huawei-cloud-mrs-host-fault-diagnose

Huawei Cloud MRS cluster fault diagnosis skill. Diagnoses service faults, instance faults, and host faults through progressive root cause localization: quick log scan first, host troubleshooting when host issues are found, detailed investigation when no conclusion is reached. Driven by the built-in LakeWatch API client and the per-component knowledge base under components/. No commands outside the knowledge base are fabricated. Applicable to MRS fault diagnosis and root cause localization scenarios where a service name or node name is provided. Trigger words: "故障诊断", "故障定位", "fault diagnosis", "fault diagnose", "MRS故障", "服务故障", "实例故障", "主机故障", "集群排查", "集群诊断", "启动失败", "停止异常", "KrbServer故障", "DBService故障", "fault troubleshooting"

Huawei Cloud MRS Host Fault Diagnosis Skill

Overview

This skill diagnoses Huawei Cloud MRS (MapReduce Service) cluster faults. Given a service name and/or node name, it progressively localizes the root cause: quick log scan first, host troubleshooting when host issues are found, detailed investigation when no conclusion is reached.

Architecture: Caller (Agent) -> lakewatch_api_client.py (Python, scripts/) -> LakeWatch API -> MRS cluster (node resource data, logs, MRS Manager proxy); per-component knowledge base (components/<service_name>.md) drives the diagnosis flow; three fault layers (host -> instance -> service) with propagation chain tracing.

Note on language: This SKILL.md and the documents under references/ are written in English per the repository spec. The knowledge base documents under fault_layer/, scenarios/, components/, and propagation.md are also in English. Commands and code blocks are English throughout.

Applicable Scenarios:

• A service is reported unhealthy and the root cause must be localized
• An instance is reported faulty on a specific node
• A host is reported unreachable or abnormal
• Progressive fault triage from quick scan to deep investigation

Typical Use Cases:

• "KrbServer出问题了，帮忙诊断一下" (service fault, no node specified)
• "8-5-225-6上的KrbServer挂了" (instance fault, service + node specified)
• "8-5-225-6出问题了" (host fault, node only)
• "MRS集群KrbServer启动失败，集群ID xxx"
• "DBService停止异常，节点8-5-225-6"

Critical Constraints

Important constraints:

1. Read-only: This skill only runs information-gathering commands (view logs, query status, collect resource data). It MUST NOT run any start/stop, modify, or delete operations.
2. User confirmation for repair: The skill only provides executable repair suggestions; it MUST NOT directly execute any repair operation. All repair actions require user confirmation.
3. Strict execution: Diagnose strictly according to the knowledge base content under this skill directory. Fabricating diagnostic commands outside the knowledge base is prohibited.

Prerequisites

1. Python Requirements

• Python >= 3.7
• Dependencies: pyyaml (YAML parsing), cryptography (Windows AES password encryption only)
• Linux uses CryptoAPI for password encryption (no cryptography dependency)
• Verify installation: python3 --version (Linux) / python --version (Windows)

This skill does NOT require KooCLI (hcloud). It calls the LakeWatch API through scripts/lakewatch_api_client.py. For the LakeWatch client setup, see CLI Installation Guide.

2. LakeWatch Credential Configuration

• A valid LakeWatch service account (username + password)
• The password MUST be encrypted with --encrypt-password and stored in scripts/lakewatch_api_config.yaml (auth.encrypted_password). Never store the plaintext password.
• Security Rules:
  • Never expose the LakeWatch password in conversation or command output
  • Never ask the user to input the plaintext password in conversation; use the interactive --encrypt-password flow
  • The token is cached locally with owner-only file permissions (Win: %TEMP%\lakewatch_token\, Linux: /tmp/lakewatch_token/)

3. Access Permissions

• Reachability to the LakeWatch service endpoint (configured in scripts/lakewatch_api_config.yaml server.host/port)
• The LakeWatch account must have permission to call the MRS Manager proxy and collect node resource/log data on the target cluster
• See IAM Policies for the access model and required roles

4. Dependent Skill: huawei-cloud-mrs-host-alarm-diagnose

This skill references the per-alarm diagnosis knowledge base from the huawei-cloud-mrs-host-alarm-diagnose skill (sibling directory under skills/bigdata/mrs/). When the fault diagnosis flow encounters a known alarm (12006/12007/25000/25500/27001), it loads the corresponding document from ../huawei-cloud-mrs-host-alarm-diagnose/alarms/<alarm_id>.md.

• If the alarm skill exists, load the referenced document and follow its diagnosis flow
• If NOT exist, inform the user and proceed with the generic fault diagnosis flow
• The dependency is document-level reference only (loading markdown by relative path), NOT a direct skill call. Both skills share the same LakeWatch API client and config format.

Command Format Standard

This skill uses the LakeWatch API client instead of KooCLI. The unified command format is:

bash

# Linux
python3 <skill_dir>/scripts/lakewatch_api_client.py -a <api_name> -p 'key1=value1' -p 'key2=value2'

# Windows
python <skill_dir>/scripts/lakewatch_api_client.py -a <api_name> -p 'key1=value1' -p 'key2=value2'

Element	Rule	Example
python3 / python	Linux uses python3, Windows uses python	python3 lakewatch_api_client.py
-a, --api	API name to call (defined in lakewatch_api_config.yaml)	-a collect_alarm_node_res_data
-p, --param	API parameter in key=value form, repeatable	-p 'cluster_id=xxx'
Quoting	Every -p value MUST be wrapped in single quotes to prevent shell parsing of [] {} | ()	-p 'keywords=["ERROR"]'

Windows (PowerShell) quote rule: every " inside a value must be replaced with """ (including " inside [] and {}), otherwise the server returns {"message":"Unknown exception","success":false,"code":"500"}:

powershell

# Correct on Windows
-p 'keywords=["""ERROR"""]'
-p 'env={"""PID""":"""123"""}'

# Wrong on Windows (will fail)
-p 'keywords=["ERROR"]'

Linux (bash) quote rule: keep " as-is inside the value, wrap the whole value in single quotes:

bash

# Correct on Linux
-p 'keywords=["ERROR","Exception"]'
-p 'env={"PID":"123"}'

For the full API catalog, parameters, and the token/encryption mechanism, see LakeWatch API Client.

Workflow

Step 1: Determine Fault Entry

Extract fault information from the user input and determine the diagnosis entry:

User Description	Entry	Step 1 Action
Has service_name, no node_name (e.g. "KrbServer出问题了")	Service fault	Check all instance statuses, find faulty instances
Has service_name + node_name (e.g. "8-5-225-6上的KrbServer挂了")	Instance fault	Directly check that instance
Has node_name, no service_name (e.g. "8-5-225-6出问题了")	Host fault	Check host status, then check instances on the host

Step 2: Locate the Fault Object

Entry A: Service Fault (has service_name, no node_name)

Load components/<service_name>.md for component config. Query OMS primary/standby nodes, check process on each node:

bash

python3 lakewatch_api_client.py -a query-management-node-info \
  -p 'cluster_id=<cluster_id>'

bash

python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=process-basic-info' \
  -p 'env={"process_name":"<process_name>"}' \
  -p 'node_name=<node_name>'

Decision:

Result	Next Step
All node processes normal	Step 4 detailed investigation
Some node processes missing	Step 3 quick log scan (for faulty nodes)
API call failed (node unreachable)	Step 4 host troubleshooting

Entry B: Instance Fault (has service_name + node_name)

Load components/<service_name>.md. Directly check process on that node:

bash

python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=process-basic-info' \
  -p 'env={"process_name":"<process_name>"}' \
  -p 'node_name=<node_name>'

Decision:

Result	Next Step
Process normal	Step 4 detailed investigation
Process missing	Step 3 quick log scan
API call failed (node unreachable)	Step 4 host troubleshooting

Entry C: Host Fault (has node_name, no service_name)

Query OMS primary/standby nodes, query node IP, ping the faulty node from OMS active node:

bash

python3 lakewatch_api_client.py -a query-management-node-info \
  -p 'cluster_id=<cluster_id>'

bash

python3 lakewatch_api_client.py -a query-node-ip \
  -p 'cluster_id=<cluster_id>' \
  -p 'node_name=<node_name>'

bash

python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=ping-check' \
  -p 'env={"TARGET_IP":"<target_ip>"}' \
  -p 'node_name=<oms_active_node>'

Decision:

Result	Next Step
Ping failed	Step 4 host troubleshooting (network/hardware)
Ping succeeded	Check all component processes on the host, find faulty instances -> Step 3 quick log scan

Step 3: Quick Log Scan

For the faulty node, quickly scan three layers of logs (Controller -> NodeAgent -> component), looking for clear ERROR:

bash

# Controller log
python3 lakewatch_api_client.py -a collect_alarm_log_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'alarm_time=<alarm_time>' \
  -p 'log_directory=/var/log/Bigdata/controller' \
  -p 'log_file_name=exe.log*' \
  -p 'keywords=["<service_name>","ERROR","fail","timeout","Exception"]' \
  -p 'log_type=local' \
  -p 'node_name=<oms_active_node>'

# NodeAgent script log
python3 lakewatch_api_client.py -a collect_alarm_log_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'alarm_time=<alarm_time>' \
  -p 'log_directory=/var/log/Bigdata/nodeagent/scriptlog' \
  -p 'log_file_name=*.log*' \
  -p 'keywords=["<service_name>","ERROR","fail","exit"]' \
  -p 'log_type=local' \
  -p 'node_name=<node_name>'

If service_name is known, also check the component's own log (path from components/<service_name>.md):

bash

python3 lakewatch_api_client.py -a collect_alarm_log_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'alarm_time=<alarm_time>' \
  -p 'log_directory=<log_directory>' \
  -p 'log_file_name=<log_file_name>' \
  -p 'keywords=["ERROR","Exception","FATAL","fail","OOM"]' \
  -p 'log_type=local' \
  -p 'node_name=<node_name>'

Decision:

Log Result	Next Step
Clear ERROR (e.g. OOM/permission/port conflict/config missing)	Output root cause
Log shows node unreachable / Agent timeout	Step 4 host troubleshooting
Multiple faulty nodes on same host	Step 4 host troubleshooting
No clear conclusion	Step 4 detailed investigation

Step 4: Detailed Investigation

When the quick log scan yields no conclusion, collect complete data:

1. Load Data Collection to collect process/port/HA/resource/alarm/framework logs
2. Load Instance Fault Diagnosis for instance-level diagnosis (includes scenario identification)
3. If needed, load Service Fault Diagnosis for service-level diagnosis
4. If host issue is found, load Host Fault Diagnosis for host-level diagnosis

Step 5: Propagation Chain Tracing

Load Propagation Chain to trace the root cause propagation path and impact scope.

Step 6: Output Diagnosis Conclusion

markdown

## Diagnosis Result

| Item | Content |
|------|---------|
| Diagnosis time | [time] |
| Cluster ID | [cluster_id] |
| Faulty component | [service_name] |
| Faulty node | [node_name] |

### Diagnosis Process

| Step | Result |
|------|--------|
| Instance status | [which nodes normal/abnormal] |
| Quick log scan | [found/not found clear ERROR] |
| Host troubleshooting | [normal/abnormal: ...] |
| Detailed investigation | [process/port/HA/resource results] |

### Propagation Path

[root cause] -> [propagation] -> [symptom] (single-layer root cause if no propagation)

### Root Cause Analysis

**Root cause layer**: [host/instance/service]
**Root cause type**: [specific reason]

### Repair Suggestion

| Priority | Operation | Description | Needs user confirmation |
|----------|-----------|-------------|-------------------------|
| 1 | [operation] | [description] | Yes |

Core Commands

Query OMS Primary/Standby Nodes

bash

python3 lakewatch_api_client.py -a query-management-node-info \
  -p 'cluster_id=<cluster_id>'

Query Node IP

bash

python3 lakewatch_api_client.py -a query-node-ip \
  -p 'cluster_id=<cluster_id>' \
  -p 'node_name=<node_name>'

Collect Node Resource Data

bash

# Process basic info
python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=process-basic-info' \
  -p 'env={"process_name":"<process_name>"}' \
  -p 'node_name=<node_name>'

# Port check
python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=port-check' \
  -p 'env={"PORT":"<port>"}' \
  -p 'node_name=<node_name>'

# HA resource status
python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=ha-resource-status' \
  -p 'node_name=<node_name>'

# Disk space / Memory / CPU load
python3 lakewatch_api_client.py -a collect_alarm_node_res_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'strategy_name=disk-space' \
  -p 'node_name=<node_name>'

Supported strategy_name values include: system-load, memory-usage, disk-space, disk-io, network-io, file-handle, port-check, high-cpu-processes, high-memory-process, zombie-process, dns-check, network-connectivity-test, process-basic-info, process-file-descriptor, jstack-thread-dump, disk-health-check, disk-smart-info, ha-resource-status, omm-process-tree, and more. See LakeWatch API Client for the full list.

Collect Alarm Log Data

bash

python3 lakewatch_api_client.py -a collect_alarm_log_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'alarm_time=<alarm_time>' \
  -p 'log_directory=<log_directory>' \
  -p 'log_file_name=<log_file_name>' \
  -p 'keywords=["ERROR","Exception"]' \
  -p 'log_type=local'

When the log time format is non-standard ISO (e.g. [2026-07-07 20:54:25,171]), pass time_pattern:

bash

python3 lakewatch_api_client.py -a collect_alarm_log_data \
  -p 'cluster_id=<cluster_id>' \
  -p 'alarm_time=2026/07/07 20:54:00 GMT+08:00' \
  -p 'log_directory=/var/log/Bigdata/omm/oms/pms' \
  -p 'log_file_name=pms*.log' \
  -p 'keywords=["ERROR","Exception"]' \
  -p 'log_type=local' \
  -p 'time_pattern=^\[([0-9]{4})-([0-9]{2})-([0-9]{2}) ([0-9]{2}):([0-9]{2}):([0-9]{2})||ymdHMS'

Proxy MRS Manager GET API

bash

# Query cluster services
python3 lakewatch_api_client.py -a access_manager_get \
  -p 'cluster_id=<cluster_id>' \
  -p 'target_url=api/v2/clusters/<cluster_id>/services'

# Query host processes
python3 lakewatch_api_client.py -a access_manager_get \
  -p 'cluster_id=<cluster_id>' \
  -p 'target_url=api/v2/clusters/<cluster_id>/hosts/<node_name>/processes'

# Query active alarms
python3 lakewatch_api_client.py -a access_manager_get \
  -p 'cluster_id=<cluster_id>' \
  -p 'target_url=api/v2/clusters/<cluster_id>/alarms'

target_url MUST NOT start with /. The proxy requires Agent >= 1.0.5 and reported OMS node info. Only GET is supported currently.

Parameter Confirmation

Parameter	Required/Optional	Description	Default
cluster_id	Required	MRS cluster ID	N/A
service_name	Conditionally required	Faulty component (required for service/instance fault entry)	N/A
node_name	Conditionally required	Faulty node (required for instance/host fault entry)	N/A
alarm_time	Optional	Fault occurrence time, format yyyy/MM/dd HH:mm:ss GMT+X:XX	Current time
strategy_name	Required by collect_alarm_node_res_data	Resource collection strategy	N/A
log_directory	Required by collect_alarm_log_data	Log directory, must be under /var/log/	N/A
log_file_name	Required by collect_alarm_log_data	Log file name, no path separators	N/A
keywords	Required by collect_alarm_log_data	Log keyword filter, JSON array	N/A
log_type	Required by collect_alarm_log_data	local or hdfs	N/A
time_pattern	Optional	Non-standard log time regex, format regex||format	N/A
target_url	Required by access_manager_get	MRS Manager API path, must NOT start with /	N/A

Output Format

The diagnosis report is output in Markdown, containing:

• Diagnosis result table: diagnosis time, cluster ID, faulty component, faulty node
• Diagnosis process: step-by-step results (instance status, quick log scan, host troubleshooting, detailed investigation)
• Propagation path: root cause -> propagation -> symptom (single-layer if no propagation)
• Root cause analysis: root cause layer (host/instance/service) + root cause type
• Repair suggestion table: priority, operation, description, needs-user-confirmation (all repair actions require user confirmation)

See the template in the Workflow -> Step 6 section.

Verification Method

See Verification Method for the installation, configuration, and function verification steps.

Best Practices

1. Determine entry first: Based on user-provided information (service_name, node_name), determine whether the entry is service fault, instance fault, or host fault before starting diagnosis.
2. Progressive investigation: Always start with quick log scan (Step 3); only escalate to detailed investigation (Step 4) when no clear conclusion is reached.
3. Substitute placeholders: Replace <cluster_id>, <alarm_time>, <node_name>, <target_ip>, <process_name>, etc. with actual user-provided values; never hardcode them.
4. Quote parameters: Always wrap -p values in single quotes; on Windows PowerShell, escape " as """ to avoid code:500 errors.
5. Time format: alarm_time must follow yyyy/MM/dd HH:mm:ss GMT+X:XX; for non-standard log time formats, pass time_pattern.
6. Summarize results: Use a summarization tool to condense command output before analysis; large raw outputs should not be analyzed directly.
7. Reflect after diagnosis: After completing the checks, reflect on whether the root cause is confirmed; if not, re-check for missed steps.
8. Read-only: All commands are read-only; repair steps are suggestions only and require user confirmation before execution.
9. Command failure handling: When a command fails, skip the current check item and continue with the other checks; do not abort the whole diagnosis.

References

Document	Description
CLI Installation Guide	Python dependencies and LakeWatch client setup
IAM Policies	LakeWatch/MRS Manager access model and required roles
Verification Method	Installation, configuration, and function verification
Acceptance Criteria	Pass/fail criteria for skill testing
Fault Diagnosis Workflow	Progressive fault diagnosis workflow design
LakeWatch API Client	Full API catalog, parameters, token and encryption mechanism
Related Commands	Common LakeWatch API commands quick reference
huawei-cloud-mrs-host-alarm-diagnose (sibling skill)	Dependency: per-alarm diagnosis knowledge base (../huawei-cloud-mrs-host-alarm-diagnose/alarms/<alarm_id>.md). See Prerequisites section 4 for details.
Data Collection	Complete data collection flow (Step 4)
Host Fault Diagnosis	Host layer diagnosis
Instance Fault Diagnosis	Instance layer diagnosis (includes scenario identification)
Service Fault Diagnosis	Service layer diagnosis
Propagation Chain	Root cause propagation path tracing
Common Scenario	6-phase common diagnosis framework for all scenarios
scenarios/<scenario>.md	Scenario-specific checks (install/start/stop/uninstall/reinstall/reinstall_host/scale_out/scale_in)
components/<service_name>.md	Per-component configuration (process, port, log path, etc.)
components/_template.md	Template for new component configuration

Notes

• Security: This skill is read-only. It never exposes the LakeWatch password; the password is encrypted via --encrypt-password and stored in lakewatch_api_config.yaml. Repair steps are suggestions only.
• No KooCLI: This skill does not use hcloud; it calls the LakeWatch API through lakewatch_api_client.py. Do not mix in hcloud commands.
• Command failure: When a command fails, skip the current check item and continue with the other checks; do not abort the whole diagnosis.
• Known limitations: The access_manager_get proxy only supports GET requests (PUT is not yet available on the Agent side); collect_alarm_log_data requires log_directory to be under /var/log/; some strategy_name values require extra env parameters.
• Cross-skill dependency: This skill references alarm diagnosis documents from the huawei-cloud-mrs-host-alarm-diagnose skill (e.g. ../huawei-cloud-mrs-host-alarm-diagnose/alarms/12006.md, 12007.md). See Prerequisites section 4 for the dependency declaration and handling rules. If the alarm skill is not installed, inform the user and proceed with the generic fault diagnosis flow.