Dubbo之——幾種負載均衡演算法
阿新 • • 發佈：2019-02-03
1、RandomLoadBalance演算法
public class RandomLoadBalance extends AbstractLoadBalance { public static final String NAME = "random"; private final Random random = new Random(); protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) { int length = invokers.size(); // 總個數 int totalWeight = 0; // 總權重 boolean sameWeight = true; // 權重是否都一樣 for (int i = 0; i < length; i++) { int weight = getWeight(invokers.get(i), invocation); totalWeight += weight; // 累計總權重 if (sameWeight && i > 0 && weight != getWeight(invokers.get(i - 1), invocation)) { sameWeight = false; // 計算所有權重是否一樣 } } if (totalWeight > 0 && ! sameWeight) { // 如果權重不相同且權重大於0則按總權重數隨機 int offset = random.nextInt(totalWeight); // 並確定隨機值落在哪個片斷上 for (int i = 0; i < length; i++) { offset -= getWeight(invokers.get(i), invocation); if (offset < 0) { return invokers.get(i); } } } // 如果權重相同或權重為0則均等隨機 return invokers.get(random.nextInt(length)); } }
2、RoundRobinLoadBalance演算法
public class RoundRobinLoadBalance extends AbstractLoadBalance { public static final String NAME = "roundrobin"; private final ConcurrentMap<String, AtomicPositiveInteger> sequences = new ConcurrentHashMap<String, AtomicPositiveInteger>(); private final ConcurrentMap<String, AtomicPositiveInteger> weightSequences = new ConcurrentHashMap<String, AtomicPositiveInteger>(); protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) { String key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName(); int length = invokers.size(); // 總個數 int maxWeight = 0; // 最大權重 int minWeight = Integer.MAX_VALUE; // 最小權重 for (int i = 0; i < length; i++) { int weight = getWeight(invokers.get(i), invocation); maxWeight = Math.max(maxWeight, weight); // 累計最大權重 minWeight = Math.min(minWeight, weight); // 累計最小權重 } if (maxWeight > 0 && minWeight < maxWeight) { // 權重不一樣 AtomicPositiveInteger weightSequence = weightSequences.get(key); if (weightSequence == null) { weightSequences.putIfAbsent(key, new AtomicPositiveInteger()); weightSequence = weightSequences.get(key); } int currentWeight = weightSequence.getAndIncrement() % maxWeight; List<Invoker<T>> weightInvokers = new ArrayList<Invoker<T>>(); for (Invoker<T> invoker : invokers) { // 篩選權重大於當前權重基數的Invoker if (getWeight(invoker, invocation) > currentWeight) { weightInvokers.add(invoker); } } int weightLength = weightInvokers.size(); if (weightLength == 1) { return weightInvokers.get(0); } else if (weightLength > 1) { invokers = weightInvokers; length = invokers.size(); } } AtomicPositiveInteger sequence = sequences.get(key); if (sequence == null) { sequences.putIfAbsent(key, new AtomicPositiveInteger()); sequence = sequences.get(key); } // 取模輪循 return invokers.get(sequence.getAndIncrement() % length); } }
3、LeastActionLoadBalance演算法
public class LeastActiveLoadBalance extends AbstractLoadBalance { public static final String NAME = "leastactive"; private final Random random = new Random(); protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) { int length = invokers.size(); // 總個數 int leastActive = -1; // 最小的活躍數 int leastCount = 0; // 相同最小活躍數的個數 int[] leastIndexs = new int[length]; // 相同最小活躍數的下標 int totalWeight = 0; // 總權重 int firstWeight = 0; // 第一個權重，用於於計算是否相同 boolean sameWeight = true; // 是否所有權重相同 for (int i = 0; i < length; i++) { Invoker<T> invoker = invokers.get(i); int active = RpcStatus.getStatus(invoker.getUrl(), invocation.getMethodName()).getActive(); // 活躍數 int weight = invoker.getUrl().getMethodParameter(invocation.getMethodName(), Constants.WEIGHT_KEY, Constants.DEFAULT_WEIGHT); // 權重 if (leastActive == -1 || active < leastActive) { // 發現更小的活躍數，重新開始 leastActive = active; // 記錄最小活躍數 leastCount = 1; // 重新統計相同最小活躍數的個數 leastIndexs[0] = i; // 重新記錄最小活躍數下標 totalWeight = weight; // 重新累計總權重 firstWeight = weight; // 記錄第一個權重 sameWeight = true; // 還原權重相同標識 } else if (active == leastActive) { // 累計相同最小的活躍數 leastIndexs[leastCount ++] = i; // 累計相同最小活躍數下標 totalWeight += weight; // 累計總權重 // 判斷所有權重是否一樣 if (sameWeight && i > 0 && weight != firstWeight) { sameWeight = false; } } } // assert(leastCount > 0) if (leastCount == 1) { // 如果只有一個最小則直接返回 return invokers.get(leastIndexs[0]); } if (! sameWeight && totalWeight > 0) { // 如果權重不相同且權重大於0則按總權重數隨機 int offsetWeight = random.nextInt(totalWeight); // 並確定隨機值落在哪個片斷上 for (int i = 0; i < leastCount; i++) { int leastIndex = leastIndexs[i]; offsetWeight -= getWeight(invokers.get(leastIndex), invocation); if (offsetWeight <= 0) return invokers.get(leastIndex); } } // 如果權重相同或權重為0則均等隨機 return invokers.get(leastIndexs[random.nextInt(leastCount)]); } }
4、ConsistentHashLoadBalance演算法
public class ConsistentHashLoadBalance extends AbstractLoadBalance {
private final ConcurrentMap<String, ConsistentHashSelector<?>> selectors = new ConcurrentHashMap<String, ConsistentHashSelector<?>>();
@SuppressWarnings("unchecked")
@Override
protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
String key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName();
int identityHashCode = System.identityHashCode(invokers);
ConsistentHashSelector<T> selector = (ConsistentHashSelector<T>) selectors.get(key);
if (selector == null || selector.getIdentityHashCode() != identityHashCode) {
selectors.put(key, new ConsistentHashSelector<T>(invokers, invocation.getMethodName(), identityHashCode));
selector = (ConsistentHashSelector<T>) selectors.get(key);
}
return selector.select(invocation);
}
private static final class ConsistentHashSelector<T> {
private final TreeMap<Long, Invoker<T>> virtualInvokers;
private final int replicaNumber;
private final int identityHashCode;
private final int[] argumentIndex;
public ConsistentHashSelector(List<Invoker<T>> invokers, String methodName, int identityHashCode) {
this.virtualInvokers = new TreeMap<Long, Invoker<T>>();
this.identityHashCode = System.identityHashCode(invokers);
URL url = invokers.get(0).getUrl();
this.replicaNumber = url.getMethodParameter(methodName, "hash.nodes", 160);
String[] index = Constants.COMMA_SPLIT_PATTERN.split(url.getMethodParameter(methodName, "hash.arguments", "0"));
argumentIndex = new int[index.length];
for (int i = 0; i < index.length; i ++) {
argumentIndex[i] = Integer.parseInt(index[i]);
}
for (Invoker<T> invoker : invokers) {
for (int i = 0; i < replicaNumber / 4; i++) {
byte[] digest = md5(invoker.getUrl().toFullString() + i);
for (int h = 0; h < 4; h++) {
long m = hash(digest, h);
virtualInvokers.put(m, invoker);
}
}
}
}
public int getIdentityHashCode() {
return identityHashCode;
}
public Invoker<T> select(Invocation invocation) {
String key = toKey(invocation.getArguments());
byte[] digest = md5(key);
Invoker<T> invoker = sekectForKey(hash(digest, 0));
return invoker;
}
private String toKey(Object[] args) {
StringBuilder buf = new StringBuilder();
for (int i : argumentIndex) {
if (i >= 0 && i < args.length) {
buf.append(args[i]);
}
}
return buf.toString();
}
private Invoker<T> sekectForKey(long hash) {
Invoker<T> invoker;
Long key = hash;
if (!virtualInvokers.containsKey(key)) {
SortedMap<Long, Invoker<T>> tailMap = virtualInvokers.tailMap(key);
if (tailMap.isEmpty()) {
key = virtualInvokers.firstKey();
} else {
key = tailMap.firstKey();
}
}
invoker = virtualInvokers.get(key);
return invoker;
}
private long hash(byte[] digest, int number) {
return (((long) (digest[3 + number * 4] & 0xFF) << 24)
| ((long) (digest[2 + number * 4] & 0xFF) << 16)
| ((long) (digest[1 + number * 4] & 0xFF) << 8)
| (digest[0 + number * 4] & 0xFF))
& 0xFFFFFFFFL;
}
private byte[] md5(String value) {
MessageDigest md5;
try {
md5 = MessageDigest.getInstance("MD5");
} catch (NoSuchAlgorithmException e) {
throw new IllegalStateException(e.getMessage(), e);
}
md5.reset();
byte[] bytes = null;
try {
bytes = value.getBytes("UTF-8");
} catch (UnsupportedEncodingException e) {
throw new IllegalStateException(e.getMessage(), e);
}
md5.update(bytes);
return md5.digest();
}
}
}