Table of Contents
- 1. Concurrency
- 2. Improvements and issues with concurrency
- 3. Concurrency in Java
- 4. The Java memory model
- 5. Immutability and Defensive Copies
- 6. Threads in Java
- 7. Threads pools with the Executor Framework
- 8. Futures and Callables
- 9. Nonblocking algorithms
- 10. Fork-Join in Java 7
- 11. Deadlock
- 12. About this website
- 13. Links and Literature
Concurrency is the ability to run several programs or several parts of a program in parallel. If a time consuming task can be performed asynchronously or in parallel, this improve the throughput and the interactivity of the program.
A modern computer has several CPU's or several cores within one CPU. The ability to leverage these multi-cores can be the key for a successful high-volume application.
A process runs independently and isolated of other processes. It cannot directly access shared data in other processes. The resources of the process, e.g. memory and CPU time, are allocated to it via the operating system.
A thread is a so called lightweight process. It has its own call stack, but can access shared data of other threads in the same process. Every thread has its own memory cache. If a thread reads shared data it stores this data in its own memory cache. A thread can re-read the shared data.
A Java application runs by default in one process. Within a Java application you work with several threads to achieve parallel processing or asynchronous behavior.
Within a Java application you work with several threads to achieve parallel processing or asynchronous behavior.
Concurrency promises to perform certain task faster as these tasks can be divided into subtasks and these subtasks can be executed in parallel. Of course the runtime is limited by parts of the task which can be performed in parallel.
The theoretical possible performance gain can be calculated by the following rule which is referred to as Amdahl's Law.
If F is the percentage of the program which can not run in parallel and N is the number of processes, then the maximum performance gain is 1/ (F+ ((1-F)/n)).
Threads have their own call stack, but can also access shared data. Therefore you have two basic problems, visibility and access problems.
A visibility problem occurs if thread A reads shared data which is later changed by thread B and thread A is unaware of this change.
An access problem can occur if several thread access and change the same shared data at the same time.
Visibility and access problem can lead to
-
Liveness failure: The program does not react anymore due to problems in the concurrent access of data, e.g. deadlocks.
-
Safety failure: The program creates incorrect data.
A Java program runs in its own process and by default in one thread. Java supports threads as part of the Java language via theThread code. The Java application can create new threads via this class.
Java 1.5 also provides improved support for concurrency with the in the java.util.concurrent package.
Java provides locks to protect certain parts of the code to be executed by several threads at the same time. The simplest way of locking a certain method or Java class is to define the method or class with the synchronized keyword.
The synchronized keyword in Java ensures:
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that only a single thread can execute a block of code at the same time
-
that each thread entering a synchronized block of code sees the effects of all previous modifications that were guarded by the same lock
Synchronization is necessary for mutually exclusive access to blocks of and for reliable communication between threads.
You can use the synchronized keyword for the definition of a method. This would ensure that only one thread can enter this method at the same time. Another threads which is calling this method would wait until the first threads leaves this method.
public synchronized void critial() {
// some thread critical stuff
// here
}
You can also use the synchronized keyword to protect blocks of code within a method. This block is guarded by a key, which can be either a string or an object. This key is called the lock. All code which is protected by the same lock can only be executed by one thread at the same time
For example the following datastructure will ensure that only one thread can access the inner block of the add() and next()methods.
package de.vogella.pagerank.crawler;
import java.util.ArrayList;
import java.util.List;
public class CrawledSites {
private List<String> crawledSites = new ArrayList<String>();
private List<String> linkedSites = new ArrayList<String>();
public void add(String site) {
synchronized (this) {
if (!crawledSites.contains(site)) {
linkedSites.add(site);
}
}
}
public String next() {
if (linkedSites.size() == 0) {
return null;
}
synchronized (this) {
// Need to check again if size has changed
if (linkedSites.size() > 0) {
String s = linkedSites.get(0);
linkedSites.remove(0);
crawledSites.add(s);
return s;
}
return null;
}
}
}
If a variable is declared with the volatile keyword then it is guaranteed that any thread that reads the field will see the most recently written value. The volatile keyword will not perform any mutual exclusive lock on the variable.
As of Java 5 write access to a volatile variable will also update non-volatile variables which were modified by the same thread. This can also be used to update values within a reference variable, e.g. for a volatile variable person. In this case you must use a temporary variable person and use the setter to initialize the variable and then assign the temporary variable to the final variable. This will then make the address changes of this variable and the values visible to other threads.
The Java memory model describes the communication between the memory of the threads and the main memory of the application.
It defines the rules how changes in the memory done by threads are propagated to other threads. The Java memory model also defines the situations in which a thread re-fresh its own memory from the main memory.
It also describes which operations are atomic and the ordering of the operations.
An atomic operation is an operation which is performed as a single unit of work without the possibility of interference from other operations.
The Java language specification guarantees that reading or writing a variable is an atomic operation(unless the variable is of typelong or double). Operations variables of type long or double are only atomic if they declared with the volatile keyword. .
Assume i is defined as int. The i++ (increment) operation it not an atomic operation in Java. This also applies for the other numeric types, e.g. long. etc).
The i++ operation first reads the value which is currently stored in i (atomic operations) and then it adds one to it (atomic operation). But between the read and the write the value of i might have changed.
Since Java 1.5 the java language provides atomic variables, e.g. AtomicInteger or AtomicLong which provide methods likegetAndDecrement(), getAndIncrement() and getAndSet() which are atomic.
The simplest way to avoid problems with concurrency is to share only immutable data between threads. Immutable data is data which cannot changed.
To make a class immutable make
-
all its fields final
-
the class declared as final
-
the this reference is not allowed to escape during construction
-
Any fields which refer to mutable data objects are
-
private
-
have no setter method
-
they are never directly returned of otherwise exposed to a caller
-
if they are changed internally in the class this change is not visible and has no effect outside of the class
-
An immutable class may have some mutable data which is uses to manages its state but from the outside this class nor any attribute of this class can get changed.
For all mutable fields, e.g. Arrays, that are passed from the outside to the class during the construction phase, the class needs to make a defensive-copy of the elements to make sure that no other object from the outside still can change the data
You must protect your classes from calling code. Assume that calling code will do its best to change your data in a way you didn't expect it. While this is especially true in case of immutable data it is also true for non-immutable data which you still not expect that this data is changed outside your class.
To protect your class against that you should copy data you receive and only return copies of data to calling code.
The following example creates a copy of a list (ArrayList) and returns only the copy of the list. This way the client of this class cannot remove elements from the list.
package de.vogella.performance.defensivecopy;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
public class MyDataStructure {
List<String> list = new ArrayList<String>();
public void add(String s) {
list.add(s);
}
public List<String> getList() {
return Collections.unmodifiableList(list);
}
}
The base means for concurrency are is the java.lang.Threads class. A Thread executes an object of type java.lang.Runnable.
Runnable is an interface with defines the run() method. This method is called by the Thread object and contains the work which should be done. Therefore the "Runnable" is the task to perform. The Thread is the worker who is doing this task.
The following demonstrates a task (Runnable) which counts the sum of a given range of numbers. Create a new Java project calledde.vogella.concurrency.threads for the example code of this section.
package de.vogella.concurrency.threads;
public class MyRunnable implements Runnable {
private final long countUntil;
MyRunnable(long countUntil) {
this.countUntil = countUntil;
}
@Override
public void run() {
long sum = 0;
for (long i = 1; i < countUntil; i++) {
sum += i;
}
System.out.println(sum);
}
}
The following example demonstrate the usage of the Thread and the Runnable class.
package de.vogella.concurrency.threads;
import java.util.ArrayList;
import java.util.List;
public class Main {
public static void main(String[] args) {
// We will store the threads so that we can check if they are done
List<Thread> threads = new ArrayList<Thread>();
// We will create 500 threads
for (int i = 0; i < 500; i++) {
Runnable task = new MyRunnable(10000000L + i);
Thread worker = new Thread(task);
// We can set the name of the thread
worker.setName(String.valueOf(i));
// Start the thread, never call method run() direct
worker.start();
// Remember the thread for later usage
threads.add(worker);
}
int running = 0;
do {
running = 0;
for (Thread thread : threads) {
if (thread.isAlive()) {
running++;
}
}
System.out.println("We have " + running + " running threads. ");
} while (running > 0);
}
}
Using the Thread class directly has the following disadvantages.
-
Creating a new thread causes some performance overhead
-
Too many threads can lead to reduced performance, as the CPU needs to switch between these threads.
-
You cannot easily control the number of threads, therefore you may run into out of memory errors due to too many threads.
The java.util.concurrent package offers improved support for concurrency compared to the direct usage of Threads. This package is described in the next section.
Tip
You find this examples in the source section in Java project calledde.vogella.concurrency.threadpools.Thread pools manage a pool of worker threads. The thread pools contains a work queue which holds tasks waiting to get executed.
A thread pool can be described as a collection of Runnable objects (work queue) and a connections of running threads. These threads are constantly running and are checking the work query for new work. If there is new work to be done they execute this Runnable. The Thread class itself provides a method, e.g. execute(Runnable r) to add a new Runnable object to the work queue.
The Executor framework provides example implementation of the java.util.concurrent.Executor interface, e.g. Executors.newFixedThreadPool(int n) which will create n worker threads. The ExecutorService adds life cycle methods to the Executor, which allows to shutdown the Executor and to wait for termination.
Tip
If you want to use one thread pool with one thread which executes several runnables you can use theExecutors.newSingleThreadExecutor() method.Create again the Runnable.
package de.vogella.concurrency.threadpools;
public class MyRunnable implements Runnable {
private final long countUntil;
MyRunnable(long countUntil) {
this.countUntil = countUntil;
}
@Override
public void run() {
long sum = 0;
for (long i = 1; i < countUntil; i++) {
sum += i;
}
System.out.println(sum);
}
}
Now you run your runnables with the executor framework.
package de.vogella.concurrency.threadpools;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class Main {
private static final int NTHREDS = 10;
public static void main(String[] args) {
ExecutorService executor = Executors.newFixedThreadPool(NTHREDS);
for (int i = 0; i < 500; i++) {
Runnable worker = new MyRunnable(10000000L + i);
executor.execute(worker);
}
// This will make the executor accept no new threads
// and finish all existing threads in the queue
executor.shutdown();
// Wait until all threads are finish
executor.awaitTermination();
System.out.println("Finished all threads");
}
}
In case the threads should return some value (result-bearing threads) then you can use the java.util.concurrent.Callableclass.
The code examples for this section are created in a Java project called de.vogella.concurrency.callables.
The executor framework presented in the last chapter works with Runnables. Runnable do not return result.
In case you expect your threads to return a computed result you can use java.util.concurrent.Callable. The Callable object allows to return values after completion.
The Callable object uses generics to define the type of object which is returned.
If you submit a Callable object to an Executor the framework returns an object of type java.util.concurrent.Future. ThisFuture object can be used to check the status of a Callable and to retrieve the result from the Callable.
On the Executor you can use the method submit to submit a Callable and to get a future. To retrieve the result of the future use the get() method.
package de.vogella.concurrency.callables;
import java.util.concurrent.Callable;
public class MyCallable implements Callable<Long> {
@Override
public Long call() throws Exception {
long sum = 0;
for (long i = 0; i <= 100; i++) {
sum += i;
}
return sum;
}
}
package de.vogella.concurrency.callables;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
public class CallableFutures {
private static final int NTHREDS = 10;
public static void main(String[] args) {
ExecutorService executor = Executors.newFixedThreadPool(NTHREDS);
List<Future<Long>> list = new ArrayList<Future<Long>>();
for (int i = 0; i < 20000; i++) {
Callable<Long> worker = new MyCallable();
Future<Long> submit = executor.submit(worker);
list.add(submit);
}
long sum = 0;
System.out.println(list.size());
// now retrieve the result
for (Future<Long> future : list) {
try {
sum += future.get();
} catch (InterruptedException e) {
e.printStackTrace();
} catch (ExecutionException e) {
e.printStackTrace();
}
}
System.out.println(sum);
executor.shutdown();
}
}
Java 5.0 provides supports for additional atomic operations. This allows to develop algorithm which are non-blocking algorithm, e.g. which do not require synchronization, but are based on low-level atomic hardware primitives such as compare-and-swap (CAS). A compare-and-swap operation check if the variable has a certain value and if it has this value it will perform this operation.
Non-blocking algorithm are usually much faster then blocking algorithms as the synchronization of threads appears on a much finer level (hardware).
For example this created a non-blocking counter which always increases. This example is contained in the project calledde.vogella.concurrency.nonblocking.counter.
package de.vogella.concurrency.nonblocking.counter;
import java.util.concurrent.atomic.AtomicInteger;
public class Counter {
private AtomicInteger value = new AtomicInteger();
public int getValue(){
return value.get();
}
public int increment(){
return value.incrementAndGet();
}
// Alternative implementation as increment but just make the
// implementation explicit
public int incrementLongVersion(){
int oldValue = value.get();
while (!value.compareAndSet(oldValue, oldValue+1)){
oldValue = value.get();
}
return oldValue+1;
}
}
And a test.
package de.vogella.concurrency.nonblocking.counter;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
public class Test {
private static final int NTHREDS = 10;
public static void main(String[] args) {
final Counter counter = new Counter();
List<Future<Integer>> list = new ArrayList<Future<Integer>>();
ExecutorService executor = Executors.newFixedThreadPool(NTHREDS);
for (int i = 0; i < 500; i++) {
Callable<Integer> worker = new Callable<Integer>() {
@Override
public Integer call() throws Exception {
int number = counter.increment();
System.out.println(number);
return number ;
}
};
Future<Integer> submit= executor.submit(worker);
list.add(submit);
}
// This will make the executor accept no new threads
// and finish all existing threads in the queue
executor.shutdown();
// Wait until all threads are finish
while (!executor.isTerminated()) {
}
Set<Integer> set = new HashSet<Integer>();
for (Future<Integer> future : list) {
try {
set.add(future.get());
} catch (InterruptedException e) {
e.printStackTrace();
} catch (ExecutionException e) {
e.printStackTrace();
}
}
if (list.size()!=set.size()){
throw new RuntimeException("Double-entries!!!");
}
}
}
The interesting part is how the incrementAndGet() method is implemented. It uses a CAS operation.
public final int incrementAndGet() {
for (;;) {
int current = get();
int next = current + 1;
if (compareAndSet(current, next))
return next;
}
}
The JDK itself makes more and more use of non-blocking algorithms to increase performance for every developer. Developing correct non-blocking algorithm is not a trivial task.
For more information on non-blocking algorithm, e.g. examples for a non-blocking Stack and non-block LinkedList, please seehttp://www.ibm.com/developerworks/java/library/j-jtp04186/index.html
Java 7 introduce a new parallel mechanism for compute intensive tasks, the fork-join framework. The fork-join framework allows you to distribute a certain task on several workers and then wait for the result.
E For Java 6.0 you can download the package (jsr166y) from Download site
For testing create the Java project "de.vogella.performance.forkjoin". If you are not using Java 7 you also need to jsr166y.jar to the classpath.
Create first a algorithm package and then the following class.
package algorithm;
import java.util.Random;
public class Problem {
private final int[] list = new int[2000000];
public Problem() {
Random generator = new Random(19580427);
for (int i = 0; i < list.length; i++) {
list[i] = generator.nextInt(500000);
}
}
public int[] getList() {
return list;
}
}
Define now the Solver class as shown in the following example coding.
Tip
The API defines other top classes, e.g. RecursiveAction, AsyncAction. Check the Javadoc for details.package algorithm;
import java.util.Arrays;
import jsr166y.forkjoin.RecursiveAction;
public class Solver extends RecursiveAction {
private int[] list;
public long result;
public Solver(int[] array) {
this.list = array;
}
@Override
protected void compute() {
if (list.length == 1) {
result = list[0];
} else {
int midpoint = list.length / 2;
int[] l1 = Arrays.copyOfRange(list, 0, midpoint);
int[] l2 = Arrays.copyOfRange(list, midpoint, list.length);
Solver s1 = new Solver(l1);
Solver s2 = new Solver(l2);
forkJoin(s1, s2);
result = s1.result + s2.result;
}
}
}
Now define a small test class for testing it efficiency.
package testing;
import jsr166y.forkjoin.ForkJoinExecutor;
import jsr166y.forkjoin.ForkJoinPool;
import algorithm.Problem;
import algorithm.Solver;
public class Test {
public static void main(String[] args) {
Problem test = new Problem();
// check the number of available processors
int nThreads = Runtime.getRuntime().availableProcessors();
System.out.println(nThreads);
Solver mfj = new Solver(test.getList());
ForkJoinExecutor pool = new ForkJoinPool(nThreads);
pool.invoke(mfj);
long result = mfj.getResult();
System.out.println("Done. Result: " + result);
long sum = 0;
// check if the result was ok
for (int i = 0; i < test.getList().length; i++) {
sum += test.getList()[i];
}
System.out.println("Done. Result: " + sum);
}
}
A concurrent application has the risk of a deadlock. A set of processes are deadlocked if all processes are waiting for an event which another process in the same set has to cause.
For example if thread A waits for a lock on object Z which thread B holds and thread B wait for a look on object Y which is hold be process A then these two processes are looked and cannot continue in their processing.
http://java.sun.com/docs/books/tutorial/essential/concurrency/index.html Introduction to Concurrency
http://www.briangoetz.com/pubs.html Article series from Brian Goetz including lots about concurrency
http://www.ibm.com/developerworks/library/j-jtp0730.html Thread pools and work queues by Brian Goetz
http://www.ibm.com/developerworks/java/library/j-jtp04186/index.html Introduction to nonblocking algorithms by Brian Goetz
http://www.ibm.com/developerworks/java/library/j-jtp11137.html Java theory and practice: Stick a fork in it, Part 1 by Brian Goetz
http://www.ibm.com/developerworks/java/library/j-jtp03048.html Java theory and practice: Stick a fork in it, Part 2 by Brian Goetz