Is CsvPath an easy or hard language?

Source: Dev.to

CsvPath and the CsvPath Validation Language (CVL)

CsvPath Framework includes the CsvPath Validation Language, abbreviated CVL.
CVL is a document‑oriented, tabular‑data validation language similar to XSD, Schematron, DDL, and JSONQuery. It can validate CSV, Excel, JSONL files, and in‑memory data frames (e.g., Pandas, Polars). Within the CsvPath Framework, CVL provides validation and upgrading capabilities for a larger data‑pre‑boarding architecture.

Line‑by‑line validation

CVL is line‑based: it validates data as it streams, line by line, rather than loading the entire data set into memory.
Other languages (XSD, DDL, etc.) operate on the full data set at once, trading memory usage for speed. While DDL has strategies for handling data larger than RAM, CVL’s streaming approach enables certain tasks that full‑set languages find difficult, and vice‑versa. Runtime environments can mitigate many of these limitations.

Declarative nature

All the languages mentioned—including CVL—are declarative. You describe what should happen or what you are testing, and the runtime determines how to achieve the result.

Schema‑based and rules‑based

• Schema definition in CVL resembles SQL’s DDL.
• Rule definition is akin to Schematron and SQL queries.

CVL is intentionally simple grammatically, but it still offers hundreds of functions for business‑logic validation. Choosing the right functions for a given task is often the biggest learning curve.

Why a tabular‑data validation language?

• CSV and similar formats are ubiquitous and extremely flexible, which can hurt productivity and increase risk.
• Data often originates from external partners, making early detection of problems essential.
• Errors propagate and amplify as data moves inward through an enterprise, raising the cost of remediation.

Validating as close to the source as possible minimizes blast radius and downstream firefighting.

Learning curve: easy or hard?

The effort required to learn CVL depends on your goals:

CVL is designed for automation, not one‑off scripting. Treat CVL scripts as code: version them, write tests, keep them simple and reusable. When used properly, CVL is easy to learn and easy to use, though, like any powerful language, it can be misused.

Example scenario

A retail data partner sends weekly CSV files of orders. You need to validate incoming files before they enter the enterprise system.

Sample data

ID,date,time,store,address,city,state,zip,category,type,shelf,vendor,product name,UPC,SKU,unit,quantity,a price
03358993,03/21/2024,10:24:14,Bob's store,1 Lakeshore Drive, Chicago, IL, 33581,OFFICE,PAPER,1-5,Sams Paper,20lbs Ream,0301024855,,per each,8,20.99
03358994,03/21/2024,10:31:28,Fred's store,1 Lakeshore Drive, Chicago, IL, 33581,OPERA,WRITING,1-5,Biz Pen,10-pack Black,0541931855,0432950078,per each,2,4
03358995,03/21/2024,11:26:18,Mary's story, 1 Lakeshore Drive, Chicago, IL, 33581,FOOD,CANDY,7,Starbursts,Single,3583900656,0899920453,per each,1,1.29d

Validation goals

• Detect bad prices (e.g., missing decimal places).
• Flag wrong categories.
• Identify missing SKU values.
• Identify missing UPC values.

CVL script (CsvPath)

validation-mode:no-raise, no-stop, print, no-fail, collect
logic-mode:OR

${FILE}[1*][
  # --- Category check ---
  @in = in(#category, "OFFICE|COMPUTING|FURNITURE|PRINT|FOOD|OTHER")
  not(@in.asbool) ->
    error.category("Bad category $.headers.category at line $.csvpath.count_lines", fail())

  # --- Price format check ---
  @price_format = exact(end(), /\$?\d*\.\d{2}/)
  not(@price_format.asbool) ->
    error.price("Bad price $.headers.'a price' at line $.csvpath.count_lines", fail())

  # --- SKU presence check ---
  not(#SKU) ->
    error.sku("No SKU at line $.csvpath.count_lines", fail())

  # --- UPC presence check ---
  not(#UPC) ->
    error.upc("No UPC at line $.csvpath.count_lines", fail())
]

In a production environment you would likely split these into separate CsvPath files and run them as a named‑paths group. The example above embeds the file path (${FILE}) directly for illustration.

Key points to remember

• Logic mode (OR vs. AND) determines how multiple rules combine.
• validation‑mode controls error handling (no-raise, no-stop, print, etc.).
• Use asbool when you need a boolean result from a function.
• Assignments (@var = …) do not determine match success; only the subsequent predicate does.
• Header names with spaces or special characters require quoting ('a price').
• fail() signals that the current line should be considered invalid.
• Adding descriptive names to errors (error.category, error.price, …) helps downstream processing.

Conclusion

CVL can feel easy once you understand its declarative, line‑by‑line nature and become familiar with its core functions. The learning curve is mainly about remembering the syntax nuances and best practices for structuring validation scripts. For large‑scale, repeatable data‑pre‑boarding pipelines, CVL offers a concise, maintainable solution that scales far better than ad‑hoc scripting.

For more details and advanced examples, visit the official documentation at .