Ruby Concurrency Compared

Slide 1

Slide 1 text

Ruby Concurrency Compared Anil Wadghule  @anildigital Making Software. Better. simple software solutions to big business problems.

Slide 2

Slide 2 text

Slide 3

Slide 3 text

🏯%🗾

Slide 4

Slide 4 text

❤🎧🗺🎵

Slide 5

Slide 5 text

❤🎧 Yoko Kanno - Japanese Music Composer

Slide 6

Slide 6 text

No content

Slide 7

Slide 7 text

RubyKaigi TShirt, June 2006, Premshree’s Personal Weblog

Slide 8

Slide 8 text

👕❤

Slide 9

Slide 9 text

@anildigital Outline Some basics about concurrency Concurrency models Java Clojure (STM) Node.js Python Erlang / Elixir Go Ruby 10

Slide 10

Slide 10 text

Concurrency vs. Parallelism Obligatory

Slide 11

Slide 11 text

@anildigital Concurrency vs. parallelism 12 Concurrent = Two queues and one coﬀee machine. Parallel = Two queues and two coﬀee machines. Joe Armstrong

Slide 12

Slide 12 text

@anildigital Concurrency vs. parallelism 13 Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once. http://blog.golang.org/concurrency-is-not-parallelism Rob Pike

Slide 13

Slide 13 text

@anildigital Concurrency vs. parallelism 14 Lady Gaga I cannot text you with a drink in my hand, eh

Slide 14

Slide 14 text

@anildigital Concurrency vs. parallelism 15 Lady Gaga Concurrent I will put down this drink to text you, then put my phone away and continue drinking, eh

Slide 15

Slide 15 text

@anildigital Concurrency vs. parallelism 16 Lady Gaga Parallel I can text you with one hand while I use the other to drink, eh

Slide 16

Slide 16 text

@anildigital Three walls https://www.technologyreview.com/s/421186/why-cpus-arent-getting-any-faster/ Power wall Faster computers get really hot Memory wall Memory buses are not fast enough to handle these increase clock speeds ILP wall (Instruction level parallelism) Instruction pipeline really means digging deeper power hole 17 Dr. David Patterson, Berkeley

Slide 17

Slide 17 text

@anildigital Power wall + Memory wall + ILP wall Taken together, they mean. Computers will stop getting faster Furthermore, if an engineer optimizes one wall he aggravates the other two. Solution - Multi core processors 18

Slide 18

Slide 18 text

@anildigital Computer performance Latency - Amount of time it takes to complete particular program on hardware Throughput - Number of operations per second Utilisation - Utilising best use of multicore systems Speedup - (Speciﬁc to parallel programming, your algorithm runs faster on parallel hardware) Power Consumption (No one cares) 19

Slide 19

Slide 19 text

@anildigital Scheduling - Preemptive vs Non-preemptive A scheduling algorithm is Preemptive - If the active process or task or thread can be temporarily suspended to execute a more important process or task or thread Non-preemptive - If the active process or task or thread cannot be suspended i.e. runs to completion 20

Slide 20

Slide 20 text

@anildigital Scheduling - Cooperative A scheduling algorithm is Cooperative - When currently running process voluntarily gives up executing to allow another process to run. e.g. yield 21

Slide 21

Slide 21 text

@anildigital Concurrency models Threads / Mutexes Software Transactional Memory Actors Evented Coroutines CSP Processes / IPC 22

Slide 22

Slide 22 text

@anildigital Comparing concurrency models / techniques 23 Model Execution Scheduling Communication Concurrent/ Parallel Implementation Mutexes Threads Preemptive Shared memory (locks) C/P Mutex Software Transactional Memory Threads Preemptive Shared memory (commit/abort) C/P Clojure STM Processes & IPC Processes Preemptive Shared memory  (message passing) C/P Resque/Forking CSP Threads/Processes Preemptive Message passing (channels) C/P Golang / concurrent-ruby Actors Threads/Processes Preemptive Message passing (mailboxes) Erlang / Elixir / Akka / concurrent-ruby Futures & Promises Threads Cooperative Message passing (itself) C/P concurrent-ruby / Celluloid Co-routines 1 process / thread Cooperative Message passing C Fibers Evented 1 process / thread Cooperative Shared memory C EventMachine

Slide 23

Slide 23 text

Java Concurrency 24

Slide 24

Slide 24 text

@anildigital Java Concurrency - Threads Threads Shared mutability is root of all Evil ( Deadlocks / Race conditions ) Solutions With synchronization / mutexes / locks 25

Slide 25

Slide 25 text

@anildigital Java Concurrency - Threads Pros No scheduling needed by program (preemptive) Operating system does it for you Commonly used Cons Context switching / Scheduling overhead Deadlocks / Race conditions Synchronization / Locking issues 26

Slide 26

Slide 26 text

@anildigital Java Concurrency - java.util.concurrent java.util.concurrent 27

Slide 27

Slide 27 text

@anildigital Java Concurrency - java.util.concurrent 28

Slide 28

Slide 28 text

@anildigital Locks - java.util.concurrent ReentrantLock ReentrantReadWriteLock Condition 29

Slide 29

Slide 29 text

@anildigital Locks - java.util.concurrent ReentrantLock 30 private ReentrantLock lock; public void foo(){ ... lock.lock(); ... } public void bar(){ ... lock.unlock(); ... }

Slide 30

Slide 30 text

@anildigital Locks - java.util.concurrent ReentrantReadWriteLock 31 ReadWriteLock readWriteLock=new ReentrantReadWriteLock(); readWriteLock.readLock().lock(); // multiple readers can enter this section // if not locked for writing, and not writers waiting // to lock for writing. readWriteLock.readLock().unlock();   readWriteLock.writeLock().lock(); // only one writer can enter this section, // and only if no threads are currently reading. readWriteLock.writeLock().unlock();

Slide 31

Slide 31 text

@anildigital Locks - java.util.concurrent Condition 32 class BoundedBuffer { final Lock lock = new ReentrantLock(); final Condition notFull = lock.newCondition(); final Condition notEmpty = lock.newCondition(); final Object[] items = new Object[100]; int putptr, takeptr, count; public void put(Object x) throws InterruptedException { lock.lock(); try { while (count == items.length) notFull.await(); items[putptr] = x; if (++putptr == items.length) putptr = 0; ++count; notEmpty.signal(); } finally { lock.unlock(); } } public Object take() throws InterruptedException { lock.lock(); try { while (count == 0) notEmpty.await(); Object x = items[takeptr]; if (++takeptr == items.length) takeptr = 0; --count; notFull.signal(); return x; } finally { lock.unlock(); } } }

Slide 32

Slide 32 text

@anildigital Locks - java.util.concurrent Condition 33 class BoundedBuffer { final Lock lock = new ReentrantLock(); final Condition notFull = lock.newCondition(); final Condition notEmpty = lock.newCondition(); ...

Slide 33

Slide 33 text

@anildigital Locks - java.util.concurrent Condition 34 ...  public Object take() throws InterruptedException { lock.lock(); try { while (count == 0) notEmpty.await(); Object x = items[takeptr]; if (++takeptr == items.length) takeptr = 0; --count; notFull.signal(); return x; } finally { lock.unlock(); } } }

Slide 34

Slide 34 text

@anildigital Locks - java.util.concurrent Condition 35 ... public void put(Object x) throws InterruptedException { lock.lock(); try { while (count == items.length) notFull.await(); items[putptr] = x; if (++putptr == items.length) putptr = 0; ++count; notEmpty.signal(); } finally { lock.unlock(); } }  ...

Slide 35

Slide 35 text

@anildigital Semaphores - java.util.concurrent Semaphores A thread synchronization construct To avoid missed signals between threads or to guard a critical section like locks 36

Slide 36

Slide 36 text

@anildigital Semaphores - java.util.concurrent Semaphores 37 class Pool { private static final int MAX_AVAILABLE = 100; private final Semaphore available = new Semaphore(MAX_AVAILABLE, true); public Object getItem() throws InterruptedException { available.acquire(); return getNextAvailableItem(); } public void putItem(Object x) { if (markAsUnused(x)) available.release(); } ...

Slide 37

Slide 37 text

@anildigital CountdownLatch - java.util.concurrent CountdownLatch Allows one or more threads to wait for a given set of operations to complete. 38

Slide 38

Slide 38 text

@anildigital CountdownLatch - java.util.concurrent CountdownLatch await countdown 39

Slide 39

Slide 39 text

@anildigital CountdownLatch - java.util.concurrent CountdownLatch 40 CountDownLatch latch = new CountDownLatch(3); Waiter waiter = new Waiter(latch); Decrementer decrementer = new Decrementer(latch); new Thread(waiter) .start(); new Thread(decrementer).start(); Thread.sleep(4000);

Slide 40

Slide 40 text

@anildigital CountdownLatch - java.util.concurrent CountdownLatch 41 public class Waiter implements Runnable{ CountDownLatch latch = null; public Waiter(CountDownLatch latch) { this.latch = latch; } public void run() { try { latch.await(); } catch (InterruptedException e) { e.printStackTrace(); } System.out.println("Waiter Released"); } }

Slide 41

Slide 41 text

@anildigital CountdownLatch - java.util.concurrent CountdownLatch 42 public class Decrementer implements Runnable { CountDownLatch latch = null;  public Decrementer(CountDownLatch latch) { this.latch = latch; } public void run() { try { Thread.sleep(1000); this.latch.countDown(); Thread.sleep(1000); this.latch.countDown(); Thread.sleep(1000); this.latch.countDown(); } catch (InterruptedException e) { e.printStackTrace(); } } }

Slide 42

Slide 42 text

@anildigital Barrier - java.util.concurrent CyclicBarrier (multi-party synchronization) Can synchronize threads progressing through some algorithm. It is a barrier that all threads must wait at, until all threads reach it, before any of the threads can continue. 43

Slide 43

Slide 43 text

@anildigital Barrier - java.util.concurrent CyclicBarrier (multi-party synchronization) 44 Thread 1 Cyclic Barrier 1 Thread 2 wait 44 Cyclic Barrier 2 wait wait wait

Slide 44

Slide 44 text

@anildigital Exchanger - java.util.concurrent Exchanger represents a kind of rendezvous point where two threads can exchange objects 45

Slide 45

Slide 45 text

@anildigital Exchanger - java.util.concurrent Exchanger 46 46 46 Object 1 Thread 1 Thread 1 Object 2 Exchanger

Slide 46

Slide 46 text

@anildigital Exchanger - java.util.concurrent Exchanger Exchanger exchanger = new Exchanger(); ExchangerRunnable exchangerRunnable1 = new ExchangerRunnable(exchanger, "A"); ExchangerRunnable exchangerRunnable2 = new ExchangerRunnable(exchanger, "B"); new Thread(exchangerRunnable1).start(); new Thread(exchangerRunnable2).start();

Slide 47

Slide 47 text

@anildigital Exchanger - java.util.concurrent Exchanger public class ExchangerRunnable implements Runnable{ Exchanger exchanger = null; Object object = null; public ExchangerRunnable(Exchanger exchanger, Object object) { this.exchanger = exchanger; this.object = object; } public void run() { try { Object previous = this.object; this.object = this.exchanger.exchange(this.object); System.out.println( Thread.currentThread().getName() + " exchanged " + previous + " for " + this.object ); } catch (InterruptedException e) { e.printStackTrace(); } } }

Slide 48

Slide 48 text

@anildigital Exchanger - java.util.concurrent Exchanger Thread-0 exchanged A for B Thread-1 exchanged B for A Output

Slide 49

Slide 49 text

@anildigital Atomic variable Not possible to forget to acquire the lock when necessary. Second, because no locks are involved, it’s impossible for an operation on an atomic variable to deadlock. 50

Slide 50

Slide 50 text

@anildigital Atomic variables - java.util.concurrent AtomicBoolean AtomicInteger AtomicLong AtomicReference AtomicStampedReference AtomicIntegerArray AtomicLongArray AtomicReferenceArray 51

Slide 51

Slide 51 text

@anildigital Atomic variables - java.util.concurrent AtomicBoolean 52 AtomicBoolean atomicBoolean = new AtomicBoolean(true); boolean expectedValue = true; boolean newValue = false; boolean wasNewValueSet = atomicBoolean.compareAndSet( expectedValue, newValue);

Slide 52

Slide 52 text

@anildigital Atomic variables - java.util.concurrent AtomicInteger 53 AtomicInteger atomicInteger = new AtomicInteger(123); int expectedValue = 123; int newValue = 234; atomicInteger.compareAndSet(expectedValue, newValue);    System.out.println(atomicInteger.getAndAdd(10)); System.out.println(atomicInteger.addAndGet(10));

Slide 53

Slide 53 text

@anildigital Atomic variables - java.util.concurrent AtomicReference 54 AtomicReference atomicReference = new AtomicReference(); String initialReference = "the initially referenced string"; AtomicReference atomicReference = new AtomicReference(initialReference);

Slide 54

Slide 54 text

@anildigital Atomic variables - java.util.concurrent AtomicIntegerArray 55 int[] ints = new int[10]; ints[5] = 123; AtomicIntegerArray array = new AtomicIntegerArray(ints); int value = array.get(5); array.set(5, 999); boolean swapped = array.compareAndSet(5, 999, 123); int newValue = array.addAndGet(5, 3); int oldValue = array.getAndAdd(5, 3); int newValue = array.incrementAndGet(5) int oldValue = array.getAndIncrement(5); int newValue = array.decrementAndGet(5); int oldValue = array.getAndDecrement(5);

Slide 55

Slide 55 text

@anildigital Queues - java.util.concurrent BlockingQueue ArrayBlockingQueue DelayQueue LinkedBlockingQueue PriorityBlockingQueue SynchronousQueue BlockingDeque LinkedBlockingDeque 56

Slide 56

Slide 56 text

@anildigital Queues - java.util.concurrent BlockingQueue 57 Thread 1 Thread 2 BlockingQueue Put Take

Slide 57

Slide 57 text

@anildigital Queues - java.util.concurrent BlockingQueue public class BlockingQueueExample { public static void main(String[] args) throws Exception { BlockingQueue queue = new ArrayBlockingQueue(1024); Producer producer = new Producer(queue); Consumer consumer = new Consumer(queue); new Thread(producer).start(); new Thread(consumer).start(); Thread.sleep(4000); } }

Slide 58

Slide 58 text

@anildigital Queues - java.util.concurrent BlockingQueue public class Producer implements Runnable { protected BlockingQueue queue = null; public Producer(BlockingQueue queue) { this.queue = queue; } public void run() { try { queue.put("1"); Thread.sleep(1000); queue.put("2"); Thread.sleep(1000); queue.put("3"); } catch (InterruptedException e) { e.printStackTrace(); } } }

Slide 59

Slide 59 text

@anildigital Queues - java.util.concurrent BlockingQueue public class Consumer implements Runnable{ protected BlockingQueue queue = null; public Consumer(BlockingQueue queue) { this.queue = queue; } public void run() { try { System.out.println(queue.take()); System.out.println(queue.take()); System.out.println(queue.take()); } catch (InterruptedException e) { e.printStackTrace(); } } }

Slide 60

Slide 60 text

@anildigital Queues - java.util.concurrent BlockingDeque 61 Thread 1 Thread 2 BlockingDeqeue Put /  Take Put /  Take

Slide 61

Slide 61 text

@anildigital Queues - java.util.concurrent BlockingDeque LinkedBlockingDeque 62

Slide 62

Slide 62 text

@anildigital ConcurrentHashMap - java.util.concurrent ConcurrentHashMap 63 ConcurrentMap concurrentMap = new ConcurrentHashMap(); concurrentMap.put("key", "value"); Object value = concurrentMap.get("key");

Slide 63

Slide 63 text

@anildigital ConcurrentHashMap - java.util.concurrent ConcurrentHashMap 64

Slide 64

Slide 64 text

@anildigital Lists - java.util.concurrent CopyOnWriteArrayList Thread safe variant of ArrayList 65

Slide 65

Slide 65 text

@anildigital ThreadPool - java.util.concurrent Instead of creating new thread for the task, it can be passed to a thread pool ThreadPool reuses threads Internally it uses BlockingQueue for threads ExecutorService is a thread pool implementation 66

Slide 66

Slide 66 text

@anildigital ThreadPool - java.util.concurrent ExecutorService 67 ExecutorService executorService = Executors.newFixedThreadPool(10); executorService.execute(new Runnable() { public void run() { System.out.println("Asynchronous task"); } }); executorService.shutdown();

Slide 67

Slide 67 text

@anildigital ThreadPool - java.util.concurrent ExecutorService newFixedThreadPool newWorkStealingPool newSingleThreadExecutor newCachedThreadPool newScheduledThreadPool 68

Slide 68

Slide 68 text

@anildigital ForkJoinPool - java.util.concurrent ForkJoinPool was added in Java 7 Similar to ExecutorService but one difference Makes it easy for tasks To split their work up into smaller tasks Tasks are then submitted to the ForkJoinPool too. Uses work stealing algorithm Next level of ExecutorService 69

Slide 69

Slide 69 text

@anildigital ForkJoinPool - java.util.concurrent Basic fork & join algorithm 70 Result solve(Problem problem) { if (problem is small) directly solve problem else { split problem into independent parts fork new subtasks to solve each part join all subtasks compose result from subresults } }

Slide 70

Slide 70 text

@anildigital ForkJoinPool - java.util.concurrent Splitting and joining of tasks. 71 Task 71 71 71 Task Task Task Task Task Task Join

Slide 71

Slide 71 text

@anildigital Real world usage Threads & mutexes are still used by programmers Provides very low level APIs to handle concurrency Fine grained concurrency with more power java.util.concurrent is robust. Still there are chances of bugs. 72

Slide 72

Slide 72 text

Amdahl’s Law 73

Slide 73

Slide 73 text

@anildigital Amdhal’s Law 74 Amdhal’s Law, 1967

Slide 74

Slide 74 text

@anildigital Amdhal’s Law To predict the theoretical maximum speedup for program processing using multiple processors. The speedup limited by the time needed for the sequential fraction of the program If N is the number of processors, s is the time spent by a processor on serial part of a program, and p is the time spent by a processor on a parallel part of a program, then the maximum possible speedup is given by: 1 / (s+p/N) Synchronization & communication overhead 75

Slide 75

Slide 75 text

Clojure STM   (Software Transactional Memory) 76

Slide 76

Slide 76 text

@anildigital Clojure STM https://en.wikipedia.org/wiki/Software_transactional_memory “…completing an entire transaction veriﬁes that other threads have not concurrently made changes to memory that it accessed in the past. This ﬁnal operation, in which the changes of a transaction are validated and, if validation is successful, made permanent, is called a commit…” 77

Slide 77

Slide 77 text

@anildigital Clojure STM Software transactional memory (STM) Any method of coordinating multiple concurrent modiﬁcations to a shared set of storage locations. Similar to garbage collection has displaced the need for manual memory management. Characterized as providing the same kind of systematic simpliﬁcation of another error-prone programming practice - manual lock management 78

Slide 78

Slide 78 text

@anildigital Clojure STM “Don’t wait on lock, just check when we’re ready to commit” 79 # Thread 2   atomic {  - read variable  - increment variable  # going to write but Thread1 has written a variable…  # notices Thread1 changed data, so ROLLS BACK  - write variable  } # Thread 1  atomic {   - read a variable  - increment a variable  - write a variable  }

Slide 79

Slide 79 text

@anildigital State, Identify and Value http://clojure.org/about/state State, Identify, and Value 72, {1, 2, 3}, “cat”, [4, 5, 7] (def sarah {:name "Sarah" :age 25 :wears-glasses? false}) An identify is an entity that has state State is a value at a point in time Value is something that doesn’t change e.g. Set of my favourite foods. I will prefer different foods in future, that will be a different set 80

Slide 80

Slide 80 text

@anildigital Issues with state based OOP langs Imperative programming and state OOP, complects identity and state No way to obtain state independent of identity without copying There is no way to associate the identity’s state with a different value other than in-place memory mutation. Objects not treated as values Clojure says, OO doesn’t have to be in this way 81

Slide 81

Slide 81 text

@anildigital Mutability issues Mutable values + Identities = Complexity 82 # in Ruby favorite_langs_joe = Set.new ["Clojure", "Ruby"]    favorite_langs_bob = favorite_langs_joe    favorite_langs_joe << "Elixir"  favorite_langs_joe   # favorite_langs_bob   #

Slide 82

Slide 82 text

@anildigital Clojure STM Immutable data New values are functions of old values An identity’s state at any point in time is an immutable value Values can always be observed and new values calculated from old values without coordination Values will never change “in hand” Values can be safely shared between threads 83

Slide 83

Slide 83 text

@anildigital Clojure STM - Atomic references Atomic references to values Deﬁnes mutable data structures which are concurrency aware Models Identity by way of references to immutable data Dereferencing a reference type gives you its (immutable) value Changes to an identity’s state are controlled / coordinated by the system 84

Slide 84

Slide 84 text

@anildigital Clojure STM - Atomic references These mutable variables are in concert with Clojure’s persistent data structures to separate identify from state. Allowing accessing mutable variables from multiple threads without dangers of deadlock or race conditions In all cases the program will see stable views of the values in the world 85

Slide 85

Slide 85 text

@anildigital Clojure STM 86 Model Usage Functions Atoms Synchronised, Independent updates pure Refs Synchronised, Coordinated updates pure Agents Asynchronous, Independent updates any Vars Thread-local updates any Clojure Reference Containers

Slide 86

Slide 86 text

@anildigital Clojure STM 87 Refs Atoms Agents coordinated independent synchronous asynchronous

Slide 87

Slide 87 text

@anildigital Clojure Reference Types Isolate the state and constraint the ways in which that state can be changed. Clojure STM 88 Value Value Reference Type λ deref

Slide 88

Slide 88 text

@anildigital Clojure STM - Atom atomic variables very similar to java.util.concurrent Atomic Variables Persistent data structures separate identity from state. 89 (def my-atom (atom 42)) #'user/my-atom (deref my-atom) 42 @my-atom 42

Slide 89

Slide 89 text

@anildigital Clojure STM - Atom To update an atom 90 (swap! my-atom inc) 43 @my-atom 43 (swap! my-atom +2) 45

Slide 90

Slide 90 text

@anildigital Clojure STM - Atom Validators 91 (def non-negative (atom 0 :validator #(>= %0))) #'user/non-negative (reset! non-negative 42) 42 (reset! non-negative -1) IllegalStateException Invalid reference state

Slide 91

Slide 91 text

@anildigital Clojure STM - Atom Watchers 92 (def a atom (0)) (add-watch a :print #(println "Changed from " %3 " to " %4)) # (swap! a + 2) Changed from 0 to 2

Slide 92

Slide 92 text

@anildigital Clojure STM - Agents Agents are an uncoordinated, asynchronous reference type. concurrency aware. Agents work in concert with persistent data structures to maintain the separation of identity and state. Changes to an agent’s state are independent of changes to other agents’ states, and that all such changes are made away from the thread of execution that schedules them. I/O and other side-effecting functions may be safely used in conjunction with agents. Agents are STM-aware, so that they may be safely used in the context of retrying transactions 93

Slide 93

Slide 93 text

@anildigital Clojure STM - Agents Agents 94 (def my-agent (agent 0)) #'user/my-agent @my-agent 0 (send my-agent inc) # (@my-agent) 1 (send my-agent + 2) # (@my-agent) 3

Slide 94

Slide 94 text

@anildigital Clojure STM - Agents 95 Agents Actors Agent’s value can be retrieved with deref. Actor encapsulates state and provides no direct means to accept it. Agent does not encapsulates behaviour. Function is provided by sender Actor’s can encapsulate behaviour Agent’s error reporting is more primitive. Actor’s error detection and recovery is sophisticated Agents provide no support for distribution Actors can be remote Composing agents cannot deadlock Composing actors can deadlock

Slide 95

Slide 95 text

@anildigital Clojure STM - Agents Unlike refs and atoms, it is perfectly safe to use agents to coordinate I/O and perform other blocking operations. Use their own thread pool send For ﬁxed thread pool. Never used for actions that might perform I/O or other blocking operations send-off for growing thread pool Ideal for guarding a contested resource. Blocking IO. 96

Slide 96

Slide 96 text

@anildigital Clojure STM - Refs Refs enable coordinated, synchronous changes to multiple values concurrently No possibility of the Involved refs ever being in an observable inconsistent state Race conditions among the involved refs. Deadlocks. No manual use of locks, monitors, or other low-level synchronization primitive 97

Slide 97

Slide 97 text

@anildigital Clojure STM - Refs Ideal for co-ordinating changes to multiple states Operations are atomic, consistent and isolated from other transactions Manual transaction control (dosync) Operations alter, ref-set, commute, ensure 98

Slide 98

Slide 98 text

@anildigital Clojure STM - Refs STM Transactions Atomic Consistent Isolated ACID without D (Durability) 99

Slide 99

Slide 99 text

@anildigital Clojure STM - Refs Refs 100 (def my-ref (ref 0)) #'user/my-ref @my-ref 0 (ref-set my-ref 42) IllegalStateException No transaction running (alter my-ref inc) IllegalStateException No transaction running

Slide 100

Slide 100 text

@anildigital Clojure STM - Refs A transaction is created with dosync 101 (dosync (ref-set my-ref 42)) 42 @my-ref 42 (dosync (alter my-ref inc)) 43  @my-ref 43

Slide 101

Slide 101 text

@anildigital Clojure STM - Pros & Cons Pros Increased concurrency No thread needs to wait to access a resource Smaller scope that needs synchronizing - modifying disjoint parts of a system of a data structure Cons Aborting transactions Places limitations on the behaviour of transactions - they cannot perform any operation that cannot be undone, including most I/O 102

Slide 102

Slide 102 text

@anildigital Clojure - Futures Futures Evaluated within a thread pool that is shared with potentially blocking agent actions. This pooling of resources can make futures more efﬁcient than creating native threads as needed. Using future is much more concise than setting up and starting a native thread. Clojure futures (the value returned by future) are instances of java.util.concurrent.Future, which can make it easier to interoperate with Java APIs that expect them. Dereferencing a future blocks until value is available 103

Slide 103

Slide 103 text

@anildigital Clojure - Futures Futures 104 (def do-something (future (Thread/sleep 10000) 28)) #'user/do-something do-something # (realized? do-something) false .... .... 10 seconds after (realized? do-something) true @do-something 28 do-something #

Slide 104

Slide 104 text

@anildigital Clojure - Delays Delays Construct that suspends some body of code, Evaluating only upon demand, when it is dereferenced Only evaluate their body of code once Caches the return value. 105 (def d (delay (println "Running...") :done!)) #'user/d (deref d) Running... :done!    @d :done!    (realized? d) true

Slide 105

Slide 105 text

@anildigital Clojure - Promises Used in similar way as delay or future They block when you dereference them if they don’t have value until they do You don’t immediately give them value, but provide them one by calling deliver 106 (def result (promise)) (future (println "The result is: " @result))  (Thread/sleep 2000)    (deliver result 42)  "The result is: 42”

Slide 106

Slide 106 text

@anildigital Clojure - Vars Identities (Atom, Agent, Refs) referred (eventually) to a single value Real world needs names that refer to different values at different points in time With vars you can override the value Concurrency aware Limitation We can override their value only within the scope of a particular function call, and nowhere else. 107

Slide 107

Slide 107 text

@anildigital Clojure - Vars 108 (def x :mouse) #'user/x   (def box (fn [] x)) #'user/box  (box) :mouse  (def x :cat) #'user/x    (box) :cat Example

Slide 108

Slide 108 text

Node.js 109

Slide 109

Slide 109 text

@anildigital Node.js Architecture API Node Bindings  (socket, http, etc) Asynchronous I/O  (libuv) V8 Event  Loop  (libuv) DNS  (c-ares) Crypto  (OpenSSL) Main Single Thread

Slide 110

Slide 110 text

@anildigital Node.js Single threaded Cooperative multitasking (Node.js waits for some asynchronous I/O to yield to the next waiting process.) Slow network operations event loops are preferred You can serve more number of connections You don’t have to spend a process/thread for a connection libuv abstracts best of platforms in Node.js libuv internally uses thread pool. (e.g. File IO) 111

Slide 111

Slide 111 text

@anildigital Node.js server

Slide 112

Slide 112 text

@anildigital Node.js Error handling 113 https://www.joyent.com/node-js/production/design/errors

Slide 113

Slide 113 text

@anildigital Node.js Evented servers are really good for very light requests, but if you have long running request, it falls on its face. Pros Avoid polling. CPU bound vs IO bound Scales well vs spawning many threads No deadlocks Cons You block the even loop, all goes bad Program ﬂow is “spaghettish” Callback hell Hard to debug, you lose the stack 114

Slide 114

Slide 114 text

Python concurrency 115

Slide 115

Slide 115 text

@anildigital coroutines with asyncio in Python New trend in asynchronous programming Coroutines allow asynchronous code can be paused and resumed functions which cooperatively multitasks with other function Program puts coroutine on hold for async and control goes to event loop 116

Slide 116

Slide 116 text

@anildigital coroutines with asyncio in Python Coroutines are a language construct Event loop is a scheduler for coroutine Coroutines are best of the both the worlds, They can wait as callbacks do. They yield to each other so less chance of race conditions Coroutines are extremely lightweight and never preempted Coroutines wait on socket and if something happens on socket they become runnable. Compared to multi-threaded, co-routines can run sequence of operations in a single function. Exception handling works too Limitation Coroutines execute on single thread only 117

Slide 117

Slide 117 text

Actor model 118

Slide 118

Slide 118 text

@anildigital Actor model 119 Carl Hewitt, Peter Bishop Richard Steiger A Universal Modular ACTOR Formalism for Artiﬁcial Intelligence, 1973

Slide 119

Slide 119 text

@anildigital Actor model Mathematical model of concurrent computation. The Actor model treats “Actors” as the universal primitives of concurrent digital computation Actors communicate with messages In response to a message that it receives, Actor can Make local decisions Create more Actors Send more messages Determine how to respond to the next message received 120

Slide 120

Slide 120 text

CSP - Communicating Sequential Processes 121

Slide 121

Slide 121 text

@anildigital CSP 122 CSP, 1978 - Paper by Tony Hoare,

Slide 122

Slide 122 text

@anildigital CSP Practically applied to in industry as a tool for specifying and verifying the concurrent aspects of variety of different systems Processes - No threads. No shared memory. Fixed number of processes. Channels - Communication is synchronous (Unlike Actor model) Inﬂuences on design Go, Limbo 123

Slide 123

Slide 123 text

@anildigital CSP 124 Adaptation among languages Message passing style of programming Addressable processes Unknown Processes with Channels OCaml, Go, Clojure Erlang 124

Slide 124

Slide 124 text

@anildigital CSP Pros Uses message passing and channels heavily, alternative to locks Cons Handling very big messages, or a lot of messages, unbounded buffers Messaging is essentially a copy of shared 125

Slide 125

Slide 125 text

@anildigital CSP & Actor Model Two approaches from seventies changed concurrency today. Go, Akka (JVM), Elixir (Erlang) 126

Slide 126

Slide 126 text

Elixir / Erlang concurrency 127

Slide 127

Slide 127 text

@anildigital Elixir Elixir is written on top of Erlang. Shares runtime of Erlang & can use Erlang libraries. A functional programming language Immutable data structures 128

Slide 128

Slide 128 text

@anildigital Elixir - Robust & Fault Tolerant Distributed Concurrent Fault tolerance Highly Resilient Scalable (Horizontal and Vertical) 129

Slide 129

Slide 129 text

@anildigital Elixir - Processes Everything is process “Green” processes. Elixir process != Operating system process Elixir process != Operating system thread Completely isolated in execution time and space 130

Slide 130

Slide 130 text

@anildigital Elixir/Erlang VM CPU CPU CPU CPU Scheduler Scheduler Scheduler Scheduler Process Process Process Process Process Process Process Process Process Process Process Process Process Process Process Process OS Thread OS Thread OS Process BEAM OS Thread OS Thread

Slide 131

Slide 131 text

@anildigital Elixir - Processes Each Elixir process has own local GC No stop-the world garbage collection (like Ruby/Java) Much easier for process scheduler to schedule Very little overhead Cheap to create thousands of Elixir processes Concurrency is not difﬁcult in Erlang / Elixir and in built (not conventional) Isolated crashes. Process crash doesn’t affect other processes. 132

Slide 132

Slide 132 text

@anildigital Elixir - Actor model Implements Actor model (similar to Postal service) Processes are actors Every actor has its address Actors communicate via messages asynchronously “Do not communicate by sharing memory; instead, share memory by communicating” 133

Slide 133

Slide 133 text

@anildigital Elixir - Actor model Actor do one of the following 3 things Create more actors Send messages to other actors whose addressees it knows Deﬁne how it reacts to a message Actors may receive messages in random order 134

Slide 134

Slide 134 text

@anildigital Elixir - Actor model 135 defmodule Talker do def loop do receive do {:greet, name} -> IO.puts("Hello #{name}") {:praise, name} -> IO.puts("#{name}, you're amazing") {:celebrate, name, age} -> IO.puts("Here's to another #{age} years, #{name}") end loop end end pid = spawn(&Talker.loop/0) send(pid, {:greet, "Huey"}) send(pid, {:praise, "Dewey"}) send(pid, {:celebrate, "Louie", 16}) # Outputs  Hello Huey Dewey, you're amazing Here's to another 16 years, Louie

Slide 135

Slide 135 text

@anildigital Elixir - Actor model vs. CSP 136 CSP Actor model Send & Receive may block (synchronous) Only receive blocks Messages are delivered when they are sent No guarantee of delivery of messages Synchronous Send message and forget Works on one machine Work on multiple machines (Distributed by default) Lacks fault tolerance Fault tolerance

Slide 136

Slide 136 text

@anildigital Elixir - Actor model Pros Uses message passing heavily No shared state (avoid locks, easier to scale) Easier to maintain code. Declarative Cons When shared state is required, doesn’t ﬁt well Handling of very big messages, or a lot of messages Messaging is essentially a copy of data 137

Slide 137

Slide 137 text

@anildigital Elixir / Erlang advanced tools OTP Deﬁnes systems in terms of hierarchies of applications. Agents Background process that maintain state State can be accessed at different places within a process or node, or across multiple nodes. Good at dealing with very-speciﬁc background activities, 138

Slide 138

Slide 138 text

@anildigital Elixir / Erlang advanced tools Nodes Instances of Erlang VM Can be connected to there Nodes Remote execution Supervisors Has one purpose. It manages one or more worker processes. Uses process-linking and monitoring facilities of Erlang VM Heart of Reliability 139

Slide 139

Slide 139 text

@anildigital Elixir / Erlang advanced tools Tasks Execute one particular operation throughout their lifetime async / await 140 task = Task.async(fn -> do_some_work() end) res = do_some_other_work() res + Task.await(task)

Slide 140

Slide 140 text

Go concurrency 141

Slide 141

Slide 141 text

@anildigital Go Built-in concurrency based on Tony Hoare’s CSP paper. goroutines Lightweight processes Runs concurrently and parallel Communicate using channels Channels is something you create and pass around as object Read and write operations on channels block e.g. send from process A will not complete unless process B receives 142

Slide 142

Slide 142 text

@anildigital goroutine Go - goroutine 143 func f(from string) { for i := 0; i < 3; i++ { fmt.Println(from, ":", i) } } func main() { f("direct") go f("goroutine") go func(msg string) { fmt.Println(msg) }("going") var input string fmt.Scanln(&input) fmt.Println("done") } $ go run goroutines.go direct : 0 direct : 1 direct : 2 goroutine : 0 going goroutine : 1 goroutine : 2 done Output

Slide 143

Slide 143 text

@anildigital Channels Go - Channels 144 Output package main import "fmt" func main() { messages := make(chan string) go func() { messages <- "ping" }() msg := <-messages fmt.Println(msg) } $ go run channels.go ping

Slide 144

Slide 144 text

@anildigital Go - Channels Channels are means of synchronization For multiple channels we use select statement select allows you to listen for multiple channels There is default case and timeout case. e.g. for network IO if nothing happens, a timeout will happen. timeout case is important (in a highly concurrent system with network calls) 145

Slide 145

Slide 145 text

@anildigital Go - Channels select 146 c1 := make(chan string) c2 := make(chan string) go func() { time.Sleep(time.Second * 1) c1 <- "one" }() go func() { time.Sleep(time.Second * 2) c2 <- "two" }() for i := 0; i < 2; i++ { select { case msg1 := <-c1: fmt.Println("received", msg1) case msg2 := <-c2: fmt.Println("received", msg2) } } $ time go run select.go received one received two real 0m2.245s Output

Slide 146

Slide 146 text

@anildigital Go - goroutines goroutines run in user space and are scheduled by user space runtime in Golang start with small stack and can be efﬁciently grown can be multiplexed on operating system thread has very well deﬁned scheduling order for IO, scheduler will preempt it, it will make resume goroutine once IO is complete. Java/Ruby lack the concept of goroutine or lightweight thread or select (important for CSP). Without concept of cheap threads to communicate between channels. It won’t be possible to have go like concurrency. In Java/Ruby, it would be heavy weight thread, Not as performant. 147

Slide 147

Slide 147 text

Ruby concurrency 148

Slide 148

Slide 148 text

@anildigital Ruby - Fibers A Fiber is a lightweight thread that uses cooperative multitasking instead of preemptive multitasking. A running fiber must explicitly "yield" to allow another fiber to run, which makes their implementation much easier than kernel or user threads. A fiber is a unit of execution that must be manually scheduled by the application. Fibers run in the context of the threads that schedule them. Each thread can schedule multiple fibers. In general, fibers do not provide advantages over a well-designed multithreaded application.However, using fibers can make it easier to port applications that were designed to schedule their own threads. 149

Slide 149

Slide 149 text

@anildigital Ruby - Fibers 150 Thread 1 Thread 2 Fiber 1 Fiber 2 Blocking IO  (red) CPU Time (orange) Co-operative: 60ms Blocking IO  (red) CPU Time   (orange) 10 quanta * 10 ms =   100 ms Cooperative - Handling execution rights between one and another, saving local state

Slide 150

Slide 150 text

@anildigital Ruby - Fibers Pros Expressive state: state based computations much easier to understand and implement No need for locks (cooperative scheduling) Scales vertically (add more CPU power) Cons Single thread: Harder to paralleize/scale horizontally (Use more cases, add more nodes) Constrained to have all components work together symbiotically Fibers are more of a code organization pattern than a way of doing concurrent tasks. 151

Slide 151

Slide 151 text

@anildigital Ruby - Threads Thread.new Deadlocks and Race conditions Mutex # For thread safety 152

Slide 152

Slide 152 text

@anildigital Ruby - GVL What is GVL? Global VM Lock (aka GIL - Global Interpreter Lock) What happens with GVL? With GVL, only one thread executes a time Thread must request a lock If lock is available, it is acquired If not, the thread blocks and waits for the lock to become available Ruby’s runtime guarantees thread safety. But it makes no guarantees about your code. 153

Slide 153

Slide 153 text

@anildigital Ruby - GVL Blocking or long-running operations happens outside of GVL You can still write performant concurrent (as good as Java, Node.js) in a Ruby app if it does only heavy IO Multithreaded CPU-bound requests GVL is still issue. Ruby is fast enough for IO (network) heavy applications (In most cases) 154

Slide 154

Slide 154 text

@anildigital Ruby - Why GVL? Makes developer’s life easier (It’s harder to corrupt data) Avoids race conditions C extensions It makes C extensions development easier Most C libraries are not thread safe Parts of Ruby’s implementation aren’t thread safe (Hash for instance) 155

Slide 155

Slide 155 text

@anildigital Ruby - Multiprocess vs. Multithreading 156 fork do puts "Hello Kaigi" end Thread.new do puts "Hello Kaigi" end Multiprocess Multithreading

Slide 156

Slide 156 text

@anildigital 157 Processes Threads Uses more memory Uses less memory If parent dies before children have exited, children can become zombie processes All threads die when the process dies (no chance of zombies) More expensive for forked processes to switch context since OS needs to save and reload everything Threads have considerably less overhead since they share address space and memory Forked processes are given a new virtual memory space (process isolation) Threads share the same memory, so need to control and deal with concurrent memory issues Requires inter-process communication Can "communicate" via queues and shared memory Slower to create and destroy Faster to create and destroy Easier to code and debug Can be signiﬁcantly more complex to code and debug Ruby - Multiprocess vs. Multithreading

Slide 157

Slide 157 text

@anildigital Who uses multiprocessing and multithreading 158 Who uses multiprocesses? Who uses multithreading? Resque Sidekiq Unicorn Puma 1.x Sidekiq Pro Thin Puma 2 (Clustered mode) Puma 2 (Clustered mode)

Slide 158

Slide 158 text

@anildigital Who uses multiprocessing? GitHub - Unicorn Shopify - Unicorn Gitlab - Unicorn Basecamp - Unicorn for web requests 159

Slide 159

Slide 159 text

@anildigital Who uses multithreading? Basecamp uses Puma for ActionCable Heroku’s recommended server now is Puma 160

Slide 160

Slide 160 text

@anildigital Ruby lacked better concurrency abstractions Java has java.util.concurrent, Ruby didn't not have actor model Ruby didn’t have STM Ruby didn’t have better concurrency abstractions. Ruby has concurrent-ruby gem now concurrent-ruby gem provides concurrency aware abstractions (Inspired from other languages) 161

Slide 161

Slide 161 text

@anildigital concurrent-ruby 162

Slide 162

Slide 162 text

@anildigital concurrent-ruby 163

Slide 163

Slide 163 text

@anildigital concurrent-ruby 164

Slide 164

Slide 164 text

@anildigital concurrent-ruby General-purpose Concurrency Abstractions Async - A mixin module that provides simple asynchronous behavior to a class. Loosely based on Erlang's gen_server. Future - An asynchronous operation that produces a value. Promise - Similar to Futures, with more features. ScheduledTask - Like a Future scheduled for a speciﬁc future time. TimerTask - A Thread that periodically wakes up to perform work at regular intervals. 165

Slide 165

Slide 165 text

@anildigital concurrent-ruby Thread-safe Value Objects, Structures, and Collections Array - A thread-safe subclass of Ruby's standard Array. Hash - A thread-safe subclass of Ruby's standard Hash. Map - A hash-like object that should have much better performance characteristic Tuple - A ﬁxed size array with volatile (synchronized, thread safe) getters/setters. 166

Slide 166

Slide 166 text

@anildigital concurrent-ruby Value objects Maybe - A thread-safe, immutable object representing an optional value. Delay - Lazy evaluation of a block yielding an immutable result. Based on Clojure's delay. 167

Slide 167

Slide 167 text

@anildigital concurrent-ruby Thread-safe variables Agent - A way to manage shared, mutable, asynchronous, independent, state. Based on Clojure's Agent. Atom - A way to manage shared, mutable, synchronous, independent state. Based on Clojure's Atom. AtomicBoolean - A boolean value that can be updated atomically. AtomicFixnum - A numeric value that can be updated atomically. AtomicReference - An object reference that may be updated atomically. Exchanger - A synchronization point at which threads can pair and swap elements within pairs. Based on Java's Exchanger. Java-inspired ThreadPools and Other Executors 168

Slide 168

Slide 168 text

@anildigital concurrent-ruby Thread Synchronization Classes and Algorithms CountDownLatch - A synchronization object that allows one thread to wait on multiple other threads. CyclicBarrier - A synchronization aid that allows a set of threads to all wait for each other to reach a common barrier point. Event - Old school kernel-style event. ReadWriteLock - A lock that supports multiple readers but only one writer. ReentrantReadWriteLock - A read/write lock with reentrant and upgrade features. Semaphore - A counting-based locking mechanism that uses permits. 169

Slide 169

Slide 169 text

@anildigital concurrent-ruby Edge features Actor - Implements the Actor Model, where concurrent actors exchange messages. New Future Framework Channel: Communicating Sequential Processes (CSP) - Functionally equivalent to Go channels with additional inspiration from Clojure core.async. 170

Slide 170

Slide 170 text

Books to learn more about concurrency

Slide 171

Slide 171 text

172

Slide 172

Slide 172 text

173

Slide 173

Slide 173 text

174

Slide 174

Slide 174 text

@anildigital References http://www.braveclojure.com/concurrency/ http://www.slideshare.net/crazyinventor/ruby-concurrency-44303019 http://tutorials.jenkov.com/java-concurrency/index.html The Pragmatic Bookshelf | Seven Concurrency Models in Seven Weeks https://cmdrdats.wordpress.com/2012/08/14/a-look-at-clojure-concurrency-primitives-delay-future-and-promise/ http://neilk.net/blog/2013/04/30/why-you-should-use-nodejs-for-CPU-bound-tasks/ http://www.edn.com/design/systems-design/4368705/The-future-of-computers--Part-1-Multicore-and-the-Memory-Wall https://www.toptal.com/ruby/ruby-concurrency-and-parallelism-a-practical-primer https://www.nateberkopec.com/2015/07/29/scaling-ruby-apps-to-1000-rpm.html www.slideshare.net/crazyinventor/ruby-concurrency-44303019 http://merbist.com/2011/10/03/about-concurrency-and-the-gil/ http://www.jstorimer.com/blogs/workingwithcode/8085491-nobody-understands-the-gil http://www.slideshare.net/JerryDAntonio/everything-you-know-about-the-gil-is-wrong https://aphyr.com/posts/306-clojure-from-the-ground-up-state https://gobyexample.com/ http://codepodcast.com 175

Slide 175

Slide 175 text

LinkedIn linkedin.com/company/equal-experts Twitter @EqualExperts Web www.equalexperts.com UNITED KINGDOM +44 203 603 7830 helloUK@equalexperts.com Equal Experts UK Ltd 30 Brock Street London NW1 3FG INDIA +91 20 6607 7763 helloIndia@equalexperts.com Equal Experts India Private Ltd Ofﬁce No. 4-C Cerebrum IT Park No. B3 Kumar City, Kalyani Nagar Pune, 411006 CANADA +1 403 775 4861 helloCanada@equalexperts.com Equal Experts Devices Inc 205 - 279 Midpark way S.E.  T2X 1M2  Calgary, Alberta PORTUGAL +351 211 378 414+ helloPortugal@equalexperts.com Equal Experts Portugal Rua Tomás da Fonseca   - Torres de Lisboa Torre G, 5º Andar 1600-209 Lisboa USA helloUSA@equalexperts.com Equal Experts Inc 1460 Broadway New York NY 10036 ͘Π͢;͚ͪͬ͜Δͯ