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trait TwitterElem
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Next, let s define a class that represents a status. The structure of the class is based on the definition of the Status in the API document.
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/* <status> created_at id
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CHAPTER 8 PARSERS BECAUSE BNF IS NOT JUST FOR ACADEMICS ANYMORE
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text source truncated in_reply_to_status_id in_reply_to_user_id favorited */ case class TwitterStatus(id: Long, createdAt: String, text: String, source: String, truncated: Boolean, inReplyToStatus: Option[Long], inReplyToUser: Option[Long], favorited: Boolean, user: TwitterUser) extends TwitterElem
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In order to convert from the JSON data, we need to extract each field and build up all the parameters necessary to construct a TwitterStatus. Using is and sGet from SafeMap along with the for comprehension, we extract the data from the JSON data and create a type-safe TwitterStatus instance.
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object TwitterStatus extends SafeMap { def apply(in: Any): Option[TwitterStatus] = for {m <- in.is[Map[String, Any]] id <- m.sGet[String]("id").map(_.toLong) createdAt <- m.sGet[String]("created_at") text <- m.sGet[String]("text") source <- m.sGet[String]("source") truncated <- m.sGet[Boolean]("truncated") inRepSt = m.sGet[Double]("in_reply_to_status_id").map(_.toLong) inRepUsr = m.sGet[Double]("in_reply_to_user_id").map(_.toLong) fav = m.sGet[Boolean]("favorited") getOrElse false userObj <- m.sGet[Map[String, Any]]("user") user <- TwitterUser(userObj) } yield new TwitterStatus(id, createdAt, text, source, truncated, inRepSt, inRepUsr, fav, user)
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CHAPTER 8 PARSERS BECAUSE BNF IS NOT JUST FOR ACADEMICS ANYMORE
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The fromList method extracts a series of TwitterStatus instances from a List of JSON data.
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def fromList(in: Any): List[TwitterStatus] = { for {list <- in.is[List[Any]].toList item <- list st <- apply(item) } yield st } } TwitterStatus includes a TwitterUser instance. We define the TwitterUser class the same way we defined TwitterStatus. /* <user> id name screen_name description location profile_image_url url protected followers_count */ case class TwitterUser(id: Long, name: String, screenName: String, description: Option[String], location: Option[String], image: Option[String], url: Option[String], protectd: Boolean, followerCount: Option[Int]) extends TwitterElem object TwitterUser extends SafeMap { def apply(in: Any): Option[TwitterUser] = for {m <- in.is[Map[String, Any]] id <- m.sGet[String]("id").map(_.toLong)
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CHAPTER 8 PARSERS BECAUSE BNF IS NOT JUST FOR ACADEMICS ANYMORE
name <- m.sGet[String]("name") scrName <- m.sGet[String]("screen_name") desc = m.sGet[String]("description") loc = m.sGet[String]("location") image = m.sGet[String]("profile_image_url") url = m.sGet[String]("url") prot = m.sGet[Boolean]("protected") getOrElse false fc = m.sGet[Double]("followers_count").map(_.toInt) } yield new TwitterUser(id, name, scrName, desc, loc, image, url, prot, fc) }
We ve got our classes all defined. Let s use the REPL to see how well it works.
scala> import scala.io._ import scala.io._ scala> val timeline = "http://twitter.com/statuses/public_timeline.json"
timeline: java.lang.String = http://twitter.com/statuses/public_timeline.json
scala> val src = Source.fromURL(timeline)
src: scala.io.Source = non-empty iterator
scala> val json = JSON.run(src.mkString)
json: JSON.ParseResult[JSON.RootType] = [1.11894] parsed: List( )
scala> json.map(TwitterStatus.fromList _)
res0: JSON.ParseResult[List[TwitterStatus]] = List(TwitterStatus(1242017450, ))
CHAPTER 8 PARSERS BECAUSE BNF IS NOT JUST FOR ACADEMICS ANYMORE
Very cool. We ve layered our Twitter-specific code on top of the JSON Parser in a way that allows us to parse Twitter-related data. The json.map(TwitterStatus.fromList _) piece reinforces the value of functional programming. We were able to convert a ParseResult that returned JSON data into a ParseResult that returns Twitter data just by mapping the JSON data into Twitter data.
Summary
Scala s parser combinator library demonstrates the flexibility of Scala s syntax, the usefulness of implicit conversions, and the power of functional composition. The parser combinator is an excellent example of a domain-specific language. The domain is parsing text, and the syntax is nearly one-for-one with BNF. We were able to create the JSON Parser by taking the ECMAScript spec and translate it directly into Scala code. This library also gives you some idea of the kind of domain-specific languages you can create using Scala. There s nothing specific in the Scala compiler for the parser combinator library it s just that, a library. On a practical level, using a single language Scala for defining your Parser rather than using a tool like ANTLR9 means that you and your team use a single language for describing your system. This means that your brain thinks Scala. This means that you edit code in a single language and take advantage of the type safety of the language. This concludes our tour of Scala the language. In the next chapter, we ll explore how you can integrate Scala into your projects, better build teams and divide work using Scala, and take advantage of the power of Scala without losing the infrastructure that you ve built around Java.
9. http://www.antlr.org/
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