Extended TSV format

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tsvx: Strictly typed TSV format with metadata

TSV is a nice format. It's human-readable, very fast to parse, and concise. It's possible to process as a streaming format.

It's also painfully hard to work with due to lack of standardization and typing. Each file requires a different set of heuristics to interpret. Those heuristics might break 80GB into a 100GB file, when an oddball case comes up. Those heuristics also translate into a tremendous amount of unnecessary ETL work. Even importing a TSV/CSV into a spreadsheet causes a dialog to pop up.

This is a proposal for an enhanced TSV format. If this proposal is successful, a spreadsheet would be able to open a MySQL export without prompting the user for help, and most data will flow freely between tools like pandas, tableau, R, and others (excepting issues of formatting and precision).

tsvx files are a tab-separated format, where:

  • Columns are statically typed, with declared types, and standardized string escaping, dates, and similar for other data types.
  • We standardize column headers which provide metadata about column names, types, and contents.
  • We standardize overall metadata for overall file description, providence, creation time, authors, and otherwise.

This is repository includes a simple parser for such files so we can experiment with this concept. It also includes scripts to export from MySQL, both on a table level and on a whole database level.

What tsvx looks like

title: Food inventory
created-date: 2016-10-29T15:25:29.449640
Food Name   Weight   Price       Expiration Date
foodname    weight   netprice    exp             (variables)
str         int      float       ISO8601-date    (types)
String      Number   Number      String          (json)
Tuna        300      5.13        2017-10-12
Salmon      150      7.18        2018-10-12
Swordfish   250      9.41        2016-11-13


We have nice standards for documents (XML, as well as other markup languages). We have nice standards for objects and configuration data (YAML and JSON). While numerical and tabular data is becoming increasingly important, we don't have any nice standards for representing it; it is all very ad-hoc.

It is also increasingly being interchanged across tools. We see organizations which have terabytes of data in systems like Hadoop and Vertica, gigabytes in databases and flat files, and kilobytes of hand-curated data in spreadsheets, all of which is integrated in common systems.

Our design goals are:

  • Human-readability
  • Compatibility. Excel, Google Docs, Hadoop, LibreOffice, MySQL, PostgreSQL, Python, R, Vertica, and other tools should open each other's imports and exports, maintaining basic type information without additional scripting.
  • Simple parsing. Processing scripts are simpler and less brittle. With tsvx, adding a column or changing column order doesn't break scripts.
  • In-Line Documentation. We have many TSV files sitting around, often years old, and no idea of what they contain, or how to regenerate them on current data.
  • Backwards-compatibility. A tsvx file should open reasonably in spreadsheets unaware of tsvx
  • Fast, single-pass processing. tsvx files are usable up to perhaps 10GB of data.
  • Extensibility. If it becomes popular, it should be able to replace formats from tools like mysqldump with something less ad-hoc.


We are in a request-for-comments period. If we decide this is a good idea, we'll make this more production-ready, and perhaps draft an IETF RFC or similar standard.

File Structure

The file has three sections:

  • File metadata (optional) -- Information about the file itself.
  • Column headings (required) -- Metadata about the names and types in the columns
  • Data (required) -- What one might traditionally find in a TSV file.

Sections are separated by a line of all dashes, containing at least three dashes. If the first section is omitted, the file should start with a line of dashes.

File Metadata

The metadata is a YAML dictionary. The first line must contain a colon, but we recommend having all lines in this format. Standard fields include:

  • authors -- JSON list of authors
  • created-date -- ISO8601 date and time of when the file was created
  • description -- A multiline description
  • generator -- Some identifier of the program or source which created the file
  • modified-date -- ISO8601 date and time of last modification
  • title -- A single line title/description

Headers/Column Metadata

First line of the headers are human-readable column headers (required). Following lines define additional information about each column. Required is types, which says how the data ought to be interpreted. Currently defined types are:

  • ISO8601-date -- Date as 2014-12-30
  • ISO8601-datetime -- Date and time as 2014-12-30T11:59:00.01
  • bool -- Boolean.trueorfalse`
  • float -- Floating point number, either containing a space or otherwise
  • int -- Integer
  • str -- JSON-encoded string (quotes omitted)

As a fallback, we strongly recommend a json section. All columns should be defined as one of three JSON types:

  • Boolean -- true or false
  • Number -- Integer or floating point
  • String -- Most other data types fall into this category

If this is missing, parsers should treat unrecognized types as strings.

variables is also strongly recommended. variables gives useful variable names to assign in a program. These should be letters, numbers, and underscores, but may not begin with a number. This is convenient for automatic parsers and parser generators.

In addition, there may be headers for other metadata, such as units, or vendor extensions.

Vendor Extensions

Vendors may add arbitrary extensions to both metadata and headers. The keys should begin with a program name and a dash. For example, mysql could add rows called mysql-types and mysql-keys to the headers, which would allow imports/exports to maintain both type information, and which columns are unique. mysql could also place global column metadata (such as multi-column constraints or storage engine) in the file-wide metadata with keys such as mysql-constraints and mysql-engine, or just in its own YAML section.

Such extensions should pay close attention to human readability.

Getting started

This is a before-and-after example of the same code, with a TSV file in Python, versus with a tsvx file and the library:


for line in tsvx.reader(open("file.tsvx"))


f = open("file.tsv")
# Skip headers
# Break on tabs
split_lines = (l[:-1].split('\t') for l in f)
# Parse to types
parsed_lines = (
   [l[0], int(l[1]), float(l[2]), dateutil.parser.parse(l[3])]
   for l in split_lines

for foodname, weight, price, expiration in parsed_lines:
   do_some_stuff(foodname, weight, price, expiration)

There is a also a small file in the repo, example/example.py, which shows how the prototype reference library works. Note that both the API and file format are still mutable -- we are actively soliciting feedback.

A More Complex File

And a more complex example, to show how extension fit in:

title: Food inventory
description: A sample tsvx file
created-date: 2016-10-29T15:25:29.449640
generator: myoffice.py
myoffice-version: 2.7
Food Name   Weight     Price       Expiration Date
foodname    weight     price       expiration      (variables)
str         int        float       ISO8601-date    (types)
            kg         dollars/kg                  (units)
String      Number     Number      String          (json)
VARCHAR(80) SMALLINT   DOUBLE      VARCHAR(20)     (mysql-types)
primary                                            (mysql-keys)
inventory   shipweight gross       exp             (myoffice-schema)
%s          %i         %.2f        YYYY-MM-DD      (myoffice-format-strings)
Tuna        300        5.13        2017-10-12
Salmon      150        7.18        2018-10-24
Swordfish   250        9.41        2016-11-13