azure-monitor-ingestion-java
Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".
Best use case
azure-monitor-ingestion-java is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".
Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "azure-monitor-ingestion-java" skill to help with this workflow task. Context: Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-monitor-ingestion-java/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-monitor-ingestion-java Compares
| Feature / Agent | azure-monitor-ingestion-java | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
| 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
```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](../query/SKILL.md) 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
| 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 |Related Skills
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