Approaches to application request throttling
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Speaking from experience building MyGet.org: users are insane. If you are lucky, they use your service, but in reality, they probably abuse. Crazy usage patterns resulting in more requests than expected, request bursts when users come back to the office after the weekend, and more! These all pose a potential threat to the health of our web application and may impact other users or the service as a whole. Ideally, we can apply some filtering at the front door: limit the number of requests over a given timespan, limiting bandwidth, ... In this talk, we’ll explore the simple yet complex realm of rate limiting. We’ll go over how to decide on which resources to limit, what the limits should be and where to enforce these limits – in our app, on the server, using a reverse proxy like Nginx or even an external service like CloudFlare or Azure API management. The takeaway? Know when and where to enforce rate limits so you can have both a happy application as well as happy customers.
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Approaches to application request throttling
- 1. 1 Approaches for application request throttling Maarten Balliauw @maartenballiauw
- 2. 3 Who am I? Maarten Balliauw Antwerp, Belgium Developer Advocate, JetBrains Founder, MyGet AZUG Focus on web ASP.NET MVC, Azure, SignalR, ... Former MVP Azure & ASPInsider Big passion: Azure http://blog.maartenballiauw.be @maartenballiauw
- 3. 4 Agenda Users and traffic patterns Rate limiting and considerations Which resources? Which limits? Who to limit? Who not to limit? What when a limit is reached? Where to limit?
- 4. 5 Users...
- 5. 6 MyGet Hosted private package repository – www.myget.org NuGet, NPM, Bower, Maven, VSIX, PHP Composer, Symbols, ... HTTP-based Web UI for managing things API for various package managers PUT/POST – Upload package DELETE – Delete package via API GET – Fetch metadata or binary
- 6. 7 We’re using background workers Example: package upload PUT/POST binary and metadata to front-end PackageAddedEvent on queue with many handlers handled on back-end ProcessSymbols UpdateLatestVersion Indexing ...
- 7. 8 What could possibly go wrong... Too many uploads incoming! Front-end IIS server needs workers to read the incoming network stream Application logic has to check credentials, subscription, quota Back-end Delays in queue processing (luckily workers can process at their own pace) Too many uploads that are too slow! Front-end IIS server needs lots of workers to slowly copy from the network stream Workers == threads == memory == synchronization == not a happy place
- 8. 9 What could possibly go wrong... Too many downloads! Application logic has to check credentials, subscription, quota 404’s still need that application logic... Package managers are crazy! Total # requests Total # 404’s % 404’s # of packages in solution 200 800 600 # on NuGet.org 190 200 10 5% # on MyGet feed 1 5 200 195 97,5% # on MyGet feed 2 4 200 196 98% # on company-internal TeamCity 1 200 199 99,5%
- 9. 12 Other examples Web UI requests Trying to register spam accounts Trying to brute-force login/password reset Trying to validate credit card numbers via a form on your site Robots / Crawlers Imagine a spider adding 20k items to a shopping cart For us, usually fine (e.g. Googlebot by default up to 5 req/sec) Limiting is easy with rel=“nofollow” and robots.txt crawl-delay
- 12. 15 Rate limiting – what? Limits # of requests in a given timeframe Or limits bandwidth, or another resource – up to you Helps eliminate: Unexpected traffic patterns Unwanted traffic patterns (e.g. script kiddie brute-force login) Potentiallly damaging traffic patterns (accidental and malicious)
- 13. 16 Rate limit everything. - Maarten Balliauw
- 14. 17 Rate limiting – everything??? Everything that could slow down or break your application Typically everything that depends on a scarce or external resource CPU Memory Disk I/O Database External API So yes, everything...
- 15. 18 Let’s do this! Database with table Events UserIdentifier – who do we limit ActionIdentifier – what do we limit When – event timestamp so we can apply a query Filter attribute SELECT COUNT(*) FROM Events WHERE UserIdentifier = <user> AND ActionIdentifier = <action> AND When >= NOW() – X INSERT INTO Events (<user>, <action>, NOW()) DELETE FROM Events WHERE UserIdentifier = <user> AND ActionIdentifier = <action> AND When < NOW() – X
- 18. 21 That database was a bad idea! Very flexible in defining various limits or doing combinations Very flexible in changing limits, e.g. changing the time period The database will suffer at scale... Every request is at least 2 – 3 queries Constant index churn We need to manually run DELETE to remove old events Database size!
- 19. 22 That database was a bad idea! We created a denial of service opportunity! SELECT, INSERT, DELETE for every request Consider a simpler technique to limit # of operations Ideally just a simple counter “Buckets”
- 20. 24 Quantized buckets Create “buckets” per <identifier> and <timespan> Use incr <bucket> on Redis and get back the current count per <timespan> public string GetBucketName(string operation, TimeSpan timespan) { var bucket = Math.Floor( DateTime.UtcNow.Ticks / timespan.TotalMilliseconds / 10000); return $"{operation}_{bucket}"; } Console.WriteLine(GetBucketName("someaction", TimeSpan.FromMinutes(10))); // someaction_106062120 <-- this will be the key for +/- 10 minutes
- 21. 25 Quantized buckets Super easy and super cheap (atomic write and read on Redis, auto-expire LRU) Not accurate... (but that may be ok) (n-1)x2 / 10 sec Theoretically: max. 6 / 10 sec
- 22. 26 Leaky bucket “Imagine a bucket where water is poured in at the top and leaks from the bottom. If the rate at which water is poured in exceeds the rate at which it leaks, the bucket overflows.“ Widely used in telecommunications to deal with bandwidth/bursts.
- 24. 28 Bucket approaches QUANTIZED BUCKET Create “buckets” per <identifier>_<timespan> No rolling window (new bucket every <timespan>) Simple key/value store is sufficient, 1 atomic read+write Old keys can auto-expire (unlike our DB approach) LEAKY BUCKET Get <delta> tokens, with maximum <count> per <timespan> Rolling window, smooths traffic, allows bursts when bucket has capacity Need to store # tokens, last refill, concurrency needs to be taken into account Or use a FIFO queue of timestamps (works great with Redis sorted set)
- 25. 29 Redis sorted set as a bucket Demo
- 26. 30 Cool! That’s it, right?
- 28. 32 Things to decide on Decide on the resources to limit Decide on a sensible limit Come up with an identifier to limit on Decide on exceptions to the rule
- 29. 33 Which resources to limit? ...
- 30. 34 Rate limit everything. - Maarten Balliauw
- 31. 35 What are sensible limits? Approach 1 1. Figure out current # of requests for a certain resource 2. Set limits 3. Get angry phone calls from customers Approach 2 1. Figure out current # of requests for a certain resource 2. Set limits, but only log when a request would be limited 3. Analyze logs, set new limits, ... 4. Start rate limiting 5. Keep measuring
- 32. 36 Will you allow bursts or not? Laddering! Different buckets per identifier and resource... 10 requests per second can be 36000 requests per hour. But 10 requests per second could also be 1000 requests per hour. Bucket Operation A Operation B Operation C Per second 10 10 100 Per minute 60 60 500 Per hour 3600 600 500 ... Steady flow of max. 10/sec Steady flow of max. 10/sec, but only 600/hour max. Bursts of up to 100/sec, but only 500/hour max.
- 33. 37 What will be the identifier? Per IP address? But what with NAT/proxy? Per user? But how do you limit anonymous users? Per session? But what when the user starts a new session for every request? Or what if there is no such thing as a session? Per browser? But everyone uses Chrome!
- 34. 38 What will be the identifier? Probably a combination! IP address (debatable) + User token (or “anonymous”) + Session token + Headers (user agent + accept-language + some cookie + ...)
- 35. 39 Decide on exceptions Do we rate limit all users? Do we have separate limits for certain users? Dynamic limiting Do we rate limit all IP addresses? What about ourselves? What about our monitoring tools? What about web crawlers? What about certain datacenter ranges? (https://github.com/client9/ipcat) “IP addresses that end web consumers should not be using"
- 37. 41 What when the user hits the limit? Do we just “black hole” and close the connection? Do you tell the user? API: status code 429 Too Many Requests Web: error page stating rate limit exceeded / captcha (StackOverflow)
- 38. 42 Try to always tell the user Format? Depends on Accept header (text/html vs. application/json) Tell them why they were throttled Can be a simple link to API documentation Tell them when to retry (e.g. GitHub does this even before rate limiting) Status: 200 OK X-RateLimit-Limit: 5000 X-RateLimit-Remaining: 4999 X-RateLimit-Reset: 1372700873
- 39. 43 Where do we limit?
- 40. 44 Rate limiting – where? MvcThrottle Runs as action filter Requests per timespan Per action, user, IP, ... (so knows about actions) Owin.Limits Runs as OWIN middleware Bandwidth, concurrent requests, ... No knowledge about application specifics Many, many others
- 42. 46 How far do we allow traffic before saying no? KNOWLEDGE ABOUT THE OPERATION RESOURCES SPENT
- 43. 47 How far do we allow traffic before saying no? KNOWLEDGE ABOUT THE OPERATION RESOURCES SPENT
- 44. 48 What options are there? In our application ActionFilter / Middleware / HttpModule / ... Easy to add custom logic, based on request details On the server Outside of our server Outside of our datacenter
- 45. 49 What options are there? In our application On the server IIS has dynamic IP restrictions, bit rate throttling, <limits /> Kestrel minimum speed throttle Found these less flexible in terms of configuraton... E.g. IIS dynamic IP restrictions returns 403 Forbidden, wth! Not a big fan, as these are usually HttpModules anyway (and thus hit our app) Outside of our server Outside of our datacenter
- 46. 50 What options are there? In our application On the server Outside of our server Reverse proxy (IIS Application Request Routing, NGinx, HAProxy, Squid, ...) Traffic does not even hit our application server, yay! Outside of our datacenter
- 47. 51 Rate limiting with NGinx Demo
- 48. 52 What options are there? In our application On the server Outside of our server Outside of our datacenter Azure API management, CloudFlare Filters traffic very early in the request, yay! Often also handle DDoS attacks Often more expensive
- 49. 54 Conclusion
- 50. 55 Conclusion Users are crazy! (typically unintended) We need rate limiting Decide on the resources to limit (tip: everything) Decide on a sensible limit (tip: measure) Come up with an identifier to limit on Decide on exceptions What when the user reaches a limit? Decide where in the request/response flow to limit
Editor's Notes
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- Prerequisites: create database and make sure it works! Open demo 01 - DemoLetsDoThis.sln In Startup.cs explain adding EF context and show how EventsContext is built Next, show RateLimitFilter is applied to every request Implementation of RateLimitFilter Uses an identifier for the user (either User.Identity.Username or “anonymous” + IP address) Uses ControllerActionDescriptor to determine controller + action We then check if there are > 5 requests to this resource We always add an event to the DB – DANGEROUS!!! And drop older events Show in Fiddler, requesting: http://localhost:56983/api/hello/maarten
- Open DemoTokenBuckets.sln Show our quantized bucket implementation QuantizedBucket We give it a rate and an interval, so 5 requests per 5 seconds We use the bucket name, which is based on Math.Floor(Now.Ticks / TimeSpan.Ticks) When bucket empty, block Bucket auto-refills when Math.Floor(Now.Ticks / TimeSpan.Ticks) returns another value Run the app, show request rate allows 5 new requests every 5 seconds Explain again that at the end of my 5 seconds, I could potentially run 8 requests per 10 seconds Leaky bucket fixes this – show LeakyBucket implementation Also takes rate and interval Whenever we consume, we calculate the number of tokens left by looking at the timespan that elapsed between requests. Downside: need to store both # tokens left and last refill time Run the app, show we get a better distribution like in our pizza oven, where we are effectively flattening our traffic now.
- Prerequisites: run Redis! Open Patterns03RateLimiting.sln Show our HelloWorldController, say we use an API key this time In WebApiConfig, note we add ThrottlingHandler Run the application, and in Fiddler request http://localhost:9400/api/helloworld Add X-ApiKey header See we are rate limited per API key, open ThrottlingHandler We are using a sorted set as the data type Sorted set has two things: a key, and a score. We’ll use the current timestamp for both, but important is the score is our tmestamp 1) Drop old items with a score below our reference time 2) Count elements 3) Add element with score == now
- Open MvcThrottle, in project MvcThrottle.Demo Show HomeController, show EnableThrottling attribute Run the application - http://localhost:53048/Home/About – see it in action after a few refreshes Mention we can respond to throttlign depending on the client type! Open MvcThrottleCustomFilter See filterContext.HttpContext.Request.AcceptTypes.Any(accept => accept.Contains("html")) -> custom view result Mention we can filter based on client IP In FilterConfig.cs, there is an IP whitelist of folks we never want to throttle Same goes with user agents Same goes with endpoints The REALLY nice thing: I can enable/disable per action in MVC Show BlogController REALLY NICE, throttling follows my logic The SAD thing: open 04-snapshot I did a load test – non scientific! This thing has low overhead (did a few thousand requests) but still my aplication spent 12% of its time rate limiting requests
- Run the nginx docker container from 05-nginx Show a few requests: http://localhost:8080/ proxies MyGet http://localhost:8080/F/googleanalyticstracker/api/v2 proxies a feed A few refreshes of http://localhost:8080/F/googleanalyticstracker/api/v2 get throttled So we proxy our app, and get to rate limit some calls, sweet! Open nginx.conf and go through some boiler-plate: Worker processes and worker connections (typically == to # cores) http section sets up a web server, we can add SSL etc here as well Under server, we define the different resources / just proxies www.myget.org and injects a header /Content proxies and caches (yay added bonus of NGinx) /F/ is where things get interesting – we limit requests to this one using “mylimit” Defines a key, names a zone, names the timespan, names the limit Can mix and match to create key: limit_req_zone $binary_remote_addr$http_authorization zone=mylimit:10m rate=2r/s;
- Prerequisites Create Azure API management (!!! Well upfront – takes time !!!) Force-push the 06-apim repo to it git remote add origin .......<new url>....... git push --force --set-upstream origin master Show portal – especially “API” and “PRODUCT” “API” defines API calls. From portal, show we can create this based on a Swagger definition For demo here, created manually and registered /F/* and /* to just pass-through Under products Show anonymous and unlimited Explain the idea of API management is to sell access to your API and allow people to purchase a product to get better/less/… access to an API Anonymous is all I’ll use during the demo Anonymous has policies – show rate limit is 100 per 60 sec From PUBLISHER PORTAL (click), we have a policy for –Feed endpoint as well, which is more strict Show https://ratelimitingdemo.azure-api.net/ is smooth Show a few refreshes of https://shit.azure-api.net/F/googleanalyticstracker/api/v2/ limited Requests that are limited never hit my server