CSV Validation

Lightning Talk


Adam Retter

Evolved Binary Ltd


@adamretter / adam.retter@googlemail.com

It all started with...



Talking about

  • CSV Schema Language

  • CSV Validation Tool

  • Full talk: http://adamretter.org.uk/presentations.xml#csvconf14

The National Archives

  • Archive Records of UK from OGDs, NGOs and Special Interest

  • Excellent at traditional Paper records

    • One of the largest collections in the world

    • Over 11 million historical Government and Public Records

  • However, most records today are not created on paper!

    • Predicted 2013 - 2020:

      • >6PB of Digital Records to Archive

      • 50% of which will be Born Digital

    • 2009: Existing Digital Records System will not cope...

      • 2011: Build new Digital Records Infrastructure = ME :-)

Digital Repository Infrastructure

  1. Records arrive via:

    • Hard Disks (USB etc)

    • DVD / CD / Digital Video Cassette / Tape (mostly LTO 1 to 6)

    • SFTP

  2. Load Records

  3. Test, Secure and Examine Records (Pre-Ingest)

  4. Extract Metadata and Archive (Ingest)

  5. Enable Digital Archivists (Search, Retrieval and Edit)

  6. Export Transcoded Records and Metadata (Publish / Sell)

Q: Digital Preservation...

  1. What constitutes a Record?

  2. Given a disk of files - What do you Accession?

  3. How does DRI know what it should process and how?


A: Metadata!

  1. One or more Files and Metadata (Technical, Provenance, Transcription, Closure)

  2. Records Selection Process by OGD, provided as metadata

  3. Search source for metadata and process described records

Collecting Metadata

  • TNA creates Metadata Standards for their records

  • Expects suppliers to provide Metadata alongside files (records)

  • CSV was decided upon as file format for metadata

    • XML and RDF were both considered

    • Must be achievable by non-technical staff

      • Often Gov IT Departments are outsourced

      • Installing even free applications is prohibitive (cost)

      • Likely familiarity with MS Excel (and available)

  • Past experience has shown that if the barriers to entry are too high, then suppliers will not comply

CSV Metadata Problems

  • TNA has complex metadata requirements

    • Conditional Values and Co-variance Constraints

    • Relationships: row -> row, csv -> csv, csv -> files

  • Errors are introduced

    • Human

      • Transcription mistakes

      • Rename .xls file to .csv

    • Computer

      • Poorly implemented Metadata generation

      • MS Excel can hide/mangle data e.g. #NAME?

    • Commercial - Suppliers try and cut corners

CSV Validation

  • Version 0.1 (Internal Only)

    • Command Line tool developed in Java

    • Validated metadata across 3 types of CSV files

    • Validation rules were expressed in Java DSL

    • Home Guard Collection (Proof of Concept)

      • 82,800 records checked

      • >250,000 rows of CSV data 

      • ~4.5TB of JP2000 Images validated

  • Still... many failures detected!

    • However, faster feedback (Pre-Ingest).

    • Eventually... shared with digitisation supplier

CSV Validation

  • Version 0.1 was nice... but in Version 0.2 can we have:

    • Validation rules DSL should

      • Be External (no need to recompile)
      • Writable by Domain Experts not Developers (no Java!)
      • Easily sharable with suppliers

    • Application(s) should be

      • Freely available to suppliers
      • Useable in DRI Pre-Ingest and Ingest processing

The CSV Schema Language

  • Started at TNA as text based DSL for CSV Validation Rules

  • As interest grew... Requirements exploded!

  • Now:

    • A generic CSV Schema Language

    • 60+ Expression for forming Validation Rules

    • 10+ High-level data types (Dates, Times, Numbers etc.)

    • Flexible Support for any tabular text data (CSV, TSV, etc.)

    • Open Standard (Currently... guided by TNA)

    • Freely available under MPL v2.0


https://github.com/digital-preservation/csv-schema

Design Principles of CSV Schema

  • Simple Plain-Text Expression

    • Composable by non-techies with text editor

  • Implicit Context

    • Natural to write, rules are per-column, applied row-by-row

  • Sane Defaults

    • CSV files come in all shapes, e.g. default to RFC 4180.

  • Streamable

    • CSV files may be large. Do not prohibit efficient processing.

  • NOT a Programming Language!

    • Powerful? Yes!   For programmers? No!

CSV Schema 101

  • A CSV Schema consists of:

    • Directives - modify behaviour of CSV parsing and rules

    • Rules - 1 per column, composed of expressions


CSV Data

first_name,last_name,gender,dob
Adam,Retter,33,M,1981-02-04
Elisabeth,Roberts,33,F,1980-11-13

CSV Schema

version 1.0
@totalColumns 4
first_name: length(2, *)
last_name: length(2, *)
gender: is("M") or is("F") @optional
dob: xDate

CSV Schema - Example 2

  • Global Directives control parsing of CSV


CSV Data

"Huxley"$"feline"$"Short Haired Domestic"$"10"
"Precious"$"feline"$"Short Haired Domestic"$"6"
"Mac"$"canine"$"Dalmatian"$"12"

CSV Schema

version 1.0
@separator '$' @quoted @totalColumns 4 @noHeader
name: notEmpty
class: is("feline") or is("canine")
breed: length(3, 255)
age: positiveInteger

CSV Schema - Example 3

  • Conditional Expressions and Co-Variance


CSV Data

name,animal,age,short description,notes
James,Mouse,4,,
Louise,Elephant,45,In good health,

CSV Schema

version 1.0
name: notEmpty
animal: notEmpty
age: if($animal/is("mouse"), range(0, 3), positiveInteger)
"short description": length(*, 255) @optional
notes:

CSV Schema - Example 4

  • External Expressions (mainly file checks)


CSV Data

"id","fn","checksum","classifications"
"1","image1.jp2","54229abfcfa5649e7003b83dd4755294",""
"2","image2.jp3",3d0ad5a7a8ef3b1d4e6ea33e92e4d3b5,""
"3","folder1/","",""

CSV Schema

version 1.0
id: positiveInteger unique
fn: (ends(".jp2") or ends("/")) and unique
checksum: if($fn/ends("/"), empty, checksum(file($fn,"MD5")))
classifications: regex("[0-9a-z]+(,[0-9a-z]+)*") @optional

The CSV Validator

  • Validates CSV data against CSV Schema

  • Reference Implementation

  • Runs on any JVM v6+ (written in Scala 2.11)

    • Command Line Interface

    • GUI Application

    • Scala API

    • Java API

    • Open source, available under MPL v2.0

  • Fast and efficient! Battle-tested against large datasets.

https://github.com/digital-preservation/csv-validator

Future Work

  • It's all open:

    • CSV Schema collaborators would be nice

    • Developers for CSV Validator

    • Bugfixes

    • New Features

      • CSV Schema

        • More data types, specifically numeric types

        • Expressions: any, min, max, foward/backward etc.

      • CSV Validator

        • Multi-Threading External Expressions

        • Stream error messages

  • Review regarding CSV on the Web WG products

CSV Validation Lightning Talk

By Adam Retter

CSV Validation Lightning Talk

Lightning Talk given at XML Summer School 2014

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