AZ

azure-monitor-ingestion-java

Assists with Azure Monitor custom log ingestion in Java applications.

Install

mkdir -p .claude/skills/azure-monitor-ingestion-java && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/14352" && unzip -o skill.zip -d .claude/skills/azure-monitor-ingestion-java && rm skill.zip

Installs to .claude/skills/azure-monitor-ingestion-java

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

|. Use when you need help with azure monitor ingestion java.
60 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Install the Azure Monitor Ingestion SDK for Java
  • Create synchronous and asynchronous LogsIngestionClient instances
  • Upload custom logs to Azure Monitor
  • Upload large log collections concurrently
  • Handle partial upload failures gracefully
  • Query ingested logs using Azure Monitor Query

How it works

This skill uses the Azure Monitor Ingestion SDK for Java to send custom logs to Azure Monitor via Data Collection Rules and Endpoints.

Inputs & outputs

You give it
Java application logs and Azure Monitor configuration
You get back
custom logs ingested into Azure Monitor

When to use azure-monitor-ingestion-java

  • Setting up custom log ingestion
  • Configuring DCRs in Java
  • Integrating Azure Monitor SDK

About this skill

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  # Required for all auth methods
DATA_COLLECTION_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  # Required for log upload routing
STREAM_NAME=Custom-MyTable_CL  # Required for the target DCR stream
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Client Creation

Synchronous Client

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.ingestion.LogsIngestionClient;
import com.azure.monitor.ingestion.LogsIngestionClientBuilder;

// 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();

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(credential)
    .buildAsyncClient();

Key Concepts

ConceptDescription
Data Collection Endpoint (DCE)Ingestion endpoint URL for your region
Data Collection Rule (DCR)Defines data transformation and routing to tables
Stream NameTarget stream in the DCR (e.g., Custom-MyTable_CL)
Log Analytics WorkspaceDestination for ingested logs

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 throughputLogsIngestionAsyncClient 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

ResourceURL
Maven Packagehttps://central.sonatype.com/artifact/com.azure/azure-monitor-ingestion
GitHubhttps://github.com/Azure/azure-sdk-for-java/tree/main/sdk/monitor/azure-monitor-ingestion
Product Docshttps://learn.microsoft.com/azure/azure-monitor/logs/logs-ingestion-api-overview
DCE Overviewhttps://learn.microsoft.com/azure/azure-monitor/essentials/data-collection-endpoint-overview
DCR Overviewhttps://learn.microsoft.com/azure/azure-monitor/essentials/data-collection-rule-overview
Troubleshootinghttps://github.com/Azure/azure-sdk-for-java/blob/main/sdk/monitor/azure-monitor-ingestion/TROUBLESHOOTING.md

When not to use it

  • When logs are not custom logs
  • When the target is not Azure Monitor
  • When the application is not Java-based

Prerequisites

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

Limitations

  • The skill requires a configured Data Collection Endpoint.
  • The skill requires a configured Data Collection Rule.
  • The skill requires a Log Analytics workspace.

How it compares

This skill provides a programmatic way to ingest custom logs into Azure Monitor from Java applications, unlike manual log collection or other non-Java methods.

Compared to similar skills

azure-monitor-ingestion-java side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
azure-monitor-ingestion-java (this skill)02moReviewIntermediate
common-technical-practices23moNo flagsIntermediate
minecraft-bukkit-pro904moNo flagsAdvanced
workflow-orchestration-patterns102moNo flagsAdvanced

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