How does the skill handle upload errors?

It includes patterns for using the LogsUploadErrorConsumer to handle partial failures, allowing you to log or retry specific failed log entries without aborting the entire batch.

Does this skill support asynchronous logging?

Yes, it provides implementation patterns for both synchronous (LogsIngestionClient) and asynchronous (LogsIngestionAsyncClient) operations using Project Reactor.

What is the primary library used in this skill?

The skill uses the com.azure:azure-monitor-ingestion library to interact with the Azure Monitor Logs Ingestion API.

What are the prerequisites for using this skill?

You must have a Data Collection Endpoint (DCE), a Data Collection Rule (DCR), and a target Log Analytics workspace table already configured in Azure.

Azure Monitor Log Ingestion for Java

Name: Azure Monitor Log Ingestion for Java
Author: JantonioFC

byJantonioFC

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分析と監視

Streamlines the process of sending custom logs to Azure Monitor via Data Collection Rules using the Java SDK.

This skill provides a comprehensive guide and implementation pattern for utilizing the Azure Monitor Ingestion SDK for Java. It enables developers to seamlessly transmit custom log data to Log Analytics workspaces by leveraging Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Whether implementing synchronous or asynchronous ingestion, the skill covers essential operations like batching, concurrent uploads, and robust error handling, ensuring reliable observability and performance for Java-based cloud applications.

主な機能

012 GitHub stars

02Data Collection Rule (DCR) and Endpoint (DCE) configuration

03Concurrent upload management for high-throughput logging

04Synchronous and asynchronous log ingestion support

05Best practices for batching and schema matching

06Robust error handling with partial failure management

ユースケース

01Integrating legacy Java system logs into centralized Azure Monitor dashboards

02Sending application-specific custom logs to Azure Log Analytics

03Implementing high-performance reactive logging patterns with Project Reactor

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank azure-monitor-ingestion-java

For use in Claude.ai and ChatGPT

主な機能

012 GitHub stars

02Data Collection Rule (DCR) and Endpoint (DCE) configuration

03Concurrent upload management for high-throughput logging

04Synchronous and asynchronous log ingestion support

05Best practices for batching and schema matching

06Robust error handling with partial failure management

ユースケース

01Integrating legacy Java system logs into centralized Azure Monitor dashboards

02Sending application-specific custom logs to Azure Log Analytics

03Implementing high-performance reactive logging patterns with Project Reactor

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank azure-monitor-ingestion-java

For use in Claude.ai and ChatGPT