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Fault tolerance made easy Patterns for fault tolerance implemented surprisingly easy Uwe Friedrichsen, codecentric AG, 2013-2014

Fault tolerance made easy

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Fault tolerance in general is a challenging topic. Yet we need fault toleranct designs more badly than ever in order to provide robust, highly available systems - especially in times of scale out systems becoming more and more popular. Unfortunately, most developers do not care too much about a fault tolerant design, either because they are scared by the complexity of the realm or because they do not care enough. One of the problems is that a lack of fault tolerant design does not hurt a lot in development or in QA, but it hurts a lot in production - as Michael Nygard said: "It's all about production!" (at least figuratively). In this presentation I do *not* try to give a general introduction to fault tolerant design. Instead I pick a few generic case studies that demonstrate the results of missing fault tolerant design, try to sensitize a bit about the production relevance of fault tolerant design and then go along with a few selected patterns. I picked a few patterns which are surprisingly easy to implement and help to mitigate the problems of the former case studies. This way I try to show two things: 1. A piece of architecture or design as a pattern is not necessarily hard to implement. Sometimes the code is written quicker than it takes to explain the pattern beforehand. 2. Even if fault tolerant design as a general topic might be hard, some parts of it can be implemented very easily and it's more than worth the coding effort if you look how much better your system behaves in production just from adding those few lines of code.

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Fault tolerance made easy Patterns for fault tolerance implemented surprisingly easy

Uwe Friedrichsen, codecentric AG, 2013-2014

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@ufried Uwe Friedrichsen | [email protected] | http://slideshare.net/ufried | http://ufried.tumblr.com

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It‘s all about production!

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Production

Availability

Resilience

Fault Tolerance

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Your web server doesn‘t look good …

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Pattern #1

Timeouts

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Timeouts (1) // Basics myObject.wait(); // Do not use this by default myObject.wait(TIMEOUT); // Better use this // Some more basics myThread.join(); // Do not use this by default myThread.join(TIMEOUT); // Better use this

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Timeouts (2) // Using the Java concurrent library Callable<MyActionResult> myAction = <My Blocking Action> ExecutorService executor = Executors.newSingleThreadExecutor(); Future<MyActionResult> future = executor.submit(myAction); MyActionResult result = null; try { result = future.get(); // Do not use this by default result = future.get(TIMEOUT, TIMEUNIT); // Better use this } catch (TimeoutException e) { // Only thrown if timeouts are used ... } catch (...) { ... }

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Timeouts (3) // Using Guava SimpleTimeLimiter Callable<MyActionResult> myAction = <My Blocking Action> SimpleTimeLimiter limiter = new SimpleTimeLimiter(); MyActionResult result = null; try { result = limiter.callWithTimeout(myAction, TIMEOUT, TIMEUNIT, false); } catch (UncheckedTimeoutException e) { ... } catch (...) { ... }

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Determining Timeout Duration Configurable Timeouts Self-Adapting Timeouts Timeouts in JavaEE Containers

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Pattern #2

Circuit Breaker

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Circuit Breaker (1)

Client Resource Circuit Breaker

Request

Resource unavailable

Resource available

Closed Open

Half-Open

Lifecycle

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Circuit Breaker (2)

Closed on call / pass through call succeeds / reset count call fails / count failure threshold reached / trip breaker

Open on call / fail on timeout / attempt reset

trip breaker

Half-Open on call / pass through call succeeds / reset call fails / trip breaker

trip breaker attempt reset

reset

Source: M. Nygard, „Release It!“

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Circuit Breaker (3) public class CircuitBreaker implements MyResource { public enum State { CLOSED, OPEN, HALF_OPEN } final MyResource resource; State state; int counter; long tripTime; public CircuitBreaker(MyResource r) { resource = r; state = CLOSED; counter = 0; tripTime = 0L; } ...

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Circuit Breaker (4) ... public Result access(...) { // resource access Result r = null; if (state == OPEN) { checkTimeout(); throw new ResourceUnavailableException(); } try { r = r.access(...); // should use timeout } catch (Exception e) { fail(); throw e; } success(); return r; } ...

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Circuit Breaker (5) ... private void success() { reset(); } private void fail() { counter++; if (counter > THRESHOLD) { tripBreaker(); } } private void reset() { state = CLOSED; counter = 0; } ...

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Circuit Breaker (6) ... private void tripBreaker() { state = OPEN; tripTime = System.currentTimeMillis(); } private void checkTimeout() { if ((System.currentTimeMillis - tripTime) > TIMEOUT) { state = HALF_OPEN; counter = THRESHOLD; } } public State getState() return state; } }

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Thread-Safe Circuit Breaker Failure Types Tuning Circuit Breakers Available Implementations

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Pattern #3

Fail Fast

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Fail Fast (1)

Client Resources Expensive Action

Request Uses

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Fail Fast (2)

Client Resources

Expensive Action

Request

Fail Fast Guard

Uses

Check availability

Forward

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Fail Fast (3) public class FailFastGuard { private FailFastGuard() {} public static void checkResources(Set<CircuitBreaker> resources) { for (CircuitBreaker r : resources) { if (r.getState() != CircuitBreaker.CLOSED) { throw new ResourceUnavailableException(r); } } } }

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Fail Fast (4) public class MyService { Set<CircuitBreaker> requiredResources; // Initialize resources ... public Result myExpensiveAction(...) { FailFastGuard.checkResources(requiredResources); // Execute core action ... } }

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The dreaded SiteTooSuccessfulException …

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Pattern #4

Shed Load

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Shed Load (1)

Clients Server

Too many Requests

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Shed Load (2)

Server

Too many Requests

Gate Keeper

Monitor

Requests

Request Load Data Monitor Load

Shedded Requests

Clients

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Shed Load (3) public class ShedLoadFilter implements Filter { Random random; public void init(FilterConfig fc) throws ServletException { random = new Random(System.currentTimeMillis()); } public void destroy() { random = null; } ...

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Shed Load (4) ... public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) throws java.io.IOException, ServletException { int load = getLoad(); if (shouldShed(load)) { HttpServletResponse res = (HttpServletResponse)response; res.setIntHeader("Retry-After", RECOMMENDATION); res.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE); return; } chain.doFilter(request, response); } ...

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Shed Load (5) ... private boolean shouldShed(int load) { // Example implementation if (load < THRESHOLD) { return false; } double shedBoundary = ((double)(load - THRESHOLD))/ ((double)(MAX_LOAD - THRESHOLD)); return random.nextDouble() < shedBoundary; } }

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Shed Load (6)

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Shed Load (7)

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Shedding Strategy Retrieving Load Tuning Load Shedders Alternative Strategies

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Pattern #5

Deferrable Work

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Deferrable Work (1)

Client

Requests

Request Processing

Resources

Use

Routine Work

Use

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OVERLOAD

Deferrable Work (2)

WithoutDeferrable Work

100%

OVERLOAD

With Deferrable Work

100%

Request Processing

Routine Work

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// Do or wait variant ProcessingState state = initBatch(); while(!state.done()) { int load = getLoad(); if (load > THRESHOLD) { waitFixedDuration(); } else { state = processNext(state); } } void waitFixedDuration() { Thread.sleep(DELAY); // try-catch left out for better readability }

Deferrable Work (3)

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// Adaptive load variant ProcessingState state = initBatch(); while(!state.done()) { waitLoadBased(); state = processNext(state); } void waitLoadBased() { int load = getLoad(); long delay = calcDelay(load); Thread.sleep(delay); // try-catch left out for better readability } long calcDelay(int load) { // Simple example implementation if (load < THRESHOLD) { return 0L; } return (load – THRESHOLD) * DELAY_FACTOR; }

Deferrable Work (4)

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Delay Strategy Retrieving Load Tuning Deferrable Work

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I can hardly hear you …

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Pattern #6

Leaky Bucket

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Leaky Bucket (1)

Leaky Bucket

Fill

Problem occured

Periodically

Leak

Error Handling

Overflowed?

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public class LeakyBucket { // Very simple implementation final private int capacity; private int level; private boolean overflow; public LeakyBucket(int capacity) { this.capacity = capacity; drain(); } public void drain () { this.level = 0; this.overflow = false; } ...

Leaky Bucket (2)

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... public void fill() { level++; if (level > capacity) { overflow = true; } } public void leak() { level--; if (level < 0) { level = 0; } } public boolean overflowed() { return overflow; } }

Leaky Bucket (3)

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Thread-Safe Leaky Bucket Leaking strategies Tuning Leaky Bucket Available Implementations

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Pattern #7

Limited Retries

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// doAction returns true if successful, false otherwise // General pattern boolean success = false int tries = 0; while (!success && (tries < MAX_TRIES)) { success = doAction(...); tries++; } // Alternative one-retry-only variant success = doAction(...) || doAction(...);

Limited Retries (1)

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Idempotent Actions Closures / Lambdas Tuning Retries

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More Patterns •  Complete Parameter Checking •  Marked Data •  Routine Audits

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Further reading 1.  Michael T. Nygard, Release It!,

Pragmatic Bookshelf, 2007

2.  Robert S. Hanmer, Patterns for Fault Tolerant Software, Wiley, 2007

3.  James Hamilton, On Designing and Deploying Internet-Scale Services,21st LISA Conference 2007

4.  Andrew Tanenbaum, Marten van Steen, Distributed Systems – Principles and Paradigms, Prentice Hall, 2nd Edition, 2006

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It‘s all about production!

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@ufried Uwe Friedrichsen | [email protected] | http://slideshare.net/ufried | http://ufried.tumblr.com

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