上期咱們介紹了Java8中新的時間日期API,本期咱們介紹Java8中原子性操做
LongAdder
。java
根據百度百科的定義:算法
"原子操做(atomic operation)是不須要synchronized",這是Java多線程編程的老生常談了。所謂原子操做是指不會被線程調度機制打斷的操做;這種操做一旦開始,就一直運行到結束,中間不會有任何 context switch (切換到另外一個線程)。編程
在單線程的環境中,使用Long,若是對於多線程的環境,若是使用Long的話,須要加上synchronized
關鍵字,從Java5開始,JDK提供了AtomicLong
類,AtomicLong是一個提供原子操做的Long類,經過線程安全的方式操做加減,AtomicLong提供原子操做來進行Long的使用,所以十分適合高併發狀況下的使用。安全
public class AtomicLongFeature {
private static final int NUM_INC = 1_000_000;
private static AtomicLong atomicLong = new AtomicLong(0);
private static void update() {
atomicLong.set(0);
ExecutorService executorService = Executors.newFixedThreadPool(5);
IntStream.range(0, NUM_INC).forEach(i -> {
Runnable task = () -> atomicLong.updateAndGet(n -> n + 2);
executorService.submit(task);
});
stop(executorService);
System.out.println(atomicLong.get());
}
private static void stop(ExecutorService executorService) {
try {
executorService.shutdown();
executorService.awaitTermination(60, TimeUnit.SECONDS);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
if (!executorService.isTerminated()) {
System.out.println("kill tasks");
}
executorService.shutdownNow();
}
}
public static void main(String[] args) {
update();
}
}
複製代碼
輸出: 2000000微信
爲何AtomicInteger
能支持高併發呢?看下AtomicLong
的updateAndGet
方法:多線程
public final int updateAndGet(IntUnaryOperator updateFunction) {
int prev, next;
do {
prev = get();
next = updateFunction.applyAsInt(prev);
} while (!compareAndSet(prev, next));
return next;
}
public final boolean compareAndSet(int expect, int update) {
return unsafe.compareAndSwapInt(this, valueOffset, expect, update);
}
複製代碼
緣由是每次updateAndGet
時都會調用compareAndSet
方法。併發
AtomicLong是在使用非阻塞算法實現併發控制,在一些高併發程序中很是適合,但並不能每一種場景都適合,不一樣場景要使用使用不一樣的數值類。app
AtomicLong的原理是依靠底層的cas來保障原子性的更新數據,在要添加或者減小的時候,會使用死循環不斷地cas到特定的值,從而達到更新數據的目的。那麼LongAdder又是使用到了什麼原理?難道有比cas更加快速的方式?高併發
public class LongAdderFeature {
private static final int NUM_INC = 1_000_000;
private static LongAdder longAdder = new LongAdder();
private static void update() {
ExecutorService executorService = Executors.newFixedThreadPool(5);
IntStream.range(0, NUM_INC).forEach(i -> {
Runnable task = () -> longAdder.add(2);
executorService.submit(task);
});
stop(executorService);
System.out.println(longAdder.sum());
}
private static void stop(ExecutorService executorService) {
try {
executorService.shutdown();
executorService.awaitTermination(60, TimeUnit.SECONDS);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
if (!executorService.isTerminated()) {
System.out.println("kill tasks");
}
executorService.shutdownNow();
}
}
public static void main(String[] args) {
update();
}
}
複製代碼
輸出: 2000000性能
咱們來看下LongAdder的add方法:
public void add(long x) {
Cell[] as; long b, v; int m; Cell a;
if ((as = cells) != null || !casBase(b = base, b + x)) {
boolean uncontended = true;
if (as == null || (m = as.length - 1) < 0 ||
(a = as[getProbe() & m]) == null ||
!(uncontended = a.cas(v = a.value, v + x)))
longAccumulate(x, null, uncontended);
}
}
複製代碼
咱們能夠看到一個Cell的類,那這個類是用來幹什麼的呢?
@sun.misc.Contended static final class Cell {
volatile long value;
Cell(long x) { value = x; }
final boolean cas(long cmp, long val) {
return UNSAFE.compareAndSwapLong(this, valueOffset, cmp, val);
}
// Unsafe mechanics
private static final sun.misc.Unsafe UNSAFE;
private static final long valueOffset;
static {
try {
UNSAFE = sun.misc.Unsafe.getUnsafe();
Class<?> ak = Cell.class;
valueOffset = UNSAFE.objectFieldOffset
(ak.getDeclaredField("value"));
} catch (Exception e) {
throw new Error(e);
}
}
}
複製代碼
咱們能夠看到Cell類的內部是一個volatile的變量,而後更改這個變量惟一的方式經過cas。咱們能夠猜想到LongAdder的高明之處可能在於將以前單個節點的併發分散到各個節點的,這樣從而提升在高併發時候的效率。
LongAdder在AtomicLong的基礎上將單點的更新壓力分散到各個節點,在低併發的時候經過對base的直接更新能夠很好的保障和AtomicLong的性能基本保持一致,而在高併發的時候經過分散提升了性能。
public long sum() {
Cell[] as = cells; Cell a;
long sum = base;
if (as != null) {
for (int i = 0; i < as.length; ++i) {
if ((a = as[i]) != null)
sum += a.value;
}
}
return sum;
}
複製代碼
當計數的時候,將base和各個cell元素裏面的值進行疊加,從而獲得計算總數的目的。這裏的問題是在計數的同時若是修改cell元素,有可能致使計數的結果不許確,因此缺點是LongAdder在統計的時候若是有併發更新,可能致使統計的數據有偏差。