Rip - Convert Streams to JSON with Regex

Convert stream data into JSON format using regular expressions with the Rip tool. Easily parse and structure command-line output.

Rip

Convert Stream Data to JSON

The rip command-line tool is designed to transform stream data into structured JSON format. It leverages regular expressions to parse input and define the output structure, making it an invaluable utility for data processing and manipulation directly from your terminal.

How Rip Works

rip takes a regular expression pattern and a format string as arguments. The pattern is used to capture groups from each line of the input stream. These captured groups can then be referenced within the format string to construct the desired JSON output. This allows for flexible and powerful data transformation.

Example Usage

Below is a common use case for rip: converting file listings with extensions into JSON objects.

# regex into pattern

# convert stream in JSON
ls | rip '(.*)\.(.*)' '{"name": "$1", "ext": "$2"}'

Explanation of the Example

  • ls: This command generates a list of files and directories, piping its output to rip.
  • rip '(.*)\.(.*)': This is the core of the command.
    • '(.*)\.(.*)' is the regular expression pattern.
    • (.*) captures any character (.) zero or more times (*). This creates two capturing groups.
    • \. matches the literal dot separating the filename from its extension.
  • '{"name": "$1", "ext": "$2"}': This is the JSON format string.
    • $1 refers to the content captured by the first group in the regex (the filename without the extension).
    • $2 refers to the content captured by the second group (the file extension).

The output will be a series of JSON objects, each representing a file with its name and extension.

Benefits of Using Rip

  • Stream Processing: Efficiently handles data piped from other command-line tools.
  • Flexible Parsing: Powerful regex engine allows for complex pattern matching.
  • Structured Output: Generates clean JSON, ideal for further processing by scripts or applications.
  • Command-Line Efficiency: Automate data transformations without writing complex scripts.

Further Resources