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renaming metrics #1224

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merged 11 commits into from
Aug 22, 2023
Merged

renaming metrics #1224

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29d6398
renaming metrics
dhrubo-os Aug 19, 2023
1b703f9
updating tests
dhrubo-os Aug 21, 2023
8439f45
updating test cases
dhrubo-os Aug 21, 2023
30b1761
removing the ML_NODE checking for node level stats
dhrubo-os Aug 21, 2023
1a057eb
updating constructing new set
dhrubo-os Aug 21, 2023
7e3b4de
spotless Apply
dhrubo-os Aug 21, 2023
42f2c8c
updating ML_NODE_TOTAL_MODEL_COUNT to ML_DEPLOYED_MODEL_COUNT
dhrubo-os Aug 21, 2023
04e2106
fixing metrics count
dhrubo-os Aug 22, 2023
569d8fc
spotless
dhrubo-os Aug 22, 2023
62b0e7b
fixing executing task
dhrubo-os Aug 22, 2023
0b61450
updating comment
dhrubo-os Aug 22, 2023
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2 changes: 0 additions & 2 deletions plugin/src/main/java/org/opensearch/ml/action/deploy/TransportDeployModelAction.java
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Original file line number Diff line number Diff line change
Expand Up @@ -148,8 +148,6 @@ protected void doExecute(Task task, ActionRequest request, ActionListener<MLDepl
if (!allowCustomDeploymentPlan && !deployToAllNodes) {
throw new IllegalArgumentException("Don't allow custom deployment plan");
}
// mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
DiscoveryNode[] allEligibleNodes = nodeFilter.getEligibleNodes(functionName);
Map<String, DiscoveryNode> nodeMapping = new HashMap<>();
for (DiscoveryNode node : allEligibleNodes) {
Expand Down
6 changes: 0 additions & 6 deletions plugin/src/main/java/org/opensearch/ml/action/register/TransportRegisterModelAction.java
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Expand Up @@ -234,12 +234,6 @@ private void registerModel(MLRegisterModelInput registerModelInput, ActionListen
throw new IllegalArgumentException("URL can't match trusted url regex");
}
}
// mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
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Why remove this line?

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we are already counting this in the registerMLModel function in MLModelManager class

// //TODO: track executing task; track register failures
// mlStats.createCounterStatIfAbsent(FunctionName.TEXT_EMBEDDING,
// ActionName.REGISTER,
// MLActionLevelStat.ML_ACTION_REQUEST_COUNT).increment();
boolean isAsync = registerModelInput.getFunctionName() != FunctionName.REMOTE;
MLTask mlTask = MLTask
.builder()
Expand Down
4 changes: 2 additions & 2 deletions plugin/src/main/java/org/opensearch/ml/action/stats/MLStatsNodesTransportAction.java
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Expand Up @@ -97,9 +97,9 @@ MLStatsNodeResponse createMLStatsNodeResponse(MLStatsNodesRequest mlStatsNodesRe
MLStatsInput mlStatsInput = mlStatsNodesRequest.getMlStatsInput();
// return node level stats
if (mlStatsInput.getTargetStatLevels().contains(MLStatLevel.NODE)) {
if (mlStatsInput.retrieveStat(MLNodeLevelStat.ML_NODE_JVM_HEAP_USAGE)) {
if (mlStatsInput.retrieveStat(MLNodeLevelStat.ML_JVM_HEAP_USAGE)) {
long heapUsedPercent = jvmService.stats().getMem().getHeapUsedPercent();
statValues.put(MLNodeLevelStat.ML_NODE_JVM_HEAP_USAGE, heapUsedPercent);
statValues.put(MLNodeLevelStat.ML_JVM_HEAP_USAGE, heapUsedPercent);
}

for (Enum statName : mlStats.getNodeStats().keySet()) {
Expand Down
4 changes: 1 addition & 3 deletions plugin/src/main/java/org/opensearch/ml/action/undeploy/TransportUndeployModelAction.java
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Expand Up @@ -228,8 +228,7 @@ protected MLUndeployModelNodeResponse nodeOperation(MLUndeployModelNodeRequest r
}

private MLUndeployModelNodeResponse createUndeployModelNodeResponse(MLUndeployModelNodesRequest MLUndeployModelNodesRequest) {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();

String[] modelIds = MLUndeployModelNodesRequest.getModelIds();

Expand All @@ -246,7 +245,6 @@ private MLUndeployModelNodeResponse createUndeployModelNodeResponse(MLUndeployMo
}

Map<String, String> modelUndeployStatus = mlModelManager.undeployModel(modelIds);
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).decrement();
return new MLUndeployModelNodeResponse(clusterService.localNode(), modelUndeployStatus, modelWorkerNodesMap);
}
}
36 changes: 13 additions & 23 deletions plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java
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Expand Up @@ -212,7 +212,7 @@ public MLModelManager(
public void registerModelMeta(MLRegisterModelMetaInput mlRegisterModelMetaInput, ActionListener<String> listener) {
try {
FunctionName functionName = mlRegisterModelMetaInput.getFunctionName();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(functionName, REGISTER, ML_ACTION_REQUEST_COUNT).increment();
String modelGroupId = mlRegisterModelMetaInput.getModelGroupId();
if (Strings.isBlank(modelGroupId)) {
Expand Down Expand Up @@ -322,8 +322,7 @@ public void registerMLModel(MLRegisterModelInput registerModelInput, MLTask mlTa

checkAndAddRunningTask(mlTask, maxRegisterTasksPerNode);
try {
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(mlTask.getFunctionName(), REGISTER, ML_ACTION_REQUEST_COUNT).increment();

String modelGroupId = registerModelInput.getModelGroupId();
Expand Down Expand Up @@ -384,17 +383,14 @@ public void registerMLModel(MLRegisterModelInput registerModelInput, MLTask mlTa
} catch (Exception e) {
handleException(registerModelInput.getFunctionName(), mlTask.getTaskId(), e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
}
}

private void indexRemoteModel(MLRegisterModelInput registerModelInput, MLTask mlTask, String modelVersion) {
String taskId = mlTask.getTaskId();
FunctionName functionName = mlTask.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(functionName, REGISTER, ML_ACTION_REQUEST_COUNT).increment();
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This line is to track how many register requests on function level. By removing this, can we still track that?

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Yeah, because we are tracking this in the parent function registerMLModel

mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();

String modelName = registerModelInput.getModelName();
String version = modelVersion == null ? registerModelInput.getVersion() : modelVersion;
Expand Down Expand Up @@ -443,8 +439,6 @@ private void indexRemoteModel(MLRegisterModelInput registerModelInput, MLTask ml
} catch (Exception e) {
logException("Failed to upload model", e, log);
handleException(functionName, taskId, e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
}
}

Expand All @@ -462,9 +456,6 @@ private void registerModelFromUrl(MLRegisterModelInput registerModelInput, MLTas
String taskId = mlTask.getTaskId();
FunctionName functionName = mlTask.getFunctionName();
try (ThreadContext.StoredContext context = client.threadPool().getThreadContext().stashContext()) {
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.createCounterStatIfAbsent(functionName, REGISTER, ML_ACTION_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
String modelName = registerModelInput.getModelName();
String version = modelVersion == null ? registerModelInput.getVersion() : modelVersion;
String modelGroupId = registerModelInput.getModelGroupId();
Expand Down Expand Up @@ -509,8 +500,6 @@ private void registerModelFromUrl(MLRegisterModelInput registerModelInput, MLTas
} catch (Exception e) {
logException("Failed to register model", e, log);
handleException(functionName, taskId, e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
}
}

Expand Down Expand Up @@ -693,7 +682,7 @@ private void handleException(FunctionName functionName, String taskId, Exception
&& !(e instanceof MLResourceNotFoundException)
&& !(e instanceof IllegalArgumentException)) {
mlStats.createCounterStatIfAbsent(functionName, REGISTER, MLActionLevelStat.ML_ACTION_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_FAILURE_COUNT).increment();
}
Map<String, Object> updated = ImmutableMap.of(ERROR_FIELD, MLExceptionUtils.getRootCauseMessage(e), STATE_FIELD, FAILED);
mlTaskManager.updateMLTask(taskId, updated, TIMEOUT_IN_MILLIS, true);
Expand All @@ -718,7 +707,8 @@ public void deployModel(
ActionListener<String> listener
) {
mlStats.createCounterStatIfAbsent(functionName, ActionName.DEPLOY, ML_ACTION_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
List<String> workerNodes = mlTask.getWorkerNodes();
if (modelCacheHelper.isModelDeployed(modelId)) {
if (workerNodes != null && workerNodes.size() > 0) {
Expand Down Expand Up @@ -800,7 +790,7 @@ public void deployModel(
MLExecutable mlExecutable = mlEngine.deployExecute(mlModel, params);
try {
modelCacheHelper.setMLExecutor(modelId, mlExecutable);
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT).increment();
modelCacheHelper.setModelState(modelId, MLModelState.DEPLOYED);
listener.onResponse("successful");
} catch (Exception e) {
Expand All @@ -813,7 +803,7 @@ public void deployModel(
Predictable predictable = mlEngine.deploy(mlModel, params);
try {
modelCacheHelper.setPredictor(modelId, predictable);
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT).increment();
modelCacheHelper.setModelState(modelId, MLModelState.DEPLOYED);
Long modelContentSizeInBytes = mlModel.getModelContentSizeInBytes();
long contentSize = modelContentSizeInBytes == null
Expand Down Expand Up @@ -846,7 +836,7 @@ private void handleDeployModelException(String modelId, FunctionName functionNam
&& !(e instanceof MLResourceNotFoundException)
&& !(e instanceof IllegalArgumentException)) {
mlStats.createCounterStatIfAbsent(functionName, ActionName.DEPLOY, MLActionLevelStat.ML_ACTION_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_FAILURE_COUNT).increment();
}
removeModel(modelId);
listener.onFailure(e);
Expand All @@ -855,7 +845,7 @@ private void handleDeployModelException(String modelId, FunctionName functionNam
private void setupPredictable(String modelId, MLModel mlModel, Map<String, Object> params) {
Predictable predictable = mlEngine.deploy(mlModel, params);
modelCacheHelper.setPredictor(modelId, predictable);
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT).increment();
modelCacheHelper.setModelState(modelId, MLModelState.DEPLOYED);
}

Expand Down Expand Up @@ -1056,8 +1046,8 @@ public synchronized Map<String, String> undeployModel(String[] modelIds) {
for (String modelId : modelIds) {
if (modelCacheHelper.isModelDeployed(modelId)) {
modelUndeployStatus.put(modelId, UNDEPLOYED);
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT).decrement();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT).decrement();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats
.createCounterStatIfAbsent(getModelFunctionName(modelId), ActionName.UNDEPLOY, ML_ACTION_REQUEST_COUNT)
.increment();
Expand All @@ -1070,7 +1060,7 @@ public synchronized Map<String, String> undeployModel(String[] modelIds) {
log.debug("undeploy all models {}", Arrays.toString(getLocalDeployedModels()));
for (String modelId : getLocalDeployedModels()) {
modelUndeployStatus.put(modelId, UNDEPLOYED);
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT).decrement();
mlStats.getStat(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT).decrement();
mlStats.createCounterStatIfAbsent(getModelFunctionName(modelId), ActionName.UNDEPLOY, ML_ACTION_REQUEST_COUNT).increment();
removeModel(modelId);
}
Expand Down
10 changes: 5 additions & 5 deletions plugin/src/main/java/org/opensearch/ml/plugin/MachineLearningPlugin.java
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Expand Up @@ -305,11 +305,11 @@ public Collection<Object> createComponents(
stats.put(MLClusterLevelStat.ML_MODEL_COUNT, new MLStat<>(true, new CounterSupplier()));
stats.put(MLClusterLevelStat.ML_CONNECTOR_COUNT, new MLStat<>(true, new CounterSupplier()));
// node level stats
stats.put(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_NODE_TOTAL_FAILURE_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_NODE_TOTAL_MODEL_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_NODE_TOTAL_CIRCUIT_BREAKER_TRIGGER_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_REQUEST_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_FAILURE_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_DEPLOYED_MODEL_COUNT, new MLStat<>(false, new CounterSupplier()));
stats.put(MLNodeLevelStat.ML_CIRCUIT_BREAKER_TRIGGER_COUNT, new MLStat<>(false, new CounterSupplier()));
this.mlStats = new MLStats(stats);

mlIndicesHandler = new MLIndicesHandler(clusterService, client);
Expand Down
10 changes: 9 additions & 1 deletion plugin/src/main/java/org/opensearch/ml/rest/RestMLStatsAction.java
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Expand Up @@ -21,6 +21,7 @@
import java.util.Locale;
import java.util.Map;
import java.util.Optional;
import java.util.Set;
import java.util.stream.Collectors;

import org.opensearch.client.node.NodeClient;
Expand Down Expand Up @@ -60,6 +61,12 @@ public class RestMLStatsAction extends BaseRestHandler {
private static final String QUERY_ALL_MODEL_META_DOC =
"{\"query\":{\"bool\":{\"must_not\":{\"exists\":{\"field\":\"chunk_number\"}}}}}";

private static final Set<String> ML_NODE_STAT_NAMES = EnumSet
.allOf(MLNodeLevelStat.class)
.stream()
.map(stat -> stat.name())
.collect(Collectors.toSet());

/**
* Constructor
* @param mlStats MLStats object
Expand Down Expand Up @@ -148,6 +155,7 @@ protected RestChannelConsumer prepareRequest(RestRequest request, NodeClient cli
}

MLStatsInput createMlStatsInputFromRequestParams(RestRequest request) {

MLStatsInput mlStatsInput = new MLStatsInput();
Optional<String[]> nodeIds = splitCommaSeparatedParam(request, "nodeId");
if (nodeIds.isPresent()) {
Expand All @@ -158,7 +166,7 @@ MLStatsInput createMlStatsInputFromRequestParams(RestRequest request) {
for (String state : stats.get()) {
state = state.toUpperCase(Locale.ROOT);
// only support cluster and node level stats for bwc
if (state.startsWith("ML_NODE")) {
if (ML_NODE_STAT_NAMES.contains(state)) {
mlStatsInput.getNodeLevelStats().add(MLNodeLevelStat.from(state));
} else {
mlStatsInput.getClusterLevelStats().add(MLClusterLevelStat.from(state));
Expand Down
12 changes: 6 additions & 6 deletions plugin/src/main/java/org/opensearch/ml/stats/MLNodeLevelStat.java
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Expand Up @@ -10,12 +10,12 @@
* This enum represents node level stats.
*/
public enum MLNodeLevelStat {
ML_NODE_JVM_HEAP_USAGE,
ML_NODE_EXECUTING_TASK_COUNT,
ML_NODE_TOTAL_REQUEST_COUNT,
ML_NODE_TOTAL_FAILURE_COUNT,
ML_NODE_TOTAL_MODEL_COUNT,
ML_NODE_TOTAL_CIRCUIT_BREAKER_TRIGGER_COUNT;
ML_JVM_HEAP_USAGE,
ML_EXECUTING_TASK_COUNT,
ML_REQUEST_COUNT,
ML_FAILURE_COUNT,
ML_DEPLOYED_MODEL_COUNT,
ML_CIRCUIT_BREAKER_TRIGGER_COUNT;

public static MLNodeLevelStat from(String value) {
try {
Expand Down
6 changes: 3 additions & 3 deletions plugin/src/main/java/org/opensearch/ml/task/MLExecuteTaskRunner.java
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Expand Up @@ -88,8 +88,8 @@ protected TransportResponseHandler<MLExecuteTaskResponse> getResponseHandler(Act
protected void executeTask(MLExecuteTaskRequest request, ActionListener<MLExecuteTaskResponse> listener) {
threadPool.executor(EXECUTE_THREAD_POOL).execute(() -> {
try {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats
.createCounterStatIfAbsent(request.getFunctionName(), ActionName.EXECUTE, MLActionLevelStat.ML_ACTION_REQUEST_COUNT)
.increment();
Expand All @@ -113,7 +113,7 @@ protected void executeTask(MLExecuteTaskRequest request, ActionListener<MLExecut
.increment();
listener.onFailure(e);
} finally {
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).decrement();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).decrement();
}
});
}
Expand Down
6 changes: 3 additions & 3 deletions plugin/src/main/java/org/opensearch/ml/task/MLPredictTaskRunner.java
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Expand Up @@ -201,8 +201,8 @@ protected void executeTask(MLPredictionTaskRequest request, ActionListener<MLTas
private void predict(String modelId, MLTask mlTask, MLInput mlInput, ActionListener<MLTaskResponse> listener) {
ActionListener<MLTaskResponse> internalListener = wrappedCleanupListener(listener, mlTask.getTaskId());
// track ML task count and add ML task into cache
mlStats.getStat(MLNodeLevelStat.ML_NODE_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_REQUEST_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_EXECUTING_TASK_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_REQUEST_COUNT).increment();
mlStats
.createCounterStatIfAbsent(mlTask.getFunctionName(), ActionName.PREDICT, MLActionLevelStat.ML_ACTION_REQUEST_COUNT)
.increment();
Expand Down Expand Up @@ -303,7 +303,7 @@ private void handlePredictFailure(MLTask mlTask, ActionListener<MLTaskResponse>
mlStats
.createCounterStatIfAbsent(mlTask.getFunctionName(), ActionName.PREDICT, MLActionLevelStat.ML_ACTION_FAILURE_COUNT)
.increment();
mlStats.getStat(MLNodeLevelStat.ML_NODE_TOTAL_FAILURE_COUNT).increment();
mlStats.getStat(MLNodeLevelStat.ML_FAILURE_COUNT).increment();
}
handleAsyncMLTaskFailure(mlTask, e);
listener.onFailure(e);
Expand Down
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