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A Swift library for working with Table Schema.


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This is a draft 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).


  • 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

Being a draft implementation means APIs have not been solidified and are subject to change. However, much of the foundation has been laid, there is a testing suite to keep what should be working in check, and it is being used in at least one shipping product over a subset of the available features. The approach has been implementing features on an as-needed basis.

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 {
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 {

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()