At Crashlytics, one of our founding principals has been an extreme (some would say, absurd) attention to detail. Crash detection and reporting, particularly on iOS, is a complex and esoteric problem to solve, with arcane restrictions that throw modern programming practices out the window.

Need to allocate memory at crash-time? Revisit your approach. Thinking of calling an Objective-C method? Dream on.

This focus has not gone unnoticed: many of the world’s best mobile engineering teams – that build many of the most-well-known apps – now trust our award-winning Crashlytics for iOS solution to deliver accurate, detailed, crash reports from hundreds of millions of devices around the globe.

But we’re not satisfied.

Over the past 6 months, we’ve embarked on a ground-up rewrite of our iOS SDK to take things to a whole new level and, after 2 months of intense testing, I’m extremely pleased to publicly announce the release of Crashlytics for iOS v2.

Highlights from hundreds of improvements

When we set out to design our new iOS SDK, it was the perfect opportunity to fundamentally rethink our approach. We’ve made hundreds of improvements to our iOS SDK that have lead to significant performance and stability increases. Here are some of the major ones:

1. Mach Exceptions. Better than signal handlers. 

   All widely-used crash-reporting solutions for iOS and Mac OS are currently based off signals and uncaught exceptions. By registering handlers for both of these events, it’s possible to detect and inspect the majority of crashes that occur. As our usage has exploded, however, it became painfully obvious that crashes were sneaking through. For example, it’s not possible to catch all stack-overflow crashes with a signal handler.

   Fortunately, there’s a better way. In Darwin, signals are actually implemented on top of lower-level events called Mach Exceptions. Handling these directly is the holy-grail – all crashes can be captured immediately after they happen with far more precision and accuracy. The Mach Exception API is radically more complex than signal handling, but capturing every crash more than justifies the hurdles.

2. Advanced techniques to stop secondary crashes.

   Processes that crash often end up sustaining considerable damage before the kernel takes action to terminate them. In many of the nastiest crashes we’ve seen, this can result in secondary crashes, where the crash-handling code itself is unable to operate correctly and fails, obscuring the source of the original crash. Secondary crashes have two primary causes, both due to corruption. A buffer-overrun could mangle or destroy the in-memory data-structures that Crashlytics uses to track state. Alternatively, hardware failures or disk errors could damage the temporary cache files used to record data before it was sent to our servers.

   Our new SDK goes to great lengths to address these scenarios. By carefully controlling its memory usage, our new SDK is able to pre-allocate a contiguous block of RAM that it then surrounds with guard pages, protecting against buffer-overruns. In the case of cache corruption, we’ve invested in making our file-handling code extremely defensive, so parsing cache files can’t crash unexpectedly.

3. Stack unwinding. Finding the real path to the crash.

   One of the most abstruse aspects of crash detection is stack unwinding, the seemingly omniscient ability to determine historic code execution that directly lead to the crash. In practice, it involves carefully walking up the stack in memory and searching for return addresses – the instruction pointers of the calling lines.

   Writing an ARM stack unwinder that works in most cases is relatively straightforward – the stack layout for iOS is well-defined. However, things start to fall apart when custom assembly is thrown into the mix, as there are no hard rules on what can and can’t be done. It just so happens that objc_msgSend is such a method, performing countless tricks to make dynamic method invocation in Objective-C as fast as possible. All works perfectly during normal operation, but if objc_msgSend crashes, a naïve ARM unwinder could easily miss the stack frame of the calling function. Of course, that’s the critical line you need to know!

   Our new SDK uses a vastly better technique to determine the calling instruction that works in the case of objc_msgSend and many other “creative” methods that still conform in-part to Apple’s iOS ABI.

We could not be happier to get these improvements into the hands of thousands of app developers and the feedback so far has been fantastic. Thanks to all those who have helped us test Crashlytics for iOS v2 and trust me – we’re not stopping here – the most advanced crash reporting SDK for iOS will keep getting better!

Have questions about Crashlytics for iOS v2? Send us email!

Since our launch one year ago, Crashlytics has set the bar for the most informative crash reports on mobile. Above and beyond stack traces, RAM usage, and disk utilization, we’ve sought to provide all the critical data-points that developers need to pinpoint and fix issues – device orientation, battery state, even whether the device was being held up to the ear! And we’re never satisfied.

A treasure-trove of data lies in an app’s logs and there’s no better way to debug a problem than by knowing exactly what happened leading up to the critical moment. Capturing logging data has been our number-one customer request for months and our number-one concern. We care deeply about security and end-user privacy: collecting logging data opens the door to substantial risks. We wanted to begin the path down the road to building a Splunk for Mobile.

I’m excited to announce that after focusing our R&D efforts, we think we’ve cracked it, and I wanted to share some details on our approach.

Privacy, Performance

The easiest way to deliver logging would be to capture and redirect all output from NSLog(), but this is also the easiest way to infringe user privacy. Many apps don’t take the care they should in scrubbing log lines of personally-identifiable information: names, email addresses, even passwords often appear in URLs or internal settings that might commonly get logged. Sending this data, even encrypted over SSL, would be dangerous and in-breach of most privacy policies.

Instead, we’ve chosen to introduce completely distinct logging functionality called CLSLog(), so it’s explicit what data will be collected and transmitted with Crashlytics reports.

We also took the opportunity to make some performance improvements – in our benchmarks, CLSLog() is 10X faster than NSLog() under the same conditions. Using CLSLog() could not be easier – it’s a drop-in replacement:

1 OBJC_EXTERN void CLSLog(NSString *format, ...); // Log messages to be sent with crash reports

1 NSLog(@"Detected Higgs Boson with mass %f!!", [boson mass]);
2 CLSLog(@"Detected Higgs Boson with mass %f!!", [boson mass]);

Options, Options, Options

Of course, in many case you might want your log messages to also output to the system log, or show up in Xcode’s console. For these cases, we’ve also provided CLSNSLog(), which records the output and then passes it along to NSLog():

1 OBJC_EXTERN void CLSNSLog(NSString *format, ...); // Log messages to be sent with crash reports as well as to NSLog()

But what if both could happen? In development builds, it would be ideal for everything to pass-thru to Xcode’s console so debugging was as easy as possible. In release builds, though, that’s nothing but overhead — it would be great to take advantage of the blinding speed of our 100% in-memory implementation of CLSLog().

We’ve got you covered:

 1 /**
 2 *
 3 * The CLS_LOG macro provides as easy way to gather more information in your log messages that are
 4 * sent with your crash data. CLS_LOG prepends your custom log message with the function name and
 5 * line number where the macro was used. If your app was built with the DEBUG preprocessor macro
 6 * defined CLS_LOG uses the CLSNSLog function which forwards your log message to NSLog and CLSLog.
 7 * If the DEBUG preprocessor macro is not defined CLS_LOG uses CLSLog only, for a ~10X speed-up.
 8 *
 9 * Example output:
10 * -[AppDelegate login:] line 134 $ login start
11 *
12 **/
13 #ifdef DEBUG
14 #define CLS_LOG(__FORMAT__, ...) CLSNSLog((@"%s line %d $ " __FORMAT__), __PRETTY_FUNCTION__, __LINE__, ##__VA_ARGS__)
15 #else
16 #define CLS_LOG(__FORMAT__, ...) CLSLog((@"%s line %d $ " __FORMAT__), __PRETTY_FUNCTION__, __LINE__, ##__VA_ARGS__)
17 #endif

In Debug builds, CLS_LOG() will pass-thru to NSLog, but in Release builds, it will be as fast as possible:

1 CLS_LOG(@"Higgs-Boson detected! Bailing out... %@", attributesDict);

Network Efficient

We’ve designed our custom logging functionality from the ground-up to respect your end-users network connections and your app’s performance. Since it’s implementation is entirely in-process, it’s blazingly fast with no IPC overhead. It also accepts as much data as you choose to throw at it: CLSLog() maintains an auto-scrolling 64kb buffer of your log data, which is more than enough to record what happened in the moments leading up to a crash without exploding your app’s memory requirements or your end-users cellular data plan. Believe it or not, it’s even more memory-efficient than it sounds – our advanced architecture doesn’t even require holding all 64kb in RAM!

That’s Not All…

Viewing logging information is a whole other story. Rather than explain it, I’d encourage you to head over to our SDK Overview and see for yourself! We’re hard at work on additional SDK functionality and have much more to talk about in the coming weeks – stay tuned!

TL;DR: 31 lines of Rack middleware leverage Redis for highly-performant and flexible response caching.

As Crashlytics has scaled, we’ve always been on the lookout for ways to drastically reduce the load on our systems. We recently brought production Redis servers online for some basic analytics tracking and we’ve been extremely pleased with their performance and stability. This weekend, it was time to give them something a bit more load-intensive to chew on.

The vast majority – roughly 90% – of inbound traffic to our servers is destined for the same place. Our client-side SDK, embedded in apps on hundreds of millions of devices worldwide, periodically loads configuration settings that power many of our advanced features. These settings vary by app and app version, but are otherwise identical across devices – a prime candidate for caching.

There are countless built-in and third-party techniques for Rails caching, but we sought something simple that could leverage the infrastructure we already had. Wouldn’t it be great if we could specify a cache duration in any Rails action and it would “just work”?

1 cache_response_for 10.minutes

Rack Middleware to the Rescue

One of the most powerful features of Rack-based Rails is middleware – functionality you can inject into the request processing logic to adjust how it is handled. This will let us check Redis for a cached response or fall-through to the standard Rails action.

 1 class RackRedisCache
 2   def initialize(rails)
 3     @rails = rails
 4   end
 5
 6   def call(env)
 7     cache_key = "rack::redis-cache::#{env['ORIGINAL_FULLPATH']}"
 8
 9     data = REDIS.hgetall(cache_key)
10     if data['status'] && data['body']
11       Rails.logger.info "Completed #{data['status'].to_i} from Redis cache"
12       [data['status'].to_i, JSON.parse(data['headers']), [data['body']]]
13     else
14       @rails.call(env).tap do |response|
15         response_status, response_headers, response_body = *response
16         response_cache_duration = response_headers.delete('Rack-Cache-Response-For').to_i
17
18         if response_cache_duration > 0
19           REDIS.hmset(cache_key,
20             'status', response_status,
21             'headers', response_headers.to_json,
22             'body', response_body.body
23           )
24
25           REDIS.expire(cache_key, response_cache_duration)
26           Rails.logger.info "Cached response to Redis for #{response_cache_duration} seconds."
27         end
28       end
29     end
30   end
31 end

A response in Rails consists of 3 components – the HTTP status, HTTP headers, and of course, the response body. For clarity, we store these under separate keys within a Hash in Redis, JSON-encoding the headers to convert them into a string.

If the cache key is not present, the middleware falls-through to calling the action, and then checking an internal header value to determine whether the action desires its response be cached. The final critical line leverages Redis’ key expiration functionality to ensure the cache is only valid for a given amount of time. It couldn’t get much simpler.

Implementing our DSL

To tie it all together, the ApplicationController needs a simple implementation of cache_response_for that sets the header appropriately:

1 def cache_response_for(duration)
2   headers['Rack-Cache-Response-For'] = duration
3 end

Boom. It was really that easy.

Impact?

This implementation took us only about an hour to develop and deploy, and the effects were immediate. Only 4% of these requests now fall-through to Rails, CPU usage on our API servers has plummeted, and total queries to our MongoDB cluster are down 78%. An hour well-spent. Our Redis cluster also doesn’t sweat its increased responsibility: its CPU usage is up just marginally!

Join Us!

Interested in working on these and other high-scale challenges?  We’re hiring!  Give us a shout at jobs@crashlytics.com. You can stay up to date with all our progress on Twitter, and Facebook.

We had a blast at last night’s Backbone.js MeetUp – it’s great to see such a thriving community here in Boston and to share a few of the insights we’ve had here at Crashlytics about building scalable applications with Backbone.js. The slides from our talk are up on SlideShare for viewing.

We’re looking forward to the next MeetUp and continuing to work with the Boston Backbone community – if you have any feedback or want to get in touch, leave a comment below!

Check out the slides here.

Backbone.js is a highly extensible framework that provides an MV* paradigm for Javascript applications that we use here at Crashlytics. Its lightweight architecture provides us a great foundation for building modular thick web clients. Backbone’s adaptability allows for the creation of addons and plugins to help tackle many of the problems faced when building a large scale JS application.

However, one of the problems we ran into while using Backbone at Crashlytics was the difficulty of managing the state of our application and its child components in a modular, scalable fashion. Whether it is choosing how to render a view on the DOM or conditionalizing the functionality of a model, the persistent nature of a web application requires a way to manage state-related logic.

Enter Backbone.StateManager

Backbone.StateManager is a module for Backbone.js that adds the ability to manage and utilize states in any size JavaScript application. It can be used as a stand alone object or in conjunction with a target object. Built on top of Underscore.js and Backbone.js, StateManager allows for modular state definitions, sub/pub architecture support with Backbone.Events, transition events between states, and regular expression matching.

Continuing our commitment to open source software, we’re happy to announce that Backbone.StateManager is available today on Github.

Getting Started

The core functionality of StateManager is comprised of 3 simple methods: addState, triggerState, and removeState. Because StateManager extends Backbone.Events, it is also possible to subscribe to the object and listen as it sends out signals when moving between states.

Backbone.StateManager constructor takes two arguments, a state object and an options object, but neither is required. Passed in states will be automatically added and the options are set as an instance property.

 1 var stateManager;
 2
 3 stateManager = new Backbone.StateManager();
 4 // or
 5 stateManager = new Backbone.StateManager({
 6   foo: {
 7     enter: function() {
 8       return console.log('enter bar');
 9     },
10     exit: function() {
11       return console.log('exit foo');
12     }
13   },
14   bar: {
15     enter: function() {
16       return console.log('enter bar');
17     },
18     exit: function() {
19       return console.log('exit bar');
20     }
21   }
22 });

State Definitions

A state is intended to be as modular as possible, so each state definition is expected to contain enter and exit methods that are used when entering or leaving that state. A state definition can also have a transitions property that contains several methods to be used when moving between specified states. There are 4 types of transitions that Backbone.StateManager will defaultly look for: onBeforeExitTo, onExitTo, onBeforeEnterFrom, and onEnterFrom. Each transition is a key value pair, where the value is a method and the key defines the transition type and the specified state (eg onEnterFrom:specifiedState).

 1 {
 2   enter: function() {
 3     return console.log('enter');
 4   },
 5   exit: function() {
 6     return console.log('exit');
 7   },
 8   transitions: {
 9     'onBeforeExitTo:anotherState': function() {},
10     'onExitTo:anotherState': function() {},
11     'onBeforeEnterFrom:anotherState': function() {},
12     'onEnterFrom:anotherState': function() {}
13   }
14 }

Integration

A benefit of StateManager is that it provides an easy method to painlessly add itself to any object. Through the use of the addStateManager, which takes a target object parameter, it reads in any states defined on the target, binds state methods to the target, and creates a new Backbone.StateManager. It also sets a number of convenience methods on the target, including triggerState, getCurrentState, and a reference to the StateManager at target.stateManager.

 1 var View;
 2
 3 View = Backbone.View.extend({
 4   states: {
 5     foo: {
 6       enter: function() {
 7         return console.log('enter bar');
 8       },
 9       exit: function() {
10         return console.log('exit foo');
11       },
12       transitions: {
13         'onExitTo:bar': function() {
14           return 'just exited and bar is about to be entered';
15         }
16       }
17     },
18     bar: {
19       enter: function() {
20         return console.log('enter bar');
21       },
22       exit: function() {
23         return console.log('exit bar');
24       }
25     }
26   },
27   initialize: function() {
28     return Backbone.StateManager.addStateManager(this);
29   }
30 });

Get It

• CoffeeScript
• JavaScript
• JavaScript (minified)

Feedback Welcome

Our goal is to make Backbone.StateManager as useful as possible for you to make writing powerful Backbone.js apps painless. We’re just getting started, and the feedback you provide is critical in achieving this. Please report bugs and discuss features on our Github issues page https://github.com/crashlytics/backbone.statemanager/issues.

Join the Team

Interested in diving-deep into these and other workflow-enhancing challenges?  We’re hiring!  Give us a shout at jobs@crashlytics.com. You can stay up to date with all our progress on Twitter, Facebook, and Google+.

We’re thrilled to announce Crashlytics Integrations — seamlessly send your Crashlytics issues to your favorite issue tracker!

Crashlytics Intelligence + Workflow you already use = Productivity Win

With Crashlytics Integrations, you get all of the power of our industry-leading crash analysis platform seamlessly integrated into the issue tracking tools you already have, enabling a single-glance view at your outstanding issues. When it’s time to debug, simply jump directly from your issue tracker to the Crashlytics dashboard to get a deep dive into the data.

You’re in control

We care about signal-to-noise ratio — we made sure to build an easy to use control panel so that you’re in full control over when you receive issue updates. By default, we only send along an issue when it reaches Impact Level 2 or higher. However, you can change this to be as verbose as you want.

Launch Integrations

We’re launching Integrations with five of the most-requested offerings. They are:

• JIRA 5
• Pivotal Tracker
• Redmine
• PagerDuty
• Web Hooks

You may be familiar with the first four. The fifth, Web Hooks, is a unique integration that you can use to connect Crashlytics with whatever kind of system you want. We’ll simply POST information to a URL of your choosing, in JSON format. This is great for triggering custom events inside your own development infrastructure or behind your firewall!

Bring-Your-Own

Your options don’t stop there. Faced with the endless array of third-party bug trackers and project management tools available today, it wouldn’t make sense to artificially limit what you can use!

We’re excited to announce that, starting today, we have open-sourced our Integrations implementation so that you can build your own experience and ensure Crashlytics fits seamlessly into your development workflow, whatever it may be. Head on over to GitHub and fork crashlytics-services to get started. We’re looking forward to rolling out the best contributions for everyone to benefit from!

Feedback Welcome

Our goal is to make Crashlytics Integrations as useful as possible for you and leverage the incredible infrastructure we’ve built along with the tools you use everyday to make finding and fixing issues in your apps painless.

We’re just getting started, and we’d love to hear feedback on how we can make this even better for you. Shoot us an email through Support — as you know, we read each and every email that comes through.

Join the Team

Interested in diving-deep into these and other workflow-enhancing challenges?  We’re hiring!  Give us a shout at jobs@crashlytics.com. You can stay up to date with all our progress on Twitter, Facebook, and Google+.