// for unit testing, this allows the strategy to be forced for the next time updateState
// is called. This is not to be used in prod code.
void setStrategy(DegraderLoadBalancerState.Strategy strategy) {
DegraderLoadBalancerState newState;
newState =
new DegraderLoadBalancerState(
_state.getUpdateIntervalMs(),
_state.getClusterGenerationId(),
_state.getPointsMap(),
_state.getLastUpdated(),
strategy,
_state.getCurrentOverrideDropRate(),
_state.getCurrentAvgClusterLatency(),
_state.isInitialized(),
_state.getRecoveryMap(),
_state.getServiceName(),
_state.getDegraderProperties(),
_state.getCurrentClusterCallCount());
_state = newState;
}
/**
* updateState
*
* <p>We have two mechanisms to influence the health and traffic patterns of the client. They are
* by load balancing (switching traffic from one host to another) and by degrading service
* (dropping calls). We load balance by allocating points in a consistent hash ring based on the
* computedDropRate of the individual TrackerClients, which takes into account the latency seen by
* that TrackerClient's requests. We can alternatively, if the cluster is unhealthy (by using a
* high latency watermark) drop a portion of traffic across all tracker clients corresponding to
* this cluster.
*
* <p>The reason we do not currently consider error rate when adjusting the hash ring is that
* there are legitimate errors that servers can send back for clients to handle, such as 400
* return codes. A potential improvement would be to catch transport level exceptions and 500
* level return codes, but the implication of that would need to be carefully understood and
* documented.
*
* <p>We don't want both to reduce hash points and allow clients to manage their own drop rates
* because the clients do not have a global view that the load balancing strategy does. Without a
* global view, the clients won't know if it already has a reduced number of hash points. If the
* client continues to drop at the same drop rate as before their points have been reduced, then
* the client would have its outbound request reduced by both reduction in points and the client's
* drop rate. To avoid this, the drop rate is managed globally by the load balancing strategy and
* provided to each client. The strategy will ALTERNATE between adjusting the hash ring points or
* the global drop rate in order to avoid double penalizing a client. See below:
*
* <p>Period 1 We found the average latency is greater than high water mark. Then increase the
* global drop rate for this cluster (let's say from 0% to 20%) so 20% of all calls gets dropped.
* . . Period 2 The average latency is still higher than high water mark and we found it is
* especially high for few specific clients in the cluster Then reduce the number of hash points
* for those clients in the hash ring, with the hope we'll redirect the traffic to "healthier"
* client and reduce the average latency . . Period 3 The average latency is still higher than
* high water mark Then we will alternate strategy by increasing the global rate for the whole
* cluster again . . repeat until the latency becomes smaller than high water mark and higher than
* low water mark to maintain the state. If the latency becomes lower than low water mark that
* means the cluster is getting healthier so we can serve more traffic so we'll start recovery as
* explained below
*
* <p>We also have a mechanism for recovery if the number of points in the hash ring is not enough
* to receive traffic. The initialRecoveryLevel is a number between 0.0 and 1.0, and corresponds
* to a weight of the tracker client's full hash points. e.g. if a client has a default 100 hash
* points in a ring, 0.0 means there's 0 point for the client in the ring and 1.0 means there are
* 100 points in the ring for the client. The second configuration, rampFactor, will geometrically
* increase the previous recoveryLevel if traffic still hasn't been seen for that tracker client.
*
* <p>The reason for using weight instead of real points is to allow an initialRecoveryLevel that
* corresponds to less than one hash point. This would be useful if a "cooling off" period is
* desirable for the misbehaving tracker clients i.e. given a full weight of 100 hash points,
* 0.005 initialRecoverylevel 0 hashpoints at start and rampFactor = 2 means that there will be
* one cooling off period before the client is reintroduced into the hash ring (see below).
*
* <p>Period 1 100 * 0.005 = 0.5 point -> So nothing in the hashring
*
* <p>Period 2 100 * (0.005 * 2 because of rampfactor) = 1 point -> So we'll add one point in the
* hashring
*
* <p>Another example, given initialRecoveryLevel = 0.01, rampFactor = 2, and default tracker
* client hash points of 100, we will increase the hash points in this pattern on successive
* update States: 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, etc. -> 1, 2, 4, 8, 16, 32 points in the
* hashring and aborting as soon as calls are recorded for that tracker client.
*
* <p>We also have highWaterMark and lowWaterMark as properties of the DegraderLoadBalancer
* strategy so that the strategy can make decisions on whether to start dropping traffic GLOBALLY
* across all tracker clients for this cluster. The amount of traffic to drop is controlled by the
* globalStepUp and globalStepDown properties, where globalStepUp controls how much the global
* drop rate increases per interval, and globalStepDown controls how much the global drop rate
* decreases per interval. We only step up the global drop rate when the average cluster latency
* is higher than the highWaterMark, and only step down the global drop rate when the average
* cluster latency is lower than the global drop rate.
*
* <p>This code is thread reentrant. Multiple threads can potentially call this concurrently, and
* so callers must pass in the DegraderLoadBalancerState that they based their shouldUpdate() call
* on. The multiple threads may have different views of the trackerClients latency, but this is ok
* as the new state in the end will have only taken one action (either loadbalance or
* call-dropping with at most one step). Currently we will not call this concurrently, as
* checkUpdateState will control entry to a single thread.
*
* @param clusterGenerationId
* @param oldState
* @param config
* @param trackerClientUpdaters
*/
private static DegraderLoadBalancerState doUpdateState(
long clusterGenerationId,
DegraderLoadBalancerState oldState,
DegraderLoadBalancerStrategyConfig config,
List<TrackerClientUpdater> trackerClientUpdaters) {
debug(_log, "updating state for: ", trackerClientUpdaters);
double sumOfClusterLatencies = 0.0;
double computedClusterDropSum = 0.0;
double computedClusterWeight = 0.0;
long totalClusterCallCount = 0;
boolean hashRingChanges = false;
boolean recoveryMapChanges = false;
DegraderLoadBalancerState.Strategy strategy = oldState.getStrategy();
Map<TrackerClient, Double> oldRecoveryMap = oldState.getRecoveryMap();
Map<TrackerClient, Double> newRecoveryMap = new HashMap<TrackerClient, Double>(oldRecoveryMap);
double currentOverrideDropRate = oldState.getCurrentOverrideDropRate();
double initialRecoveryLevel = config.getInitialRecoveryLevel();
double ringRampFactor = config.getRingRampFactor();
int pointsPerWeight = config.getPointsPerWeight();
DegraderLoadBalancerState newState;
for (TrackerClientUpdater clientUpdater : trackerClientUpdaters) {
TrackerClient client = clientUpdater.getTrackerClient();
double averageLatency = client.getDegraderControl(DEFAULT_PARTITION_ID).getLatency();
long callCount = client.getDegraderControl(DEFAULT_PARTITION_ID).getCallCount();
oldState.getPreviousMaxDropRate().put(client, clientUpdater.getMaxDropRate());
sumOfClusterLatencies += averageLatency * callCount;
totalClusterCallCount += callCount;
double clientDropRate =
client.getDegraderControl(DEFAULT_PARTITION_ID).getCurrentComputedDropRate();
computedClusterDropSum += client.getPartitionWeight(DEFAULT_PARTITION_ID) * clientDropRate;
computedClusterWeight += client.getPartitionWeight(DEFAULT_PARTITION_ID);
boolean recoveryMapContainsClient = newRecoveryMap.containsKey(client);
// The following block of code calculates and updates the maxDropRate if the client had been
// fully degraded in the past and has not received any requests since being fully degraded.
// To increase the chances of the client receiving a request, we change the maxDropRate, which
// influences the maximum value of computedDropRate, which is used to compute the number of
// points in the hash ring for the clients.
if (callCount == 0) {
// if this client is enrolled in the program, decrease the maxDropRate
// it is important to note that this excludes clients that haven't gotten traffic
// due solely to low volume.
if (recoveryMapContainsClient) {
// if it's the hash ring's turn to adjust, then adjust the maxDropRate.
// Otherwise, we let the call dropping strategy take it's turn, even if
// it may do nothing.
if (strategy == DegraderLoadBalancerState.Strategy.LOAD_BALANCE) {
double oldMaxDropRate = clientUpdater.getMaxDropRate();
double transmissionRate = 1.0 - oldMaxDropRate;
if (transmissionRate <= 0.0) {
// We use the initialRecoveryLevel to indicate how many points to initially set
// the tracker client to when traffic has stopped flowing to this node.
transmissionRate = initialRecoveryLevel;
} else {
transmissionRate *= ringRampFactor;
transmissionRate = Math.min(transmissionRate, 1.0);
}
double newMaxDropRate = 1.0 - transmissionRate;
clientUpdater.setMaxDropRate(newMaxDropRate);
}
recoveryMapChanges = true;
}
} // else we don't really need to change the client maxDropRate.
else if (recoveryMapContainsClient) {
// else if the recovery map contains the client and the call count was > 0
// tough love here, once the rehab clients start taking traffic, we
// restore their maxDropRate to it's original value, and unenroll them
// from the program.
// This is safe because the hash ring points are controlled by the
// computedDropRate variable, and the call dropping rate is controlled by
// the overrideDropRate. The maxDropRate only serves to cap the computedDropRate and
// overrideDropRate.
// We store the maxDropRate and restore it here because the initialRecoveryLevel could
// potentially be higher than what the default maxDropRate allowed. (the maxDropRate doesn't
// necessarily have to be 1.0). For instance, if the maxDropRate was 0.99, and the
// initialRecoveryLevel was 0.05 then we need to store the old maxDropRate.
clientUpdater.setMaxDropRate(newRecoveryMap.get(client));
newRecoveryMap.remove(client);
recoveryMapChanges = true;
}
}
double computedClusterDropRate = computedClusterDropSum / computedClusterWeight;
debug(_log, "total cluster call count: ", totalClusterCallCount);
debug(
_log,
"computed cluster drop rate for ",
trackerClientUpdaters.size(),
" nodes: ",
computedClusterDropRate);
if (oldState.getClusterGenerationId() == clusterGenerationId
&& totalClusterCallCount <= 0
&& !recoveryMapChanges) {
// if the cluster has not been called recently (total cluster call count is <= 0)
// and we already have a state with the same set of URIs (same cluster generation),
// and no clients are in rehab, then don't change anything.
debug(
_log,
"New state is the same as the old state so we're not changing anything. Old state = ",
oldState,
", config=",
config);
return new DegraderLoadBalancerState(
oldState,
clusterGenerationId,
config.getUpdateIntervalMs(),
config.getClock().currentTimeMillis());
}
// update our overrides.
double newCurrentAvgClusterLatency = -1;
if (totalClusterCallCount > 0) {
newCurrentAvgClusterLatency = sumOfClusterLatencies / totalClusterCallCount;
}
debug(_log, "average cluster latency: ", newCurrentAvgClusterLatency);
// This points map stores how many hash map points to allocate for each tracker client.
Map<URI, Integer> points = new HashMap<URI, Integer>();
Map<URI, Integer> oldPointsMap = oldState.getPointsMap();
for (TrackerClientUpdater clientUpdater : trackerClientUpdaters) {
TrackerClient client = clientUpdater.getTrackerClient();
double successfulTransmissionWeight;
URI clientUri = client.getUri();
// Don't take into account cluster health when calculating the number of points
// for each client. This is because the individual clients already take into account
// latency, and a successfulTransmissionWeight can and should be made
// independent of other nodes in the cluster. Otherwise, one unhealthy client in a small
// cluster can take down the entire cluster if the avg latency is too high.
// The global drop rate will take into account the cluster latency. High cluster-wide error
// rates are not something d2 can address.
//
// this client's maxDropRate and currentComputedDropRate may have been adjusted if it's in the
// rehab program (to gradually send traffic it's way).
double dropRate =
Math.min(
client.getDegraderControl(DEFAULT_PARTITION_ID).getCurrentComputedDropRate(),
clientUpdater.getMaxDropRate());
// calculate the weight as the probability of successful transmission to this
// node divided by the probability of successful transmission to the entire
// cluster
successfulTransmissionWeight =
client.getPartitionWeight(DEFAULT_PARTITION_ID) * (1.0 - dropRate);
// calculate the weight as the probability of a successful transmission to this node
// multiplied by the client's self-defined weight. thus, the node's final weight
// takes into account both the self defined weight (to account for different
// hardware in the same cluster) and the performance of the node (as defined by the
// node's degrader).
debug(_log, "computed new weight for uri ", clientUri, ": ", successfulTransmissionWeight);
// keep track if we're making actual changes to the Hash Ring in this updateState.
int newPoints = (int) (successfulTransmissionWeight * pointsPerWeight);
if (newPoints == 0) {
// We are choking off traffic to this tracker client.
// Enroll this tracker client in the recovery program so that
// we can make sure it still gets some traffic
Double oldMaxDropRate = clientUpdater.getMaxDropRate();
// set the default recovery level.
newPoints = (int) (initialRecoveryLevel * pointsPerWeight);
// Keep track of the original maxDropRate
if (!newRecoveryMap.containsKey(client)) {
// keep track of this client,
newRecoveryMap.put(client, oldMaxDropRate);
clientUpdater.setMaxDropRate(1.0 - initialRecoveryLevel);
}
}
points.put(clientUri, newPoints);
if (!oldPointsMap.containsKey(clientUri) || oldPointsMap.get(clientUri) != newPoints) {
hashRingChanges = true;
}
}
// Here is where we actually make the decision what compensating action to take, if any.
// if the strategy to try is Load balancing and there are new changes to the hash ring, or
// if there were changes to the members of the cluster
if ((strategy == DegraderLoadBalancerState.Strategy.LOAD_BALANCE && hashRingChanges == true)
||
// this boolean is there to make sure when we first generate a new state, we always start
// with LOAD_BALANCE
// strategy
oldState.getClusterGenerationId() != clusterGenerationId) {
// atomic overwrite
// try Call Dropping next time we updateState.
newState =
new DegraderLoadBalancerState(
config.getUpdateIntervalMs(),
clusterGenerationId,
points,
config.getClock().currentTimeMillis(),
DegraderLoadBalancerState.Strategy.CALL_DROPPING,
currentOverrideDropRate,
newCurrentAvgClusterLatency,
true,
newRecoveryMap,
oldState.getServiceName(),
oldState.getDegraderProperties(),
totalClusterCallCount);
logState(oldState, newState, config, trackerClientUpdaters);
} else {
// time to try call dropping strategy, if necessary.
// we are explicitly setting the override drop rate to a number between 0 and 1, inclusive.
double newDropLevel = Math.max(0.0, currentOverrideDropRate);
// if the cluster is unhealthy (above high water mark)
// then increase the override drop rate
//
// note that the tracker clients in the recovery list are also affected by the global
// overrideDropRate, and that their hash ring bump ups will also alternate with this
// overrideDropRate adjustment, if necessary. This is fine because the first priority is
// to get the cluster latency stabilized
if (newCurrentAvgClusterLatency > 0
&& totalClusterCallCount >= config.getMinClusterCallCountHighWaterMark()) {
// if we enter here that means we have enough call counts to be confident that our average
// latency is
// statistically significant
if (newCurrentAvgClusterLatency >= config.getHighWaterMark()
&& currentOverrideDropRate != 1.0) {
// if the cluster latency is too high and we can drop more traffic
newDropLevel = Math.min(1.0, newDropLevel + config.getGlobalStepUp());
} else if (newCurrentAvgClusterLatency <= config.getLowWaterMark()
&& currentOverrideDropRate != 0.0) {
// else if the cluster latency is good and we can reduce the override drop rate
newDropLevel = Math.max(0.0, newDropLevel - config.getGlobalStepDown());
}
// else the averageClusterLatency is between Low and High, or we can't change anything more,
// then do not change anything.
} else if (newCurrentAvgClusterLatency > 0
&& totalClusterCallCount >= config.getMinClusterCallCountLowWaterMark()) {
// if we enter here that means, we don't have enough calls to the cluster. We shouldn't
// degrade more
// but we might recover a bit if the latency is healthy
if (newCurrentAvgClusterLatency <= config.getLowWaterMark()
&& currentOverrideDropRate != 0.0) {
// the cluster latency is good and we can reduce the override drop rate
newDropLevel = Math.max(0.0, newDropLevel - config.getGlobalStepDown());
}
// else the averageClusterLatency is somewhat high but since the qps is not that high, we
// shouldn't degrade
} else {
// if we enter here that means we have very low traffic. We should reduce the
// overrideDropRate, if possible.
// when we have below 1 QPS traffic, we should be pretty confident that the cluster can
// handle very low
// traffic. Of course this is depending on the MinClusterCallCountLowWaterMark that the
// service owner sets.
// Another possible cause for this is if we had somehow choked off all traffic to the
// cluster, most
// likely in a one node/small cluster scenario. Obviously, we can't check latency here,
// we'll have to rely on the metric in the next updateState. If the cluster is still having
// latency problems, then we will oscillate between off and letting a little traffic
// through,
// and that is acceptable. If the latency, though high, is deemed acceptable, then the
// watermarks can be adjusted to let more traffic through.
newDropLevel = Math.max(0.0, newDropLevel - config.getGlobalStepDown());
}
if (newDropLevel != currentOverrideDropRate) {
overrideClusterDropRate(newDropLevel, trackerClientUpdaters);
}
// don't change the points map or the recoveryMap, but try load balancing strategy next time.
newState =
new DegraderLoadBalancerState(
config.getUpdateIntervalMs(),
clusterGenerationId,
oldPointsMap,
config.getClock().currentTimeMillis(),
DegraderLoadBalancerState.Strategy.LOAD_BALANCE,
newDropLevel,
newCurrentAvgClusterLatency,
true,
oldRecoveryMap,
oldState.getServiceName(),
oldState.getDegraderProperties(),
totalClusterCallCount);
logState(oldState, newState, config, trackerClientUpdaters);
points = oldPointsMap;
}
// adjust the min call count for each client based on the hash ring reduction and call dropping
// fraction.
overrideMinCallCount(currentOverrideDropRate, trackerClientUpdaters, points, pointsPerWeight);
return newState;
}