A Spark toolkit, not exactly like jq but inspired by it

1. RDD[String] within json or jsonArray data type

1. Number -> Scala Int, Long, Double
2. String -> Scala String
3. Object -> Scala Map
4. Array -> Scala List
5. Boolean -> Scala Boolean
6. Compose Field, such as "map1.map2.intField" -> Type above

libraryDependencies += "com.magicsoho" %% "spark-jq" % "0.1.0"

<dependency>
    <groupId>com.magicsoho</groupId>
    <artifactId>spark-jq_${your_scala_binary_version}</artifactId>
    <version>0.1.0</version>
</dependency>

0. first of all

   import sjq.RDDLike._

1. rdd.parseJson

   • parse json RDD into JSONObject RDD

2. rddJson.fields("field1", "filed2")

   • return an RDD[List(field1Type, field2Type)]

3. rddFields(n)

   • return RDD[element n in list]

4. rddJson.key[T]("field1") or rdd.field(fieldFoo)

   • return RDD[T]

5. rddJson.jsonObject("objKey")

   • return an JSONObject RDD

6. rddField.[Int|Long|Double|Boolean|List[T]|Map[T1,T2]|JSONObject]

   • map RDD[Any] into RDD[T with specified type]

0. first of all

   import sjq.Lambda._

1. addInt | addDouble

   • an RDD[(Key, (Int, Int))] use with reduceByKey(addInt)

2. addTuple2

   • an RDD[(Key, ((AnyNumber, AnyNumber), (AnyNumber, AnyNumber))] use with reduceByKey(addTuple2)

1. support regex field
2. support more format: csv, xml, ...
3. support other input data, sql, kafka, flume, ...
4. support other RDD reduce function utils

MIT