926 reads

Java Concurrency: Understanding the ‘volatile’ Keyword

by Bruno MasciFebruary 10th, 2021

Too Long; Didn't Read

In this article I would like to talk about some particularities and misconceptions about volatile and the semantics of volatile as defined from Java 5 onwards

Companies Mentioned

featured image - Java Concurrency: Understanding the ‘volatile’ Keyword

In this article, I would like to share a different approach on this subject that maybe is helpful for you, and also to talk about some particularities and misconceptions about

volatile

. There`s very good material on this topic out there (like “Java Concurrency In Practice” [JCIP]).

I encourage you to read it ;)

We will be talking about the semantics of

volatile

as defined from Java 5 onwards (on previous Java versions,

volatile

has no memory-consistency/state-visibility semantic).

Java Concurrency: What is volatile?

Short answer:

volatile

is a keyword we can apply to a field to ensure that when one thread writes a value to that field, the value written is "immediately available" to any thread that subsequently reads it (visibility feature).

Context

When multiple threads need to interact with some shared data, there are three aspects to consider:

Visibility: The effects of an action on the shared data by a thread should be observable by other threads (consistent views);
Ordering: Execution order should be the same as statements appear in the source code (sequential consistency);
Atomicity: No thread should interfere while another thread is executing some actions on the shared data.

In the absence of necessary synchronizations, the compiler, Runtime, or processors may apply all sorts of optimizations, like code reordering and caching. Those optimizations can interact with incorrectly synchronized code in ways that are not immediately obvious when the source code is examined. Even if statements execute in the order of their appearance in a thread, caching can prevent the latest values from being reflected in the main memory.

In order to prevent incorrect or unpredictable outcomes, we can use (some form of) synchronization.
It is worth mentioning that synchronization doesn’t imply the use of the

synchronized

keyword (which is based on implicit/monitor/intrinsic locks internally); for example, lock objects and the

volatile

keyword are also synchronization mechanisms.

In a multi-threaded context we usually must resort to explicit synchronization. But, in particular scenarios, there are some alternatives we could use that do not rely on synchronization, such as atomic methods and immutable objects.

To illustrate the risks of a not correctly synchronized program, let’s consider the following example. This program could simply print 42, or even print 0, or even hang forever!

@NotThreadSafe
public class NoVisibility {
  private static boolean ready;
  private static int number;

  private static class ReaderThread extends Thread {
    public void run() {
      while (!ready)
        Thread.yield();
      System.out.println(number);
    }
  }

  public static void main(String[] args) {
    new ReaderThread().start();
    number = 42;
    ready = true;
  }
}

(from JCIP book)

Why could this happen? In Java, by default, a piece of code is assumed to be executed by only one thread. As we saw, the compiler, Runtime or processor could optimize things away, as long as we get the same result as with the original code. In a multi-threaded context is a developer's responsibility to use the appropriate mechanisms to correctly synchronize accesses to a shared state.

In this particular example, the compiler could see that ‘ready’ is false (default value) and it never changes, so we would end up with an infinite loop. On the other hand, if the updated value for ‘‘ready’ is visible to ReaderThread before ‘number’ is (code reordering/caching), ReaderThread would see that ‘number’ is 0 (default value).

The happens-before relationship

From here (section 17.4.5):

“Two actions can be ordered by a happens-before relationship. If one action happens-before another, then the first is visible to and ordered before the second (for example, the write of a default value to every field of an object constructed by a thread need not happen before the beginning of that thread, as long as no read ever observes that fact).
More specifically, if two actions share a happens-before relationship, they do not necessarily have to appear to have happened in that order to any code with which they do not share a happens-before relationship."

This relationship is established by either:

The
```
synchronized 
```
construct (this also provides atomicity);
The
```
volatile 
```
construct;
```
Thread.start() 
```
and
```
Thread.join()
```
methods;
The methods of all classes in
```
java.util.concurrent
```
and its subpackages;
...

To understand the importance of this relationship, let´s review the concept of data race. “A data race occurs when a variable is written to by at least one thread and read by at least another thread, and the reads and writes are not ordered by a happens-before relationship. A correctly synchronized program is one with no data races” (from here).
More specifically: A correctly synchronized program is one whose sequentially consistent executions do not have any data races.

Volatile

volatile

is a lightweight form of synchronization that tackles the visibility and ordering aspects.

volatile

is used as a field modifier. The purpose of

volatile

is to ensure that when one thread writes a value to a field, the value written is "immediately available" to any thread that subsequently reads it.

There’s a common misconception about the relationship between

volatile

, on the one hand, and references and objects, on the other hand. In Java, there are objects and references to those objects. We can think of references as “pointers” to objects. All variables (expect for primitives like boolean or long) contain references (object references).

volatile

acts either on a primitive variable or on a reference variable. There’s no relation between

volatile

and the object referred to by the (reference) variable.

Declaring a shared variable as

volatile

ensures visibility.

volatile

variables are not cached in registers or in caches where they are hidden from other processors, so a read of a

volatile

variable always returns the most recent write by any thread. “The visibility effects of

volatile

variables extend beyond the value of the

volatile

variable itself. When thread A writes to a

volatile

variable and subsequently thread B reads that same variable, the values of all variables that were visible to A prior to writing to the

volatile

variable become visible to B after reading the volatile
variable” (from JCIP book).
In order to guarantee that the results of one action are observable to a second action, then the first must happen before the second.

volatile

also limits reordering of accesses (accesses to the reference) by preventing the compiler and Runtime from reordering of code. The ability to perceive ordering constraints among actions is only guaranteed to actions that share a happens-before relationship with them.
Under the hood,

volatile

causes reads and writes to be accompanied by a special CPU instruction known as a memory barrier. For more low-level details, you can check here and here.

From a memory visibility perspective, writing a

volatile

variable is like exiting a

synchronized

block and reading a

volatile

variable is like entering a

synchronized

block. But note that

volatile

doesn’t block as

synchronized

does.

An atomic action in concurrent programming is one that either happens completely, or it doesn't happen at all. No side effects of an atomic action are visible until the action is complete.

There’s a correlation between

volatile

and atomic actions: for non-

volatile

64-bit numeric (long and double) primitive variables, the JVM is free to treat a 64-bit read or write as two separate 32-bit operations. Using

volatile

on those types ensures the read and write operations are atomic (accessing the rest of the primitive variables and reference variables is atomic by default).

volatile

accesses do not guarantee the atomicity of composite operations such as incrementing a counter. Compound operations on shared variables must be performed atomically to prevent data races and race conditions.

When to use volatile

We can use

volatile

variables only when all the following criteria are met (from JCIP book):

Writes to the variable do not depend on its current value, or you can ensure that only a single thread ever updates the value;
The variable does not participate in invariants with other state variables
Locking is not required for any other reason while the variable is being accessed.

Some suitable scenarios are:

When we have a simple flag (like a completion, interruption, or status flag) or other primitive item of data that is accessed (read) by multiple threads;
When we have a more complex immutable object that is initialised by one thread and then accessed by others: here we want to update the reference to the immutable data structure and allow other threads to reference it.

Considerations before using volatile

Code that relies on
```
volatile 
```
variables for visibility of arbitrary state is more fragile and harder to understand than code that uses locking;
The semantics of
```
volatile 
```
is not strong enough to guarantee atomicity (atomic variables do provide atomic read-modify-write support and can often be used as “better
```
volatile 
```
variables”)
Synchronization can introduce thread contention, which occurs when two or more threads try to access the same resource simultaneously.

Examples

Several of the following examples were taken from here. You can find a lot more information about the examples and their internal working there.

1) Incrementing a counter

@NotThreadSafe
public class UnsafeCounter {
    private volatile Integer counter;
 
    public void increment() {
        counter++;
    }
}

Even though it appears as a single operation, the “counter++;” statement is not atomic. It is actually a combination of three different operations: read, update, write. So, even making counter

volatile

is not enough to avoid data races in a multi-threaded context: we may end up with lost updates.

Some thread-safe alternatives:

synchronized

, AtomicInteger.

2) Lazy-initialized Singleton Pattern

@ThreadSafe
public class Singleton {
  private static volatile Singleton INSTANCE;

  private Singleton() {
  }

  public static Singleton getInstance() {
    if (INSTANCE != null)
      return singleton;
    synchronized (Singleton.class) {
      if (INSTANCE == null)
        INSTANCE = new Singleton();
      return INSTANCE;
    }
  }
}

In this case, not making INSTANCE

volatile

could lead getInstance() to return a non-fully initialised object, breaking the Singleton pattern. Why? The assignment of the reference variable INSTANCE could happen while the object is being initialised, exposing to other threads a (wrong) state that will end up changing, even if the object is immutable! Using

volatile

, we can ensure a one-time safe publication (see Pattern #2 here) of the object.

3) Visibility

@NotThreadSafe
public class NoVisibility {
  private static boolean ready;
  private static int number;

  private static class ReaderThread extends Thread {
    public void run() {
      while (!ready)
        Thread.yield();
      System.out.println(number);
    }
  }

  public static void main(String[] args) {
    new ReaderThread().start();
    number = 42;
    ready = true;
  }
}

In this particular case, making ‘ready’

volatile

is enough to make that class thread-safe because after main() updates ‘ready’, ReaderThread will see the new value for both ‘ready’ and ‘number’ (happens-before relationship). But these kinds of constructs are fragile and we should avoid using them: let’s consider for example the case someone flips the two assignment statements.

Some thread-safe alternatives:

synchronized

, Lock.

4) Immutable collaborator

// Immutable Helper
public final class Helper {
  private final int n;

  public Helper(int n) {
    this.n = n;
  }
  // ...
}

// Mutable Foo
@NotThreadSafe
final class Foo {
  private Helper helper;

  public Helper getHelper() {
    return helper;
  }

  public void setHelper(int num) {
    helper = new Helper(num);
  }
}

Even though (mutable) Foo contains fields referring to only immutable objects (Helper), Foo is not thread-safe. Mutable objects may not be fully constructed when their references are made visible. The reason is that, while the shared object (Helper) is immutable, the reference used to access it is itself shared and mutable. In the example, that means a separate thread could observe a stale reference in the helper field of the Foo object. One solution to that issue could be making the helper field of Foo

volatile

;

volatile

guarantees an object is constructed properly before its reference is made visible.

Some thread-safe alternatives:

synchronized

, AtomicReference.

5) Compound operation

@NotThreadSafe
final class Flag {
  private volatile boolean flag = true;

  public void toggle() {  // Unsafe
    flag ^= true;
  }

  public boolean getFlag() { // Safe
    return flag;
  }
}

As in the first example we saw, here we have a composite operation.

volatile

is not enough in this case.

Some thread-safe alternatives:

synchronized

, volatile
+

synchronized

, ReadWriteLock, AtomicBoolean.

6) Arrays

@NotThreadSafe
final class Foo {
  private volatile int[] arr = new int[20];

  public int getFirst() {
    return arr[0];
  }

  public void setFirst(int n) {
    arr[0] = n;
  }
  // …
}

Here it is the array reference which is

volatile

, not the array itself.

Some thread-safe alternatives:

synchronized

, AtomicIntegerArray.

Conclusion

volatile

variables are a weaker form of synchronization than locking, which in some cases are a good alternative to locking. If we follow the conditions for using

volatile

we discussed before, maybe we can achieve thread-safety and better performance than with locking. However, code using

volatile

is often more fragile than code using locking.

volatile

is not only an alternative to locking (in some cases); it has its own uses, also.

volatile

can be combined with other synchronization mechanisms, but it is usually better to use only one mechanism on a particular shared state.

volatile

doesn’t block (unlike

synchronized

), there is no possibility for deadlocks to occur. But, just as other synchronization mechanisms, the use of

volatile

implies some performance penalties.
On highly-contended contexts, using

volatile

could be detrimental for performance.

Note that

volatile

only applies to fields. It would not make sense to apply it to method parameters or local variables given all they are local/private to the executing thread.

volatile

is related to object references, and not to operations on objects themselves.

Using simple atomic variable access is more efficient than accessing these variables through

synchronized

code, but requires more care by the programmer to avoid memory consistency errors. Whether the extra effort is worthwhile depends on the size and complexity of the application.

Volatile FAQs

-Can we use volatile without using synchronized?

Yes.

-Can we use volatile together with synchronized?

Yes.

-Should we use volatile together with synchronized?

It depends.

-Does volatile apply to an object?

No, it applies to an object reference or to a primitive type.

-Does volatile tackle the atomicity aspect?

No, but there is a special case for 64-bit primitives where it does.

-Which is the difference between volatile and synchronized?

Besides state visibility,

synchronized

keyword provides atomicity (through mutual exclusion) over a block of code. But the changes inside that block are visible to other threads only after the exit of that synchronized block.

-Does volatile imply thread-safety?

No at all:

volatile

synchronized

, etc. are just synchronization mechanisms. As developers we must use them as appropriate, and those mechanisms depend on the case at hand.

-Does volatile improve thread performance?

The opposite. The use of

volatile

implies some performance penalties.

Bibliography

https://jcip.net/
https://docs.oracle.com/javase/specs/jls/se8/html/jls-8.html#jls-8.3.1.4
https://docs.oracle.com/javase/specs/jls/se8/html/jls-8.html#jls-8.3.1.4
https://docs.oracle.com/javase/specs/jls/se8/html/jls-17.html#jls-17.4.2
https://download.oracle.com/otndocs/jcp/memory_model-1.0-pfd-spec-oth-JSpec/
https://resources.sei.cmu.edu/asset_files/TechnicalReport/2010_005_001_15239.pdf
https://javarevisited.blogspot.com/2011/06/volatile-keyword-java-example-tutorial.html
https://www.javamex.com/tutorials/synchronization_volatile.shtml
https://www.cs.umd.edu/~pugh/java/memoryModel/DoubleCheckedLocking.html
https://www.cs.umd.edu/~pugh/java/memoryModel/jsr-133-faq.html
https://www.ibm.com/developerworks/java/library/j-jtp06197/index.html
https://dzone.com/articles/demystifying-volatile-and-synchronized-again
https://www.logicbig.com/tutorials/core-java-tutorial/java-multi-threading/java-memory-model.html
https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/locks/Lock.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/locksync.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/newlocks.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/immutable.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/sync.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/locksync.html
https://docs.oracle.com/javase/tutorial/essential/concurrency/atomic.html
https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/package-summary.html#MemoryVisibility
https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/atomic/package-summary.html
https://en.wikipedia.org/wiki/Memory_barrier
https://developpaper.com/memory-barrier-and-its-application-in-jvm/
https://developpaper.com/memory-barrier-and-its-application-in-jvm-2/
https://www.infoq.com/articles/memory_barriers_jvm_concurrency/
https://dzone.com/articles/memory-barriersfences