[UIApplication sharedApplication] setShortcutItems:items];

And this is how it looks like when the user presses down on the app icon:

IMG_0344

Finally, we have to handle where to take the user after she selects one of these options. This is yet another place where we can make use of our FLKRoute object. To handle the app opening from a Quick Action, we need to implement application:performActionForShortcutItem:completionHandler: in the app delegate.

- (void)application:(UIApplication *)application performActionForShortcutItem:(UIApplicationShortcutItem *)shortcutItem completionHandler:(void (^)(BOOL))completionHandler {
    FLKRoute *route = [shortcutItem flk_route];
     [self.router navigateToRoute:route];
    completionHandler(YES);
}

Conclusion

There is a lot more to consider when shipping these features with an app. For example, with Flickr, there are various platforms the user could be using. It is important to make sure that the Spotlight index is up to date to reflect changes made anywhere. If the user has created a new album and/or left a group from his desktop browser, we need to make sure that those changes are reflected in the app, so the newly-created album can be found through Spotlight, but the newly-departed group cannot.

All of this work should be totally opaque to the user, without hogging the device’s resources and deteriorating the user experience overall. That requires some considerations around threading and network priorities. Network requests for UI-relevant data should not be blocked because we have other network requests happening at the same time. With some careful prioritizing, using NSOperationQueue and NSURLSession, we managed to accomplish this with no major problems.

Finally, we had to consider privacy, one of the pillars of Flickr. We had to be extremely careful not to violate any of the user’s settings. We’re careful to never publicly index private content, such as photos and albums. Also, photos marked “restricted” are not publicly indexed since they might expose content that some users might consider offensive.

In this blog post we went into the basics of integrating iOS 9 Search, Universal Links, and 3D Touch in Flickr for iOS. In order to focus on those features, we simplified some of our examples to demonstrate how you could get started with them in your own app, and to show what challenges we faced.

Flickr September 2014

Like this post? Have a love of online photography? Want to work with us? Flickr is hiring mobile, back-end and front-end engineers, in our San Francisco office. Find out more at flickr.com/jobs.

Perceptual Image Compression at Flickr

Archie Russell, Peter Norby, Saeideh Bakhshi

At Flickr our users really care about image quality.  They also care a lot about how responsive our apps are.  Addressing both of these concerns simultaneously is challenging;  higher quality images have larger file sizes and are slower to transfer.   Slow transfers are especially noticeable on mobile devices.   Flickr had historically aimed for high quality at the expense of larger files, but in late 2014 we implemented a method to both maintain image quality and decrease the byte-size of the images we serve to users.   As image appearance is very important to our users,  we performed an extensive user test before rolling this change out.   Here’s how we did it.

Background:  JPEG Quality Settings

Fig 1.    JPEG settings vs file size for a test image.

JPEG compression has several tuneable knobs.   The q-value is the best known of these; it adjusts the level of spatial detail stored for fine details;  a higher q-value typically keeps more detail.    However,  as q-value gets very close to 100,  file size increases dramatically,  usually without improving image appearance.

If file size and app performance isn’t an issue,  dialing up q-value is an easy way to get really nice-looking images; this is what Flickr has done in the past.    And if appearance isn’t very important,  dialing down q-value is a viable option.    But if you want both,  you’re kind of stuck.   Additionally,  q-value isn’t one-size-fits-all,  some images look great at q-value 80 while others don’t.

Another commonly adjusted setting is chroma-subsampling,  which alters the amount of color information stored in a JPEG file.    With a setting of 4:4:4,  the two chroma (color) channels in a JPG have as much information as the luminance channel.   In an image with a setting of 4:2:0, each chroma channel has only a quarter as much information as in an a 4:4:4 image.

 q=96,  chroma=4:4:4 (125KB) q=70, chroma=4:4:4 (67KB)
q=96, chroma=4:2:0 (62KB)  q=70, chroma=4:2:0 (62KB)

Table 1:   JPEG stored at different quality and chroma levels.   The upper left image is saved at high quality and chroma level; notice the color and detail in the folds of the red flag.   The lower right image has the lowest quality;  notice artifacts along the right edges of the red flag.

Perceptual JPEG Compression

Ideally we’d have an algorithm which automatically tuned all JPEG parameters to make a file smaller, but which would limit perceptible changes to the image.  Technology exists that attempts to do this and can decrease image file size by 30-50%. This compression ratio is highly dependent on image content and dimensions.

compressed: 112KB non-compressed: 224KB

Fig 2. Compressed cropped JPEG is 50% smaller than not-compressed cropped JPEG, above, with no obvious defects.  Compression ratio is similar for a compressed 2048-pixel wide JPEG (475KB) of the entire scene and its corresponding not-compressed JPEG (897KB). 

We were pleased with perceptually compressed images in non-structured examinations.  The compressed images were smaller and nearly indistinguishable from their sources.   But we wanted to really quantify how well the technology worked before considering incorporating it into Flickr.  The standard computational tools for evaluating compression, such as SSIM, are fairly simplistic and don’t do a great job at modeling how a user sees things.  To really evaluate this technology had to use a better measure of perceptibility:  human minds.

The Gamified Taste Test

To test whether our image compression would impact user perception of image quality, we put together a “taste test.”  The taste test is constructed as a game with multiple rounds where users look at both compressed and uncompressed images.  Users accumulate points the longer they play, and get more points for doing well at the game.  We maintained a leaderboard to encourage participation and used only internal testers.The game’s test images came from a diverse collection of 250 images contributed by Flickr staff.  The images came from a variety of cameras and included a number of subjects from photographers with varying skill levels.

sampling of images used in taste test
Fig 3. A sampling of images used in our taste test.

In each round, our test code randomly select a test image, and present two variants of this image side by side.  50% of the time we present the user two identical images; the rest of the time we present one compressed image and one uncompressed image.  We ask the tester if the two images look the same or different and we’d expect a user choosing randomly OR a user unable to distinguish the two cases would answer correctly about half the time.  We randomly swap the location of the compressed images to compensate for user bias to the left or the right.  If testers choose correctly, they are presented with a second question: “Which image did you prefer, and why?”

two kittens in a video game
Fig 4. Screenshot of taste test.

Our test displays images simultaneously to prevent testers noticing a longer load time for the larger, non-compressed image.  The images are presented with either 320, 640, or 1600 pixels on their longest side.  The 320 & 640px images are shown for 12 seconds before being dimmed out.  The intent behind this detail is to represent how real users interact with our images.  The 1600px images stay on screen for 20 seconds, as we expect larger images to be viewed for longer periods of time by real users.   We award 100 points per round, regardless of whether a tester chose correctly and also award a bonus of 400 points when a tester correctly identifies whether images were identical or different.  We update the tester’s score every five tests so that the user perceives an increasing score without being rewarded immediately for any particular behavior.

Taste Test Outcome and Deployment

We ran our taste test for two weeks and analyzed our results.    Although we let users play as long as they liked,  we skipped the first result per user as a “warm-up” and considered only the subsequent ten results,  this limited the potential for users training themselves to spot compression artifacts.   We disregarded users that had fewer than eleven results.

images total results # labeled “identical” by tester % labeled “identical” by tester
two identical images 368 253 68.8%
one compressed, one non-compressed 352 238 67.6%

Table 2.   Taste test results.   Testers select “identical” at nearly the same rate, whether the input is identical or not.

When our testers were presented with two identical images, they thought the images were identical only 68.8% of the time(!), and when presented with a compressed image next to a non-compressed image,  our testers thought the images were identical slightly less often:  67.6% of the time.  This difference was small enough for us,  and our statisticians told us it was statistically insignificant.  Our image pairs were so similar that multiple testers thought all images were identical and reported that the test system was buggy. We inspected the images most often labeled different, and found no significant artifacts in the compressed versions.

So even in this side-by-side test,  perceptual image compression is just barely noticeable when images are presented side-by-side.  As the Flickr website wouldn’t ever show compressed and uncompressed images at the same time, and the use of compression had large benefits in storage footprint and site performance, we elected to go forward.

At the beginning of 2014 we silently rolled out perceptual-based compression on our image thumbnails (we don’t alter the “original” images uploaded by our users).  The slight changes to image appearance went unnoticed by users, but user interactions with Flickr became much faster,  especially for users with slow connections, while our storage footprint became much smaller.  This was a best-case scenario for us.

Evaluating perceptual compression was a considerable task,  but it gave the confidence we needed to apply this compression in production to our users.    This marked the first time Flickr had adjusted image settings in years, and, it was fun.
High Score List
Fig 5.  Taste test high score list

Epilogue

After eighteen months of perceptual compression at Flickr,  we adjusted our settings slightly to shrink images an additional 15%.   For our users on mobile devices,  15% fewer bytes per image makes for a much more responsive experience.We had run a taste test on this newer setting and users were were able to spot our compression slightly more often than with our original settings.   When presented a pair of identical images, our testers declared these images identical 65.2% of the time,  when presented with different images,  of our testers declared the images identical 62% of the time.   It wasn’t as imperceptible as our original approach, but, we decided it was close enough to roll out.

Boy were we wrong!   A few very vocal users spotted the compression and didn’t like it at all.    The Flickr Help Forum had a very lively thread which Petapixel picked up.  We beat our heads against the wall considered our options and came up with a middle path between our initial and follow-on approaches,  giving us smaller, faster-to-load files while still maintaining the appearance our users expect.

Through our use of perceptual compression,  combined with our use of on-the-fly resize and COS,  we’ve been able to decrease our storage footprint dramatically, while simultaneously improving user experience. It’s a win all around but we’re not done yet — we still have a few tricks up our sleeves.

The Data Freshener

Hello Kitty car air freshener
So fresh

 

Change

You may have noticed some changes in Flickr a couple months back. Like, half the site changed. 95% even, by some metrics. Some say CHANGE IT BACK! while others welcome change. Whatever your thoughts, the changes are here, and they mean things. For example, they mean new visual design and better usability. They mean a faster site. Unfortunately, up until recently, they also meant more stale data. Yuck.

Change
Change
 

Why? What? Well…here’s the deal. We have a new-ish frontend stack we’ve been using for the past couple years now. It’s an isomorphic single-page application, runs on node.js, and is generally awesome. We call it Reboot.

hi there / i am the computer
Reboot
 

In the World of Reboot, we treat data with kid gloves. We <3 data. We never want to give it up, never want to let it down. Once we pull data from our APIs, we store the fetched data in your browser so that we don’t have to fetch it again the next time it’s needed. This means faster page loads and faster navigation, and less API traffic (and thus a more stable and scalable API). The data cached in your browser exists as long as the current Reboot session — until you refresh or leave Reboot for a non-Rebooted page.

However, this also meant that data could become stale. You change the date taken of your photo, someone else adds a comment, you navigate to a page with cached data…and you don’t see the changes. Wat? Yeah. So, this was not a huge problem until we moved lots of pages onto Reboot in the beginning of May. From that point forward, most Flickr user sessions have spent their entirety on Reboot, feeding off the same stale loaves of cached data.

bread
Staleness
 

The thinking (design / prototypes)

We considered a number of possibilities for freshening up data during a user session. A brief history of the strategies we sampled, and their results:

1. Refresh on update

Ice Tea
 

The first stab focused on updating data locally after it was changed by the user. Most of our simpler use cases already updated as expected, but some trickier cases with indirect relationships did not. For example, changing the date taken of a photo updated the data model for the photo, but deleting a photo did not necessarily ensure the photo was removed from all the cached albums, groups, and galleries to which it belonged. (Note that the photo was removed correctly from the backend, just not from the cached representation of those entities on the client.)

Cleaning up these relationships using change events between models helped, but didn’t solve all our problems. When someone outside of the local session (read: another user) changed data, it would not reflect in the current session. The only way to catch changes from outside the current session was to be more aggressive about evicting models.

2. Nuclear option

Atomic Bomb Test
 

The pendulum swung all the way in the other direction — instead of surgical removal of data models we knew to be out-of-date, what would happen if we removed all cached data on every navigation? This prototype was quick to build, and incredibly destructive. By doing this, all our cached data always remained as fresh as could be, but we essentially reverted to Web 1.0 — with the exception of the Reboot framework, everything was reloaded on every page.

Not surprisingly, this blew up API traffic (locally only! did not unleash that disaster at scale), and inflated page load times like a Jeff Koons sculpture. It did give us some baseline timing metrics we could point to as worst-case scenarios, however. The next step was to swing the pendulum back toward the middle — to a carefully-knitted solution that would preserve fast page loads and navigation, while ensuring the freshest data we could serve up.

3. Refetch on navigate

fetched
 

At this point, our challenge was to find a solution that would keep navigation fast, API traffic slim, and pick up all changes to session data, whether local or remote. We ended up with a solution we call “refetching”: evicting and requesting new data models as the model is needed by the application. But when?
We could refetch periodically or on a user action; we determined that the best time to trigger a refetch was on navigation — when the user navigates, cached models become eligible for refetching. Specifically, when the user navigates between sections of the site, refetching is triggered. This proved to be the happiest medium between speed and freshness.

A high-level outline of how the refetching strategy works:

  • The user loads a page; data are requested from the API, and models are cached. As new models are created, they’re marked as being fresh.
  • The user navigates to another site section (e.g. Photostream → Search); all freshness marks are removed from all models. They’re now all eligible for refetching.
  • As Reboot builds the new page, it requests data models from the cache. Since they no longer have their seal of freshness, they are refetched, and marked as fresh once retrieved and cached.

One important note — refetching is not triggered on browser back/forward navigation. Users expect near-immediate navigation, thanks to browser caching, when navigating to already-viewed content. Therefore, we refetch only when the user clicks a link to navigate to a new site section.

4. Miscellany

There were a couple other options we considered and rejected from the start, but they’re worth mentioning here.

One was a TTL (time-to-live) algorithm, commonly used in caching applications. TTL algorithms expire data and evict from the cache a certain amount of time after they’re written or last updated. The arbitrary nature of TTL would mean that users would sometimes have fresh data and sometimes stale; it would be fresh more often than without any solution, but freshness would vary arbitrarily and would not result in much of an improvement on user experience.

The other was to write an algorithm that tracks the amount of time since a data model was last accessed, and refetch when it grows too old. While this sounded interesting at first, it has the same flaw as a standard TTL algorithm — freshness becomes arbitrary. It’s also more complex to implement, and might end up not being worth the complexity.

The doing (implementation)

So that was it! Refetch on navigate, all done. Right?….of course not. With the general strategy in place, the devil started sneaking around in all the details. Some of the highlights:

Exemptions

It proved to be not the best idea to evict on all navigation. For example, in Reboot we often preload photo metadata models on pages with lists of photos, in order to make navigation into the photo page snappy. The refetch setup therefore has an exemption config that allows us to easily retain models when navigating into, away from, or between specific site sections.

Child models

We often have parent-child associations between data models. For example, the data model for a photo has a reference to a data model for the author of the photo. When the photo model is refetched, the person model must be refetched as well. This means the function doing the eviction and refetching has to recurse through all child models.

Collections

An issue similar to child models above, but more complex, is the case of a model containing a list of other models. For example, the data model for a person’s photostream contains a list of photo models.

What made this particularly tricky is pagination and filtering — say you load the first 2 pages of your photostream, set your view filter to private, jump to page 5, switch the view to “Date taken”, and navigate away and back to your photostream…imagine the mess of different models with partially-loaded collections. Evicting one parent model, and its children, might evict photo models from the collection within another, without properly refetching. The solution here actually lay in the controller responsible for fetching pages: if a requested page of models is not already completely in-cache, a refetch will always happen to ensure we have all the data, in its freshest state.

Refetch only once per page view

Critical to the refetch-on-navigation strategy is to refetch only once per navigation. This was not too difficult, but essential to get right. We accomplish this by adding a flag when a model is initially fetched and upserted into the cache. When navigating to a new, non-exempt site section, all those flags are cleared, and any model requested by the new page will be refetched. When refetched, the model is again upserted into the cache and marked as fresh, until the next navigation.

But did it fresh?

Go on without me
 

With the thinking and the doing out of the way, it was time to push all this to production. Because these changes are essentially pulling the rug out from underneath the data layer on every navigation, we had to tread very carefully in order to prevent any negative impact to the end user experience.

We did very thorough manual and automated testing across all of Reboot. We left the feature turned on for staff users for a while, to be able to respond to any bug reports. Finally, the time came to test on Real People. There were three things we needed to keep an eye on: errors (of course), impact on page navigation timing, and API traffic. Since refetching implies more requests for data, we needed to be sure that we were keeping the user experience smooth and fast, and also that we weren’t blowing up our data centers.

 
All In
All in
 

In order to get a good read on these things, though, we had to go all in. Letting in just a small percentage of users would not give reliable numbers for timing or traffic impacts, due to the noise inherent in relatively small sample sizes. So, we did something unusual: we turned on refetching for all users for a short period of time. We flipped on refetching and kept an eagle eye on our stats for 2 hours, then reverted; then, we took a careful look at the aggregated data to see how the experiment went.

Surprisingly, the impact on both timing and traffic was relatively low. After some thought, we decided this is most likely because the changes disproportionately impact people on long sessions, say a Flickr tab open for hours or days. Most people don’t hang around that long; they come, they go. Also, the photo page represents north of 90% of our page views, and is exempt from refetching (see Exemptions above).

So where did we end up? A negligible bump in navigation timing and API traffic, and fresher data for all. Perhaps an anticlimactic resolution, but the story we’ve heard today outlines a serious consideration for anyone building an application with a data caching layer: keep in mind from the beginning how you plan to deal with stale data, but in a way that keeps all the other benefits of a single-page application.

#CCC is a breadcat
Busting through staleness. Yep.

Real-time Resizing of Flickr Images Using GPUs

At Flickr we work with a huge number of photos. Our users upload over 27 million photos a day, and our total collection has over 12 billion photos. This is fantastic! As usage grows, we are always looking for ways to use our storage more efficiently. Recently our storage team wrote about some new commodity storage technology now in use at Flickr which increases efficiency. But we also looked into how much data we store for each photo. In the past we stored many sizes of every photo to make serving fast. We wanted to challenge that model and find the minimal set of data to store.

Thumbnail Footprint Reduction

One of our biggest opportunities for byte per photo improvement is through reduction in the footprint of Flickr’s “thumbnails”. Thumbnail is a bit of a misnomer at Flickr; our thumbnails are as large as 2048 pixels on their longest side, so at Flickr we usually refer to these as resizes.  We create these resizes in order to provide a consistent,  fast experience for our users over a variety of use cases.



Different sizes used in different contexts. From left to right: Cameraroll uses small thumbnails, to enable fast navigation through many sizes. Our Photo Page uses our largest, most detailed sizes. Search uses sizes in between these two extremes. Red panda photos by Mathias Appel.

The selection of sizes has grown semi-organically over the years, and all told, we serve eleven different resizes per photo which, in sum, use nearly as much storage as the original photo. Almost 90% of this storage is held in the handful of resizes 640px and larger, so we targeted our efforts at eliminating some of these sizes.



Left: Distribution of byte size by resize dimension. Storage is concentrated in images with largest dimensions. Right: size distribution after largest sizes eliminated.

A Few Approaches

A simple approach to this problem would be just to cease offering some of the larger sizes.    For instance, we could drop the 1600px image from our API and require the design to adjust.   However, this requires compromises that we didn’t want to take on. Instead we took on a pretty ambitious goal: maintain our largest resize, usually 2048px wide, as a source image and create any other moderate or large-sized resizes on-the-fly from this source, without sacrificing image quality or significantly affecting performance. Using the original uploaded photo as a resize source image was impractical, as these can be very large and exist in a variety of formats.

Sounds easy, right? We already resize images when users upload, so why not just use that same technology on serving. Well, almost. The problem with the naive approach is that high-quality resizing of JPEGs is a lot slower than is widely known. A tool we use frequently, GraphicsMagick, produces beautiful images but takes over 225ms to resize a 2048px JPEG down to 1600px, depending on quality settings. This is slow enough that this method would impact user experience, and would require many CPUs to handle our load. Ymagine,  a high-performance CPU-based tool we’ve open sourced,  is twice as fast as GraphicsMagick(!). We use Ymagine extensively on smaller images, but for the large sizes we’re targeting we needed even more performance. A GPU-based solution ultimately filled our needs.

Our GPU-based Solution

We created a tier of dedicated resize servers, each with an GPU co-processor. Each of these boards has two GPUs, each with 1500+ “cores”, running at just under 1GHz. These cores aren’t anywhere near as performant as a CPU core, but there are many of them. We tested a range of server-grade boards to find the best performing type for our workload. Many manufacturers offer consumer-grade boards with incredible specifications and lower price points, but these lack server-grade cooling and other features such as ECC RAM. One member of our team had experience using these lower grade boards in a previous application and recommended against it.



Resize system architecture

On these resize servers we run a fairly vanilla Apache with a plugin written in C++.  This server responds to resize requests, reads our source image from disk into shared memory,  and hands off requests off to persistent resize daemons that do all communication with our  GPUs.  A daemon-type approach is necessary due to a somewhat lengthy initialization process with our GPUs.

Our resize daemons transfer JPEGs from shared memory to GPU device memory. Once here,  the real image processing takes place. The JPEGs are decoded, cropped, sharpened, resized, re-sharpened as needed, re-encoded as JPEGs, and finally transferred back to shared memory.    From shared memory, our Apache module returns the resized JPEG to the caller.



A simple resize pipeline. Post-sharpening overcomes fuzziness introduced when downscaling.

There are several accepted resize algorithms, but to retain the Flickr “look”, we implemented the same Lanczos resize and kernel sharpening algorithms that we’ve used for years in CUDA.     This had the added benefit of being able to directly compare images generated through GraphicsMagick and our GPU-based code.

Performance

With significant optimization, this code is able to resize our 2048px JPEGs to 1600px in under 16ms. This is more than 15x faster than GraphicsMagick and nearly 10x faster than Ymagine.  Resizes from 2048px to 640px take under 10ms. Equally noteworthy,  at peak load,  each resize server can perform over 300 resizes per second.



Performance of different resize approaches.

Although these timings are quite fast,  the source image for our resizes  is larger, byte-wise, than the images it is resizing to,  requiring additional I/O. For example,  a typical 2048px source JPEG is roughly 600kB and our typical 1024px JPEGs are just under 200kB. This difference in size leads to roughly 35ms additional I/O time per resize.

Taking it slow

As our GPU code is new and images are our most important product, this change carries some risk. We’ve addressed this with extensive testing, progressive rollout and provisions for rollback. We also used some insights into our user behavior to roll this solution out in a very controlled manner.

Conclusion

This system is currently in production and as we roll it out more fully, has the potential to cut the resize footprint of the majority of our photos by 50%, with negligible impact on performance and image appearance. We also have the ability to apply this same footprint reduction technique to images uploaded in the past, which has the potential to reduce our storage growth to zero for a significant period of time.

Credits

This project would not have been possible with hard work of Peter Norby, Tague Griffith, John Ko and many others.

Introducing: Flickr PARK or BIRD

park OR bird
Zion National Park Utah by Les Haines Creative Commons License Secretary Bird by Bill Gracey Creative Commons License

tl;dr: Check it out at parkorbird.flickr.com!

We at Flickr are not ones to back down from a challenge. Especially when that challenge comes in webcomic form. And especially when that webcomic is xkcd. So, when we saw this xkcd comic we thought, “we’ve got to do that”:

xkcd-1425
Creative Commons License

In fact, we already had the technology in place to do these things.  Like the woman in the comic says, determining whether a photo with GPS info embedded into it was taken in a national park is pretty straightforward. And, the Flickr Vision team has been working for the last year or so to be able to recognize more than 1000 things in images using deep convolutional neural nets. Incidentally, one of the things we’re pretty good at recognizing is birds!

We put those things together, and thus was born parkorbird.flickr.com!

Recognizing Stuff in Images with Deep Networks

The thing we’re really excited to show off with PARK or BIRD is our image recognition technology. To recognize 1000+ things, we employ a deep convolutional neural network similar to the one depicted below.

conv-net

This model transforms an input image into a representation in which different objects and scenes are easily distinguishable by a simple binary classification algorithm, like an SVM. It does this by passing the image through a series of layers, where each layer computes a function of the output of the layer below it.

Each successive one of these layers, after training on millions of images, has learned to recognize higher- and higher-level features of images and the ways these features go together to form different objects and scenes. For example, the first layer might recognize the most basic image features, such as short straight lines, corners, and small circular arcs. The next layer might recognize higher level combinations of those features, such as circles or other basic shapes. Further layers might recognize higher-level concepts, like eyes and beaks, and even further ones might recognize heads and wings. For an example of what this looks like, check out Figure 2 in this paper by Matt Zeiler and Rob Fergus.

As the image passes through these layers, they are “activated” in different ways depending on the features they’ve seen in the input image, and at the top of this network—after the image is transformed by the bottom layer, and that transformation of the image is transformed by the next layer, and that transformation of the transformation of the image is transformed by the next layer, and so on— a short floating-point vector summarizing all of the various activations at each layer is output. We pass this floating-point vector into more than 1000 binary classifiers, each of which is trained to give us a yes/no answer to identify a specific object/scene class. And, of course, one of those classes is birds!

The Flickr Vision team is already applying this deep network to Flickr photos to help people more more easily find what they’re looking for via Flickr search, and we plan to integrate it into Flickr in other cool ways in the future. We’re also working on other innovative computer vision and image recognition technologies that will make it easier for Flickr members to find and organize their photos.

Acknowledgements

The Flickr Vision and Search team is awesome and PARK or BIRD is built upon technologies that we all pitched in on. Here we all are (at least most of us), in all our beautiful glory. Thanks Vision/Search! Thanks also to Stephen Woods, Bart Thomee, John Ko, Mike Shema, and Sean Perkins, all of whom provided a lot of help getting PARK or BIRD off the ground.

Flickr flamily floto

If this all sounds like a challenge you’re interested in helping out with, you should join us! Flickr is hiring engineers, designers and product managers in our San Francisco office. Find out more at flickr.com/jobs.

Performance improvements for photo serving

We’ve been working to make Flickr faster for our users around the world. Since the primary photo storage locations are in the US, and information on the internet travels at a finite speed, the farther away a Flickr user is located from the US, the slower Flickr’s response time will be. Recently, we looked at opportunities to improve this situation. One of the improvements involves keeping temporary copies of recently viewed photos in locations nearer to users.  The other improvement aims to get a benefit from these caches even when a user views a photo that is not already in the cache.

Regional Photo Caches

For a few years, we’ve deployed regional photo caches located in Switzerland and Singapore. Here’s how this works. When one of our users in Vietnam requests a photo, we copy it temporarily to Singapore. When a second user requests the same photo, from, say, Kuala Lumpur, the photo is already present in Singapore. Flickr can respond much faster using this copy (only a few hundred kilometers away) instead of using the original file back in the US (over 8,000 km away).

The first piece of our solution has been to create additional caches closer to our users. We expanded our regional cache footprint around two months ago. Our Australian users, among others, should now see dramatically faster load times. Australian users will now see the average image load about twice as fast as it did in March.

We’re happy with this improvement and we’re planning to add more regional caches over the next several months to help users in other regions.

Cache Prefetch

When users in locations far from the US view photos that are already in the cache, the speedup can be up to 10x, but only for the second and subsequent viewers. The first viewer still has to wait for the file to travel all the way from the US. This is important because there are so many photos on Flickr that are viewed infrequently. It’s likely that a given photo will not be present in the cache. One example is a user looking at their Auto Upload album. Auto uploaded photos are all private initially. Scrolling through this album, it’s likely that very few of the photos will be in their regional cache, since no other users would have been able to see them yet.

It turns out that we can even help the first viewer of a photo using a trick called cache warming.

To understand how caching warming works, you need to understand a bit about how we serve images. For example, say that I’m a user in Spain trying to access the photostream of a user, Martin Brock, in the US. When my request for Martin Brock’s Photostream at https://www.flickr.com/photos/martinbrock/ hits our backend servers, our code quickly determines the most recent photos Martin has uploaded that are visible to me, which sizes will fit best in my browser, and the URLs of those images. It then sends me the list of those URLs in an HTML response. The user’s web browser reads the HTML, finds the image URLs and starts loading them from the closest regional cache.


Standard image fetch

So you’re probably already guessing how to speed things up.  The trick is to take advantage of the time in between when the server knows which images will be needed and the time when the browser starts loading them from the closest cache. This period of time can be in the range of hundreds of milliseconds. We saw an opportunity during this time to send the needed images over to the viewer’s regional cache in advance of their browser requesting the images. If we can “win the race” to do this, the viewer’s experience will be much faster, since images will load from the local cache instead of loading from the US.

To take advantage of this opportunity, we created a new “cache warming” process called The Warmer. Once we’ve determined which images will be requested (the first few photos in Martin’s photostream) we send  a message from the API servers to The Warmer.

The Warmer listens for messages and, based on the user’s location, it determines from which of the Flickr regional caches the user will likely request the image. It then pushes the image out to this cache.



Optimized image fetch, with cache warming path indicated in red

Getting this to work well required a few optimizations.

Persistent connections

Yahoo encrypts all traffic between our data centers. This is great for security, but the time to set up a secure connection can be considerable. In our first iteration of The Warmer, this set up time was so long that we rarely got the photo to the cache in time to benefit a user. To eliminate this cost, we used an Nginx proxy which maintains persistent connections to our remote data centers. When we need to push an image out – a secure connection is already set up and waiting to be used.

Transport layer

The next optimization we made helped us reduce the cost of sending messages to The Warmer.  Since the data we’re sending always fits in one datagram, and we also don’t care too much if a small percentage of these messages are never received, we don’t need any of the socket and connection features of TCP. So instead of using HTTP, we created a simple JSON format for sending messages using UDP datagrams. Another reason we chose to use UDP is that if The Warmer is not available or is reacting slowly, we don’t want that to cause slowdowns in the API.

Queue management

Naturally, some images are quite popular and it would waste resources to push them to the same cache repeatedly. So, the third optimization we applied was to maintain a list of recently pushed images in The Warmer. This simple “de-deduplication” cut the number of requests made by The Warmer by 60%. Similarly, The Warmer drops any incoming requests that are more than fifty milliseconds old. This “time-to-live” provides a safety valve in case The Warmer has fallen behind and can’t catch up.


def warm_up_url(params):
  requested_jpg = params['jpg']

  colo_to_warm = params['colo_to_warm']
  curl = &quot;curl -H 'Host: &quot; + colo_to_warm + &quot;' '&quot; + keepalive_proxy + &quot;/&quot; + requested_jpg + &quot;'&quot;
  os.system(curl)

if __name__ == '__main__':

# create the worker pool

  from multiprocessing.pool import ThreadPool
  worker_pool = ThreadPool(processes=100)

  while True:

    # receive requests
    json_data, addr = sock.recvfrom(2048)

    params = json.loads(json_data)

    requested_jpg = warm_params['jpg']
    colo_to_warm =
      determine_colo_to_warm(params['http_endpoint'])

    if recently_warmed(colo_to_warm, requested_jpg) :
      continue

    if request_too_old(params) :
      continue

    # warm up urls
    params['colo_to_warm'] = colo_to_warm

    warm_result = worker_pool.apply_async(warm_up_url,(params,))

Cache Warmer pseudocode

Java

Our initial implementation of the Warmer was in Python, using a ThreadPool. This allowed very rapid prototyping and worked great — up to a point. Profiling the Python code, we found a large portion of time spent in socket calls. Since there is so little code in The Warmer, we tried porting to Java. A nearly line-for-line translation resulted in a greater than 10x increase in capacity.

Results

When we began this process, we weren’t sure whether The Warmer would be able to populate caches before the user requests came in. We were pleasantly surprised when we first enabled it at scale. In the first region where we’ve deployed The Warmer (Western Europe), we observed a reduced median latency of more than 200 ms, 95% of photos requests sped up by at least 100 ms, and for a small percentage of photos we see over 400 ms reduction in latency. As we continue to deploy The Warmer in additional regions, we expect to see similar improvements.

Next Steps

In addition to deploying more regional photo caches and continuing to improve prefetching performance, we’re looking at a few more techniques to make photos load faster.

Compression

Overall Flickr uses a light touch on compression. This results in excellent image quality at the cost of relatively large file sizes. This translates directly into longer load times for users. With a growing number of our users connecting to Flickr with wireless devices, we want to make sure we can give users a good experience regardless of whether they have a high-speed LTE connection or two-bars of 3G in the countryside. An important goal will be to make these changes with little or no loss in image quality.

We are also testing alternative image encoding formats (like WebP). Under certain conditions WebP compression may offer better image quality at the same compression ratio than JPEG can achieve.

Geolocation and routing

It turns out it’s not straightforward to know which photo cache is going to give the best performance for a user. It depends on a lot of factors, many of which change over time — sometimes suddenly. We think the best way to do this is with a system that adapts dynamically to “Internet weather.”

Cache intelligence

Today, if a user needs to see a medium sized version of an image, and that version is not already present in the cache, the user will need to wait to retrieve the image from the US, even if a larger version of the image is already in the cache. In this case, there is an opportunity to create the smaller version at the cache layer and avoid the round-trip to the US.

Overall we’re happy with these improvements and we’re excited about the additional opportunities we have to continue to make the Flickr experience super fast for our users. Thanks for following along.

Flickr flamily floto

Like what you’ve read and want to make the jump with us? We’re hiring engineers, designers and product managers in our San Francisco office. Find out more at flickr.com/jobs.

Exploring Life Without Compass

Compass is a great thing. At Flickr, we’re actually quite smitten with it. But being conscious of your friends’ friends is important (you never know who they’ll invite to your barbecue), and we’re not so sure about this “Ruby” that Compass is always hanging out with. Then there’s Ruby’s friend Bundler who, every year at the Christmas Party, tells the same stupid story about the time the police confused him with a jewelry thief. Enough is enough! We’ve got history, Compass, but we just feel it might be time to try seeing other people.

Solving for Sprites

In order to find a suitable replacement (and for a bit of closure), we had to find out what kept us relying on Compass for so long. We knew the big one coming in to this great experiment: sprites. Flickr is a huge site with many different pages, all of which have their own image folders that need to be sprited together. There are a few different options for compiling sprites on your own, but we liked spritesmith for its multiple image rendering engines. This gives us some flexibility in dependencies.

A grunt task is available for spritesmith, but it assumes you are generating only one sprite. Our setup is a bit more complex and we’d like to keep our own sprite mixin intact so we don’t actually have to change a line of code. With spritesmith and our own runner to iterate over our sprite directories, we can easily create the sprites and output the dimensions and urls via a simple Handlebars template to a Sass file.

{{#each sprites}}
    {{#each images}}
        %{{../dir}}-{{name}}-dimensions {
            width: {{coords.width}}px;
            height: {{coords.height}}px;
        }
        %{{../dir}}-{{name}}-background {
            background: image-url('{{../url}}') -{{coords.x}}px -{{coords.y}}px no-repeat;
        }
    {{/each}}
{{/each}}

You could easily put all three of these rules in the same declaration, but we have some added flexibility in mind for our mixin.

It’s important to note that, because we’re using placeholders (the % syntax in Sass), nothing is actually written out unless we use it. This keeps our compiled CSS nice and clean (just like Compass)!

@import 'path/to/generated/sprite/file'

@mixin background-sprite($icon, $set-dimensions: false) {
    @extend %#{$spritePath}-#{$icon}-background;

    @if $set-dimensions == true {
        @extend %#{$spritePath}-#{$icon}-dimensions;
    }
}

Here, our mixin uses the Sass file we generated to provide powerful and flexible sprites. Note: Although retina isn’t shown here, adding support is as simple as extending the Sass mixin with appropriate media queries. We wanted to keep the example simple for this post, but it gives you an idea of just how extensible this setup is!

Now that the big problem is solved, what about the rest of Compass’s functionality?

Completing the Package

How do we account for the remaining items in the Compass toolbox? First, it’s important to find out just how many mixins, functions, and variables are used. An easy way to find out is to compile with Sass and see how much it complains!


sass --update assets/sass:some-temp-dir

Depending on the complexity of your app, you may see quite a lot of these errors.


error assets/css/base.scss (Line 3: Undefined mixin 'font-face'.)

In total, we’re missing 16 mixins provided by Compass (and a host of variables). How do we replace all the great mixin functionality of Compass? With mixins of the same name, node-bourbon is a nice drop-in replacement.

What is the point of all this work again?

The Big Reveal

Now that we’re comfortably off Compass, how exactly are we going to compile our Sass? Well try not to blink, because this is the part that makes it all worthwhile.

Libsass is a blazing-fast C port of the Sass compiler that exposes bindings to modules like node-sass.

Just how fast? With Compass, our compile times were consistently around a minute and a half to two minutes. Taking care of spriting ourselves and using libsass for Sass compilation, we’re down to 5 seconds. When you deploy as often as we do at Flickr (in excess of 10 times a day), that adds up and turns into some huge savings!

What’s the Catch?

There isn’t one! Oh, okay. Maybe there are a few little ones. We’re pretty willing to swallow them though. Did you see that compile time?!

There are some differences, particularly with the @extend directive, between Ruby Sass and libsass. We’re anticipating that these small kinks will continue to be ironed out as the port matures. Additionally, custom functions aren’t supported yet, so some extensibility is lost in coming from Ruby (although node-sass does have support for the image-url built-in which is the only one we use, anyway).

With everything taken into account, we’re counting down the days until we make this dream a reality and turn it on for our production builds.

Flickr flamily floto

Like what you’ve read and want to make the jump with us? We’re hiring engineers, designers and product managers in our San Francisco office. Find out more at flickr.com/jobs.