Manipulate strings

String manipulation is essential for cleaning, formatting, and transforming text data. This guide covers TQL’s comprehensive string functions, from simple case changes to complex pattern matching and encoding operations.

Change text case

Transform strings to different cases for consistency and formatting:

from {name: "john smith", title: "data ENGINEER", code: "xyz-123"}
lower_name = name.to_lower()
upper_code = code.to_upper()
title_case = title.to_title()
cap_name = name.capitalize()

{
  name: "john smith",
  title: "data ENGINEER",
  code: "xyz-123",
  lower_name: "john smith",
  upper_code: "XYZ-123",
  title_case: "Data Engineer",
  cap_name: "John smith",
}

Functions explained:

• to_lower() - Converts all characters to lowercase
• to_upper() - Converts all characters to uppercase
• to_title() - Capitalizes first letter of each word
• capitalize() - Capitalizes only the first letter

Trim whitespace

Clean up strings by removing unwanted whitespace:

from {
  raw: "  hello world  ",
  prefix: "\t\tdata",
  suffix: "value   \n"
}
trimmed = raw.trim()
no_prefix = prefix.trim_start()
no_suffix = suffix.trim_end()

{
  raw: "  hello world  ",
  prefix: "\t\tdata",
  suffix: "value   \n",
  trimmed: "hello world",
  no_prefix: "data",
  no_suffix: "value"
}

Functions:

• trim() - Removes whitespace from both ends
• trim_start() - Removes whitespace from beginning
• trim_end() - Removes whitespace from end

Split and join strings

Break strings apart and combine them back together:

from {
  path: "/home/user/documents/report.pdf",
  tags: "security,network,alert"
}
parts = path.split("/")
tag_list = tags.split(",")
rejoined = parts.join("-")

{
  path: "/home/user/documents/report.pdf",
  tags: "security,network,alert",
  parts: [
    "",
    "home",
    "user",
    "documents",
    "report.pdf",
  ],
  tag_list: [
    "security",
    "network",
    "alert",
  ],
  rejoined: "-home-user-documents-report.pdf",
}

Split with regular expressions

Use split_regex() for complex splitting:

from {text: "error:42|warning:7|info:125"}
entries = text.split_regex("[:|]")

{
  text: "error:42|warning:7|info:125",
  entries: [
    "error",
    "42",
    "warning",
    "7",
    "info",
    "125",
  ],
}

Find and replace text

Replace specific text or patterns within strings:

Simple replacement

from {
  log: "User 192.168.1.1 accessed /admin",
  template: "Hello {name}, welcome to {place}"
}
masked = log.replace("192.168.1.1", "xxx.xxx.xxx.xxx")
filled = template.replace("{name}", "Alice").replace("{place}", "Tenzir")

{
  log: "User 192.168.1.1 accessed /admin",
  template: "Hello {name}, welcome to {place}",
  masked: "User xxx.xxx.xxx.xxx accessed /admin",
  filled: "Hello Alice, welcome to Tenzir",
}

Pattern-based replacement

Use replace_regex() for complex replacements:

from {
  text: "Contact us at 555-1234 or 555-5678",
  log: "Error at 2024-01-15 10:30:45: Connection failed"
}
redacted = text.replace_regex("\\d{3}-\\d{4}", "XXX-XXXX")
simple_log = log.replace_regex(
  "\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}",
  "TIMESTAMP"
)

{
  text: "Contact us at 555-1234 or 555-5678",
  log: "Error at 2024-01-15 10:30:45: Connection failed",
  redacted: "Contact us at XXX-XXXX or XXX-XXXX",
  simple_log: "Error at TIMESTAMP: Connection failed",
}

Match patterns

Check if strings match specific patterns:

from {
  email: "alice@example.com",
  url: "https://tenzir.com",
  file: "report_2024.pdf"
}
is_email = email.match_regex("^[^@]+@[^@]+\\.[^@]+$")
is_https = url.starts_with("https://")
is_pdf = file.ends_with(".pdf")

{
  email: "alice@example.com",
  url: "https://tenzir.com",
  file: "report_2024.pdf",
  is_email: true,
  is_https: true,
  is_pdf: true,
}

Pattern matching functions:

• match_regex() - Test against regular expression
• starts_with() - Check string prefix
• ends_with() - Check string suffix

Validate string content

Check what type of characters a string contains:

from {
  id: "12345",
  name: "Alice",
  code: "abc123",
  mixed: "Hello World!",
  spaces: "hello world"
}
id_numeric = id.is_numeric()
name_alpha = name.is_alpha()
code_alnum = code.is_alnum()
mixed_alpha = mixed.is_alpha()
has_lower = spaces.is_lower()
has_upper = name.is_title()

{
  id: "12345",
  name: "Alice",
  code: "abc123",
  mixed: "Hello World!",
  spaces: "hello world",
  id_numeric: true,
  name_alpha: true,
  code_alnum: true,
  mixed_alpha: false,
  has_lower: true,
  has_upper: true,
}

Validation functions:

• is_numeric() - Contains only digits
• is_alpha() - Contains only letters
• is_alnum() - Contains only letters and digits
• is_lower() - All cased characters are lowercase
• is_upper() - All cased characters are uppercase
• is_title() - String is in title case
• is_printable() - Contains only printable characters

Measure string properties

Get information about string characteristics:

from {
  text: "Hello 世界",
  emoji: "👋 Hello!",
  path: "/var/log/system.log"
}
char_count = text.length_chars()
byte_count = text.length_bytes()
reversed = emoji.reverse()
filename = path.file_name()
directory = path.parent_dir()

{
  text: "Hello 世界",
  emoji: "👋 Hello!",
  path: "/var/log/system.log",
  char_count: 8,
  byte_count: 12,
  reversed: "!olleH 👋",
  filename: "system.log",
  directory: "/var/log",
}

String property functions:

• length_chars() - Count Unicode characters
• length_bytes() - Count bytes
• reverse() - Reverse character order
• file_name() - Extract filename from path
• parent_dir() - Extract directory from path

Extract substrings

Use slice() to extract portions of strings:

from {
  text: "Hello, World!",
  id: "USER-12345-ACTIVE",
  timestamp: "2024-01-15T10:30:45"
}
greeting = text.slice(begin=0, end=5)
user_num = id.slice(begin=5, end=10)
date_part = timestamp.slice(begin=0, end=10)
status = id.slice(begin=11)

{
  text: "Hello, World!",
  id: "USER-12345-ACTIVE",
  timestamp: "2024-01-15T10:30:45",
  greeting: "Hello",
  user_num: "12345",
  date_part: "2024-01-15",
  status: "ACTIVE",
}

The slice() function parameters:

• begin - Starting position (0-based, negative counts from end)
• end - Ending position (exclusive, optional)
• stride - Step between characters (optional, can be negative)

Encode and decode strings

Transform strings between different encodings:

Base64 encoding

from {secret: "my-api-key-12345"}
encoded = secret.encode_base64()
decoded = encoded.decode_base64()

{
  secret: "my-api-key-12345",
  encoded: "bXktYXBpLWtleS0xMjM0NQ==",
  decoded: b"my-api-key-12345",
}

Hex encoding

Use encode_hex() and decode_hex():

from {data: "Hello", hex_string: "48656c6c6f"}
hex = data.encode_hex()
decoded = hex.decode_hex()
decoded_blob = hex_string.decode_hex()

{
  data: "Hello",
  hex_string: "48656c6c6f",
  hex: "48656c6c6f",
  decoded: b"Hello",
  decoded_blob: b"Hello",
}

URL encoding

from {query: "search term with spaces & special=characters"}
encoded = query.encode_url()
decoded = encoded.decode_url()

{
  query: "search term with spaces & special=characters",
  encoded: "search%20term%20with%20spaces%20%26%20special%3Dcharacters",
  decoded: b"search term with spaces & special=characters",
}

Encoding functions:

• encode_base64() / decode_base64()
• encode_hex() / decode_hex()
• encode_url() / decode_url()

Pad strings

Add characters to reach a specific length:

from {
  id: "42",
  code: "ABC"
}
padded_id = id.pad_start(5, "0")
padded_code = code.pad_end(10, "-")

{
  id: "42",
  code: "ABC",
  padded_id: "00042",
  padded_code: "ABC-------"
}

Padding functions:

• pad_start() - Add characters to the beginning
• pad_end() - Add characters to the end

Read file contents

Access text from files during processing:

from {}
hostname = file_contents("/etc/hostname")

{
  hostname: "my-server\n",
}

The file_contents() function reads the entire file as a string. The file path must be a constant expression. Use with caution on large files.

Practical examples

Clean and normalize user input

from {
  user_input: "  JOHN.SMITH@EXAMPLE.COM  ",
  phone: "(555) 123-4567"
}
email = user_input.trim().to_lower()
clean_phone = phone.replace_regex("[^0-9]", "")

{
  user_input: "  JOHN.SMITH@EXAMPLE.COM  ",
  phone: "(555) 123-4567",
  email: "john.smith@example.com",
  clean_phone: "5551234567"
}

Extract and validate identifiers

from {
  log: "User ID: ABC-123-XYZ performed action",
  url: "https://api.example.com/v2/users/42"
}
user_id = log.split("User ID: ")[1].split(" ")[0]
valid_id = user_id.match_regex("^[A-Z]{3}-\\d{3}-[A-Z]{3}$")
api_version = url.split("/")[4]
user_num = url.split("/").last()

{
  log: "User ID: ABC-123-XYZ performed action",
  url: "https://api.example.com/v2/users/42",
  user_id: "ABC-123-XYZ",
  valid_id: true,
  api_version: "v2",
  user_num: "42"
}

Build formatted output

from {
  first: "alice",
  last: "smith",
  dept: "engineering",
  id: 42
}
full_name = first.capitalize() + " " + last.to_upper()
email = first + "." + last + "@company.com"
badge = dept.to_upper().slice(begin=0, end=3) + "-" + id.string()

{
  first: "alice",
  last: "smith",
  dept: "engineering",
  id: 42,
  full_name: "Alice SMITH",
  email: "alice.smith@company.com",
  badge: "ENG-42",
}

Generate hash values

Create checksums and identifiers using hash functions:

Common hash algorithms

from {
  data: "Hello, World!",
  secret: "my-api-key-123"
}
md5 = data.hash_md5()
sha1 = data.hash_sha1()
sha224 = data.hash_sha224()
sha256 = data.hash_sha256()
sha384 = data.hash_sha384()
sha512 = data.hash_sha512()
xxh3 = data.hash_xxh3()

{
  data: "Hello, World!",
  secret: "my-api-key-123",
  md5: "65a8e27d8879283831b664bd8b7f0ad4",
  sha1: "0a0a9f2a6772942557ab5355d76af442f8f65e01",
  sha224: "72a23dfa411ba6fde01dbfabf3b00a709c93ebf273dc29e2d8b261ff",
  sha256: "dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f",
  sha384: "5485cc9b3365b4305dfb4e8337e0a598a574f8242bf17289e0dd6c20a3cd44a089de16ab4ab308f63e44b1170eb5f515",
  sha512: "374d794a95cdcfd8b35993185fef9ba368f160d8daf432d08ba9f1ed1e5abe6cc69291e0fa2fe0006a52570ef18c19def4e617c33ce52ef0a6e5fbe318cb0387",
  xxh3: "c7269dc5f8602ca5",
}

Create unique identifiers

Use hashes to generate identifiers from multiple fields:

from {
  user_id: "alice123",
  timestamp: "2024-01-15T10:30:00",
  action: "login"
}
event_id = f"{user_id}-{timestamp}-{action}".hash_sha256().slice(begin=0, end=16)
short_hash = f"{user_id}{action}".hash_md5().slice(begin=0, end=8)
numeric_id = user_id.hash_xxh3()

{
  user_id: "alice123",
  timestamp: "2024-01-15T10:30:00",
  action: "login",
  event_id: "d5f456083b8fee43",
  short_hash: "1616f7f2",
  numeric_id: "ac6dfe13bd512d81",
}

Verify data integrity

from {
  file_content: "Important document content here...",
  expected_checksum: "dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f"
}
actual_checksum = file_content.hash_sha256()
valid = actual_checksum == expected_checksum

{
  file_content: "Important document content here...",
  expected_checksum: "dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f",
  actual_checksum: "25f898ef7be64ead26e775e41778c6b5b5e5fe135d1b6658b6a27f9334c4f085",
  valid: false,
}

Network security functions

Process network data with specialized security functions:

Generate Community IDs

Use community_id() to create standardized flow hashes:

from {
  src_ip: 192.168.1.100,
  dst_ip: 10.0.0.1,
  src_port: 54321,
  dst_port: 443,
  proto: "tcp"
}
flow_id = community_id(
  src_ip=src_ip,
  dst_ip=dst_ip,
  src_port=src_port,
  dst_port=dst_port,
  proto=proto
)

{
  src_ip: 192.168.1.100,
  dst_ip: 10.0.0.1,
  src_port: 54321,
  dst_port: 443,
  proto: "tcp",
  flow_id: "1:ZSU9hCO1tdr7pj3SCLkQ0XS3uvI=",
}

Anonymize IP addresses

Use encrypt_cryptopan() for consistent IP anonymization:

from {
  client_ip: 192.168.1.100,
  server_ip: 8.8.8.8,
  internal_ip: 10.0.0.5
}
anon_client = client_ip.encrypt_cryptopan(seed="mysecretkey12345")
anon_server = server_ip.encrypt_cryptopan(seed="mysecretkey12345")
anon_internal = internal_ip.encrypt_cryptopan(seed="mysecretkey12345")

{
  client_ip: 192.168.1.100,
  server_ip: 8.8.8.8,
  internal_ip: 10.0.0.5,
  anon_client: 206.216.1.132,
  anon_server: 110.0.51.203,
  anon_internal: 109.255.195.194,
}

Best practices

1. Chain operations efficiently: Combine multiple string operations in one expression
2. Validate before transforming: Check string content before applying operations
3. Handle edge cases: Empty strings, null values, and special characters
4. Use appropriate functions: Choose length_chars() vs length_bytes() based on needs
5. Be mindful of encoding: Ensure correct encoding when dealing with international text

Related guides

• Extract structured data from text - Parse complex text formats
• Transform basic values - Convert between data types
• Filter and select data - Use string functions in filters