Imported from GhoutiYellesCH/antigravity-skills (azure-monitor-toolkit/SKILL.md). Install upstream with npx skills add GhoutiYellesCH/antigravity-skills --skill azure-monitor-toolkit. Copyright stays with the author.

Azure Monitor Toolkit

Core Capabilities

Azure Monitor Toolkit

Azure Monitor Query SDK for Python

Query logs and metrics from Azure Monitor and Log Analytics workspaces.

Installation

pip install azure-monitor-query

Environment Variables

# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>

# Metrics
AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>

Authentication

from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

Logs Query Client

Basic Query

from azure.monitor.query import LogsQueryClient
from datetime import timedelta

client = LogsQueryClient(credential)

query = """
AppRequests
| where TimeGenerated > ago(1h)
| summarize count() by bin(TimeGenerated, 5m), ResultCode
| order by TimeGenerated desc
"""

response = client.query_workspace(
    workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
    query=query,
    timespan=timedelta(hours=1)
)

for table in response.tables:
    for row in table.rows:
        print(row)

Query with Time Range

from datetime import datetime, timezone

response = client.query_workspace(
    workspace_id=workspace_id,
    query="AppRequests | take 10",
    timespan=(
        datetime(2024, 1, 1, tzinfo=timezone.utc),
        datetime(2024, 1, 2, tzinfo=timezone.utc)
    )
)

Convert to DataFrame

import pandas as pd

response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=1))

if response.tables:
    table = response.tables[0]
    df = pd.DataFrame(data=table.rows, columns=[col.name for col in table.columns])
    print(df.head())

Batch Query

from azure.monitor.query import LogsBatchQuery

queries = [
    LogsBatchQuery(workspace_id=workspace_id, query="AppRequests | take 5", timespan=timedelta(hours=1)),
    LogsBatchQuery(workspace_id=workspace_id, query="AppExceptions | take 5", timespan=timedelta(hours=1))
]

responses = client.query_batch(queries)

for response in responses:
    if response.tables:
        print(f"Rows: {len(response.tables[0].rows)}")

Handle Partial Results

from azure.monitor.query import LogsQueryStatus

response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=24))

if response.status == LogsQueryStatus.PARTIAL:
    print(f"Partial results: {response.partial_error}")
elif response.status == LogsQueryStatus.FAILURE:
    print(f"Query failed: {response.partial_error}")

Metrics Query Client

Query Resource Metrics

from azure.monitor.query import MetricsQueryClient
from datetime import timedelta

metrics_client = MetricsQueryClient(credential)

response = metrics_client.query_resource(
    resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
    metric_names=["Percentage CPU", "Network In Total"],
    timespan=timedelta(hours=1),
    granularity=timedelta(minutes=5)
)

for metric in response.metrics:
    print(f"{metric.name}:")
    for time_series in metric.timeseries:
        for data in time_series.data:
            print(f"  {data.timestamp}: {data.average}")

Aggregations

from azure.monitor.query import MetricAggregationType

response = metrics_client.query_resource(
    resource_uri=resource_uri,
    metric_names=["Requests"],
    timespan=timedelta(hours=1),
    aggregations=[
        MetricAggregationType.AVERAGE,
        MetricAggregationType.MAXIMUM,
        MetricAggregationType.MINIMUM,
        MetricAggregationType.COUNT
    ]
)

Filter by Dimension

response = metrics_client.query_resource(
    resource_uri=resource_uri,
    metric_names=["Requests"],
    timespan=timedelta(hours=1),
    filter="ApiName eq 'GetBlob'"
)

List Metric Definitions

definitions = metrics_client.list_metric_definitions(resource_uri)
for definition in definitions:
    print(f"{definition.name}: {definition.unit}")

List Metric Namespaces

namespaces = metrics_client.list_metric_namespaces(resource_uri)
for ns in namespaces:
    print(ns.fully_qualified_namespace)

Async Clients

from azure.monitor.query.aio import LogsQueryClient, MetricsQueryClient
from azure.identity.aio import DefaultAzureCredential

async def query_logs():
    credential = DefaultAzureCredential()
    client = LogsQueryClient(credential)
    
    response = await client.query_workspace(
        workspace_id=workspace_id,
        query="AppRequests | take 10",
        timespan=timedelta(hours=1)
    )
    
    await client.close()
    await credential.close()
    return response

Common Kusto Queries

// Requests by status code
AppRequests
| summarize count() by ResultCode
| order by count_ desc

// Exceptions over time
AppExceptions
| summarize count() by bin(TimeGenerated, 1h)

// Slow requests
AppRequests
| where DurationMs > 1000
| project TimeGenerated, Name, DurationMs
| order by DurationMs desc

// Top errors
AppExceptions
| summarize count() by ExceptionType
| top 10 by count_

Client Types

Best Practices

1. Use timedelta for relative time ranges
2. Handle partial results for large queries
3. Use batch queries when running multiple queries
4. Set appropriate granularity for metrics to reduce data points
5. Convert to DataFrame for easier data analysis
6. Use aggregations to summarize metric data
7. Filter by dimensions to narrow metric results

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Azure Monitor OpenTelemetry Distro for Python

One-line setup for Application Insights with OpenTelemetry auto-instrumentation.

Installation

pip install azure-monitor-opentelemetry

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Quick Start

from azure.monitor.opentelemetry import configure_azure_monitor

# One-line setup - reads connection string from environment
configure_azure_monitor()

# Your application code...

Explicit Configuration

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/"
)

With Flask

from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()

With Django

# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

# Django settings...

With FastAPI

from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

Custom Traces

from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # Do work...

Custom Metrics

from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")

counter.add(1, {"dimension": "value"})

Custom Logs

import logging
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)

Sampling

from azure.monitor.opentelemetry import configure_azure_monitor

# Sample 10% of requests
configure_azure_monitor(
    sampling_ratio=0.1
)

Cloud Role Name

Set cloud role name for Application Map:

from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME

configure_azure_monitor(
    resource=Resource.create({SERVICE_NAME: "my-service-name"})
)

Disable Specific Instrumentations

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    instrumentations=["flask", "requests"]  # Only enable these
)

Enable Live Metrics

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Azure AD Authentication

from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential

configure_azure_monitor(
    credential=DefaultAzureCredential()
)

Auto-Instrumentations Included

Configuration Options

Best Practices

1. Call configure_azure_monitor() early — Before importing instrumented libraries
2. Use environment variables for connection string in production
3. Set cloud role name for multi-service applications
4. Enable sampling in high-traffic applications
5. Use structured logging for better log analytics queries
6. Add custom attributes to spans for better debugging
7. Use AAD authentication for production workloads

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Azure Monitor Ingestion SDK for Java

Client library for sending custom logs to Azure Monitor using the Logs Ingestion API via Data Collection Rules.

Installation

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-ingestion</artifactId>
    <version>1.2.11</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-ingestion</artifactId>
    </dependency>
</dependencies>

Prerequisites

• Data Collection Endpoint (DCE)
• Data Collection Rule (DCR)
• Log Analytics workspace
• Target table (custom or built-in: CommonSecurityLog, SecurityEvents, Syslog, WindowsEvents)

Environment Variables

DATA_COLLECTION_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com
DATA_COLLECTION_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
STREAM_NAME=Custom-MyTable_CL

Client Creation

Synchronous Client

import com.azure.identity.DefaultAzureCredential;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.monitor.ingestion.LogsIngestionClient;
import com.azure.monitor.ingestion.LogsIngestionClientBuilder;

DefaultAzureCredential credential = new DefaultAzureCredentialBuilder().build();

LogsIngestionClient client = new LogsIngestionClientBuilder()
    .endpoint("<data-collection-endpoint>")
    .credential(credential)
    .buildClient();

Asynchronous Client

import com.azure.monitor.ingestion.LogsIngestionAsyncClient;

LogsIngestionAsyncClient asyncClient = new LogsIngestionClientBuilder()
    .endpoint("<data-collection-endpoint>")
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildAsyncClient();

Key Concepts

Core Operations

Upload Custom Logs

import java.util.List;
import java.util.ArrayList;

List<Object> logs = new ArrayList<>();
logs.add(new MyLogEntry("2024-01-15T10:30:00Z", "INFO", "Application started"));
logs.add(new MyLogEntry("2024-01-15T10:30:05Z", "DEBUG", "Processing request"));

client.upload("<data-collection-rule-id>", "<stream-name>", logs);
System.out.println("Logs uploaded successfully");

Upload with Concurrency

For large log collections, enable concurrent uploads:

import com.azure.monitor.ingestion.models.LogsUploadOptions;
import com.azure.core.util.Context;

List<Object> logs = getLargeLogs(); // Large collection

LogsUploadOptions options = new LogsUploadOptions()
    .setMaxConcurrency(3);

client.upload("<data-collection-rule-id>", "<stream-name>", logs, options, Context.NONE);

Upload with Error Handling

Handle partial upload failures gracefully:

LogsUploadOptions options = new LogsUploadOptions()
    .setLogsUploadErrorConsumer(uploadError -> {
        System.err.println("Upload error: " + uploadError.getResponseException().getMessage());
        System.err.println("Failed logs count: " + uploadError.getFailedLogs().size());
        
        // Option 1: Log and continue
        // Option 2: Throw to abort remaining uploads
        // throw uploadError.getResponseException();
    });

client.upload("<data-collection-rule-id>", "<stream-name>", logs, options, Context.NONE);

Async Upload with Reactor

import reactor.core.publisher.Mono;

List<Object> logs = getLogs();

asyncClient.upload("<data-collection-rule-id>", "<stream-name>", logs)
    .doOnSuccess(v -> System.out.println("Upload completed"))
    .doOnError(e -> System.err.println("Upload failed: " + e.getMessage()))
    .subscribe();

Log Entry Model Example

public class MyLogEntry {
    private String timeGenerated;
    private String level;
    private String message;
    
    public MyLogEntry(String timeGenerated, String level, String message) {
        this.timeGenerated = timeGenerated;
        this.level = level;
        this.message = message;
    }
    
    // Getters required for JSON serialization
    public String getTimeGenerated() { return timeGenerated; }
    public String getLevel() { return level; }
    public String getMessage() { return message; }
}

Error Handling

import com.azure.core.exception.HttpResponseException;

try {
    client.upload(ruleId, streamName, logs);
} catch (HttpResponseException e) {
    System.err.println("HTTP Status: " + e.getResponse().getStatusCode());
    System.err.println("Error: " + e.getMessage());
    
    if (e.getResponse().getStatusCode() == 403) {
        System.err.println("Check DCR permissions and managed identity");
    } else if (e.getResponse().getStatusCode() == 404) {
        System.err.println("Verify DCE endpoint and DCR ID");
    }
}

Best Practices

1. Batch logs — Upload in batches rather than one at a time
2. Use concurrency — Set maxConcurrency for large uploads
3. Handle partial failures — Use error consumer to log failed entries
4. Match DCR schema — Log entry fields must match DCR transformation expectations
5. Include TimeGenerated — Most tables require a timestamp field
6. Reuse client — Create once, reuse throughout application
7. Use async for high throughput — LogsIngestionAsyncClient for reactive patterns

Querying Uploaded Logs

Use azure-monitor-query to query ingested logs:

// See azure-monitor-query skill for LogsQueryClient usage
String query = "MyTable_CL | where TimeGenerated > ago(1h) | limit 10";

Reference Links

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Azure Monitor OpenTelemetry SDK for TypeScript

Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

Installation

# Distro (recommended - auto-instrumentation)
npm install @azure/monitor-opentelemetry

# Low-level exporters (custom OpenTelemetry setup)
npm install @azure/monitor-opentelemetry-exporter

# Custom logs ingestion
npm install @azure/monitor-ingestion

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...

Quick Start (Auto-Instrumentation)

IMPORTANT: Call useAzureMonitor() BEFORE importing other modules.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor({
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
  }
});

// Now import your application
import express from "express";
const app = express();

ESM Support (Node.js 18.19+)

node --import @azure/monitor-opentelemetry/loader ./dist/index.js

package.json:

{
  "scripts": {
    "start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
  }
}

Full Configuration

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";

const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
    storageDirectory: "/path/to/offline/storage",
    disableOfflineStorage: false
  },
  
  // Sampling
  samplingRatio: 1.0,  // 0-1, percentage of traces
  
  // Features
  enableLiveMetrics: true,
  enableStandardMetrics: true,
  enablePerformanceCounters: true,
  
  // Instrumentation libraries
  instrumentationOptions: {
    azureSdk: { enabled: true },
    http: { enabled: true },
    mongoDb: { enabled: true },
    mySql: { enabled: true },
    postgreSql: { enabled: true },
    redis: { enabled: true },
    bunyan: { enabled: false },
    winston: { enabled: false }
  },
  
  // Custom resource
  resource: resourceFromAttributes({ "service.name": "my-service" })
};

useAzureMonitor(options);

Custom Traces

import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("my-tracer");

const span = tracer.startSpan("doWork");
try {
  span.setAttribute("component", "worker");
  span.setAttribute("operation.id", "42");
  span.addEvent("processing started");
  
  // Your work here
  
} catch (error) {
  span.recordException(error as Error);
  span.setStatus({ code: 2, message: (error as Error).message });
} finally {
  span.end();
}

Custom Metrics

import { metrics } from "@opentelemetry/api";

const meter = metrics.getMeter("my-meter");

// Counter
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });

// Histogram
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });

// Observable Gauge
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
  result.observe(getActiveConnections(), { pool: "main" });
});

Manual Exporter Setup

Trace Exporter

import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";

const exporter = new AzureMonitorTraceExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const provider = new NodeTracerProvider({
  spanProcessors: [new BatchSpanProcessor(exporter)]
});

provider.register();

Metric Exporter

import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";

const exporter = new AzureMonitorMetricExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const meterProvider = new MeterProvider({
  readers: [new PeriodicExportingMetricReader({ exporter })]
});

metrics.setGlobalMeterProvider(meterProvider);

Log Exporter

import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";

const exporter = new AzureMonitorLogExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));

logs.setGlobalLoggerProvider(loggerProvider);

Custom Logs Ingestion

import { DefaultAzureCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";

const endpoint = "https://<dce>.ingest.monitor.azure.com";
const ruleId = "<data-collection-rule-id>";
const streamName = "Custom-MyTable_CL";

const client = new LogsIngestionClient(endpoint, new DefaultAzureCredential());

const logs = [
  {
    Time: new Date().toISOString(),
    Computer: "Server1",
    Message: "Application started",
    Level: "Information"
  }
];

try {
  await client.upload(ruleId, streamName, logs);
} catch (error) {
  if (isAggregateLogsUploadError(error)) {
    for (const uploadError of error.errors) {
      console.error("Failed logs:", uploadError.failedLogs);
    }
  }
}

Custom Span Processor

import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";

class FilteringSpanProcessor implements SpanProcessor {
  forceFlush(): Promise<void> { return Promise.resolve(); }
  shutdown(): Promise<void> { return Promise.resolve(); }
  onStart(span: Span, context: Context): void {}
  
  onEnd(span: ReadableSpan): void {
    // Add custom attributes
    span.attributes["CustomDimension"] = "value";
    
    // Filter out internal spans
    if (span.kind === SpanKind.INTERNAL) {
      span.spanContext().traceFlags = TraceFlags.NONE;
    }
  }
}

useAzureMonitor({
  spanProcessors: [new FilteringSpanProcessor()]
});

Sampling

import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";

// Sample 75% of traces
const sampler = new ApplicationInsightsSampler(0.75);

const provider = new NodeTracerProvider({ sampler });

Shutdown

import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor();

// On application shutdown
process.on("SIGTERM", async () => {
  await shutdownAzureMonitor();
  process.exit(0);
});

Key Types

import {
  useAzureMonitor,
  shutdownAzureMonitor,
  AzureMonitorOpenTelemetryOptions,
  InstrumentationOptions
} from "@azure/monitor-opentelemetry";

import {
  AzureMonitorTraceExporter,
  AzureMonitorMetricExporter,
  AzureMonitorLogExporter,
  ApplicationInsightsSampler,
  AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";

import {
  LogsIngestionClient,
  isAggregateLogsUploadError
} from "@azure/monitor-ingestion";

Best Practices

1. Call useAzureMonitor() first - Before importing other modules
2. Use ESM loader for ESM projects - --import @azure/monitor-opentelemetry/loader
3. Enable offline storage - For reliable telemetry in disconnected scenarios
4. Set sampling ratio - For high-traffic applications
5. Add custom dimensions - Use span processors for enrichment
6. Graceful shutdown - Call shutdownAzureMonitor() to flush telemetry

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Azure Monitor OpenTelemetry Exporter for Java

⚠️ DEPRECATION NOTICE: This package is deprecated. Migrate to azure-monitor-opentelemetry-autoconfigure.

See Migration Guide for detailed instructions.

Export OpenTelemetry telemetry data to Azure Monitor / Application Insights.

Installation (Deprecated)

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
    <version>1.0.0-beta.x</version>
</dependency>

Recommended: Use Autoconfigure Instead

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
    <version>LATEST</version>
</dependency>

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Basic Setup with Autoconfigure (Recommended)

Using Environment Variable

import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
import io.opentelemetry.api.OpenTelemetry;
import com.azure.monitor.opentelemetry.exporter.AzureMonitorExporter;

// Connection string from APPLICATIONINSIGHTS_CONNECTION_STRING env var
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

With Explicit Connection String

AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder, "{connection-string}");
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Creating Spans

import io.opentelemetry.api.trace.Tracer;
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.context.Scope;

// Get tracer
Tracer tracer = openTelemetry.getTracer("com.example.myapp");

// Create span
Span span = tracer.spanBuilder("myOperation").startSpan();

try (Scope scope = span.makeCurrent()) {
    // Your application logic
    doWork();
} catch (Throwable t) {
    span.recordException(t);
    throw t;
} finally {
    span.end();
}

Adding Span Attributes

import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;

Span span = tracer.spanBuilder("processOrder")
    .setAttribute("order.id", "12345")
    .setAttribute("customer.tier", "premium")
    .startSpan();

try (Scope scope = span.makeCurrent()) {
    // Add attributes during execution
    span.setAttribute("items.count", 3);
    span.setAttribute("total.amount", 99.99);
    
    processOrder();
} finally {
    span.end();
}

Custom Span Processor

import io.opentelemetry.sdk.trace.SpanProcessor;
import io.opentelemetry.sdk.trace.ReadWriteSpan;
import io.opentelemetry.sdk.trace.ReadableSpan;
import io.opentelemetry.context.Context;

private static final AttributeKey<String> CUSTOM_ATTR = AttributeKey.stringKey("custom.attribute");

SpanProcessor customProcessor = new SpanProcessor() {
    @Override
    public void onStart(Context context, ReadWriteSpan span) {
        // Add custom attribute to every span
        span.setAttribute(CUSTOM_ATTR, "customValue");
    }

    @Override
    public boolean isStartRequired() {
        return true;
    }

    @Override
    public void onEnd(ReadableSpan span) {
        // Post-processing if needed
    }

    @Override
    public boolean isEndRequired() {
        return false;
    }
};

// Register processor
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);

sdkBuilder.addTracerProviderCustomizer(
    (sdkTracerProviderBuilder, configProperties) -> 
        sdkTracerProviderBuilder.addSpanProcessor(customProcessor)
);

OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Nested Spans

public void parentOperation() {
    Span parentSpan = tracer.spanBuilder("parentOperation").startSpan();
    try (Scope scope = parentSpan.makeCurrent()) {
        childOperation();
    } finally {
        parentSpan.end();
    }
}

public void childOperation() {
    // Automatically links to parent via Context
    Span childSpan = tracer.spanBuilder("childOperation").startSpan();
    try (Scope scope = childSpan.makeCurrent()) {
        // Child work
    } finally {
        childSpan.end();
    }
}

Recording Exceptions

Span span = tracer.spanBuilder("riskyOperation").startSpan();
try (Scope scope = span.makeCurrent()) {
    performRiskyWork();
} catch (Exception e) {
    span.recordException(e);
    span.setStatus(StatusCode.ERROR, e.getMessage());
    throw e;
} finally {
    span.end();
}

Metrics (via OpenTelemetry)

import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.LongHistogram;

Meter meter = openTelemetry.getMeter("com.example.myapp");

// Counter
LongCounter requestCounter = meter.counterBuilder("http.requests")
    .setDescription("Total HTTP requests")
    .setUnit("requests")
    .build();

requestCounter.add(1, Attributes.of(
    AttributeKey.stringKey("http.method"), "GET",
    AttributeKey.longKey("http.status_code"), 200L
));

// Histogram
LongHistogram latencyHistogram = meter.histogramBuilder("http.latency")
    .setDescription("Request latency")
    .setUnit("ms")
    .ofLongs()
    .build();

latencyHistogram.record(150, Attributes.of(
    AttributeKey.stringKey("http.route"), "/api/users"
));

Key Concepts

Migration to Autoconfigure

The azure-monitor-opentelemetry-autoconfigure package provides:

• Automatic instrumentation of common libraries
• Simplified configuration
• Better integration with OpenTelemetry SDK

Migration Steps

1. Replace dependency:

   <!-- Remove -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
   </dependency>
   
   <!-- Add -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
   </dependency>

2. Update initialization code per Migration Guide

Best Practices

1. Use autoconfigure — Migrate to azure-monitor-opentelemetry-autoconfigure
2. Set meaningful span names — Use descriptive operation names
3. Add relevant attributes — Include contextual data for debugging
4. Handle exceptions — Always record exceptions on spans
5. Use semantic conventions — Follow OpenTelemetry semantic conventions
6. End spans in finally — Ensure spans are always ended
7. Use try-with-resources — Scope management with try-with-resources pattern

Reference Links

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Azure Monitor OpenTelemetry Exporter for Python

Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.

Installation

pip install azure-monitor-opentelemetry-exporter

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

When to Use

Trace Exporter

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Create exporter
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure tracer provider
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# Use tracer
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")

Metric Exporter

from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# Create exporter
exporter = AzureMonitorMetricExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure meter provider
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# Use meter
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})

Log Exporter

import logging
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# Create exporter
exporter = AzureMonitorLogExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure logger provider
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# Add handler to Python logging
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# Use logging
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")

From Environment Variable

Exporters read APPLICATIONINSIGHTS_CONNECTION_STRING automatically:

from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment
exporter = AzureMonitorTraceExporter()

Azure AD Authentication

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential()
)

Sampling

Use ApplicationInsightsSampler for consistent sampling:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# Sample 10% of traces
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))

Offline Storage

Configure offline storage for retry:

from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    storage_directory="/path/to/storage",  # Custom storage path
    disable_offline_storage=False  # Enable retry (default)
)

Disable Offline Storage

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    disable_offline_storage=True  # No retry on failure
)

Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)

Exporter Types

Configuration Options

Best Practices

1. Use BatchSpanProcessor for production (not SimpleSpanProcessor)
2. Use ApplicationInsightsSampler for consistent sampling across services
3. Enable offline storage for reliability in production
4. Use AAD authentication instead of instrumentation keys
5. Set export intervals appropriate for your workload
6. Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

Azure Monitor Ingestion SDK for Python

Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.

Installation

pip install azure-monitor-ingestion
pip install azure-identity

Environment Variables

# Data Collection Endpoint (DCE)
AZURE_DCE_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com

# Data Collection Rule (DCR) immutable ID
AZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

# Stream name from DCR
AZURE_DCR_STREAM_NAME=Custom-MyTable_CL

Prerequisites

Before using this SDK, you need:

1. Log Analytics Workspace — Target for your logs
2. Data Collection Endpoint (DCE) — Ingestion endpoint
3. Data Collection Rule (DCR) — Defines schema and destination
4. Custom Table — In Log Analytics (created via DCR or manually)

Authentication

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Upload Custom Logs

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

rule_id = os.environ["AZURE_DCR_RULE_ID"]
stream_name = os.environ["AZURE_DCR_STREAM_NAME"]

logs = [
    {"TimeGenerated": "2024-01-15T10:00:00Z", "Computer": "server1", "Message": "Application started"},
    {"TimeGenerated": "2024-01-15T10:01:00Z", "Computer": "server1", "Message": "Processing request"},
    {"TimeGenerated": "2024-01-15T10:02:00Z", "Computer": "server2", "Message": "Connection established"}
]

client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Upload from JSON File

import json

with open("logs.json", "r") as f:
    logs = json.load(f)

client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Custom Error Handling

Handle partial failures with a callback:

failed_logs = []

def on_error(error):
    print(f"Upload failed: {error.error}")
    failed_logs.extend(error.failed_logs)

client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
    on_error=on_error
)

# Retry failed logs
if failed_logs:
    print(f"Retrying {len(failed_logs)} failed logs...")
    client.upload(rule_id=rule_id, stream_name=stream_name, logs=failed_logs)

Ignore Errors

def ignore_errors(error):
    pass  # Silently ignore upload failures

client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
    on_error=ignore_errors
)

Async Client

import asyncio
from azure.monitor.ingestion.aio import LogsIngestionClient
from azure.identity.aio import DefaultAzureCredential

async def upload_logs():
    async with LogsIngestionClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        await client.upload(
            rule_id=rule_id,
            stream_name=stream_name,
            logs=logs
        )

asyncio.run(upload_logs())

Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
client = LogsIngestionClient(
    endpoint="https://example.ingest.monitor.azure.us",
    credential=credential,
    credential_scopes=["https://monitor.azure.us/.default"]
)

Batching Behavior

The SDK automatically:

• Splits logs into chunks of 1MB or less
• Compresses each chunk with gzip
• Uploads chunks in parallel

No manual batching needed for large log sets.

Client Types

Key Concepts

DCR Stream Name Format

Stream names follow patterns:

• Custom-<TableName>_CL — For custom tables
• Microsoft-<TableName> — For built-in tables

Best Practices

1. Use DefaultAzureCredential for authentication
2. Handle errors gracefully — use on_error callback for partial failures
3. Include TimeGenerated — Required field for all logs
4. Match DCR schema — Log fields must match DCR column definitions
5. Use async client for high-throughput scenarios
6. Batch uploads — SDK handles batching, but send reasonable chunks
7. Monitor ingestion — Check Log Analytics for ingestion status
8. Use context manager — Ensures proper client cleanup

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

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
AZURE_RESOURCE_ID=/subscriptions/{sub}/resourceGroups/{rg}/providers/{provider}/{resource}

Client Creation

LogsQueryClient (Sync)

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.monitor.query.LogsQueryClient;
import com.azure.monitor.query.LogsQueryClientBuilder;

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

LogsQueryAsyncClient

import com.azure.monitor.query.LogsQueryAsyncClient;

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

MetricsQueryClient (Sync)

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

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

MetricsQueryAsyncClient

import com.azure.monitor.query.MetricsQueryAsyncClient;

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

Sovereign Cloud Configuration

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

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

Key Concepts

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.