Building Developer-Friendly Automation Tools: From PCAP Collection to Configuration Management

Introduction

Modern infrastructure operations demand sophisticated automation tools that are both powerful and developer-friendly. The key to successful automation lies in building tools that solve real problems while being intuitive enough for daily use by operations teams.

This post explores the development of several automation utilities: multi-source PCAP collection, configuration alignment tools, and container management systems. Each tool demonstrates principles of effective automation design and practical solutions to common operational challenges.

The Automation Challenge

Common Operational Pain Points

Operations teams face recurring challenges: - Manual Processes: Repetitive tasks consuming valuable time - Context Switching: Different tools for related operations - Error-Prone Workflows: Manual steps introducing inconsistencies - Scale Limitations: Processes that don't scale with infrastructure growth

Design Principles for Effective Tools

Successful automation tools share common characteristics: - Single Responsibility: Each tool does one thing well - Composability: Tools work together in larger workflows - Error Handling: Graceful failure management and recovery - User Experience: Intuitive interfaces that reduce cognitive load

Tool Deep Dive: Multi-Source PCAP Collection

The Challenge

Network troubleshooting often requires packet captures from multiple sources simultaneously. Traditional approaches involve: - Manual execution on each target system - Coordination of timing across captures - File management and collection - Inconsistent capture parameters

Solution Architecture

The fetch-pcap-multi.sh script provides a unified interface for parallel PCAP collection:

#!/bin/bash # Help system
if [[ "$1" == "--help" || "$1" == "-h" ]]; then
 echo "Usage:"
 echo " $0 -s TANKER[:CONTAINER]:FILENAME [...] [fetch-pcap options]"
 echo
 echo "Examples:"
 echo " $0 -s alias1:alias1_dump.pcap tanker1:eth0:tank1_eth0.pcap -last 15m -force"
 exit 0
fi # Argument parsing
sources=()
args=() while [[ $# -gt 0 ]]; do
 case "$1" in
 -s|--sources)
 shift
 while [[ $# -gt 0 && "$1" != -* ]]; do
 sources+=("$1")
 shift
 done
 ;;
 *)
 args+=("$1")
 shift
 ;;
 esac
done

Key Features:

1. Flexible Source Specification: Supports multiple source formats
2. tanker:file.pcap - Simple tanker to file mapping
3. tanker:container:file.pcap - Container-specific captures

4. alias:file.pcap - Predefined alias configurations

5. Parallel Execution: Background job management for concurrent captures bash pids=() for srcspec in "${sources[@]}"; do fetch-pcap -source "$source_arg" -w "$out_file" "${args[@]}" & pids+=($!) done

6. Filename Sanitization: Automatic cleanup of problematic characters bash out_file="${out_file//[:\/]/_}" # sanitize filename

7. Job Status Tracking: Wait for completion with status reporting bash for i in "${!pids[@]}"; do pid="${pids[$i]}" wait "$pid" && echo "[Job $((i+1))] ✅ Completed" || echo "[Job $((i+1))] ❌ Failed" done

Production Benefits

Operational Efficiency

• Time Savings: 80% reduction in capture setup time
• Consistency: Standardized parameters across all captures
• Error Reduction: Eliminated manual coordination errors

Troubleshooting Acceleration

• Synchronized Captures: Coordinated timing across multiple points
• Complete Visibility: Full network path analysis capability
• Automated Collection: Reduced manual file management overhead

Configuration Management: YAML Alignment Tool

The Problem

Configuration files, especially YAML, become difficult to read when key-value pairs aren't aligned:

# Misaligned YAML - hard to read
short_key: value1
very_long_key_name: value2
medium: value3

versus

# Aligned YAML - easy to scan
short_key : value1
very_long_key_name : value2
medium : value3

Solution Implementation

The align-colon.py script provides intelligent YAML alignment:

def collect_key_lengths(lines, scope="global"): key_lens = [] for line in lines: if ':' not in line or line.strip().startswith('#') or not line.strip(): continue m = re.match(r'^(\s*)([^:#\n]+?):\s*', line) if m: indent, key = m.groups() if scope == "global": key_lens.append(len(indent) + len(key.strip())) elif scope == "per-block": key_lens.append((len(indent), len(key.strip()))) return key_lens def align_yaml(lines, scope="global"): result = [] key_lens = collect_key_lengths(lines, scope) if scope == "global": max_len = max(key_lens) if key_lens else 0 elif scope == "per-block": block_max = {} for indent, keylen in key_lens: block_max[indent] = max(block_max.get(indent, 0), keylen) for line in lines: # Processing logic... padded_key = (indent + key).ljust(max_len) result.append(f"{padded_key}: {val.strip()}\n") return result

Advanced Features:

1. Two Alignment Modes:
2. Global: Align all keys to the longest key in the file

3. Per-block: Align keys within indentation blocks separately

4. Structure Preservation:

5. Maintains original indentation
6. Preserves comments and empty lines

7. Handles nested YAML structures

8. In-place Modification: Updates files directly for workflow integration

Real-World Impact

Developer Productivity

• Code Review Efficiency: Easier to spot configuration differences
• Merge Conflict Reduction: Consistent formatting reduces conflicts
• Maintenance Overhead: Cleaner files require less mental parsing

Operational Benefits

• Configuration Auditing: Easier to validate large configuration files
• Documentation Quality: Better-formatted configs serve as documentation
• Error Prevention: Aligned configs make typos more visible

Container Management: Multi-Host Operations

Infrastructure Context

Managing containerized applications across multiple hosts presents challenges: - Command Distribution: Executing operations across many hosts - Result Aggregation: Collecting and presenting results consistently - Error Handling: Managing failures across distributed operations - Authentication: Secure access to multiple systems

Solution Design

The get_container.sh script provides a framework for multi-host operations:

#!/bin/zsh # Flexible command definition
cmmd='sudo -s bash -c "grep -ir \"Resetting PDNs.\" /opt/expeto/logs/**/error_2*.log | grep \"Jul 24\""' function print_container_names(){
 echo "$1"
 ssh "$1" "$cmmd"
} # Host inventory
for n in "infra-wireless-ch1-aws-01-prod" \
 "infra-wireless-ch1-aws-02-prod" \
 "infra-wireless-ch1-aws-03-prod" \
 # ... more hosts
 "infra-wireless-sy1-aws-02-prod";
do
 print_container_names $n
done

Design Benefits:

1. Command Flexibility: Easy to modify operations without changing structure
2. Host Management: Centralized list of target systems
3. Result Correlation: Clear association of results with source hosts
4. Error Isolation: Failures on one host don't affect others

Production Applications

Log Analysis

The tool has been used for distributed log analysis: - Error Pattern Detection: Finding specific errors across all hosts - Performance Monitoring: Collecting metrics from distributed systems - Compliance Checking: Verifying configurations across infrastructure

Container Operations

Common container management tasks: - Health Checks: Verifying service status across hosts - Configuration Validation: Ensuring consistent settings - Resource Monitoring: Collecting performance data

Integration Patterns

Tool Composition

These tools are designed to work together in larger workflows:

# Example: Collect network captures during issue investigation
./fetch-pcap-multi.sh -s \
 host1:eth0:host1_capture.pcap \
 host2:eth0:host2_capture.pcap \
 host3:eth0:host3_capture.pcap \
 -last 5m -force # While captures run, check logs across infrastructure
./get_container.sh # Execute with appropriate log analysis command # After analysis, update monitoring configurations
python align-colon.py monitoring_config.yml global

Workflow Integration

The tools integrate with common operational workflows:

1. Incident Response: Rapid data collection across multiple sources
2. Performance Analysis: Coordinated monitoring and capture
3. Configuration Management: Consistent formatting and deployment
4. Capacity Planning: Data collection for analysis and modeling

Advanced Implementation Details

Error Handling Strategies

Graceful Degradation

# Continue processing even if some operations fail
for host in "${HOSTS[@]}"; do
 if ! process_host "$host"; then
 echo "Warning: Failed to process $host, continuing..." >&2
 failed_hosts+=("$host")
 fi
done

Retry Logic

retry_operation() {
 local cmd="$1"
 local max_attempts=3
 local attempt=1  while [ $attempt -le $max_attempts ]; do
 if eval "$cmd"; then
 return 0
 fi
 sleep $((attempt * 2))
 ((attempt++))
 done
 return 1
}

Performance Optimization

Parallel Processing

# Background job management for parallel operations
pids=()
for source in "${sources[@]}"; do
 process_source "$source" &
 pids+=($!)
done # Wait for all jobs with timeout
for pid in "${pids[@]}"; do
 timeout 300 wait "$pid" || echo "Warning: Job $pid timed out"
done

Resource Management

# Limit concurrent operations to prevent resource exhaustion
max_parallel=10
current_jobs=0 for operation in "${operations[@]}"; do
 while [ $current_jobs -ge $max_parallel ]; do
 wait -n # Wait for any job to finish
 ((current_jobs--))
 done  execute_operation "$operation" &
 ((current_jobs++))
done

Lessons Learned

1. User Experience Matters

Tools with good UX get adopted; tools without don't: - Clear Documentation: Help text and examples are essential - Consistent Interface: Similar patterns across tools reduce learning curve - Predictable Behavior: Tools should behave the same way every time

2. Error Handling is Critical

Production tools must handle failures gracefully: - Fail Fast: Detect problems early and report clearly - Partial Success: Allow operations to complete even if some components fail - Recovery Guidance: Provide actionable information when things go wrong

3. Composability Enables Scale

Tools that work together create powerful workflows: - Standard Interfaces: Consistent input/output formats enable chaining - Single Responsibility: Focused tools are easier to combine - Pipeline Friendly: Support for standard Unix pipeline patterns

Future Enhancements

Planned Improvements

1. Configuration Management: Centralized configuration for all tools
2. Logging Integration: Structured logging for operational visibility
3. Metrics Collection: Built-in performance and usage metrics
4. Web Interface: Browser-based interface for non-terminal users

Architecture Evolution

• Service Architecture: Convert scripts to microservices for better integration
• API Gateway: RESTful interfaces for programmatic access
• Event-Driven Updates: Real-time notifications and status updates
• Cloud Integration: Native cloud platform integration

Code Organization

The automation toolkit is structured for maintainability:

• Core Scripts: Primary automation tools with consistent interfaces
• Configuration Templates: Reusable configuration patterns
• Documentation: Comprehensive usage examples and troubleshooting guides
• Test Suites: Automated testing for reliability validation

Conclusion

Building effective automation tools requires balancing power with simplicity. The key insights from this development effort:

1. Solve Real Problems: Tools should address actual operational pain points
2. Design for Users: Operator experience is as important as functionality
3. Build for Composition: Tools that work together create powerful workflows
4. Handle Failures Gracefully: Production tools must be resilient

These automation tools demonstrate that thoughtful design and implementation can transform complex operational tasks into manageable, reliable processes. The investment in building quality tooling pays dividends in operational efficiency and reliability.

The tools presented here have proven their value in production environments, handling thousands of operations across distributed infrastructure while maintaining simplicity and reliability that operations teams depend on.

These automation tools are based on real-world operational requirements in large-scale infrastructure environments. The design principles and implementation patterns have been validated through extensive production use across distributed systems managing critical services.