tableschema-swift

This is a Swift language implementation of TableSchema for defining schemas to work with tabular data.

A schema on tabular data defines types, imposes constraints, and creates foreign key relationships on fields as data values move from some physical representation to a logical one and vice versa. For instance, a stored CSV file (physical) can be loaded in-memory along with a corresponding schema descriptor to be transformed from string values to Swift Standard Library types like Date or Int (logical).

Requirements

• Source compatibility with Swift 4.2
• Target platforms
  • Apple platforms, specifically iOS and macOS
    • Full functionality in iOS >= 10 and macOS >= 10.12
  • Linux, limited by features available in swift-corelibs-foundation
• Apple’s Foundation framework is the only dependency
• Independent from any one particular physical representation

Implementation Status

Although incomplete this library is being used in at least one shipping product over a subset of the available features. Field types that are unsupported will not be transformed resulting in potential data loss on those unsupported types. Please consult the following tables. There is a testing suite to keep what should be working in check. Contributions are welcome.

Feature Status

Feature	Status
Streaming and cast on iteration	Available
Casting field types and formats	Partial
[De]serialization	Available in Tabular Data Package
Schema inference	Missing (Unlikely to implement)
Strict mode	Missing
Constraint validation	Missing
Foreign key validation	Missing
Rich (RDF) Types	Missing (Unlikely to implement)

Casting Field Types and Formats Status

Type	Formats	Additional Properties	Forward Status (Physical to Logical)	Reverse Status (Logical to Physical)
string	default, uri, binary, uuid	N/A	Available	Available
string	email	N/A	Unavailable	Unavailable
number	N/A	Any	Unavailable	Unavailable
integer	N/A	bareNumber = false	Available*	Available
integer	N/A	bareNumber = true	Available	Available
boolean	N/A	trueValues, falseValues	Available	Available
object	N/A	N/A	Available	Unavailable
array	N/A	N/A	Available	Available
date	N/A	default	Available*	Unavailable
date	N/A	any, pattern	Unavailable	Unavailable
time	N/A	default	Available*	Unavailable
time	N/A	any, pattern	Unavailable	Unavailable
datetime	N/A	default	Available*	Available*
datetime	N/A	any, pattern	Unavailable	Unavailable
year	N/A	N/A	Available*	Unavailable
yearmonth	N/A	N/A	Available*	Unavailable
duration	N/A	N/A	Available*	Unavailable
geopoint	default, array, object	N/A	Available	Unavailable
geojson	default, topo	N/A	Unavailable	Unavailable
any	N/A	N/A	Unavailable	Unavailable

* Only available on Apple products (namely iOS and macOS) due to an incomplete implementation in swift-corelibs-foundation

Integration into Your Project

This project is set up using Swift Package Manager. Ideally add it to your project’s SPM dependencies or use Xcode’s integrated Swift Package Manager. Alternatively, generate your own Xcode .xcodeproj to integrate with your build system using:

swift package generate-xcodeproj --xcconfig-overrides ./Configuration.xcconfig

Example Usage

Cast on Iteration from a Data Source

Deserializing of data (from, say, a CSV file) can be accomplished by setting up a Table with an iterator that provides row information using a TableProvider data source. This allows for the data source to stream data rather than necessarily loading everything in-memory. Table is agnostic from the specific data source but expects the data source to convert to String representations.

let sourcePath = "import.csv"
let sourceDialect = DialectalCSV.Dialect()

let fields = [Field("name", type: .string), Field("birthday", type: .date)]
let schema = Schema(fields)

guard let provider = MyTableProvider(atPath: sourcePath, dialect: sourceDialect) else {
    fatalError("Oops")
}
let table = Table(provider: AnyTableProvider(provider), schema: schema)
let objects = table.map { $0 }

And defining MyTableProvider together with a CSV parsing library like DialectalCSV:

class MyTableProvider: TableProvider {

    private let handler: DialectalCSV.InputHandler
    private let streamIterator: DialectalCSV.InputIterator

    init?(atPath path: String, dialect: DialectalCSV.Dialect) {
        guard let handler = DialectalCSV.InputHandler(atPath: path, dialect: dialect) else {
            return nil
        }
        self.handler = handler
        self.streamIterator = handler.makeIterator()
    }

    // MARK: - TableProvider

    var header: Header? {
        return self.streamIterator.header
    }

    // MARK: - Sequence

    func makeIterator() -> AnyIterator<[String?]> {
        return AnyIterator {
            return self.streamIterator.next()
        }
    }

}

Reverse Casting (Logical to Physical)

Cast the entire data set in-memory:

let objects = [[Any?]]()
let rows = objects.map { schema.reverseCast(row: $0) }

Or streaming output using a CSV parsing library like DialectalCSV:

let objects: [[Any?]] = [["River Tam", Date(timeIntervalSince1970: 16725225600)],["Simon Tam", nil]]

let destinationPath = "export.csv"
var destinationDialect = DialectalCSV.Dialect()
destinationDialect.nullSequence = "null"

FileManager.default.createFile(atPath: destinationPath, contents: nil)
guard let outputHandler = DialectalCSV.OutputHandler(atPath: destinationPath, dialect: destinationDialect) else {
    fatalError("Oops")
}

let header = schema.fields.map { $0.name }
try? outputHandler.open(header: header)

for object in objects {
    let row = schema.reverseCast(row: object).map { $0 }
    try? outputHandler.append(records: [row])
}

try? outputHandler.close()