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protected synchronized Object[] getArray() { return array; } public synchronized void add(Object element) { int len = arraylength; Object[] newArray = new Object[len+1]; Systemarraycopy(array, 0, newArray, 0, len); newArray[len] = element; array = newArray; } public Iterator iterator() { return new Iterator() { protected final Object[] snapshot = getArray();
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protected int cursor = 0; public boolean hasNext() { return cursor < snapshotlength; } public Object next() { try { return snapshot[cursor++]; } catch (IndexOutOfBoundsException ex) { throw new NoSuchElementException(); } } }; } }
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(The utilconcurrent package available from the online supplement contains a version of this class that conforms to the javautilList interface) This class would be horribly inefficient if used in contexts involving frequent modifications of large collections, but it is well suited for most multicast applications, as illustrated in 352 and 364 2442 Optimistic Updates Optimistic updates employ a weaker protocol than other copy-on-write techniques: Rather than engaging locks for the entire duration of state update methods, they employ synchronization only at the beginnings and ends of update methods Typically, each method takes the form: 1 Get a copy of the current state representation (while holding a lock) 2 Construct a new state representation (without holding any locks) 3 Commit to the new state only if the old state has not changed since obtaining it Optimistic update techniques limit synchronization to very brief intervals just long enough to access and later update state representations This tends to provide very good performance on multiprocessors, at least under appropriate usage conditions The main added requirement here over conventional copy-on-write techniques is dealing with the possibility that Step 3 will fail because some other thread has independently updated the state representation before the current thread has had a chance to do so The potential for failure introduces two concerns (discussed in more detail in 311) that limit the range of applicability of optimistic update techniques: Failure protocols The choices are either to retry the entire method sequence or to propagate the failure back to a client, which can then take evasive action The most common choice is to retry However, this can lead to livelock the optimistic analog of indefinite blocking in which methods continuously spin without making any further progress While the probability of livelock is normally vanishingly small, the action may never complete and may expend a lot of CPU resources repeatedly attempting to do so For this reason, optimistic update techniques are bad choices for classes used in
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contexts that may encounter massive thread contention of unbounded duration However, some specialized wait-free optimistic algorithms have been proven to succeed after a bounded number of attempts regardless of contention (see Further Readings) Side effects Because they may fail, the actions performed while constructing new state representations cannot include any irrevocable side effects For example, they should not write to files, create threads, or draw to GUIs unless these actions can themselves be meaningfully cancelled upon failure (see 312) 2443 Atomic commitment The heart of any optimistic technique is an atomic commitment method that is used instead of assignment statements It must conditionally swap in a new state representation only if the existing state representation is the one expected by the caller There are many ways to distinguish and track different state representations, for example, using version numbers, transaction identifiers, time stamps, and signature codes But it is by far most convenient and most common to simply rely on the reference identity of the state object Here is a generic example:
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class Optimistic {
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private State state; // reference to representation object private synchronized State getState() { return state; } private synchronized boolean commit(State assumed, State next) { if (state == assumed) { state = next; return true; } else return false; } }
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There are several common minor variations in how the commit method is defined For example, the version usually named compareAndSwap returns the current value, which may be either the new or the old value depending on whether the operation committed successfully The increasing popularity of optimistic techniques in systems-level concurrent programming is in part due to (and in part the cause of) the fact that most modern processors include an efficient built-in compareAndSwap instruction or one of its variants While these are not directly accessible from the Java programming language, it is in principle possible for optimizing compilers to map constructs to use such instructions (Even if not, optimistic updates are still efficient) In a purely optimistic class, most update methods take a standard form: getting the initial state, building a new state representation, and then committing if possible, else looping or throwing an exception However, methods that do not rely on any particular initial states can be written more simply, unconditionally swapping in the new state For example, here is an optimistic version of the Dot class:
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class OptimisticDot { protected ImmutablePoint loc; public OptimisticDot(int x, int y) { loc = new ImmutablePoint(x, y); } public synchronized ImmutablePoint location() { return loc; } protected synchronized boolean commit(ImmutablePoint assumed, ImmutablePoint next) { if (loc == assumed) { loc = next; return true; } else return false; } public synchronized void moveTo(int x, int y) { // bypass commit since the operation is unconditional loc = new ImmutablePoint(x, y); } public void shiftX(int delta) { boolean success = false; do { ImmutablePoint old = location(); ImmutablePoint next = new ImmutablePoint(oldx() + delta, oldy()); success = commit(old, next); } while (!success); } }
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If the potential for prolonged interference is a concern, rather than simply spinning, the loop in shiftX can use an exponential back-off scheme as discussed in 3115
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