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Transformations

Transformations can be applied easily during conversions. For example, field names can be modified.

Implicit Formats​

Formats can handle different configurations that we want to apply to schemas, like transforming field names, defining precision for numeric types and so on.

They can be used by creating an implicit val with a Formats class or by importing one of the available implicit vals in Formats object

DefaultFormats​

DefaultFormats is a trait that applies no transformation to field names

To use it, you can create an implicit val:

import org.datatools.bigdatatypes.formats.{Formats, DefaultFormats}
implicit val formats: Formats = DefaultFormats

or just import the one available:

import org.datatools.bigdatatypes.formats.Formats.implicitDefaultFormats
info

Formats is required in the scope for all the transformations, even if no transformations are needed. In this case, implicitDefaultFormats can be imported and no transformation will be applied.

SnakifyFormats​

SnakifyFormats is a trait that converts camelCase field names to snake_case names

To use it, you can create an implicit val:

import org.datatools.bigdatatypes.formats.{Formats, SnakifyFormats}
implicit val formats: Formats = SnakifyFormats

or just import the one available:

import org.datatools.bigdatatypes.formats.Formats.implicitSnakifyFormats

Creating a custom Formats​

Formats can be extended, so if we want to transform keys differently, for example adding a suffix to all of our fields

import org.datatools.bigdatatypes.formats.Formats
trait SuffixFormats extends Formats {
  override def transformKey[A <: SqlType](name: String, t: A): String = key + "_at"
}
object SuffixFormats extends SuffixFormats

All your field names will have "_at" at the end.

t is the Type of the field so you can decide how to transform your keys based on the type with a simple pattern matching