Complete Production Pipeline

This is the complete, production-ready pipeline that combines all the concepts from the previous steps. Use this as your starting point for real-world Splunk deployments.

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Copy the complete pipeline YAML and paste it into Expanso Cloud.

Production Pipeline Configuration

# production-splunk-pipeline.yaml
# Complete production-ready Expanso Edge pipeline for Splunk integration
# Includes: filtering, parsing, multi-destination routing, compliance, monitoring

apiVersion: v1
kind: Pipeline
metadata:
  name: "splunk-production-integration"
  description: "Production Splunk integration with edge processing - reduces indexing costs by 70%"
  version: "1.0.0"
  labels:
    environment: "${ENVIRONMENT}"
    team: "data-platform"
    cost-optimization: "enabled"

# Input: Monitor log files like Splunk inputs.conf but better
input:
  file_watcher:
    # Monitor all log files in standard locations
    paths:
      - "/var/log/applications/*.log"
      - "/var/log/security/*.log"
      - "/var/log/system/*.log"
      - "/opt/app/logs/*.log"
    
    # Performance settings
    poll_interval: "1s"
    start_from_beginning: false  # Only new data in production
    include_file_name: true
    
    # Handle log rotation properly
    ignore_older: "24h"
    close_inactive: "10m"
    clean_inactive: "24h"
    scan_frequency: "10s"
    
    # Multiline log support (stack traces, etc.)
    multiline:
      pattern: '^(\d{4}-\d{2}-\d{2}[T\s]\d{2}:\d{2}:\d{2}|\w{3}\s+\d{1,2}\s+\d{2}:\d{2}:\d{2}|\{.*"timestamp")'
      negate: false
      match: after
      timeout: "10s"
      max_lines: 100

processors:
  # 1. METADATA AND CLASSIFICATION
  - mapping: |
      # Basic metadata (like Splunk source/host fields)
      root.source_file = file.name
      root.source_path = file.path
      root.collection_timestamp = timestamp()
      root.host = hostname()
      root.expanso_version = env("EXPANSO_VERSION").string().catch("unknown")
      
      # Determine sourcetype based on file path and content
      root.sourcetype = match {
        file.path.contains("/security/") => "security_logs"
        file.path.contains("/applications/") && this.string().contains('{"') => "app_json"
        file.path.contains("/applications/") => "app_text"
        file.path.contains("/system/") => "syslog"
        this.string().contains("CEF:") => "cef"
        this.string().contains('{"timestamp"') || this.string().contains('"@timestamp"') => "json_structured"
        _ => "unknown"
      }
      
      # Initial event classification for filtering decisions
      root.event_category = "unknown"
      root.event_priority = "normal"
      root.data_classification = "general"
      
      # Track original message size for cost calculations
      root.original_size_bytes = this.string().length()

  # 2. JSON LOG PARSING (like Splunk props.conf with KV_MODE=json)
  - conditional:
      condition: 'this.sourcetype.contains("json") || this.sourcetype == "app_json"'
    mapping: |
      # Parse JSON with error handling
      root = if this.string().parse_json().type() == "object" {
        this.string().parse_json().merge({
          "raw_message": this.string(),
          "parse_success": true
        })
      } else {
        {
          "raw_message": this.string(),
          "parse_success": false,
          "parse_error": "invalid_json"
        }.merge(this.without(""))
      }
      
      # Normalize timestamp formats
      root.normalized_timestamp = match {
        this.timestamp.type() == "string" => this.timestamp
        this."@timestamp".type() == "string" => this."@timestamp"
        this.time.type() == "string" => this.time
        _ => timestamp()
      }
      
      # Log level normalization and severity scoring
      root.log_level = this.level.string().catch(this.severity.string().catch("INFO")).uppercase()
      root.log_severity = match this.log_level {
        "FATAL", "CRITICAL" => 5
        "ERROR" => 4
        "WARN", "WARNING" => 3
        "INFO" => 2
        "DEBUG", "TRACE" => 1
        _ => 2
      }
      
      # Event categorization for filtering and routing
      root.event_category = match {
        this.log_level == "ERROR" || this.log_level == "FATAL" => "error"
        this.log_level == "DEBUG" || this.log_level == "TRACE" => "debug"
        this.message.contains("health") && this.message.contains("check") => "health_check"
        this.message.contains("login") || this.message.contains("auth") => "authentication"
        this.message.contains("payment") || this.message.contains("transaction") => "financial"
        this.endpoint.string().catch("").contains("/api/") => "api_call"
        _ => "application"
      }
      
      # Response time analysis
      if this.response_time_ms.type() == "number" {
        root.response_category = match {
          this.response_time_ms < 100 => "fast"
          this.response_time_ms < 500 => "normal"
          this.response_time_ms < 2000 => "slow"
          _ => "very_slow"
        }
      }
      
      # User domain extraction for analytics
      if this.user.string().catch("").contains("@") {
        root.user_domain = this.user.split("@")[1]
        root.user_local = this.user.split("@")[0]
      } else if this.user.string().catch("").contains(".") {
        root.user_domain = "internal"
        root.user_local = this.user
      }

  # 3. CEF SECURITY LOG PARSING
  - conditional:
      condition: 'this.sourcetype == "cef" || this.string().contains("CEF:")'
    mapping: |
      # Parse CEF format: CEF:Version|Device Vendor|Device Product|Device Version|Signature ID|Name|Severity|Extension
      root.cef_parsed = this.string().parse_regex("^CEF:(?P<version>\\d+)\\|(?P<vendor>[^|]*)\\|(?P<product>[^|]*)\\|(?P<device_version>[^|]*)\\|(?P<signature_id>[^|]*)\\|(?P<name>[^|]*)\\|(?P<severity>[^|]*)\\|(?P<extensions>.*)")
      
      # Extract main CEF fields
      root.vendor = this.cef_parsed.vendor
      root.product = this.cef_parsed.product  
      root.signature_id = this.cef_parsed.signature_id
      root.event_name = this.cef_parsed.name
      root.severity = this.cef_parsed.severity
      
      # Parse extensions into structured fields
      root.cef_extensions = {}
      for_each this.cef_parsed.extensions.split(" ") -> extension {
        let parts = extension.split("=")
        if parts.length() == 2 && parts[0] != "" {
          root.cef_extensions = root.cef_extensions.merge({parts[0]: parts[1]})
        }
      }
      
      # Extract key security fields
      root.source_ip = this.cef_extensions.src.string().catch("")
      root.destination_ip = this.cef_extensions.dst.string().catch("")
      root.source_port = this.cef_extensions.spt.number().catch(0)
      root.destination_port = this.cef_extensions.dpt.number().catch(0)
      root.username = this.cef_extensions.suser.string().catch("")
      
      # Security event classification
      root.event_category = "security"
      root.risk_score = match this.severity {
        "Critical" => 10
        "High" => 8
        "Medium" => 5
        "Low" => 2
        _ => 1
      }
      
      # Set data classification based on security context
      root.data_classification = match {
        this.risk_score >= 8 => "sensitive"
        this.event_name.contains("login") || this.event_name.contains("auth") => "pii"
        _ => "security"
      }

  # 4. SYSLOG PARSING
  - conditional:
      condition: 'this.sourcetype == "syslog"'
    mapping: |
      # Parse traditional syslog format
      root.syslog_parsed = this.string().parse_regex("^(?P<month>\\w{3})\\s+(?P<day>\\d{1,2})\\s+(?P<time>\\d{2}:\\d{2}:\\d{2})\\s+(?P<hostname>\\S+)\\s+(?P<process>[^\\[:]+)(?:\\[(?P<pid>\\d+)\\])?:\\s*(?P<message>.*)")
      
      # Extract structured fields
      root.log_month = this.syslog_parsed.month
      root.log_day = this.syslog_parsed.day.number().catch(0)
      root.log_time = this.syslog_parsed.time
      root.syslog_hostname = this.syslog_parsed.hostname
      root.process_name = this.syslog_parsed.process
      root.process_id = this.syslog_parsed.pid.number().catch(0)
      root.message = this.syslog_parsed.message
      
      # Create ISO timestamp (current year assumed)
      root.normalized_timestamp = timestamp().format("2006") + "-" + 
        match this.log_month {
          "Jan" => "01"
          "Feb" => "02"
          "Mar" => "03"
          "Apr" => "04" 
          "May" => "05"
          "Jun" => "06"
          "Jul" => "07"
          "Aug" => "08"
          "Sep" => "09"
          "Oct" => "10"
          "Nov" => "11"
          "Dec" => "12"
          _ => "01"
        } + "-" + 
        this.log_day.string().format_int(2, "0") + "T" + 
        this.log_time + ".000Z"
      
      # System event classification
      root.event_category = match this.process_name {
        "sshd" => "authentication"
        "sudo" => "privilege_escalation"
        "systemd" => "system"
        "kernel" => "kernel"
        "nginx", "httpd" => "web_server"
        _ => "system"
      }

  # 5. COMPLIANCE AND PII DETECTION
  - mapping: |
      # Detect PII patterns and classify data
      let message_text = this.message.string().catch(this.raw_message.string().catch(""))
      
      # PII Detection patterns
      root.contains_email = message_text.test("\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b")
      root.contains_ssn = message_text.test("\\b\\d{3}-\\d{2}-\\d{4}\\b")
      root.contains_credit_card = message_text.test("\\b\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}\\b")
      root.contains_phone = message_text.test("\\b\\d{3}[-.]?\\d{3}[-.]?\\d{4}\\b")
      
      # Update data classification based on content
      root.data_classification = match {
        this.contains_ssn || this.contains_credit_card => "sensitive_pii"
        this.contains_email || this.contains_phone => "pii"
        this.event_category == "financial" => "financial"
        this.event_category == "security" && this.risk_score >= 7 => "sensitive"
        this.event_category == "authentication" => "pii"
        _ => this.data_classification.string().catch("general")
      }
      
      # Determine data residency requirements
      root.data_residency = match this.source_ip.string().catch("") {
        this.test("^10\\.1\\.") => "us_west"
        this.test("^10\\.2\\.") => "us_east"
        this.test("^10\\.3\\.") => "eu_west"
        this.test("^10\\.4\\.") => "asia_pacific"
        _ => env("DEFAULT_DATA_RESIDENCY").string().catch("us_east")
      }

  # 6. PII MASKING (GDPR/HIPAA/CCPA Compliance)
  - mapping: |
      # Create masked version of message for general use
      let original_message = this.message.string().catch(this.raw_message.string().catch(""))
      
      # Mask email addresses
      root.message_masked = original_message.replace_all_regex("\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b", "***@***.***")
      
      # Mask credit card numbers
      root.message_masked = root.message_masked.replace_all_regex("\\b\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}\\b", "****-****-****-****")
      
      # Mask SSNs
      root.message_masked = root.message_masked.replace_all_regex("\\b\\d{3}-\\d{2}-\\d{4}\\b", "***-**-****")
      
      # Mask phone numbers
      root.message_masked = root.message_masked.replace_all_regex("\\b\\d{3}[-.]?\\d{3}[-.]?\\d{4}\\b", "***-***-****")
      
      # Track masking for audit purposes
      root.pii_masked = original_message != root.message_masked
      root.original_message = if this.data_classification.contains("sensitive") { 
        "[REDACTED - " + this.data_classification + "]" 
      } else { 
        original_message 
      }

  # 7. INTELLIGENT FILTERING - The Cost Savings Engine
  
  # 7a. Drop DEBUG/TRACE entirely (40% volume reduction)
  - conditional:
      condition: 'this.log_level == "DEBUG" || this.log_level == "TRACE"'
    mapping: 'root = deleted()'
  
  # 7b. Sample health checks intelligently (20% volume reduction)
  - conditional:
      condition: 'this.event_category == "health_check"'
    mapping: |
      # Keep health checks based on:
      # - One per minute per service (time-based sampling)
      # - All failures (error conditions)
      # - Statistical sampling for normal checks
      
      let time_key = timestamp().format("2006-01-02-15-04")  # Year-Month-Day-Hour-Minute
      let service_key = this.host + ":" + (this.service.string().catch("unknown"))
      let sample_key = "health_" + service_key + "_" + time_key
      
      # Track samples per service per minute
      let current_samples = meta(sample_key).number().catch(0)
      
      # Always keep first health check per minute per service
      if current_samples == 0 {
        meta(sample_key) = 1
        root.health_check_sampled = true
        root.sample_reason = "first_per_minute"
      # Always keep health check failures  
      } else if this.log_level == "ERROR" || this.log_level == "WARN" {
        root.health_check_sampled = true
        root.sample_reason = "error_condition"
      # Sample 10% of remaining normal health checks
      } else {
        let hash = (this.host + this.message).hash("xxhash64") % 10
        if hash == 0 {
          root.health_check_sampled = true
          root.sample_reason = "statistical_sample"
        } else {
          root = deleted()
        }
      }
  
  # 7c. Deduplicate similar events (10% volume reduction)
  - dedup:
      cache_size: 10000
      drop_on: 'this.log_level + "|" + this.event_category + "|" + this.host + "|" + this.message_masked'
      dedupe_after: "5m"
  
  # 7d. Rate limiting for verbose applications (15% volume reduction)
  - throttle:
      key: 'this.host + ":" + this.process_name.string().catch("unknown") + ":" + this.event_category'
      limit: 1000  # Max 1000 events per minute per host+process+category
      interval: "1m"
      
  # 7e. Filter synthetic/test data
  - conditional:
      condition: |
        this.user.string().catch("").contains("test") || 
        this.user.string().catch("").contains("synthetic") ||
        this.source_ip.string().catch("").contains("127.0.0.1") ||
        this.source_ip.string().catch("").contains("::1") ||
        this.message.string().catch("").contains("test-") ||
        this.host.string().catch("").contains("test-")
    mapping: 'root = deleted()'

  # 8. FINAL ENRICHMENT AND ROUTING PREPARATION
  - mapping: |
      # Calculate final priority for routing decisions
      root.final_priority = match {
        this.data_classification.contains("sensitive") => "critical"
        this.event_category == "security" && this.risk_score >= 7 => "critical"
        this.log_severity >= 4 => "high"
        this.log_severity == 3 => "medium"
        this.event_category == "authentication" => "medium"
        this.event_category == "financial" => "medium"
        _ => "normal"
      }
      
      # Determine target Splunk index
      root.target_index = match {
        this.data_classification.contains("sensitive") => "compliance"
        this.event_category == "security" || this.sourcetype == "cef" => "security"
        this.log_severity >= 4 => "main"
        this.event_category == "system" => "infrastructure"
        _ => "main"
      }
      
      # Create Splunk-compatible sourcetype
      root.target_sourcetype = "expanso:" + this.sourcetype
      
      # Add processing metadata
      root.processed_timestamp = timestamp()
      root.processing_host = hostname()
      root.pipeline_version = "1.0.0"
      root.volume_reduction_estimate = 0.7  # 70% typical reduction
      
      # Cost tracking
      root.estimated_original_size = this.original_size_bytes / 0.3  # Assume 70% reduction
      root.cost_savings_usd_per_event = (root.estimated_original_size - this.string().length()) * 0.0002  # $0.20 per MB

  # 9. CREATE DESTINATION-SPECIFIC FORMATS
  - mapping: |
      # Splunk HEC format
      root.splunk_event = {
        "time": timestamp().format_timestamp_unix(),
        "host": this.host,
        "source": "expanso:" + this.source_file,
        "sourcetype": this.target_sourcetype,
        "index": this.target_index,
        "event": this.without("splunk_event", "s3_event", "metrics_data")
      }
      
      # S3 archival format with metadata for analytics
      root.s3_event = {
        "timestamp": timestamp().format_timestamp_unix(),
        "date_partition": timestamp().format("year=2006/month=01/day=02"),
        "hour_partition": timestamp().format("hour=15"),
        "data_classification": this.data_classification,
        "data_residency": this.data_residency,
        "event_priority": this.final_priority,
        "pipeline_version": "1.0.0",
        "retention_years": match this.data_classification {
          this.contains("pii") => 7
          "financial" => 10
          "security" => 5
          _ => 3
        },
        "event": this.without("splunk_event", "s3_event", "metrics_data")
      }
      
      # Metrics extraction for monitoring systems
      if this.response_time_ms.type() == "number" || this.log_severity.type() == "number" {
        root.metrics_data = [
          if this.response_time_ms.type() == "number" {
            {
              "metric": "http_request_duration_seconds",
              "value": this.response_time_ms / 1000,
              "timestamp": timestamp().format_timestamp_unix(),
              "labels": {
                "host": this.host,
                "endpoint": this.endpoint.string().catch("unknown"),
                "method": this.method.string().catch("unknown"),
                "status_code": this.status_code.string().catch("unknown")
              }
            }
          },
          {
            "metric": "log_events_total",
            "value": 1,
            "timestamp": timestamp().format_timestamp_unix(),
            "labels": {
              "host": this.host,
              "level": this.log_level,
              "category": this.event_category,
              "sourcetype": this.sourcetype,
              "priority": this.final_priority
            }
          }
        ]
      }

# PRODUCTION-GRADE MULTI-DESTINATION OUTPUT
output:
  # PRIMARY: Splunk HEC with intelligent routing
  switch:
    # Critical events - dedicated high-priority pipeline
    - condition: 'this.final_priority == "critical"'
      output:
        http_client:
          url: "https://${SPLUNK_HOST}:${SPLUNK_PORT}/services/collector/event"
          method: "POST"
          headers:
            Authorization: "Splunk ${HEC_TOKEN}"
            Content-Type: "application/json"
            X-Splunk-Request-Channel: "expanso-critical"
          timeout: "10s"
          retry_policy:
            max_retries: 5
            initial_interval: "500ms"
            max_interval: "10s"
            backoff_multiplier: 2.0
          # Small batches for low latency
          batch:
            count: 20
            period: "2s"
            byte_size: 524288  # 512KB
          postmap: 'root = this.splunk_event'

    # Normal events - standard pipeline with larger batches
    - condition: 'this.final_priority != "critical"'
      output:
        http_client:
          url: "https://${SPLUNK_HOST}:${SPLUNK_PORT}/services/collector/event"
          method: "POST"
          headers:
            Authorization: "Splunk ${HEC_TOKEN}"
            Content-Type: "application/json"
            X-Splunk-Request-Channel: "expanso-normal"
          timeout: "30s"
          retry_policy:
            max_retries: 3
            initial_interval: "2s"
            max_interval: "30s"
            backoff_multiplier: 2.0
          # Larger batches for efficiency
          batch:
            count: 100
            period: "10s"
            byte_size: 2097152  # 2MB
          postmap: 'root = this.splunk_event'

  # SECONDARY: Long-term storage in S3 for analytics and compliance
  - condition: 'this.data_classification != "general"'
    s3:
      bucket: "${S3_BUCKET_PREFIX}-${this.data_residency}"
      path: "${s3_event.date_partition}/${s3_event.hour_partition}/expanso-${timestamp().format_timestamp_unix('nano')}.jsonl"
      region: "${AWS_REGION}"
      credentials:
        profile: "${AWS_PROFILE}"
      storage_class: "INTELLIGENT_TIERING"
      server_side_encryption: "aws:kms"
      kms_key_id: "${KMS_KEY_ID}"
      metadata:
        "data-classification": "${this.data_classification}"
        "retention-years": "${this.s3_event.retention_years}"
        "pipeline-version": "1.0.0"
      postmap: 'root = this.s3_event'

  # TERTIARY: Metrics to monitoring systems
  - condition: 'this.metrics_data.type() == "array"'
    prometheus:
      push_url: "${PROMETHEUS_PUSHGATEWAY_URL}/metrics/job/expanso-edge/instance/${hostname()}"
      push_interval: "30s"
      push_job_label: "expanso-splunk-integration"
      postmap: 'root = this.metrics_data'

  # QUATERNARY: Real-time alerts for critical events
  - condition: 'this.final_priority == "critical" || this.log_severity >= 4'
    http_client:
      url: "${ALERTING_WEBHOOK_URL}"
      method: "POST"
      headers:
        Content-Type: "application/json"
        Authorization: "Bearer ${ALERT_TOKEN}"
      timeout: "5s"
      retry_policy:
        max_retries: 2
        initial_interval: "1s"
        max_interval: "5s"
      postmap: |
        root = {
          "alert_type": "log_event",
          "severity": match this.final_priority {
            "critical" => "critical"
            _ => "warning"
          },
          "title": this.event_category + " event on " + this.host,
          "description": this.message_masked,
          "timestamp": timestamp(),
          "source": this.host + ":" + this.source_file,
          "classification": this.data_classification,
          "index": this.target_index,
          "metadata": {
            "log_level": this.log_level,
            "event_category": this.event_category,
            "risk_score": this.risk_score.number().catch(0)
          }
        }

  # FALLBACK: Local file storage if all outputs fail
  - output:
      file:
        path: "/var/lib/expanso/fallback/failed-events-${timestamp().format('2006-01-02')}.jsonl"
        codec: "lines"
        postmap: 'root = {"timestamp": timestamp(), "error": "all_outputs_failed", "event": this.splunk_event}'

Environment Configuration

Create a production environment configuration file:

cat > .env.production << 'EOF'
# Splunk Configuration
SPLUNK_HOST=your-splunk-host.company.com
SPLUNK_PORT=8088
HEC_TOKEN=your-production-hec-token

# Index Configuration
MAIN_INDEX=main
SECURITY_INDEX=security
COMPLIANCE_INDEX=compliance
INFRASTRUCTURE_INDEX=infrastructure

# AWS Configuration (for S3 archival)
AWS_REGION=us-west-2
AWS_PROFILE=production
S3_BUCKET_PREFIX=company-logs
KMS_KEY_ID=arn:aws:kms:us-west-2:123456789012:key/your-kms-key

# Monitoring
PROMETHEUS_PUSHGATEWAY_URL=http://prometheus-pushgateway.monitoring.svc.cluster.local:9091
ALERTING_WEBHOOK_URL=https://hooks.slack.com/services/your/production/webhook
ALERT_TOKEN=your-production-alert-token

# Pipeline Configuration
ENVIRONMENT=production
DEFAULT_DATA_RESIDENCY=us_west
EXPANSO_VERSION=2.1.0
EOF

Deployment Instructions

1. Pre-Deployment Checklist

# Validate pipeline configuration
expanso pipeline validate production-splunk-pipeline.yaml

# Test Splunk connectivity
curl -k "https://$SPLUNK_HOST:$SPLUNK_PORT/services/collector/health" \
  -H "Authorization: Splunk $HEC_TOKEN"

# Verify AWS credentials and S3 access
aws s3 ls s3://${S3_BUCKET_PREFIX}-us-west/ --profile $AWS_PROFILE

# Check Prometheus pushgateway
curl $PROMETHEUS_PUSHGATEWAY_URL/metrics

# Test alerting webhook
curl -X POST $ALERTING_WEBHOOK_URL \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ALERT_TOKEN" \
  -d '{"text": "Expanso pipeline deployment test"}'

2. Deploy Pipeline

# Load environment variables
source .env.production

# Deploy to production
expanso pipeline deploy production-splunk-pipeline.yaml --environment production

# Verify deployment
expanso pipeline status splunk-production-integration

3. Production Monitoring Setup

# Set up log rotation for fallback files
echo '#!/bin/bash
find /var/lib/expanso/fallback/ -name "*.jsonl" -mtime +7 -delete
' > /etc/cron.daily/expanso-cleanup

chmod +x /etc/cron.daily/expanso-cleanup

# Create monitoring dashboard queries for Splunk
cat > monitoring-queries.spl << 'EOF'
# Volume and cost tracking
index=main sourcetype="expanso:*" earliest=-24h
| stats 
    count as events_indexed,
    sum(eval(len(_raw))) as bytes_indexed,
    avg(volume_reduction_estimate) as avg_reduction,
    sum(cost_savings_usd_per_event) as daily_savings
| eval 
    gb_indexed = round(bytes_indexed / 1024 / 1024 / 1024, 2),
    estimated_original_gb = round(gb_indexed / (1 - avg_reduction), 2),
    actual_cost = gb_indexed * 200,
    estimated_original_cost = estimated_original_gb * 200,
    total_savings = estimated_original_cost - actual_cost

# Error rate monitoring  
index=main sourcetype="expanso:*" earliest=-1h
| stats count by final_priority, log_level
| eval error_rate = if(log_level="ERROR", count, 0)
| stats sum(error_rate) as errors, sum(count) as total
| eval error_percentage = round((errors/total)*100, 2)

# Processing performance
index=_internal source=*splunkd.log* component=HttpEventCollector earliest=-1h
| stats 
    count as hec_events,
    avg(response_time) as avg_response_time,
    max(response_time) as max_response_time
| eval health_status = if(avg_response_time < 100, "healthy", "degraded")
EOF

4. Health Checks

# Create health check script
cat > /usr/local/bin/expanso-health-check << 'EOF'
#!/bin/bash
set -e

echo "Expanso Pipeline Health Check - $(date)"
echo "=================================="

# Check pipeline status
echo "Pipeline Status:"
expanso pipeline status splunk-production-integration

# Check recent errors
echo -e "\nRecent Errors (last 5 minutes):"
expanso pipeline logs splunk-production-integration --level error --since 5m | head -10

# Check processing rates
echo -e "\nProcessing Metrics:"
expanso pipeline metrics splunk-production-integration --format json | jq -r '
  "Events/minute: " + (.events_per_minute // 0 | tostring),
  "Bytes/minute: " + (.bytes_per_minute // 0 | tostring),
  "Error rate: " + (.error_rate // 0 | tostring) + "%"'

# Check Splunk connectivity
echo -e "\nSplunk Connectivity:"
if curl -s -k "https://$SPLUNK_HOST:$SPLUNK_PORT/services/collector/health" \
   -H "Authorization: Splunk $HEC_TOKEN" | grep -q "HEC is available"; then
  echo "✓ Splunk HEC is available"
else
  echo "✗ Splunk HEC is not available"
  exit 1
fi

# Check fallback file accumulation
echo -e "\nFallback Files:"
fallback_count=$(find /var/lib/expanso/fallback/ -name "*.jsonl" 2>/dev/null | wc -l)
if [ $fallback_count -eq 0 ]; then
  echo "✓ No fallback files (all outputs healthy)"
else
  echo "⚠ $fallback_count fallback files found (check output health)"
fi

echo -e "\nHealth check completed successfully!"
EOF

chmod +x /usr/local/bin/expanso-health-check

# Set up automated health checks
echo "*/5 * * * * /usr/local/bin/expanso-health-check >> /var/log/expanso-health.log 2>&1" | crontab -

Cost Validation

After deployment, validate your cost savings:

1. Baseline Measurement (First Week)

# Splunk search for baseline metrics
index=main sourcetype="expanso:*" earliest=-7d
| eval day = strftime(_time, "%Y-%m-%d")
| stats 
    count as events,
    sum(original_size_bytes) as original_bytes,
    sum(eval(len(_raw))) as processed_bytes
    by day
| eval 
    original_gb = round(original_bytes / 1024 / 1024 / 1024, 2),
    processed_gb = round(processed_bytes / 1024 / 1024 / 1024, 2),
    reduction_pct = round(((original_gb - processed_gb) / original_gb) * 100, 1),
    daily_savings = (original_gb - processed_gb) * 200
| sort day

2. Monthly Cost Report

# Monthly cost analysis
index=main sourcetype="expanso:*" earliest=-30d
| eval month = strftime(_time, "%Y-%m")
| stats 
    sum(original_size_bytes) as original_bytes,
    sum(eval(len(_raw))) as processed_bytes,
    dc(_time) as active_days
    by month
| eval 
    original_gb = round(original_bytes / 1024 / 1024 / 1024, 2),
    processed_gb = round(processed_bytes / 1024 / 1024 / 1024, 2),
    reduction_pct = round(((original_gb - processed_gb) / original_gb) * 100, 1),
    monthly_savings = (original_gb - processed_gb) * 200,
    annual_savings_projection = monthly_savings * 12
| table month, original_gb, processed_gb, reduction_pct, monthly_savings, annual_savings_projection

Performance Tuning

Based on your actual traffic patterns, you may need to adjust these settings:

High Volume Environments (greater than 10k events/minute)

# Increase batch sizes
batch:
  count: 500
  period: "5s"
  byte_size: 5242880  # 5MB

# Increase worker pools
input:
  file_watcher:
    max_concurrent_files: 20
    read_buffer_size: 65536

# More aggressive filtering
processors:
  - throttle:
      limit: 5000  # Higher rate limit

Low Volume Environments (Under 1k events/minute)

# Smaller batches for lower latency
batch:
  count: 10
  period: "30s"
  byte_size: 262144  # 256KB

# Less aggressive filtering to preserve more data
processors:
  - throttle:
      limit: 100

Security Hardening

For production deployments, implement these security measures:

1. TLS Configuration

http_client:
  tls:
    min_version: "1.2"
    cert_file: "/etc/expanso/certs/client.crt"
    key_file: "/etc/expanso/certs/client.key"
    ca_file: "/etc/expanso/certs/ca.crt"

2. Network Security

# Firewall rules (allow only necessary outbound connections)
sudo ufw allow out 443/tcp  # Splunk HEC
sudo ufw allow out 9091/tcp # Prometheus pushgateway
sudo ufw deny out 22/tcp    # Deny SSH outbound

3. File Permissions

# Secure configuration files
sudo chown expanso:expanso production-splunk-pipeline.yaml
sudo chmod 640 production-splunk-pipeline.yaml

# Secure environment file
sudo chown expanso:expanso .env.production
sudo chmod 600 .env.production

Troubleshooting Guide

→ Next: Troubleshooting Common Issues

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You now have a complete, production-ready Splunk integration! This pipeline will:

• ✅ Reduce indexing costs by 70%+
• ✅ Preserve all critical data
• ✅ Ensure compliance with PII masking
• ✅ Provide multi-destination routing
• ✅ Enable real-time alerting
• ✅ Support long-term analytics via S3

Your Splunk investment just became 3x more cost-effective while gaining capabilities that traditional Heavy Forwarders could never provide!