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The weighted load balancing policy uses a number of factors as weights along with the number of in-flight requests for each host to score and then sort the list of known live hosts.
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265
...java/com/datastax/oss/driver/internal/core/loadbalancing/WeightedLoadBalancingPolicy.java
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you under the Apache License, Version 2.0 (the | ||
| * "License"); you may not use this file except in compliance | ||
| * with the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package com.datastax.oss.driver.internal.core.loadbalancing; | ||
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| import static java.util.concurrent.TimeUnit.SECONDS; | ||
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| import com.datastax.oss.driver.api.core.config.DefaultDriverOption; | ||
| import com.datastax.oss.driver.api.core.context.DriverContext; | ||
| import com.datastax.oss.driver.api.core.metadata.Node; | ||
| import com.datastax.oss.driver.api.core.session.Request; | ||
| import com.datastax.oss.driver.api.core.session.Session; | ||
| import com.datastax.oss.driver.internal.core.util.ArrayUtils; | ||
| import com.datastax.oss.driver.internal.core.util.collection.QueryPlan; | ||
| import com.datastax.oss.driver.internal.core.util.collection.SimpleQueryPlan; | ||
| import edu.umd.cs.findbugs.annotations.NonNull; | ||
| import java.util.ArrayList; | ||
| import java.util.Comparator; | ||
| import java.util.List; | ||
| import java.util.Objects; | ||
| import java.util.Queue; | ||
| import java.util.Random; | ||
| import java.util.Set; | ||
| import java.util.concurrent.ThreadLocalRandom; | ||
| import org.slf4j.Logger; | ||
| import org.slf4j.LoggerFactory; | ||
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|
||
| /** | ||
| * A load balancing policy that optimally balances between sending load to local token holder, | ||
| * rack replicas, and local datacenter replicas (in that order). | ||
| * | ||
| * The default weights are good for the vast majority of use cases, but you can tweak them to get different behavior. | ||
| */ | ||
| public class WeightedLoadBalancingPolicy extends DefaultLoadBalancingPolicy { | ||
| private static final Logger LOG = | ||
| LoggerFactory.getLogger(WeightedLoadBalancingPolicy.class); | ||
| // Each client will randomly skew so traffic is introduced gradually to a newly up replica | ||
| // Each client will start sending at a period between 15s and 30, and they will gradually | ||
| // increase load over the next 15 seconds. | ||
| private static final long DELAY_TRAFFIC_SKEW_MILLIS = SECONDS.toMillis(15); | ||
| private static final long DELAY_TRAFFIC_MILLIS = | ||
| DELAY_TRAFFIC_SKEW_MILLIS + ThreadLocalRandom.current().nextLong(DELAY_TRAFFIC_SKEW_MILLIS); | ||
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| // By default we will only score this many nodes, the rest will get added on without scoring. | ||
| // We don't usually need to score every single node if there are more than a few. | ||
| static final int DEFAULT_SCORED_PLAN_SIZE = 8; | ||
| // Default multiplicative weights. Think of this like "How much concurrency must occur | ||
| // before I fail off this node to the next". Note that these defaults are intentionally | ||
| // meant to shed load to unloaded rack coordinators when a replica set is all | ||
| // relatively heavily loaded (specifically 3x as loaded). | ||
| static final double DEFAULT_WEIGHT_NON_RACK = 4.0; | ||
| static final double DEFAULT_WEIGHT_NON_REPLICA = 12.0; | ||
| static final double DEFAULT_WEIGHT_STARTING = 16.0; | ||
| static final double DEFAULT_WEIGHT_UNHEALTHY = 64.0; | ||
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| private final int planSize; | ||
| private final double weightNonRack; | ||
| private final double weightNonReplica; | ||
| private final double weightStarting; | ||
| private final double weightUnhealthy; | ||
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| public WeightedLoadBalancingPolicy( | ||
| @NonNull DriverContext context, | ||
| @NonNull String profileName) { | ||
| super(context, profileName); | ||
| this.planSize = profile.getInt(DefaultDriverOption.LOAD_BALANCING_SCORED_PLAN_SIZE, DEFAULT_SCORED_PLAN_SIZE); | ||
| // Choices of weights will change how this load balancer prefers endpoints. | ||
| // The weight is relative to the outstanding concurrency. | ||
| this.weightNonRack = profile.getDouble( | ||
| DefaultDriverOption.LOAD_BALANCING_WEIGHT_NON_RACK, DEFAULT_WEIGHT_NON_RACK); | ||
| this.weightNonReplica = profile.getDouble( | ||
| DefaultDriverOption.LOAD_BALANCING_WEIGHT_NON_REPLICA, DEFAULT_WEIGHT_NON_REPLICA); | ||
| this.weightStarting = profile.getDouble( | ||
| DefaultDriverOption.LOAD_BALANCING_WEIGHT_STARTING, DEFAULT_WEIGHT_STARTING); | ||
| this.weightUnhealthy = profile.getDouble( | ||
| DefaultDriverOption.LOAD_BALANCING_WEIGHT_UNHEALTHY, DEFAULT_WEIGHT_UNHEALTHY); | ||
| } | ||
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| @NonNull | ||
| @Override | ||
| public Queue<Node> newQueryPlan(Request request, Session session) { | ||
| if (session == null) { | ||
| return super.newQueryPlan(request, null); | ||
| } | ||
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| // Take a copy of nodes and reference to replicas since the node map is concurrent | ||
| Set<Node> dcNodeSet = getLiveNodes().dc(getLocalDatacenter()); | ||
| Set<Node> replicaSet = getReplicas(request, session); | ||
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| long nowNanos = nanoTime(); | ||
| long nowMillis = milliTime(); | ||
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| // collect known replica nodes | ||
| List<NodeScore> nodeScores = new ArrayList<>(this.planSize); | ||
| for (Node replicaNode : replicaSet) { | ||
| if (dcNodeSet.contains(replicaNode)) { | ||
| nodeScores.add(new NodeScore(replicaNode, | ||
| getWeightedScore(replicaNode, session, nowMillis, nowNanos, true))); | ||
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| if (nodeScores.size() == this.planSize) { | ||
| break; // TODO (akhaku) add the rest of the nodes once we port the tests to OSS | ||
| } | ||
| } | ||
| } | ||
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| // collect any non-replicas, if we need to and there are some available | ||
| if (nodeScores.size() < this.planSize && nodeScores.size() < dcNodeSet.size()) { | ||
| Random rand = getRandom(); | ||
| final Node[] dcNodes = dcNodeSet.toArray(new Node[0]); | ||
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| for (int i = 0; i < dcNodes.length; i++) { | ||
| // pick a random target; shuffle it up front, so we don't revisit | ||
| int nextIndex = i + rand.nextInt(dcNodes.length - i); | ||
| ArrayUtils.swap(dcNodes, i, nextIndex); | ||
| Node dcNode = dcNodes[i]; | ||
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| // skip replicas; they were already inserted | ||
| // otherwise, found a valid node: score it | ||
| if (!replicaSet.contains(dcNode)) { | ||
| nodeScores.add(new NodeScore(dcNode, | ||
| getWeightedScore(dcNode, session, nowMillis, nowNanos, false))); | ||
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| // if we scored, we might by now have already scored enough of what we need | ||
| if (nodeScores.size() == this.planSize || nodeScores.size() == dcNodes.length) { | ||
| break; | ||
| } | ||
| } | ||
| } | ||
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| // by now we've scored everything we need to meet planSize, or if not, at least everything available | ||
| } | ||
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| // At this point we have a small, typically 8 element array containing all local | ||
| // datacenter replicas and potentially some random choices from the rest of the datacenter. | ||
| // | ||
| // We now rank nodes by a score function that takes into account outstanding requests weighted | ||
| // by replica status, rack placement, uptime, and health status. In general, we expect to | ||
| // see the following order | ||
| // 1. Rack replicas | ||
| // 2. Datacenter replicas | ||
| // 3. Rack nodes | ||
| // 4. Datacenter nodes | ||
| // We expect these orderings to move around when nodes are overloaded. For example if the | ||
| // local zone replica has too much load we will failover to other replicas. If those | ||
| // replicas are too slow we will failover to other rack nodes. | ||
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| // sort, extract, convert to query plan | ||
| nodeScores.sort(Comparator.comparingDouble(NodeScore::getScore)); | ||
| Node[] candidate = new Node[nodeScores.size()]; | ||
| for (int i = 0; i < candidate.length; i++) { | ||
| candidate[i] = nodeScores.get(i).getNode(); | ||
| } | ||
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| QueryPlan queryPlan = candidate.length == 0 ? QueryPlan.EMPTY : new SimpleQueryPlan((Object[]) candidate); | ||
| return maybeAddDcFailover(request, queryPlan); | ||
| } | ||
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| protected String getLocalRack() { | ||
| return ""; // TODO (akhaku) internally we passed it through the context, for OSS perhaps something like the local DC helper? | ||
| } | ||
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| protected boolean inRack(Node node) { | ||
| if (node == null || node.getRack() == null) return false; | ||
| return node.getRack().equals(this.getLocalRack()); | ||
| } | ||
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| protected double getWeightedScore(Node node, Session session, long nowMillis, long nowNanos, boolean isReplica) { | ||
| int base = Math.min(32768, 1 + getInFlight(node, session)); | ||
| double weight = 1.0; | ||
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| if (!inRack(node)) weight *= this.weightNonRack; // 4.0 | ||
| if (!isReplica) weight *= this.weightNonReplica; // 12.0 | ||
| if (isUnhealthy(node, session, nowNanos)) weight *= this.weightUnhealthy; // 64.0 | ||
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| // We want to gradually ramp up traffic, shedding heavily at first and then allowing it back | ||
| // in gradually. Note: | ||
| // | ||
| // 1. We cannot use nowNanos for this since node.getUpSinceMillis uses | ||
| // System.currentTimeMillis (which may have no relation to nano time). | ||
| // 2. getUpSinceMillis might return 0 or -1, in either case don't consider it freshly up. | ||
| // 3. When a client starts up everything will appear to be freshly up, which is fine since | ||
| // all nodes will randomly be shuffled to the front and back. | ||
| long millisSinceUp = nowMillis - node.getUpSinceMillis(); | ||
| if (millisSinceUp < (DELAY_TRAFFIC_MILLIS + DELAY_TRAFFIC_SKEW_MILLIS)) { | ||
| double pShed = 1.0 - ((double) millisSinceUp / (DELAY_TRAFFIC_MILLIS + DELAY_TRAFFIC_SKEW_MILLIS)); | ||
| if (pShed > getRandom().nextDouble()) { | ||
| if (LOG.isTraceEnabled()) { | ||
| String shed = String.format("%.2f", pShed); | ||
| LOG.trace("[{}] shedding at startup [pShed={}, millisSinceUp={}]", node, shed, millisSinceUp); | ||
| } | ||
| weight *= this.weightStarting; // 16.0 | ||
| } | ||
| } | ||
|
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| double score = base * weight; | ||
| if (LOG.isDebugEnabled()) { | ||
| String msg = String.format("score=%.2f [base=%d, weight=%.2f]", score, base, weight); | ||
| LOG.debug("[{}] {}", node, msg); | ||
| } | ||
| return score; | ||
| } | ||
|
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| protected long milliTime() { | ||
| return System.currentTimeMillis(); | ||
| } | ||
|
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| protected Random getRandom() { | ||
| return ThreadLocalRandom.current(); | ||
| } | ||
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| /** | ||
| * Wraps a Node alongside its score. | ||
| * | ||
| * Calculating scores is expensive, and not stable (could vary). By wrapping them we can be sure the score | ||
| * is calculated only once and does not change during processing. | ||
| */ | ||
| static class NodeScore { | ||
| final double score; | ||
| final Node node; | ||
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| public NodeScore(Node node, double score) { | ||
| this.node = node; | ||
| this.score = score; | ||
| } | ||
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| public Node getNode() { | ||
| return node; | ||
| } | ||
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| public double getScore() { | ||
| return score; | ||
| } | ||
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| @Override | ||
| public boolean equals(Object o) { | ||
| if (this == o) { | ||
| return true; | ||
| } | ||
| if (o == null || getClass() != o.getClass()) { | ||
| return false; | ||
| } | ||
| NodeScore nodeScore = (NodeScore) o; | ||
| return Double.compare(score, nodeScore.score) == 0 && Objects.equals(node, nodeScore.node); | ||
| } | ||
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| @Override | ||
| public int hashCode() { | ||
| return Objects.hash(score, node); | ||
| } | ||
| } | ||
| } |
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