PureCSV

This is a fork of https://github.com/melrief/PureCSV with Scala 2.13 support and a few extra features.

PureCSV is a Scala library for working with the CSV format. The library gets rid of most of the boilerplate required to work with CSV

scala> import purecsv.unsafe._
scala> case class Event(ts: Long, msg: String)
scala> val records = CSVReader[Event].readCSVFromString("1,foo\n2,bar")
records: List[Event] = List(Event(1,foo), Event(2,bar))
scala> records.toCSV()
res0: String =
1,"foo"
2,"bar

In this example, a case class called Event is defined and immediately used to read to and write from CSV.

Add the library to your project

Add the Sonatype release repository to the resolvers and then add the library to the project libraryDependencies:

resolvers += Resolver.sonatypeRepo("releases")

libraryDependencies += "io.kontainers" %% "purecsv" % "1.3.10"

The library works for Scala 2.12.x and 2.13.x.

Use the library

Writing to CSV

To convert a value to a CSV String use toCSV

scala> import purecsv.safe._
scala> class Interval(val start: Long, val end: Long)
scala> new Interval(10,20).toCSV()
res1: String = 1,10
scala> Seq(new Interval(1,10),new Interval(11,20)).toCSV("|")
res2: String =
1|10
11|20

To write CSV values to a file use writeCSVToFile or writeCSVToFileName

scala> import purecsv.unsafe._
scala> class Interval(val start: Long, val end: Long) { override def toString: String = s"Interval($start,$end)" }
scala> Seq(new Interval(1,10),new Interval(11,20)).writeCSVToFileName("/tmp/example.csv")
scala> scala.io.Source.fromFile("/tmp/example.csv").getLines.toList
res0: List[String] = List(1,10, 11,20)
scala> CSVReader[Interval].readCSVFromFile("/tmp/example.csv")
res2: List[Interval] = List(Interval(1,10), Interval(11,20))

Reading from CSV

Reading from CSV is a bit different from writing to CSV. There are two different ways to read from CSV Strings, one safe and the other unsafe. See next paragraph for the difference. Other than that, a CSVReader instance should be used to read data

scala> import purecsv.unsafe._
scala> class Interval(val start: Long, val end: Long) { override def toString: String = s"Interval($start,$end)" }
scala> CSVReader[Interval].readCSVFromFile("/tmp/example.csv")
res2: List[Interval] = List(Interval(1,10), Interval(11,20))

If the source has a header, it is possible to skip it.

scala> import purecsv.unsafe._
scala> case class Address(name: String, address: String)
scala> Seq(Address("alice","wonderland")).writeCSVToFileName("/tmp/example.csv", header=Some(Seq("name","address")))
scala> scala.io.Source.fromFile("/tmp/example.csv").getLines.toList
res2: List[String] = List(name,address, "alice","wonderland")
scala> CSVReader[Address].readCSVFromFileName("/tmp/example.csv", headers = Headers.ReadAndIgnore)
res1: List[Address] = List(Address(alice,wonderland))

An optional header -> field mapping can be provided when reading a CSV.

scala> import purecsv.safe._
scala> case class Address(name: String, address: String)
scala> val csv = """address,full name
     | wonderland,alice""".stripMargin
csv: String =
address,full name
wonderland,alice
scala> CSVReader[Address].readCSVFromString(csv, headerMapping = Map("full name" -> "name"))
res1: List[scala.util.Try[Address]] = List(Success(Address(alice,wonderland)))

Headers

By default purecsv assumes that headers are present in csv that is being read (headers is set by default to Headers.ParseHeaders). For Headers.ParseHeaders ordering and number (value for absent columns will be set to None / empty string) of columns in csv does not matter.

Other options for Headers are

• Headers.None - csv does not contain headers
• Headers.ReadAndIgnore - csv contains headers that should be skipped

In both cases ordering of columns matters.

Reading from CSV: Safe vs Unsafe

For reading values, the library comes in two flavors: one safe and one unsafe. Only one should be imported. The safe flavor captures errors

scala> import purecsv.safe._
scala> case class Person(name: String, age: Int)
scala> CSVReader[Person].readCSVFromString("alice,1")
res0: List[scala.util.Try[Person]] = List(Success(Person(alice,1)))
scala> CSVReader[Person].readCSVFromString("alice,nan")
res1: List[scala.util.Try[Person]] = List(Failure(java.lang.NumberFormatException: For input string: "nan"))

The unsafe flavor, instead, ignores errors

scala> import purecsv.unsafe._
scala> case class Person(name: String, age: Int)
scala> CSVReader[Person].readCSVFromString("alice,1")
res0: List[Person] = List(Person(alice,1))
scala> CSVReader[Person].readCSVFromString("alice,nan")
java.lang.NumberFormatException: For input string: "nan"
  at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
  at java.lang.Integer.parseInt(Integer.java:492)
  ...

The two flavors solve different problems: the safe one helps dealing with inputs that could potentially be not well formatted or event erroneous while the unsafe flavor reads data that is well formatted, for instance the one created by this library.

The safe flavor has also another characteristic: it helps the user understanding which records gave errors by collecting them

scala> val result = CSVReader[Person].readCSVFromString("alice,1\nbob,nan\ncharlie,2\ndelta,\n")
result: List[scala.util.Try[Person]] = List(Success(Person(alice,1)), Failure(java.lang.NumberFormatException: For input string: "nan"), Success(Person(charlie,2)), Failure(java.lang.NumberFormatException: For input string: ""))
scala> import purecsv.safe.tryutil._
scala> val (successes,failures) = result.getSuccessesAndFailures
successes: List[(Int, Person)] = List((1,Person(alice,1)), (3,Person(charlie,2)))
failures: List[(Int, Throwable)] = List((2,java.lang.NumberFormatException: For input string: "nan"), (4,java.lang.NumberFormatException: For input string: ""))

The method getSuccessesAndFailures returns a pair where the first element are the successes and the second element are the failures with associated the record number. From the information above we know that record 1 and 3 were read successfully while record 2 and 4 were not.

Extending the library

PureCSV has been designed with flexibility and extensibility in mind. It is possible to add new field types as well as change the way standard types are read from String. It is also possible to change the way CSV is parsed.

Add new field types

Boolean, Int and most of the Scala types are field types, meaning that it is possible to convert their values from/to String. To add a new type A, you have to supply a purecsv.safe.converter.StringConverter[A]. You can have a look at the defaults string converters to see how this is done for primitive types. For completeness, we can do an example showing how to create a StringConverter for non-trivial types, like ISO 8601 for dates. We want to avoid to reinvent the wheel and thus we are going to use the Joda-Time library that provides the Joda DateTime structure where to store datetimes as well as the parsers. We can add our StringConverter[DateTime] in this way

implicit val dateTimeStringConverter = new StringConverter[DateTime] {
  override def tryFrom(str: String): Try[DateTime] = {
    Try(ISODateTimeFormat.dateTimeParser().parseDateTime(str))
  }

  override def to(dateTime: DateTime): String = {
    ISODateTimeFormat.dateTime().print(dateTime)
  }
}

After importing this converter, PureCSV will be able to read DateTime fields.

How it works

The library is based on Shapeless Generic system. Everything that has a Generic instance can be used with PureCSV. A Generic instance defines a representation for a given type in terms of heterogenous lists and function to convert from/to that representation

scala> import shapeless._
scala> case class Foo(i: Int)
scala> :t Generic[Foo]
shapeless.Generic[Foo]{type Repr = shapeless.::[Int,shapeless.HNil]}
scala> Generic[Foo].to(Foo(1))
res4: shapeless.::[Int,shapeless.HNil] = 1 :: HNil

Note that also structures that don't have an automatically generated Generic instance can be used by manually define the Generic. So given a type

class Event2(val ts: Long, var msg: String) {
  override def equals(o: Any): Boolean = o match {
    case other:Event2 => (this.ts ## other.ts && this.msg ## other.msg)
    case _ => false
  }
  override def toString: String = s"Event($ts, $msg)"
}

One can define the Generic instance as

implicit val fooGeneric = new Generic[Event2] {
  override type Repr = Long :: String :: HNil
  override def from(r: Repr): Event2 = {
    val ts :: msg :: HNil = r
    new Event2(ts, msg)
  }
  override def to(t: Event2): Repr = t.ts :: t.msg :: HNil
}

and then use that structure with PureCSV

scala> val conv = RawFieldsConverter[Event2]

License

Apache License v2

Special Thanks

To the Shapeless developers for their amazing library.