A specialized diagnostic toolkit for analyzing health, configurations, and logs of open-source big data ecosystems on Linux systems.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install emr-analyer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install emr-analyer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is EMR Analyzer?
EMR Analyzer is a comprehensive technical skill designed to manage and troubleshoot open-source big data applications within Linux environments. It serves as a bridge between complex distributed systems and actionable insights, providing developers with automated tools to check service health, retrieve optimized parameters, and diagnose task failures across a wide array of services including YARN, HDFS, Spark, Hive, and Kafka. By leveraging this skill on Openclaw Skills, teams can significantly reduce the Mean Time to Resolution (MTTR) for cluster-wide issues.
The tool is particularly effective for environments running Alibaba Cloud EMR or similar big data distributions. It intelligently scans standard configuration paths and log directories to provide a unified view of the cluster's operational status. Whether you are dealing with resource contention in YARN or connection bottlenecks in ZooKeeper, this skill provides the technical depth required to maintain high-availability big data infrastructure.
EMR Analyzer Use Cases
- Monitoring overall cluster health and identifying non-running services.
- Troubleshooting failed Spark or Hive jobs by automatically parsing error logs.
- Auditing resource configurations for YARN memory or Kafka broker settings.
- Optimizing JVM heap sizes and GC settings based on current performance bottlenecks.
- Validating port listening status and systemd service health across distributed nodes.
How EMR Analyzer Works
- The user initiates a query regarding cluster health, specific error messages, or performance tuning.
- The skill identifies the target service (e.g., HDFS, Spark) based on the context of the input.
- It executes backend Python scripts such as check_service_status.py to verify process and port availability.
- For troubleshooting, it runs analyze_logs.py to scan for known error patterns like OOM, timeouts, or permission denials.
- The check_config.py script is called to fetch current parameters from XML or shell configuration files.
- The final output synthesizes diagnostic data with prioritized remediation steps and optimization suggestions.
EMR Analyzer Setup
To use this skill, ensure you have SSH access to the target Linux servers with sudo privileges. Python 3 must be installed in the environment.
# Example: Check the status of all services in the cluster
python3 {baseDir}/scripts/check_service_status.py all
# Example: Analyze logs for a specific service like Hive
python3 {baseDir}/scripts/analyze_logs.py hive 100
EMR Analyzer Data Schema & Taxonomy
The skill organizes its diagnostic data and metadata using the following structures:
| Data Category | Source Paths | Format |
|---|---|---|
| Configurations | /etc/taihao-apps/ or /etc/ecm/ | XML, Properties, YAML |
| Service Logs | /var/log/hadoop-*/, /var/log/hive/ | Plain text / Log files |
| Health Status | Systemd, Netstat, PS | JSON Objects |
| Tuning Rules | Built-in Expert Knowledge Base | Structured Recommendations |
Metadata includes service versions, process IDs, listening ports, and error frequency counts.
EMR Analyzer Advanced Features
- Multi-service health aggregation for high-level cluster reporting.
- Intelligent log pattern matching for complex errors like HDFS SafeMode or Kafka Leader election failures.
- Automated configuration fallback logic to support multiple EMR directory versions.
- Integration with Openclaw Skills to provide real-time repair suggestions based on matched error signatures.
- Detailed performance audit reports comparing current settings against industry best practices.
SKILL.md
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