Regtransfers/azure-monitor-ingestion-java

@ 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

Concept; Description

Data Collection Endpoint (DCE); Ingestion endpoint URL for your region Data Collection Rule (DCR); Defines data transformation and routing to tables Stream Name; Target stream in the DCR (e.g., Custom-MyTable_CL) Log Analytics Workspace; Destination 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 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

Resource; URL

Maven Package; https://central.sonatype.com/artifact/com.azure/azure-monitor-ingestion GitHub; https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/monitor/azure-monitor-ingestion Product Docs; https://learn.microsoft.com/azure/azure-monitor/logs/logs-ingestion-api-overview DCE Overview; https://learn.microsoft.com/azure/azure-monitor/essentials/data-collection-endpoint-overview DCR Overview; https://learn.microsoft.com/azure/azure-monitor/essentials/data-collection-rule-overview Troubleshooting; https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/monitor/azure-monitor-ingestion/TROUBLESHOOTING.md

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

@ Limitations

• Use this skill only when the task clearly matches the scope described above.
• never treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.