eks-app-log-analysis（eks 应用程序日志分析）

EKS App Log Analysis

Analyze EKS application logs during FIS fault injection experiments to understand how applications respond to infrastructure failures. Supports real-time monitoring and post-hoc analysis modes.

Output Language Rule

Detect the language of the user's conversation and use the same language for all output.

• Chinese input -> Chinese output
• English input -> English output

Prerequisites

Required tools:

• kubectl — configured with access to target EKS cluster
• AWS CLI — for querying FIS experiment status
• A prepared/executed FIS experiment directory (from aws-fis-experiment-prepare or aws-fis-experiment-execute)

Workflow

digraph log_analysis_flow {
    "Receive input path" [shape=box];
    "Detect mode" [shape=diamond];
    "Real-time mode" [shape=box];
    "Post-hoc mode" [shape=box];
    "Read service list" [shape=box];
    "Auto-discover + confirm app dependencies" [shape=box];
    "Start background log collection" [shape=box];
    "Batch fetch historical logs" [shape=box];
    "Frontend polling + insight display" [shape=box];
    "Experiment complete?" [shape=diamond];
    "Generate analysis report" [shape=box];

    "Receive input path" -> "Detect mode";
    "Detect mode" -> "Real-time mode" [label="directory with README"];
    "Detect mode" -> "Post-hoc mode" [label="*-experiment-results.md"];
    "Real-time mode" -> "Read service list";
    "Post-hoc mode" -> "Read service list";
    "Read service list" -> "Auto-discover + confirm app dependencies";
    "Auto-discover + confirm app dependencies" -> "Start background log collection" [label="real-time"];
    "Auto-discover + confirm app dependencies" -> "Batch fetch historical logs" [label="post-hoc"];
    "Start background log collection" -> "Frontend polling + insight display";
    "Frontend polling + insight display" -> "Experiment complete?";
    "Experiment complete?" -> "Frontend polling + insight display" [label="No, continue"];
    "Experiment complete?" -> "Generate analysis report" [label="Yes"];
    "Batch fetch historical logs" -> "Generate analysis report";
}

Step 1: Detect Mode and Load Context

The user provides either:

• Directory path (e.g., ./2026-03-31-14-30-22-az-power-interruption-my-cluster/) → Real-time mode
• Report file path (e.g., ./2026-03-31-...-experiment-results.md) → Post-hoc mode

Real-time mode: The directory contains a README.md from the prepare skill. Extract the experiment template ID and region from it.

Post-hoc mode: The file is an experiment results report (contains "FIS Experiment Results"). Extract experiment ID, start time, end time, and region from it.

Step 2: Read Service List

Extract affected AWS services from:

• expected-behavior.md in the experiment directory (real-time mode), or
• the experiment results report (post-hoc mode)

Look for service name headings (e.g., "### RDS (cluster-xxx)") to build the list. Present the detected service list to the user.

Step 3: Collect Application Dependencies

3a. Auto-Discover Potential Dependencies

For each affected AWS service, automatically discover EKS applications that may depend on it:

1. Get the service's endpoint (e.g., RDS cluster endpoint, ElastiCache primary endpoint, EC2 private IP/DNS) via AWS CLI
2. Search all pod environment variables across namespaces for references to that endpoint
3. Search ConfigMaps across namespaces for references to that endpoint
4. Present discovered namespace/deployment candidates to the user, noting where the match was found (env var name, ConfigMap name)

3b. User Confirmation and Manual Supplement

Ask the user to confirm the auto-discovered dependencies and add any that were missed. Store the final mapping as SERVICE_APP_MAP (service → list of namespace/deployment pairs).

Step 4: Log Collection

Shell scripting rule: Use multi-line scripts. Do NOT chain commands with && on a single line — variables get lost after background & processes.

All logs should be saved to a temp directory: /tmp/{timestamp}-fis-app-logs/, organized by service name subdirectories.

Real-time Mode: Background Collection

For each application in SERVICE_APP_MAP, start background kubectl logs -f processes for regular containers only (excluding FIS-injected ephemeral containers):

1. Resolve the deployment's pod label selector from .spec.selector.matchLabels
2. Get the list of regular container names from the deployment spec: kubectl get deployment {DEPLOYMENT} -n {NAMESPACE} \ -o jsonpath='{.spec.template.spec.containers[*].name}' Do NOT use --all-containers=true — FIS pod-level fault injection (e.g., pod-network-latency, pod-cpu-stress) injects ephemeral containers into target pods. Using --all-containers would pull in FIS agent logs (noise) alongside application logs. Always use --container={name} to collect only regular containers.
3. For each regular container, start a background log stream: kubectl logs -f --selector={labels} -n {NAMESPACE} \ --container={CONTAINER_NAME} --timestamps --prefix=true \ --max-log-requests=20 \ >> {LOG_DIR}/{service-name}/{deployment}.log & Use --selector={labels} (NOT deployment/xxx) — this captures logs from all matching pods, including those recreated during the experiment.
4. Record each background PID to {LOG_DIR}/.pids for cleanup

Post-hoc Mode: Batch Fetch

In post-hoc mode, pods may have been terminated during the experiment. First detect whether Container Insights is available, then choose the log source accordingly.

Step 4a: Detect Container Insights

Check whether the EKS cluster has Container Insights enabled:

• Look for amazon-cloudwatch-observability EKS addon (via aws eks describe-addon)
• Or check for CloudWatch agent / Fluent Bit daemonset in amazon-cloudwatch namespace

Step 4b: CloudWatch Logs (preferred, if Container Insights is enabled)

Query CloudWatch Logs Insights against the log group /aws/containerinsights/{CLUSTER_NAME}/application for the experiment time window (START_TIME to END_TIME). Filter by kubernetes.namespace_name and kubernetes.labels.app (or pod name pattern) for each deployment. This captures complete logs including from pods that no longer exist.

Step 4c: kubectl logs (fallback, no Container Insights)

Use kubectl logs --selector={labels} --since-time={START_TIME} with --container={CONTAINER_NAME} --timestamps --prefix=true for each regular container (same container discovery as real-time mode Step 2). Do NOT use --all-containers. Note: this only retrieves logs from currently running pods — logs from pods terminated during the experiment are lost.

Step 5: Real-time Monitoring Display

Poll every 30 seconds while the experiment is running. For each service group and each application:

1. Read the last 30 seconds of collected logs from the log file
2. Count error-level entries (match: error, exception, fail, refused, timeout) and warning-level entries (match: warn, retry)
3. Display a per-app summary: error count, warning count, last 5 error lines
4. Detect recovery signals (connected, restored, success, recovered) in recent lines and report if found

Step 6: Check Experiment Status (Real-time Mode)

Use aws fis list-experiments to check if the experiment with the matching template ID is still in running state. When the experiment completes (or is not found), proceed to report generation.

Step 7: Generate Analysis Report

After experiment completes (or immediately in post-hoc mode), generate the report:

TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S)
# Save the report in the experiment directory (EXPERIMENT_DIR)
REPORT_FILE="${EXPERIMENT_DIR}/${TIMESTAMP}-app-log-analysis.md"

Report structure:

# Application Log Analysis Report

**Experiment ID:** {EXPERIMENT_ID}
**Analysis Time:** {TIMESTAMP}
**Time Range:** {START_TIME} - {END_TIME}
**Duration:** {DURATION}

## Summary

| Service | Application | Total Errors | Peak Error Rate | Recovery Time |
|---------|-------------|--------------|-----------------|---------------|
| {service} | {app} | {count} | {rate}/min | {time} |

## Per-Service Application Analysis

### {Service Name} ({resource_id})

#### {Application Name} ({namespace}/{deployment})

**Error Timeline:**

| Time (UTC) | Level | Message |
|------------|-------|---------|
| {HH:MM:SS} | ERROR | {truncated message} |
| ... | ... | ... |

**Key Error Patterns:**

| Pattern | Count | First Occurrence | Last Occurrence |
|---------|-------|------------------|-----------------|
| Connection refused | {n} | {time} | {time} |
| Timeout | {n} | {time} | {time} |

**Log Sample (Critical Errors):**

{5-10 lines of actual error logs}

**Insights:**
- {insight_1}: Error spike at {time}, correlates with {service} failover
- {insight_2}: Recovery detected at {time}, {duration} after fault injection ended
- {insight_3}: Application retry mechanism worked/failed because...

(Repeat for each application)

## Cross-Service Correlation

| Time | Event | RDS Impact | ElastiCache Impact | Application Response |
|------|-------|------------|--------------------|--------------------|
| {time} | Fault injection start | - | - | First errors appear |
| {time} | {service} failover | Connection errors | - | Retrying... |
| {time} | Recovery | Connections restored | - | Normal operation |

## Recommendations

1. **{Issue}:** {description}
   - **Impact:** {what happened}
   - **Recommendation:** {what to improve}

## Appendix: Log File Locations

**Raw log directory:** `{LOG_DIR}`

To view raw logs after the analysis, navigate to the temp directory shown above.
These files will persist until the system clears `/tmp`.

| Application | Log File |
|-------------|----------|
| {app} | `{LOG_DIR}/{service}/{app}.log` |

Step 8: Cleanup (Real-time Mode)

Kill all background kubectl logs processes recorded in {LOG_DIR}/.pids. Remove the PID file after cleanup.

Error Handling

Output Files

{EXPERIMENT_DIR}/                                 # Experiment directory
└── {timestamp}-app-log-analysis.md               # Analysis report

/tmp/{timestamp}-fis-app-logs/                    # Temp directory for raw logs
├── rds-cluster-xxx/
│   ├── app-backend.log
│   └── api-server.log
├── elasticache-redis-xxx/
│   └── cache-layer.log
└── .pids (temporary, cleaned up)

Usage Examples

# Real-time monitoring (during experiment)
"Analyze app logs for ./2026-03-31-14-30-22-az-power-interruption-my-cluster/"
"Monitor application behavior in the experiment directory"
"实时监控应用日志"

# Post-hoc analysis (after experiment)
"Analyze app logs using ./2026-03-31-14-35-00-az-power-interruption-my-cluster-experiment-results.md"
"分析实验报告中的应用表现"
"Check what happened to applications during the experiment"

Integration with Other Skills

• aws-fis-experiment-prepare — Reads README.md and expected-behavior.md for context
• aws-fis-experiment-execute — Reads *-experiment-results.md for time range and service list
• Does NOT modify any files from other skills