1. datapackage-js

A library for working with Data Packages.

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

1.1. Features

Package class for working with data packages
Resource class for working with data resources
Profile class for working with profiles
validate function for validating data package descriptors
infer function for inferring data package descriptors

1.2. Getting Started

1.2.1. Installation

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

NPM

$ npm install datapackage@latest # v1.0
$ npm install datapackage # v0.8

CDN

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

1.2.2. 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 {Package} = require('datapackage')

const descriptor = {
  resources: [
    {
      name: 'example',
      profile: 'tabular-data-resource',
      data: [
        ['height', 'age', 'name'],
        ['180', '18', 'Tony'],
        ['192', '32', 'Jacob'],
      ],
      schema:  {
        fields: [
          {name: 'height', type: 'integer'},
          {name: 'age', type: 'integer'},
          {name: 'name', type: 'string'},
        ],
      }
    }
  ]
}

const dataPackage = await Package.load(descriptor)
const resource = dataPackage.getResource('example')
await resource.read() // [[180, 18, 'Tony'], [192, 32, 'Jacob']]

1.3. Documentation

1.3.1. Package

A class for working with data packages. It provides various capabilities like loading local or remote data package, inferring a data package descriptor, saving a data package descriptor and many more.

Consider we have some local csv files in a data directory. Let's create a data package based on this data using a Package class:

data/cities.csv

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,"41.89,12.51"

data/population.csv

city,year,population
london,2017,8780000
paris,2017,2240000
rome,2017,2860000

First we create a blank data package::

const dataPackage = await Package.load()

Now we're ready to infer a data package descriptor based on data files we have. Because we have two csv files we use glob pattern **/*.csv:

await dataPackage.infer('**/*.csv')
dataPackage.descriptor
//{ profile: 'tabular-data-package',
//  resources:
//   [ { path: 'data/cities.csv',
//       profile: 'tabular-data-resource',
//       encoding: 'utf-8',
//       name: 'cities',
//       format: 'csv',
//       mediatype: 'text/csv',
//       schema: [Object] },
//     { path: 'data/population.csv',
//       profile: 'tabular-data-resource',
//       encoding: 'utf-8',
//       name: 'population',
//       format: 'csv',
//       mediatype: 'text/csv',
//       schema: [Object] } ] }

An infer method has found all our files and inspected it to extract useful metadata like profile, encoding, format, Table Schema etc. Let's tweak it a little bit:

dataPackage.descriptor.resources[1].schema.fields[1].type = 'year'
dataPackage.commit()
dataPackage.valid // true

Because our resources are tabular we could read it as a tabular data:

await dataPackage.getResource('population').read({keyed: true})

//[ { city: 'london', year: 2017, population: 8780000 },
//  { city: 'paris', year: 2017, population: 2240000 },
//  { city: 'rome', year: 2017, population: 2860000 } ]

Let's save our descriptor on the disk. After it we could update our datapackage.json as we want, make some changes etc:

await dataPackage.save('datapackage.json')

To continue the work with the data package we just load it again but this time using local datapackage.json:

const dataPackage = await Package.load('datapackage.json')
// Continue the work

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

`async Package.load(descriptor, {basePath, strict=false})`

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

descriptor (String/Object) - data package descriptor as local path, url or object. If ththe path has a zip file extension it will be unzipped to the temp directory first.
basePath (String) - base path for all relative paths
strict (Boolean) - strict flag to alter validation behavior. Setting it to true leads to throwing errors on any operation with invalid descriptor
(errors.DataPackageError) - raises error if something goes wrong
(Package) - returns data package class instance

`package.valid`

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

`package.errors`

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

`package.profile`

(Profile) - returns an instance of Profile class (see below).

`package.descriptor`

(Object) - returns data package descriptor

`package.resources`

(Resource[]) - returns an array of Resource instances (see below).

`package.resourceNames`

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

`package.getResource(name)`

Get data package resource by name.

name (String) - data resource name
(Resource/null) - returns Resource instances or null if not found

`package.addResource(descriptor)`

Add new resource to data package. The data package descriptor will be validated with newly added resource descriptor.

descriptor (Object) - data resource descriptor
(errors.DataPackageError) - raises error if something goes wrong
(Resource/null) - returns added Resource instance or null if not added

`package.removeResource(name)`

Remove data package resource by name. The data package descriptor will be validated after resource descriptor removal.

name (String) - data resource name
(errors.DataPackageError) - raises error if something goes wrong
(Resource/null) - returns removed Resource instances or null if not found

`async package.infer(pattern=false)`

Infer a data package metadata. If pattern is not provided only existent resources will be inferred (added metadata like encoding, profile etc). If pattern is provided new resoures with file names mathing the pattern will be added and inferred. It commits changes to data package instance.

pattern (String) - glob pattern for new resources
(Object) - returns data package descriptor

`package.commit({strict})`

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

strict (Boolean) - alter strict mode for further work
(errors.DataPackageError) - raises error if something goes wrong
(Boolean) - returns true on success and false if not modified

const dataPackage = await Package.load({
    name: 'package',
    resources: [{name: 'resource', data: ['data']}]
})

dataPackage.name // package
dataPackage.descriptor.name = 'renamed-package'
dataPackage.name // package
dataPackage.commit()
dataPackage.name // renamed-package

`async package.save(target)`

Save data package to target destination. If target path has a zip file extension the package will be zipped and saved entirely. If it has a json file extension only the descriptor will be saved.

target (String) - path where to save a data package
(errors.DataPackageError) - raises error if something goes wrong
(Boolean) - returns true on success

1.3.2. Resource

A class for working with data resources. You can read or iterate tabular resources using the iter/read methods and all resource as bytes using rowIter/rowRead methods.

Consider we have some local csv file. It could be inline data or remote link - all supported by Resource 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 resource. We use static Resource.load method instantiate a resource. Because resource is tabular we could use resource.read method with a keyed option to get an array of keyed rows:

const resource = await Resource.load({path: 'data.csv'})
resource.tabular // true
resource.headers // ['city', 'location']
await resource.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 resource metadata:

await resource.infer()
resource.descriptor
//{ path: 'data.csv',
//  profile: 'tabular-data-resource',
//  encoding: 'utf-8',
//  name: 'data',
//  format: 'csv',
//  mediatype: 'text/csv',
// schema: { fields: [ [Object], [Object] ], missingValues: [ '' ] } }
await resource.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 resource.descriptor.schema. Resource descriptor could be changed in-place but all changes should be commited by resource.commit():

resource.descriptor.schema.missingValues = 'N/A'
resource.commit()
resource.valid // false
resource.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 recource descriptor validity. And it's not valid! We should use an array for missingValues property. Also don't forget to have an empty string as a missing value:

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

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

await resource.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 our resource descriptor:

await resource.save('dataresource.json')

Let's check newly-crated dataresource.json. It contains path to our data file, inferred metadata and our missingValues tweak:

{
    "path": "data.csv",
    "profile": "tabular-data-resource",
    "encoding": "utf-8",
    "name": "data",
    "format": "csv",
    "mediatype": "text/csv",
    "schema": {
        "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 dataresource.json file and then open it again. But this time let's read our resoure as a byte stream:

const resource = await Resource.load('dataresource.json')
const stream = await resource.rawIter({stream: true})
stream.on('data', (data) => {
  // handle data chunk as a Buffer
})

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

`async Resource.load(descriptor, {basePath, strict=false})`

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

descriptor (String/Object) - data resource descriptor as local path, url or object
basePath (String) - base path for all relative paths
strict (Boolean) - strict flag to alter validation behavior. Setting it to true leads to throwing errors on any operation with invalid descriptor
(errors.DataPackageError) - raises error if something goes wrong
(Resource) - returns resource class instance

`resource.valid`

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

`resource.errors`

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

`resource.profile`

(Profile) - returns an instance of Profile class (see below).

`resource.descriptor`

(Object) - returns resource descriptor

`resource.name`

(String) - returns resource name

`resource.inline`

(Boolean) - returns true if resource is inline

`resource.local`

(Boolean) - returns true if resource is local

`resource.remote`

(Boolean) - returns true if resource is remote

`resource.multipart`

(Boolean) - returns true if resource is multipart

`resource.tabular`

(Boolean) - returns true if resource is tabular

`resource.source`

(Array/String) - returns data or path property

Combination of resource.source and resource.inline/local/remote/multipart provides predictable interface to work with resource data.

`resource.headers`

Only for tabular resources

(String[]) - returns data source headers

`resource.schema`

Only for tabular resources

It returns Schema instance to interact with data schema. Read API documentation - tableschema.Schema.

(tableschema.Schema) - returns schema class instance

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

Only for tabular resources

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 (Boolean) - if true foreign key fields will be checked and resolved to its references
stream (Boolean) - return Node Readable Stream of table rows
(errors.DataPackageError) - 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 resource.read({keyed, extended, cast=true, relations=false, limit})`

Only for tabular resources

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) - flag to disable data casting if false
relations (Boolean) - if true foreign key fields will be checked and resolved to its references
limit (Number) - integer limit of rows to return
(errors.DataPackageError) - raises any error occured in this process
(Array[]) - returns array of rows (see table.iter)

`resource.checkRelations()`

Only for tabular resources

It checks foreign keys and raises an exception if there are integrity issues.

(errors.DataPackageError) - raises if there are integrity issues
(Boolean) - returns True if no issues

`await resource.rawIter({stream=false})`

Iterate over data chunks as bytes. If stream is true Node Stream will be returned.

stream (Boolean) - Node Stream will be returned
(Iterator/Stream) - returns Iterator/Stream

`await resource.rawRead()`

Returns resource data as bytes.

(Buffer) - returns Buffer with resource data

`async resource.infer()`

Infer resource metadata like name, format, mediatype, encoding, schema and profile. It commits this changes into resource instance.

(Object) - returns resource descriptor

`resource.commit({strict})`

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

strict (Boolean) - alter strict mode for further work
(errors.DataPackageError) - raises error if something goes wrong
(Boolean) - returns true on success and false if not modified

`async resource.save(target)`

For now only descriptor will be saved.

Save resource to target destination.

target (String) - path where to save a resource
(errors.DataPackageError) - raises error if something goes wrong
(Boolean) - returns true on success

1.3.3. Profile

A component to represent JSON Schema profile from Profiles Registry:

await profile = Profile.load('data-package')

profile.name // data-package
profile.jsonschema // JSON Schema contents

const {valid, errors} = profile.validate(descriptor)
for (const error of errors) {
  // inspect Error objects
}

`async Profile.load(profile)`

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

profile (String) - profile name in registry or URL to JSON Schema
(errors.DataPackageError) - raises error if something goes wrong
(Profile) - returns profile class instance

`profile.name`

(String/null) - returns profile name if available

`profile.jsonschema`

(Object) - returns profile JSON Schema contents

`profile.validate(descriptor)`

Validate a data package descriptor against the profile.

descriptor (Object) - retrieved and dereferenced data package descriptor
(Object) - returns a {valid, errors} object

1.3.4. Validate

A standalone function to validate a data package descriptor:

const {valid, errors} = await validate({name: 'Invalid Datapackage'})
for (const error of errors) {
  // inspect Error objects
}

`async validate(descriptor)`

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

descriptor (String/Object) - data package descriptor (local/remote path or object)
(Object) - returns a {valid, errors} object

1.3.5. Infer

A standalone function to infer a data package descriptor.

const descriptor = await infer('**/*.csv')
//{ profile: 'tabular-data-resource',
//  resources:
//   [ { path: 'data/cities.csv',
//       profile: 'tabular-data-resource',
//       encoding: 'utf-8',
//       name: 'cities',
//       format: 'csv',
//       mediatype: 'text/csv',
//       schema: [Object] },
//     { path: 'data/population.csv',
//       profile: 'tabular-data-resource',
//       encoding: 'utf-8',
//       name: 'population',
//       format: 'csv',
//       mediatype: 'text/csv',
//       schema: [Object] } ] }

`async infer(pattern, {basePath})`

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

pattern (String) - glob file pattern
(Object) - returns data package descriptor

1.3.6. Foreign Keys

The library supports foreign keys described in the Table Schema specification. It means if your data package descriptor use resources[].schema.foreignKeys property for some resources a data integrity will be checked on reading operations.

Consider we have a data package:

const DESCRIPTOR = {
  'resources': [
    {
      'name': 'teams',
      'data': [
        ['id', 'name', 'city'],
        ['1', 'Arsenal', 'London'],
        ['2', 'Real', 'Madrid'],
        ['3', 'Bayern', 'Munich'],
      ],
      'schema': {
        'fields': [
          {'name': 'id', 'type': 'integer'},
          {'name': 'name', 'type': 'string'},
          {'name': 'city', 'type': 'string'},
        ],
        'foreignKeys': [
          {
            'fields': 'city',
            'reference': {'resource': 'cities', 'fields': 'name'},
          },
        ],
      },
    }, {
      'name': 'cities',
      'data': [
        ['name', 'country'],
        ['London', 'England'],
        ['Madrid', 'Spain'],
      ],
    },
  ],
}

Let's check relations for a teams resource:

const {Package} = require('datapackage')

const package = await Package.load(DESCRIPTOR)
teams = package.getResource('teams')
await teams.checkRelations()
// tableschema.exceptions.RelationError: Foreign key "['city']" violation in row "4"

As we could see there is a foreign key violation. That's because our lookup table cities doesn't have a city of Munich but we have a team from there. We need to fix it in cities resource:

package.descriptor['resources'][1]['data'].push(['Munich', 'Germany'])
package.commit()
teams = package.getResource('teams')
await teams.checkRelations()
// True

Fixed! But not only a check operation is available. We could use relations argument for resource.iter/read methods to dereference a resource relations:

await teams.read({keyed: true, relations: true})
//[{'id': 1, 'name': 'Arsenal', 'city': {'name': 'London', 'country': 'England}},
// {'id': 2, 'name': 'Real', 'city': {'name': 'Madrid', 'country': 'Spain}},
// {'id': 3, 'name': 'Bayern', 'city': {'name': 'Munich', 'country': 'Germany}}]

Instead of plain city name we've got a dictionary containing a city data. These resource.iter/read methods will fail with the same as resource.check_relations error if there is an integrity issue. But only if relations: true flag is passed.

1.3.7. Errors

`errors.DataPackageError`

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 ...
    }
  }
}

1.4. 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

1.5. 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.

1.5.1. v1.0

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

1.5.2. v0.8

First stable version of the library.

1. datapackage-js

1.1. Features

1.2. Getting Started

1.2.1. Installation

NPM

CDN

1.2.2. Examples

1.3. Documentation

1.3.1. Package

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

package.valid

package.errors

package.profile

package.descriptor

package.resources

package.resourceNames

package.getResource(name)

package.addResource(descriptor)

package.removeResource(name)

async package.infer(pattern=false)

package.commit({strict})

async package.save(target)

1.3.2. Resource

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

resource.valid

resource.errors

resource.profile

resource.descriptor

resource.name

resource.inline

resource.local

resource.remote

resource.multipart

resource.tabular

resource.source

resource.headers

resource.schema

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

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

resource.checkRelations()

await resource.rawIter({stream=false})

await resource.rawRead()

async resource.infer()

resource.commit({strict})

async resource.save(target)

1.3.3. Profile

async Profile.load(profile)

profile.name

profile.jsonschema

profile.validate(descriptor)

1.3.4. Validate

async validate(descriptor)

1.3.5. Infer

async infer(pattern, {basePath})

1.3.6. Foreign Keys

1.3.7. Errors

errors.DataPackageError

1.4. Contributing

1.5. Changelog

1.5.1. v1.0

1.5.2. v0.8

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`async Package.load(descriptor, {basePath, strict=false})`

`package.valid`

`package.errors`

`package.profile`

`package.descriptor`

`package.resources`

`package.resourceNames`

`package.getResource(name)`

`package.addResource(descriptor)`

`package.removeResource(name)`

`async package.infer(pattern=false)`

`package.commit({strict})`

`async package.save(target)`

`async Resource.load(descriptor, {basePath, strict=false})`

`resource.valid`

`resource.errors`

`resource.profile`

`resource.descriptor`

`resource.name`

`resource.inline`

`resource.local`

`resource.remote`

`resource.multipart`

`resource.tabular`

`resource.source`

`resource.headers`

`resource.schema`

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

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

`resource.checkRelations()`

`await resource.rawIter({stream=false})`

`await resource.rawRead()`

`async resource.infer()`

`resource.commit({strict})`

`async resource.save(target)`

`async Profile.load(profile)`

`profile.name`

`profile.jsonschema`

`profile.validate(descriptor)`

`async validate(descriptor)`

`async infer(pattern, {basePath})`

`errors.DataPackageError`