azure-monitor-query-java

Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. Triggers: "LogsQueryClient java", "MetricsQueryClient java", "kusto query java", "log analytics java", "azure monitor query java". Note: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.

npx skills add https://github.com/microsoft/skills --skill azure-monitor-query-java

Azure Monitor Query SDK for Java

DEPRECATION NOTICE: This package is deprecated in favor of:

  • azure-monitor-query-logs — For Log Analytics queries
  • azure-monitor-query-metrics — For metrics queries

See migration guides: Logs Migration | Metrics Migration

Client library for querying Azure Monitor Logs and Metrics.

Installation

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-query</artifactId>
    <version>1.5.9</version>
</dependency>

Or use Azure SDK BOM:

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-sdk-bom</artifactId>
            <version>{bom_version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-monitor-query</artifactId>
    </dependency>
</dependencies>

Prerequisites

  • Log Analytics workspace (for logs queries)
  • Azure resource (for metrics queries)
  • TokenCredential with appropriate permissions

Environment Variables

LOG_ANALYTICS_WORKSPACE_ID=xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx  # Required for Log Analytics workspace queries
AZURE_RESOURCE_ID=/subscriptions/{sub}/resourceGroups/{rg}/providers/{provider}/{resource}  # Required for metrics queries against a resource
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Client Creation

LogsQueryClient (Sync)

import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
import com.azure.monitor.query.LogsQueryClient;
import com.azure.monitor.query.LogsQueryClientBuilder;

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

LogsQueryClient logsClient = new LogsQueryClientBuilder()
    .credential(credential)
    .buildClient();

LogsQueryAsyncClient

import com.azure.monitor.query.LogsQueryAsyncClient;

LogsQueryAsyncClient logsAsyncClient = new LogsQueryClientBuilder()
    .credential(credential)
    .buildAsyncClient();

MetricsQueryClient (Sync)

import com.azure.monitor.query.MetricsQueryClient;
import com.azure.monitor.query.MetricsQueryClientBuilder;

MetricsQueryClient metricsClient = new MetricsQueryClientBuilder()
    .credential(credential)
    .buildClient();

MetricsQueryAsyncClient

import com.azure.monitor.query.MetricsQueryAsyncClient;

MetricsQueryAsyncClient metricsAsyncClient = new MetricsQueryClientBuilder()
    .credential(credential)
    .buildAsyncClient();

Sovereign Cloud Configuration

// Azure China Cloud - Logs
LogsQueryClient logsClient = new LogsQueryClientBuilder()
    .credential(credential)
    .endpoint("https://api.loganalytics.azure.cn/v1")
    .buildClient();

// Azure China Cloud - Metrics
MetricsQueryClient metricsClient = new MetricsQueryClientBuilder()
    .credential(credential)
    .endpoint("https://management.chinacloudapi.cn")
    .buildClient();

Key Concepts

ConceptDescription
LogsLog and performance data from Azure resources via Kusto Query Language
MetricsNumeric time-series data collected at regular intervals
Workspace IDLog Analytics workspace identifier
Resource IDAzure resource URI for metrics queries
QueryTimeIntervalTime range for the query

Logs Query Operations

Basic Query

import com.azure.monitor.query.models.LogsQueryResult;
import com.azure.monitor.query.models.LogsTableRow;
import com.azure.monitor.query.models.QueryTimeInterval;
import java.time.Duration;

LogsQueryResult result = logsClient.queryWorkspace(
    "{workspace-id}",
    "AzureActivity | summarize count() by ResourceGroup | top 10 by count_",
    new QueryTimeInterval(Duration.ofDays(7))
);

for (LogsTableRow row : result.getTable().getRows()) {
    System.out.println(row.getColumnValue("ResourceGroup") + ": " + row.getColumnValue("count_"));
}

Query by Resource ID

LogsQueryResult result = logsClient.queryResource(
    "{resource-id}",
    "AzureMetrics | where TimeGenerated > ago(1h)",
    new QueryTimeInterval(Duration.ofDays(1))
);

for (LogsTableRow row : result.getTable().getRows()) {
    System.out.println(row.getColumnValue("MetricName") + " " + row.getColumnValue("Average"));
}

Map Results to Custom Model

// Define model class
public class ActivityLog {
    private String resourceGroup;
    private String operationName;
    
    public String getResourceGroup() { return resourceGroup; }
    public String getOperationName() { return operationName; }
}

// Query with model mapping
List<ActivityLog> logs = logsClient.queryWorkspace(
    "{workspace-id}",
    "AzureActivity | project ResourceGroup, OperationName | take 100",
    new QueryTimeInterval(Duration.ofDays(2)),
    ActivityLog.class
);

for (ActivityLog log : logs) {
    System.out.println(log.getOperationName() + " - " + log.getResourceGroup());
}

Batch Query

import com.azure.monitor.query.models.LogsBatchQuery;
import com.azure.monitor.query.models.LogsBatchQueryResult;
import com.azure.monitor.query.models.LogsBatchQueryResultCollection;
import com.azure.core.util.Context;

LogsBatchQuery batchQuery = new LogsBatchQuery();
String q1 = batchQuery.addWorkspaceQuery("{workspace-id}", "AzureActivity | count", new QueryTimeInterval(Duration.ofDays(1)));
String q2 = batchQuery.addWorkspaceQuery("{workspace-id}", "Heartbeat | count", new QueryTimeInterval(Duration.ofDays(1)));
String q3 = batchQuery.addWorkspaceQuery("{workspace-id}", "Perf | count", new QueryTimeInterval(Duration.ofDays(1)));

LogsBatchQueryResultCollection results = logsClient
    .queryBatchWithResponse(batchQuery, Context.NONE)
    .getValue();

LogsBatchQueryResult result1 = results.getResult(q1);
LogsBatchQueryResult result2 = results.getResult(q2);
LogsBatchQueryResult result3 = results.getResult(q3);

// Check for failures
if (result3.getQueryResultStatus() == LogsQueryResultStatus.FAILURE) {
    System.err.println("Query failed: " + result3.getError().getMessage());
}

Query with Options

import com.azure.monitor.query.models.LogsQueryOptions;
import com.azure.core.http.rest.Response;

LogsQueryOptions options = new LogsQueryOptions()
    .setServerTimeout(Duration.ofMinutes(10))
    .setIncludeStatistics(true)
    .setIncludeVisualization(true);

Response<LogsQueryResult> response = logsClient.queryWorkspaceWithResponse(
    "{workspace-id}",
    "AzureActivity | summarize count() by bin(TimeGenerated, 1h)",
    new QueryTimeInterval(Duration.ofDays(7)),
    options,
    Context.NONE
);

LogsQueryResult result = response.getValue();

// Access statistics
BinaryData statistics = result.getStatistics();
// Access visualization data
BinaryData visualization = result.getVisualization();

Query Multiple Workspaces

import java.util.Arrays;

LogsQueryOptions options = new LogsQueryOptions()
    .setAdditionalWorkspaces(Arrays.asList("{workspace-id-2}", "{workspace-id-3}"));

Response<LogsQueryResult> response = logsClient.queryWorkspaceWithResponse(
    "{workspace-id-1}",
    "AzureActivity | summarize count() by TenantId",
    new QueryTimeInterval(Duration.ofDays(1)),
    options,
    Context.NONE
);

Metrics Query Operations

Basic Metrics Query

import com.azure.monitor.query.models.MetricsQueryResult;
import com.azure.monitor.query.models.MetricResult;
import com.azure.monitor.query.models.TimeSeriesElement;
import com.azure.monitor.query.models.MetricValue;
import java.util.Arrays;

MetricsQueryResult result = metricsClient.queryResource(
    "{resource-uri}",
    Arrays.asList("SuccessfulCalls", "TotalCalls")
);

for (MetricResult metric : result.getMetrics()) {
    System.out.println("Metric: " + metric.getMetricName());
    for (TimeSeriesElement ts : metric.getTimeSeries()) {
        System.out.println("  Dimensions: " + ts.getMetadata());
        for (MetricValue value : ts.getValues()) {
            System.out.println("    " + value.getTimeStamp() + ": " + value.getTotal());
        }
    }
}

Metrics with Aggregations

import com.azure.monitor.query.models.MetricsQueryOptions;
import com.azure.monitor.query.models.AggregationType;

Response<MetricsQueryResult> response = metricsClient.queryResourceWithResponse(
    "{resource-id}",
    Arrays.asList("SuccessfulCalls", "TotalCalls"),
    new MetricsQueryOptions()
        .setGranularity(Duration.ofHours(1))
        .setAggregations(Arrays.asList(AggregationType.AVERAGE, AggregationType.COUNT)),
    Context.NONE
);

MetricsQueryResult result = response.getValue();

Query Multiple Resources (MetricsClient)

import com.azure.monitor.query.MetricsClient;
import com.azure.monitor.query.MetricsClientBuilder;
import com.azure.monitor.query.models.MetricsQueryResourcesResult;

MetricsClient metricsClient = new MetricsClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint("{endpoint}")
    .buildClient();

MetricsQueryResourcesResult result = metricsClient.queryResources(
    Arrays.asList("{resourceId1}", "{resourceId2}"),
    Arrays.asList("{metric1}", "{metric2}"),
    "{metricNamespace}"
);

for (MetricsQueryResult queryResult : result.getMetricsQueryResults()) {
    for (MetricResult metric : queryResult.getMetrics()) {
        System.out.println(metric.getMetricName());
        metric.getTimeSeries().stream()
            .flatMap(ts -> ts.getValues().stream())
            .forEach(mv -> System.out.println(
                mv.getTimeStamp() + " Count=" + mv.getCount() + " Avg=" + mv.getAverage()));
    }
}

Response Structure

Logs Response Hierarchy

LogsQueryResult
├── statistics (BinaryData)
├── visualization (BinaryData)
├── error
└── tables (List<LogsTable>)
    ├── name
    ├── columns (List<LogsTableColumn>)
    │   ├── name
    │   └── type
    └── rows (List<LogsTableRow>)
        ├── rowIndex
        └── rowCells (List<LogsTableCell>)

Metrics Response Hierarchy

MetricsQueryResult
├── granularity
├── timeInterval
├── namespace
├── resourceRegion
└── metrics (List<MetricResult>)
    ├── id, name, type, unit
    └── timeSeries (List<TimeSeriesElement>)
        ├── metadata (dimensions)
        └── values (List<MetricValue>)
            ├── timeStamp
            ├── count, average, total
            ├── maximum, minimum

Error Handling

import com.azure.core.exception.HttpResponseException;
import com.azure.monitor.query.models.LogsQueryResultStatus;

try {
    LogsQueryResult result = logsClient.queryWorkspace(workspaceId, query, timeInterval);
    
    // Check partial failure
    if (result.getStatus() == LogsQueryResultStatus.PARTIAL_FAILURE) {
        System.err.println("Partial failure: " + result.getError().getMessage());
    }
} catch (HttpResponseException e) {
    System.err.println("Query failed: " + e.getMessage());
    System.err.println("Status: " + e.getResponse().getStatusCode());
}

Best Practices

  1. Use batch queries — Combine multiple queries into a single request
  2. Set appropriate timeouts — Long queries may need extended server timeout
  3. Limit result size — Use top or take in Kusto queries
  4. Use projections — Select only needed columns with project
  5. Check query status — Handle PARTIAL_FAILURE results gracefully
  6. Cache results — Metrics don't change frequently; cache when appropriate
  7. Migrate to new packages — Plan migration to azure-monitor-query-logs and azure-monitor-query-metrics

Reference Links

ResourceURL
Maven Packagehttps://central.sonatype.com/artifact/com.azure/azure-monitor-query
GitHubhttps://github.com/Azure/azure-sdk-for-java/tree/main/sdk/monitor/azure-monitor-query
API Referencehttps://learn.microsoft.com/java/api/com.azure.monitor.query
Kusto Query Languagehttps://learn.microsoft.com/azure/data-explorer/kusto/query/
Log Analytics Limitshttps://learn.microsoft.com/azure/azure-monitor/service-limits#la-query-api
Troubleshootinghttps://github.com/Azure/azure-sdk-for-java/blob/main/sdk/monitor/azure-monitor-query/TROUBLESHOOTING.md

Plus de skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Créez des agents Azure AI Foundry à l’aide du SDK Python Microsoft Agent Framework (agent-framework-azure-ai). À utiliser lors de la création d’agents persistants avec AzureAIAgentsProvider, de l’utilisation d’outils hébergés (interpréteur de code, recherche de fichiers, recherche web), de l’intégration de serveurs MCP, de la gestion de fils de conversation ou de l’implémentation de réponses en streaming. Couvre les outils de fonction, les sorties structurées et les agents multi-outils.
development
airunway-aks-setup
microsoft
Configurez AI Runway sur AKS — du cluster nu au modèle en cours d'exécution. Couvre la vérification du cluster, l'installation du contrôleur, l'évaluation GPU, la configuration du fournisseur et le premier déploiement. QUAND : « configurer AI Runway », « intégrer un cluster AKS », « installer AI Runway », « configuration airunway », « déployer un modèle sur AKS », « inférence GPU sur AKS », « configuration KAITO sur AKS », « exécuter LLM sur AKS », « vLLM sur AKS », « configurer le service de modèles sur AKS », « contrôleur AI Runway ».
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrumentez les applications navigateur/web avec le SDK JavaScript Application Insights (@microsoft/applicationinsights-web). Utilisez-le pour la surveillance des utilisateurs réels (RUM) — vues de page, clics, dépendances AJAX/fetch, exceptions, événements personnalisés et traces d’agents GenAI côté navigateur corrélées aux traces OpenTelemetry backend. Couvre le script de chargement du SDK et la configuration npm, les extensions de framework (React, React Native, Angular), Click Analytics, les initialiseurs de télémétrie et les conventions sémantiques OTel GenAI pour les spans d’agents/outils/modèles émises depuis le navigateur.
devops
azure-ai-anomalydetector-java
microsoft
Créez des applications de détection d'anomalies avec le SDK Azure AI Anomaly Detector pour Java. Utilisez-le lors de l'implémentation de la détection d'anomalies univariées/multivariées, de l'analyse de séries temporelles ou de la surveillance basée sur l'IA.
development
azure-ai-language-conversations-py
microsoft
Implémentez la compréhension du langage conversationnel (CLU) à l’aide du SDK Python azure-ai-language-conversations. Utilisez-le lorsque vous travaillez avec ConversationAnalysisClient pour analyser l’intention et les entités d’une conversation, créer des fonctionnalités de NLP ou intégrer la compréhension du langage dans des applications.
development
azure-ai-ml-py
microsoft
SDK v2 d’Azure Machine Learning pour Python. Utiliser pour les espaces de travail ML, les tâches, les modèles, les jeux de données, le calcul et les pipelines. Déclencheurs : « azure-ai-ml », « MLClient », « espace de travail », « registre de modèles », « tâches d’entraînement », « jeux de données ».
development