如何将 JSON 的 RDD 转换为 Dataframe？

Question

I have an RDD that has been created from some JSON, each record in the RDD contains key/value pairs. My RDD looks like:

myRdd.foreach(println)

{"sequence":89,"id":8697344444103393,"trackingInfo":{"location":"Browse","row":0,"trackId":14170286,"listId":"cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585","videoId":80000778,"rank":0,"requestId":"ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171"},"type":["Play","Action","Session"],"time":527636408955},1],
{"sequence":153,"id":8697389197662617,"trackingInfo":{"location":"Browse","row":0,"trackId":14170286,"listId":"cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585","videoId":80000778,"rank":0,"requestId":"ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171"},"type":["Play","Action","Session"],"time":527637852762},1],
{"sequence":155,"id":8697389381205360,"trackingInfo":{"location":"Browse","row":0,"trackId":14170286,"listId":"cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585","videoId":80000778,"rank":0,"requestId":"ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171"},"type":["Play","Action","Session"],"time":527637858607},1],
{"sequence":136,"id":8697374208897843,"trackingInfo":{"location":"Browse","row":0,"trackId":14170286,"listId":"cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585","videoId":80000778,"rank":0,"requestId":"ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171"},"type":["Play","Action","Session"],"time":527637405129},1],
{"sequence":189,"id":8697413135394406,"trackingInfo":{"row":0,"trackId":14272744,"requestId":"284929d9-6147-4924-a19f-4a308730354c-3348447","rank":0,"videoId":80075830,"location":"PostPlay\/Next"},"type":["Play","Action","Session"],"time":527638558756},1],
{"sequence":130,"id":8697373887446384,"trackingInfo":{"location":"Browse","row":0,"trackId":14170286,"listId":"cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585","videoId":80000778,"rank":0,"requestId":"ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171"},"type":["Play","Action","Session"],"time":527637394083}]

I would to convert each record to a row in a spark dataframe, the nested fields in trackingInfo should be there own columns and the type list should be its own column also.

So far I've tired to split it using a case class :

case class Event(
    sequence: String, 
    id: String, 
    trackingInfo:String,
    location:String, 
    row:String, 
    trackId: String, 
    listrequestId: String, 
    videoId:String, 
    rank: String, 
    requestId: String, 
    `type`:String, 
    time: String)

val dataframeRdd = myRdd.map(line => line.split(",")).
    map(array => Event(
        array(0).split(":")(1),
        array(1).split(":")(1),
        array(2).split(":")(1),
        array(3).split(":")(1),
        array(4).split(":")(1),
        array(5).split(":")(1),
        array(6).split(":")(1),
        array(7).split(":")(1),
        array(8).split(":")(1),
        array(9).split(":")(1),
        array(10).split(":")(1),
        array(11).split(":")(1)
        ))

However I keep getting java.lang.ArrayIndexOutOfBoundsException: 1 errors.

What is the best way to do this ? As you can see record number 5 has a slight difference in the ordering of some attributes. Is it possible to parse based on attribute names instead of splitting on "," etc.

I'm using Spark 1.6.x

Answer 1

Your json rdd seems to be invalid jsons . You need to convert them to valid jsons as

val validJsonRdd = myRdd.map(x => x.replace(",1],", ",").replace("}]", "}"))

then you can use the sqlContext to read the valid rdd jsons into a dataframe as

val df = sqlContext.read.json(validJsonRdd)

which should give you dataframe ( i used the invalid json you provided in the question)

+----------------+--------+------------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------+
|id              |sequence|time        |trackingInfo                                                                                                                             |type                   |
+----------------+--------+------------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------+
|8697344444103393|89      |527636408955|[cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585,Browse,0,ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171,0,14170286,80000778]|[Play, Action, Session]|
|8697389197662617|153     |527637852762|[cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585,Browse,0,ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171,0,14170286,80000778]|[Play, Action, Session]|
|8697389381205360|155     |527637858607|[cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585,Browse,0,ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171,0,14170286,80000778]|[Play, Action, Session]|
|8697374208897843|136     |527637405129|[cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585,Browse,0,ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171,0,14170286,80000778]|[Play, Action, Session]|
|8697413135394406|189     |527638558756|[null,PostPlay/Next,0,284929d9-6147-4924-a19f-4a308730354c-3348447,0,14272744,80075830]                                                  |[Play, Action, Session]|
|8697373887446384|130     |527637394083|[cd7c2c7a-00f6-4035-867f-d1dd7d89972d_6625365X3XX1505943605585,Browse,0,ac12f4e1-5644-46af-87d1-ec3b92ce4896-4071171,0,14170286,80000778]|[Play, Action, Session]|
+----------------+--------+------------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------+

and the schema for the dataframe is

root
 |-- id: long (nullable = true)
 |-- sequence: long (nullable = true)
 |-- time: long (nullable = true)
 |-- trackingInfo: struct (nullable = true)
 |    |-- listId: string (nullable = true)
 |    |-- location: string (nullable = true)
 |    |-- rank: long (nullable = true)
 |    |-- requestId: string (nullable = true)
 |    |-- row: long (nullable = true)
 |    |-- trackId: long (nullable = true)
 |    |-- videoId: long (nullable = true)
 |-- type: array (nullable = true)
 |    |-- element: string (containsNull = true)

I hope the answer is helpful

Answer 2

您可以使用 sqlContext.read.json(myRDD.map(_._2)) 将 json 读入数据帧