sickn33/antigravity-awesome-skills/azure-monitor-ingestion-java

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Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE).

Install

npx vskill@latest install sickn33/antigravity-awesome-skills/azure-monitor-ingestion-java

SKILL.md

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

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

Or use Azure SDK BOM:

xml
<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

bash
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
java
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
java
import com.azure.monitor.ingestion.LogsIngestionAsyncClient;
LogsIngestionAsyncClient asyncClient = new LogsIngestionClientBuilder()
.endpoint("<data-collection-endpoint>")
.credential(new DefaultAzureCredentialBuilder().build())
.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
java
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:

java
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:

java
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
java
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

java
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

java
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:

java
// 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 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.
  • Do not 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.

Serialize record property names, timestamp formats and values to the exact Data Collection Rule stream schema; a POJO timestamp or console log does not prove ingestion. Await async completion with error handling and check the target workspace separately before declaring success. Use an authorized test stream, bound batch volume and avoid logging private records.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
36,035
Repository forks
5,907
Repository npm downloads, 7 days
6,751
7-day trend score
74

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 2 May 2026, 5 months ago.

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