contains_line

Status:

● Tested

Category:

CSV Data

Function Name:

contains_line

Tests if row in a CSV file exists that matches the given entry layout.

This test function verifies that a CSV file contains a specific row matching the provided entry pattern. It supports exact value matching, regex patterns, and timestamp tolerance for flexible CSV data validation.

Parameters

Parameter	Type	Description
dst	string	Required. The CSV file path to check. Supports glob patterns for dynamic path resolution.
entry	list	Required. Array of values representing the expected CSV row. Supports exact values, regex patterns, and placeholder matching.

Usage Example

Basic Exact Matching

tests:
  - name: test_csv_contains_line_exact_match
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/users.csv'
      entry: ['1', 'John Doe', 'john@example.com', 'Admin']
    description: "Test csv_contains_line with exact match"

Mixed Data Types

tests:
  - name: test_csv_contains_line_mixed_types
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/access_logs.csv'
      entry: ['2024-01-15', '10:30:45', '192.168.1.100', 'GET', '/api/users', '200']
    description: "Test csv_contains_line with mixed data types"

Regex Pattern Matching

tests:
  - name: test_csv_contains_line_email_regex
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/users.csv'
      entry: ['1', 'John Doe', '{{email_regex}}', 'Admin']
    description: "Test csv_contains_line with email regex pattern"

  - name: test_csv_contains_line_ip_regex
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/access_logs.csv'
      entry: ['2024-01-15', '10:30:45', '{{ip_regex}}', 'GET', '/api/users', '200']
    description: "Test csv_contains_line with IP address regex pattern"

Direct Regex Patterns

tests:
  - name: test_csv_contains_line_decimal_regex
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/transactions.csv'
      entry: ['TXN003', '3.0.1', 'completed', !re '\d+\.\d{2}']
    description: "Test csv_contains_line with decimal number regex"

Timestamp with Tolerance

tests:
  - name: test_csv_contains_line_current_timestamp
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/events.csv'
      entry: ['EVENT001', 'user_login', '{{ now | tolerance(30) | format("%Y-%m-%dT%H:%M:%S%z") }}', 'success']
    description: "Test csv_contains_line with current timestamp and tolerance"

Complex Pattern Combinations

tests:
  - name: test_csv_contains_line_mixed_regex_timestamp
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/events.csv'
      entry: ['{{uuid_regex}}', 'system_update', '{{ now | tolerance(30) | format("%Y-%m-%dT%H:%M:%S%z") }}', 'info']
    description: "Test csv_contains_line with both regex and timestamp"

  - name: test_csv_contains_line_all_regex_patterns
    function: csv.contains_line
    parameter:
      dst: '/home/adare/test_csv/transactions.csv'
      entry: ['{{uuid_regex}}', '{{version_regex}}', 'failed', !re '\d+\.\d{2}']
    description: "Test csv_contains_line with multiple regex patterns"

Expected Failure Cases

tests:
  - name: test_csv_contains_line_not_found
    function: csv.contains_line
    expect_to_fail: true
    parameter:
      dst: '/home/adare/test_csv/users.csv'
      entry: ['999', 'Nonexistent User', 'none@example.com', 'Guest']
    description: "Test csv_contains_line with non-existent entry"

  - name: test_csv_contains_line_wrong_column_count
    function: csv.contains_line
    expect_to_fail: true
    parameter:
      dst: '/home/adare/test_csv/users.csv'
      entry: ['1', 'John Doe']  # Missing columns
    description: "Test csv_contains_line with wrong column count"

Common Use Cases

Log File Analysis

Validate that CSV log files contain specific entries with exact or pattern-based matching

Data Integrity Verification

Ensure CSV data files contain expected records with proper formatting

API Response Logging

Verify that API access logs contain specific request patterns and response codes

User Data Validation

Check that user data exports contain expected user records with proper email formats

Transaction Monitoring

Validate financial or system transaction logs contain expected entries with amounts and IDs

Pattern Matching Features

Exact Value Matching

• Direct string comparison for precise matches

• Supports all data types (strings, numbers, dates)

Regex Pattern Matching

• Use variable placeholders like {{email_regex}} for reusable patterns

• Direct regex with !re syntax for inline patterns

• Common patterns: email validation, IP addresses, UUIDs, version numbers

Timestamp Tolerance

• Use {{ now | tolerance(seconds) }} for time-based matching

• Flexible formatting with format() filter

• Useful for matching recently created records

Column Count Validation

• Automatically validates that the entry has the correct number of columns

• Fails if the expected entry doesn’t match the CSV structure

Return Values

Success

Returns success when a matching row is found in the CSV file

Failure

Returns failure when:

• No matching row is found for the given pattern

• Column count mismatch between entry and CSV rows

• Regex patterns don’t match actual values

• Timestamp values fall outside tolerance range

• CSV parsing errors occur

Execution Error

Returns execution error when:

• Permission denied accessing the file

• Invalid regex patterns provided

• System I/O errors occur

Example Results

# Success case
result: success

# Failure case - no match found
result: failed
details:
  - "no matching row found for pattern: ['999', 'Nonexistent User', 'none@example.com', 'Guest']"
  - "analyzed 5 rows in CSV file"
  - "closest matches:"
  - "  [1] row 0: ['1', 'John Doe', 'john@example.com', 'Admin']"
  - "      failures: col0('1' != '999'), col1('John Doe' != 'Nonexistent User'), col2('john@example.com' != 'none@example.com'), col3('Admin' != 'Guest')"

# Failure case - column count mismatch
result: failed
details:
  - "no matching row found for pattern: ['1', 'John Doe']"
  - "analyzed 5 rows in CSV file"
  - "closest matches:"
  - "  [1] row 0: ['1', 'John Doe', 'john@example.com', 'Admin']"
  - "      failures: column_count(4 != 2)"

# Execution error case
result: execution_error
error: "PermissionError: [Errno 13] Permission denied"
context: "Cannot read CSV file /root/protected.csv"