Step 2: Parse Data Like props.conf + transforms.conf

In Splunk, props.conf and transforms.conf handle parsing and field extraction. This example uses the Bloblang mapping language to perform those operations at collection time.

Splunk Parsing Configuration Example

props.conf + transforms.conf Setup

The Splunk example uses these props.conf and transforms.conf entries:

# props.conf
[json_logs]
KV_MODE = json
TIMESTAMP_FIELDS = timestamp
TIME_FORMAT = %Y-%m-%dT%H:%M:%S.%3NZ
EXTRACT-user_info = (?<user>\w+\.\w+)
EXTRACT-response_time = response_time_ms=(?<response_time>\d+)
EVAL-log_severity = case(level=="ERROR", 3, level=="WARN", 2, level=="INFO", 1, 1=1, 0)

[cef]
EXTRACT-cef_fields = CEF:\d+\|(?<vendor>[^|]*)\|(?<product>[^|]*)\|(?<version>[^|]*)\|(?<signature_id>[^|]*)\|(?<name>[^|]*)\|(?<severity>[^|]*)\|(?<extensions>.*)
EVAL-risk_score = case(severity=="Critical", 10, severity=="High", 8, severity=="Medium", 5, severity=="Low", 2, 1=1, 1)

[syslog]
EXTRACT-syslog_fields = ^(?<month>\w+)\s+(?<day>\d+)\s+(?<time>\d+:\d+:\d+)\s+(?<host>\w+)\s+(?<process>[^[]*)\[?(?<pid>\d+)?\]?:\s*(?<message>.*)

SPL Search-Time Field Extraction

# Extract fields during search
index=main sourcetype=json_logs
| rex field=message "Database connection timeout.*database=\"(?<db_name>[^\"]+)\""
| eval response_category = case(response_time_ms < 100, "fast", response_time_ms < 500, "normal", 1=1, "slow")
| eval user_domain = split(user, "@")[1]
| eval hour_of_day = strftime(_time, "%H")

Expanso Bloblang Mapping

In this example, parsing happens at collection time with the following mapping:

Update Pipeline with Parsing

cat > ~/splunk-edge-pipeline.yaml << 'EOF'
apiVersion: v1
kind: Pipeline
metadata:
  name: "splunk-edge-collection"
  description: "Parse data like props.conf during collection"

input:
  file_watcher:
    paths:
      - "/var/log/expanso-demo/app.log"
      - "/var/log/expanso-demo/security.log"
      - "/var/log/expanso-demo/system.log"
    poll_interval: "1s"
    include_file_name: true
    multiline:
      pattern: '^(\d{4}-\d{2}-\d{2}T|\w{3}\s+\d{1,2}\s|\{"timestamp")'
      negate: false
      match: after

processors:
  # 1. Basic metadata (same as Step 1)
  - mapping: |
      root.source_file = file.name
      root.collection_timestamp = timestamp()
      root.host = hostname()

      root.sourcetype = match file.name {
        this.contains("app.log") => "json_logs"
        this.contains("security.log") => "cef"
        this.contains("system.log") => "syslog"
        _ => "unknown"
      }

  # 2. Parse JSON logs (equivalent to KV_MODE=json)
  - conditional:
      condition: 'this.sourcetype == "json_logs"'
    mapping: |
      # Parse JSON if not already parsed
      root = if this.type() == "object" {
        this
      } else {
        this.parse_json().catch({"raw_message": this, "parse_error": true})
      }

      # Add computed fields (like Splunk EVAL)
      root.log_severity = match this.level {
        "ERROR" => 3
        "WARN" => 2
        "INFO" => 1
        _ => 0
      }

      # Response time categorization
      root.response_category = if this.response_time_ms.type() == "number" {
        match {
          this.response_time_ms < 100 => "fast"
          this.response_time_ms < 500 => "normal"
          _ => "slow"
        }
      }

      # Extract user domain (equivalent to SPL split)
      root.user_domain = if this.user.contains("@") {
        this.user.split("@")[1]
      } else if this.user.contains(".") {
        "internal"
      } else {
        "unknown"
      }

      # Time-based fields
      root.hour_of_day = timestamp().format("15")
      root.day_of_week = timestamp().format("Monday")

  # 3. Parse CEF logs (Security events)
  - conditional:
      condition: 'this.sourcetype == "cef"'
    mapping: |
      # Parse CEF format: CEF:Version|Device Vendor|Device Product|Device Version|Signature ID|Name|Severity|Extension
      root.cef = this.string().parse_regex("CEF:(?P<version>\\d+)\\|(?P<vendor>[^|]*)\\|(?P<product>[^|]*)\\|(?P<device_version>[^|]*)\\|(?P<signature_id>[^|]*)\\|(?P<name>[^|]*)\\|(?P<severity>[^|]*)\\|(?P<extensions>.*)")

      # Flatten main CEF fields to root level
      root.vendor = this.cef.vendor
      root.product = this.cef.product
      root.signature_id = this.cef.signature_id
      root.event_name = this.cef.name
      root.severity = this.cef.severity

      # Parse extensions (key=value pairs)
      root.extensions = {}
      for_each this.cef.extensions.split(" ") -> value {
        let kv = value.split("=")
        if kv.length() == 2 {
          root.extensions = root.extensions.merge({kv[0]: kv[1]})
        }
      }

      # Add risk scoring (like Splunk EVAL)
      root.risk_score = match this.severity {
        "Critical" => 10
        "High" => 8
        "Medium" => 5
        "Low" => 2
        _ => 1
      }

      # Extract IP addresses for geo enrichment
      root.source_ip = this.extensions.src.string().catch("")
      root.dest_ip = this.extensions.dst.string().catch("")

  # 4. Parse Syslog (traditional format)
  - conditional:
      condition: 'this.sourcetype == "syslog"'
    mapping: |
      # Parse syslog format
      root.syslog = this.string().parse_regex("(?P<month>\\w+)\\s+(?P<day>\\d+)\\s+(?P<time>\\d+:\\d+:\\d+)\\s+(?P<hostname>\\w+)\\s+(?P<process>[^\\[:]*)\\[?(?P<pid>\\d+)?\\]?:\\s*(?P<message>.*)")

      # Flatten and enhance
      root.log_month = this.syslog.month
      root.log_day = this.syslog.day.number()
      root.log_time = this.syslog.time
      root.log_hostname = this.syslog.hostname
      root.process_name = this.syslog.process
      root.process_id = this.syslog.pid.number().catch(0)
      root.message = this.syslog.message

      # Create structured timestamp
      root.timestamp = timestamp().format("2006") + "-01-" + this.log_day.string().format_int(2, "0") + "T" + this.log_time + ".000Z"

      # Process classification
      root.process_category = match this.process_name {
        "sshd" => "authentication"
        "nginx" => "web_server"
        "systemd" => "system"
        "kernel" => "kernel"
        _ => "other"
      }

  # 5. Final enrichment and routing
  - mapping: |
      # Determine target index (like outputs.conf)
      root.target_index = match this.sourcetype {
        "cef" => "security"
        "syslog" => if this.process_category == "authentication" { "security" } else { "main" }
        _ => "main"
      }

      # Add data volume tracking
      root.raw_size_bytes = this.string().length()

      # Classification for filtering (used in Step 3)
      root.event_class = match {
        this.sourcetype == "cef" => "security"
        this.level == "ERROR" => "error"
        this.level == "DEBUG" => "debug"
        this.process_name == "nginx" && this.message.contains("GET /api/health") => "health_check"
        _ => "normal"
      }

output:
  stdout:
    format: "json"
EOF

Side-by-Side: SPL vs Bloblang Translations

Here are common Splunk field extraction patterns and their Bloblang equivalents:

1. Regular Expression Extraction

SPL:

| rex field=message "response_time=(?<response_time>\d+)"

Bloblang:

root.response_time = this.message.parse_regex("response_time=(?P<response_time>\\d+)").response_time.number()

2. Conditional Field Creation

SPL:

| eval priority = case(level="ERROR", "high", level="WARN", "medium", 1=1, "low")

Bloblang:

root.priority = match this.level {
"ERROR" => "high"
"WARN" => "medium"
_ => "low"
}

3. String Manipulation

SPL:

| eval user_domain = split(user, "@")[1]
| eval upper_user = upper(user)

Bloblang:

root.user_domain = this.user.split("@")[1]
root.upper_user = this.user.uppercase()

4. Time-Based Fields

SPL:

| eval hour = strftime(_time, "%H")
| eval is_business_hours = if(hour>=9 AND hour<=17, "yes", "no")

Bloblang:

root.hour = timestamp().format("15")
root.is_business_hours = if this.hour.number() >= 9 && this.hour.number() <= 17 { "yes" } else { "no" }

5. JSON Path Extraction

SPL:

| spath input=json_field path=user.preferences.theme output=theme

Bloblang:

root.theme = this.json_field.user.preferences.theme

Test Your Parsing

1. Deploy Updated Pipeline

# Deploy the enhanced pipeline
expanso pipeline deploy ~/splunk-edge-pipeline.yaml

# Monitor parsed output
expanso pipeline logs splunk-edge-collection -f

2. Generate Test Events

Add some complex events to test parsing:

# JSON with nested data
echo '{"timestamp":"'$(date -u +"%Y-%m-%dT%H:%M:%S.%3NZ")'","level":"ERROR","message":"Database connection timeout","user":"[email protected]","database":"users_db","response_time_ms":2500,"retry_count":3}' >> $TEST_DATA_DIR/app.log

# CEF security event
echo 'CEF:0|Company|WebApp|1.0|700|Suspicious Login|High|src=203.0.113.25 dst=192.168.1.5 suser=admin act=login outcome=failure reason=multiple_attempts dpt=443' >> $TEST_DATA_DIR/security.log

# Syslog event
echo "$(date '+%b %d %H:%M:%S') web01 sshd[54321]: Failed password for invalid user hacker from 203.0.113.50 port 12345 ssh2" >> $TEST_DATA_DIR/system.log

3. Verify Parsed Output

You should see enriched output like:

{
  "timestamp": "2024-02-10T10:30:15.123Z",
  "level": "ERROR",
  "message": "Database connection timeout",
  "user": "[email protected]",
  "database": "users_db",
  "response_time_ms": 2500,
  "retry_count": 3,
  "log_severity": 3,
  "response_category": "slow",
  "user_domain": "company.com",
  "hour_of_day": "10",
  "sourcetype": "json_logs",
  "target_index": "main",
  "event_class": "error",
  "raw_size_bytes": 187
}

Parsing Locations Represented in This Tutorial

Operation	Splunk snippet	Pipeline snippet
Field extraction	SPL command during search	Bloblang mapping before HEC
Parsing point	During search	During pipeline processing
Output shape	Search fields	Structured JSON

Advanced Bloblang Techniques

1. Error Handling

root.parsed_data = this.raw_json.parse_json().catch({
  "parse_error": true,
  "raw_data": this.raw_json
})

2. Conditional Processing

if this.level == "DEBUG" {
  root = deleted()  # Drop debug logs entirely
}

3. Data Enrichment

# GeoIP lookup (if service available)
root.geo = this.source_ip.http_request("GET", "http://geoip-service/lookup/" + this.source_ip).parse_json().catch({})

4. Array Processing

# Process array fields
root.user_tags = this.tags.map_each(tag -> tag.uppercase())

What's Next?

The pipeline now parses data at collection time. Next, implement filtering before data reaches the indexers.

→ Next Step: Step 3: Filter Before Indexing

Key takeaway: Bloblang performs the example's parsing before HEC ingestion, while SPL is used here to illustrate analogous field extraction. Compare correctness, maintainability, and performance with your actual workload.

Splunk Parsing Configuration Example​

props.conf + transforms.conf Setup​

SPL Search-Time Field Extraction​

Expanso Bloblang Mapping​

Update Pipeline with Parsing​

Side-by-Side: SPL vs Bloblang Translations​

1. Regular Expression Extraction​

2. Conditional Field Creation​

3. String Manipulation​

4. Time-Based Fields​

5. JSON Path Extraction​

Test Your Parsing​

1. Deploy Updated Pipeline​

2. Generate Test Events​

3. Verify Parsed Output​

Parsing Locations Represented in This Tutorial​

Advanced Bloblang Techniques​

1. Error Handling​

2. Conditional Processing​

3. Data Enrichment​

4. Array Processing​

What's Next?​

Splunk Parsing Configuration Example

props.conf + transforms.conf Setup

SPL Search-Time Field Extraction

Expanso Bloblang Mapping

Update Pipeline with Parsing

Side-by-Side: SPL vs Bloblang Translations

1. Regular Expression Extraction

2. Conditional Field Creation

3. String Manipulation

4. Time-Based Fields

5. JSON Path Extraction

Test Your Parsing

1. Deploy Updated Pipeline

2. Generate Test Events

3. Verify Parsed Output

Parsing Locations Represented in This Tutorial

Advanced Bloblang Techniques

1. Error Handling

2. Conditional Processing

3. Data Enrichment

4. Array Processing

What's Next?