tableschema-js

A library for working with Table Schema.

Version v1.0 includes various important changes. Please read a migration guide.

Features

• Table class for working with data and schema
• Schema class for working with schemas
• Field class for working with schema fields
• validate function for validating schema descriptors
• infer function that creates a schema based on a data sample

Getting started

Installation

The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify tableschema version range in your package.json file e.g. tabulator: ^1.0 which will be added by default by npm install --save.

NPM

$ npm install jsontableschema # v0.2
$ npm install tableschema@latest # v1.0-alpha

CDN

<script src="//unpkg.com/tableschema/dist/tableschema.min.js"></script>

Examples

Code examples in this readme requires Node v8.3+ or proper modern browser . Also you have to wrap code into async function if there is await keyword used. You could see even more example in examples directory.

const {Table} = require('tableschema')

const table = await Table.load('data.csv')
await table.infer() // infer a schema
await table.read({keyed: true}) // read the data
await table.schema.save() // save the schema
await table.save() // save the data

Documentation

Table

A table is a core concept in a tabular data world. It represents a data with a metadata (Table Schema). Let's see how we could use it in practice.

Consider we have some local csv file. It could be inline data or remote link - all supported by Table class (except local files for in-brower usage of course). But say it's data.csv for now:

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,N/A

Let's create and read a table. We use static Table.load method and table.read method with a keyed option to get array of keyed rows:

const table = await Table.load('data.csv')
table.headers // ['city', 'location']
await table.read({keyed: true})
// [
//   {city: 'london', location: '51.50,-0.11'},
//   {city: 'paris', location: '48.85,2.30'},
//   {city: 'rome', location: 'N/A'},
// ]

As we could see our locations are just a strings. But it should be geopoints. Also Rome's location is not available but it's also just a N/A string instead of JavaScript null. First we have to infer Table Schema:

await table.infer()
table.schema.descriptor
// { fields:
//   [ { name: 'city', type: 'string', format: 'default' },
//     { name: 'location', type: 'geopoint', format: 'default' } ],
//  missingValues: [ '' ] }
await table.read({keyed: true})
// Fails with a data validation error

Let's fix not available location. There is a missingValues property in Table Schema specification. As a first try we set missingValues to N/A in table.schema.descriptor. Schema descriptor could be changed in-place but all changes sould be commited by table.schema.commit():

table.schema.descriptor['missingValues'] = 'N/A'
table.schema.commit()
table.schema.valid // false
table.schema.errors
// Error: Descriptor validation error:
//   Invalid type: string (expected array)
//    at "/missingValues" in descriptor and
//    at "/properties/missingValues/type" in profile

As a good citiziens we've decided to check out schema descriptor validity. And it's not valid! We sould use an array for missingValues property. Also don't forget to have an empty string as a missing value:

table.schema.descriptor['missingValues'] = ['', 'N/A']
table.schema.commit()
table.schema.valid // true

All good. It looks like we're ready to read our data again:

await table.read({keyed: true})
// [
//   {city: 'london', location: [51.50,-0.11]},
//   {city: 'paris', location: [48.85,2.30]},
//   {city: 'rome', location: null},
// ]

Now we see that:

• locations are arrays with numeric lattide and longitude
• Rome's location is a native JavaScript null

And because there are no errors on data reading we could be sure that our data is valid againt our schema. Let's save it:

await table.schema.save('schema.json')
await table.save('data.csv')

Our data.csv looks the same because it has been stringified back to csv format. But now we have schema.json:

{
    "fields": [
        {
            "name": "city",
            "type": "string",
            "format": "default"
        },
        {
            "name": "location",
            "type": "geopoint",
            "format": "default"
        }
    ],
    "missingValues": [
        "",
        "N/A"
    ]
}

If we decide to improve it even more we could update the schema file and then open it again. But now providing a schema path and iterating thru the data using Node Streams:

const table = await Table.load('data.csv', {schema: 'schema.json'})
const stream = await table.iter({stream: true})
stream.on('data', (row) => {
  // handle row ['london', [51.50,-0.11]] etc
  // keyed/extended/cast supported in a stream mode too
})

It was onle basic introduction to the Table class. To learn more let's take a look on Table class API reference.

async Table.load(source, {schema, strict=false, headers=1, ...parserOptions})

Factory method to instantiate Table class. This method is async and it should be used with await keyword or as a Promise. If references argument is provided foreign keys will be checked on any reading operation.

• source (String/Array[]/Stream/Function) - data source (one of):
  • local CSV file (path)
  • remote CSV file (url)
  • array of arrays representing the rows
  • readable stream with CSV file contents
  • function returning readable stream with CSV file contents
• schema (Object) - data schema in all forms supported by Schema class
• strict (Boolean) - strictness option to pass to Schema constructor
• headers (Integer/String[]) - data source headers (one of):
  • row number containing headers (source should contain headers rows)
  • array of headers (source should NOT contain headers rows)
• parserOptions (Object) - options to be used by CSV parser. All options listed at http://csv.adaltas.com/parse/#parser-options. By default ltrim is true according to the CSV Dialect spec.
• (errors.TableSchemaError) - raises any error occured in table creation process
• (Table) - returns data table class instance

table.headers

• (String[]) - returns data source headers

table.schema

• (Schema) - returns schema class instance

async table.iter({keyed, extended, cast=true, relations=false, stream=false})

Iter through the table data and emits rows cast based on table schema (async for loop). With a stream flag instead of async iterator a Node stream will be returned. Data casting could be disabled.

• keyed (Boolean) - iter keyed rows
• extended (Boolean) - iter extended rows
• cast (Boolean) - disable data casting if false
• relations (Object) - object of foreign key references in a form of {resource1: [{field1: value1, field2: value2}, ...], ...}. If provided foreign key fields will checked and resolved to its references
• stream (Boolean) - return Node Readable Stream of table rows
• (errors.TableSchemaError) - raises any error occured in this process
• (AsyncIterator/Stream) - async iterator/stream of rows:
  • [value1, value2] - base
  • {header1: value1, header2: value2} - keyed
  • [rowNumber, [header1, header2], [value1, value2]] - extended

async table.read({keyed, extended, cast=true, relations=false, limit})

Read the whole table and returns as array of rows. Count of rows could be limited.

• keyed (Boolean) - flag to emit keyed rows
• extended (Boolean) - flag to emit extended rows
• cast (Boolean) - disable data casting if false
• relations (Object) - object of foreign key references in a form of {resource1: [{field1: value1, field2: value2}, ...], ...}. If provided foreign key fields will checked and resolved to its references
• limit (Number) - integer limit of rows to return
• (errors.TableSchemaError) - raises any error occured in this process
• (Array[]) - returns array of rows (see table.iter)

async table.infer({limit=100})

Infer a schema for the table. It will infer and set Table Schema to table.schema based on table data.

• limit (Number) - limit rows samle size
• (Object) - returns Table Schema descriptor

async table.save(target)

Save data source to file locally in CSV format with , (comma) delimiter

• target (String) - path where to save a table data
• (errors.TableSchemaError) - raises an error if there is saving problem
• (Boolean) - returns true on success

Schema

A model of a schema with helpful methods for working with the schema and supported data. Schema instances can be initialized with a schema source as a url to a JSON file or a JSON object. The schema is initially validated (see validate below). By default validation errors will be stored in schema.errors but in a strict mode it will be instantly raised.

Let's create a blank schema. It's not valid because descriptor.fields property is required by the Table Schema specification:

const schema = await Schema.load({})
schema.valid // false
schema.errors
// Error: Descriptor validation error:
//         Missing required property: fields
//         at "" in descriptor and
//         at "/required/0" in profile

To do not create a schema descriptor by hands we will use a schema.infer method to infer the descriptor from given data:

schema.infer([
  ['id', 'age', 'name'],
  ['1','39','Paul'],
  ['2','23','Jimmy'],
  ['3','36','Jane'],
  ['4','28','Judy'],
])
schema.valid // true
schema.descriptor
//{ fields:
//   [ { name: 'id', type: 'integer', format: 'default' },
//     { name: 'age', type: 'integer', format: 'default' },
//     { name: 'name', type: 'string', format: 'default' } ],
//  missingValues: [ '' ] }

Now we have an inferred schema and it's valid. We could cast data row against our schema. We provide a string input by an output will be cast correspondingly:

schema.castRow(['5', '66', 'Sam'])
// [ 5, 66, 'Sam' ]

But if we try provide some missing value to age field cast will fail because for now only one possible missing value is an empty string. Let's update our schema:

schema.castRow(['6', 'N/A', 'Walt'])
// Cast error
schema.descriptor.missingValues = ['', 'N/A']
schema.commit()
schema.castRow(['6', 'N/A', 'Walt'])
// [ 6, null, 'Walt' ]

We could save the schema to a local file. And we could continue the work in any time just loading it from the local file:

await schema.save('schema.json')
const schema = await Schema.load('schema.json')

It was onle basic introduction to the Schema class. To learn more let's take a look on Schema class API reference.

async Schema.load(descriptor, {strict=false})

Factory method to instantiate Schema class. This method is async and it should be used with await keyword or as a Promise.

• descriptor (String/Object) - schema descriptor:
  • local path
  • remote url
  • object
• strict (Boolean) - flag to alter validation behaviour:
  • if false error will not be raised and all error will be collected in schema.errors
  • if strict is true any validation error will be raised immediately
• (errors.TableSchemaError) - raises any error occured in the process
• (Schema) - returns schema class instance

schema.valid

• (Boolean) - returns validation status. It always true in strict mode.

schema.errors

• (Error[]) - returns validation errors. It always empty in strict mode.

schema.descriptor

• (Object) - returns schema descriptor

schema.primaryKey

• (str[]) - returns schema primary key

schema.foreignKeys

• (Object[]) - returns schema foreign keys

schema.fields

• (Field[]) - returns an array of Field instances.

schema.fieldNames

• (String[]) - returns an array of field names.

schema.getField(name)

Get schema field by name.

• name (String) - schema field name
• (Field/null) - returns Field instance or null if not found

schema.addField(descriptor)

Add new field to schema. The schema descriptor will be validated with newly added field descriptor.

• descriptor (Object) - field descriptor
• (errors.TableSchemaError) - raises any error occured in the process
• (Field/null) - returns added Field instance or null if not added

schema.removeField(name)

Remove field resource by name. The schema descriptor will be validated after field descriptor removal.

• name (String) - schema field name
• (errors.TableSchemaError) - raises any error occured in the process
• (Field/null) - returns removed Field instances or null if not found

schema.castRow(row)

Cast row based on field types and formats.

• row (any[]) - data row as an array of values
• (any[]) - returns cast data row

schema.infer(rows, {headers=1})

Infer and set schema.descriptor based on data sample.

• rows (Array[]) - array of arrays representing rows.
• headers (Integer/String[]) - data sample headers (one of):
  • row number containing headers (rows should contain headers rows)
  • array of headers (rows should NOT contain headers rows)
• {Object} - returns Table Schema descriptor

schema.commit({strict})

Update schema instance if there are in-place changes in the descriptor.

• strict (Boolean) - alter strict mode for further work
• (errors.TableSchemaError) - raises any error occured in the process
• (Boolean) - returns true on success and false if not modified

const descriptor = {fields: [{name: 'field', type: 'string'}]}
const schema = await Schema.load(descriptor)

schema.getField('name').type // string
schema.descriptor.fields[0].type = 'number'
schema.getField('name').type // string
schema.commit()
schema.getField('name').type // number

async schema.save(target)

Save schema descriptor to target destination.

• target (String) - path where to save a descriptor
• (errors.TableSchemaError) - raises any error occured in the process
• (Boolean) - returns true on success

Field

Class represents field in the schema.

Data values can be cast to native Javascript types. Casting a value will check the value is of the expected type, is in the correct format, and complies with any constraints imposed by a schema.

{
    'name': 'birthday',
    'type': 'date',
    'format': 'default',
    'constraints': {
        'required': True,
        'minimum': '2015-05-30'
    }
}

Following code will not raise the exception, despite the fact our date is less than minimum constraints in the field, because we do not check constraints of the field descriptor

var dateType = field.castValue('2014-05-29')

And following example will raise exception, because we set flag 'skip constraints' to false, and our date is less than allowed by minimum constraints of the field. Exception will be raised as well in situation of trying to cast non-date format values, or empty values

try {
    var dateType = field.castValue('2014-05-29', false)
} catch(e) {
    // uh oh, something went wrong
}

Values that can't be cast will raise an Error exception. Casting a value that doesn't meet the constraints will raise an Error exception.

Available types, formats and resultant value of the cast:

| Type | Formats | Casting result | | ---- | ------- | -------------- | | any | default | Any | | array | default | Array | | boolean | default | Boolean | | date | default, any, | Date | | datetime | default, any, | Date | | duration | default | moment.Duration | | geojson | default, topojson | Object | | geopoint | default, array, object | [Number, Number] | | integer | default | Number | | number | default | Number | | object | default | Object | | string | default, uri, email, binary | String | | time | default, any, | Date | | year | default | Number | | yearmonth | default | [Number, Number] |

new Field(descriptor, {missingValues=['']})

Constructor to instantiate Field class.

• descriptor (Object) - schema field descriptor
• missingValues (String[]) - an array with string representing missing values
• (errors.TableSchemaError) - raises any error occured in the process
• (Field) - returns field class instance

field.name

• (String) - returns field name

field.type

• (String) - returns field type

field.format

• (String) - returns field format

field.required

• (Boolean) - returns true if field is required

field.constraints

• (Object) - returns an object with field constraints

field.descriptor

• (Object) - returns field descriptor

field.castValue(value, {constraints=true})

Cast given value according to the field type and format.

• value (any) - value to cast against field
• constraints (Boolean/String[]) - gets constraints configuration
  • it could be set to true to disable constraint checks
  • it could be an Array of constraints to check e.g. ['minimum', 'maximum']
• (errors.TableSchemaError) - raises any error occured in the process
• (any) - returns cast value

field.testValue(value, {constraints=true})

Test if value is compliant to the field.

• value (any) - value to cast against field
• constraints (Boolean/String[]) - constraints configuration
• (Boolean) - returns if value is compliant to the field

Validate

validate() validates whether a schema is a validate Table Schema accordingly to the specifications. It does not validate data against a schema.

Given a schema descriptor validate returns Promise with a validation object:

const {validate} = require('tableschema')

const {valid, errors} = await validate('schema.json')
for (const error of errors) {
  // inspect Error objects
}

async validate(descriptor)

This funcion is async so it has to be used with await keyword or as a Promise.

• descriptor (String/Object) - schema descriptor (one of):
  • local path
  • remote url
  • object
• (Object) - returns {valid, errors} object

Infer

Given data source and headers infer will return a Table Schema as a JSON object based on the data values.

Given the data file, example.csv:

id,age,name
1,39,Paul
2,23,Jimmy
3,36,Jane
4,28,Judy

Call infer with headers and values from the datafile:

const descriptor = await infer('data.csv')

The descriptor variable is now a JSON object:

{
  fields: [
    {
      name: 'id',
      title: '',
      description: '',
      type: 'integer',
      format: 'default'
    },
    {
      name: 'age',
      title: '',
      description: '',
      type: 'integer',
      format: 'default'
    },
    {
      name: 'name',
      title: '',
      description: '',
      type: 'string',
      format: 'default'
    }
  ]
}

async infer(source, {headers=1, ...options})

This funcion is async so it has to be used with await keyword or as a Promise.

• source (String/Array[]/Stream/Function) - data source (one of):
  • local CSV file (path)
  • remote CSV file (url)
  • array of arrays representing the rows
  • readable stream with CSV file contents
  • function returning readable stream with CSV file contents
• headers (String[]) - array of headers
• options (Object) - any Table.load options
• (errors.TableSchemaError) - raises any error occured in the process
• (Object) - returns schema descriptor

Errors

errors.TableSchemaError

Base class for the all library errors. If there are more than one error you could get an additional information from the error object:

try {
  // some lib action
} catch (error) {
  console.log(error) // you have N cast errors (see error.errors)
  if (error.multiple) {
    for (const error of error.errors) {
        console.log(error) // cast error M is ...
    }
  }
}

errors.tableSchemaError.rowNumber

• (Number/undefined) - row number of the error if available

errors.tableSchemaError.columnNumber

• (Number/undefined) - column number of the error if available

Contributing

The project follows the Open Knowledge International coding standards. There are common commands to work with the project:

$ npm install
$ npm run test
$ npm run build

Changelog

Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.

v1.4

Improved behaviour:

• Now the infer functions support formats inferring

v1.3

New API added:

• error.rowNumber if available
• error.columnNumber if available

v1.2

New API added:

• Table.load and infer now accept Node Stream as a source argument

v1.1

New API added:

• Table.load and infer now accepts parserOptions

v1.0

This version includes various big changes. A migration guide is under development and will be published here.

v0.2

First stable version of the library.