read json key-values with hive/sql and spark

Question

I am trying to read this json file into a hive table, the top level keys i.e. 1,2.., here are not consistent.

{
    "1":"{\"time\":1421169633384,\"reading1\":130.875969,\"reading2\":227.138275}",
    "2":"{\"time\":1421169646476,\"reading1\":131.240628,\"reading2\":226.810211}",
    "position": 0
}

I only need the time and readings 1,2 in my hive table as columns ignore position. I can also do a combo of hive query and spark map-reduce code. Thank you for the help.

Update , here is what I am trying

val hqlContext = new HiveContext(sc)

val rdd = sc.textFile(data_loc)

val json_rdd = hqlContext.jsonRDD(rdd)
json_rdd.registerTempTable("table123")
println(json_rdd.printSchema())
hqlContext.sql("SELECT json_val from table123 lateral view explode_map( json_map(*, 'int,string')) x as json_key, json_val ").foreach(println)

It throws the following error :

Exception in thread "main" org.apache.spark.sql.hive.HiveQl$ParseException: Failed to parse: SELECT json_val from temp_hum_table lateral view explode_map( json_map(*, 'int,string')) x as json_key, json_val
    at org.apache.spark.sql.hive.HiveQl$.createPlan(HiveQl.scala:239)
    at org.apache.spark.sql.hive.ExtendedHiveQlParser$$anonfun$hiveQl$1.apply(ExtendedHiveQlParser.scala:50)
    at org.apache.spark.sql.hive.ExtendedHiveQlParser$$anonfun$hiveQl$1.apply(ExtendedHiveQlParser.scala:49)
    at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:136)
    at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:135)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
    at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:222)

Answer 1

This would work, if you rename "1" and "2" (key names) to "x1" and "x2" (inside the json file or in the rdd):

val resultrdd = sqlContext.sql("SELECT x1.time, x1.reading1, x1.reading1, x2.time, x2.reading1, x2.reading2 from table123  ")
resultrdd.flatMap(row => (Array( (row(0),row(1),row(2)), (row(3),row(4),row(5)) )))

This would give you an RDD of tuples with time, reading1 and reading2. If you need a SchemaRDD, you would map it to a case class inside the flatMap transformation, like this:

case class Record(time: Long, reading1: Double, reading2: Double)
resultrdd.flatMap(row => (Array( Record(row.getLong(0),row.getDouble(1),row.getDouble(2)), 
        Record(row.getLong(3),row.getDouble(4),row.getDouble(5))  )))
val schrdd = sqlContext.createSchemaRDD(resultrdd)

Update:

In the case of many nested keys, you can parse the row like this:

val allrdd = sqlContext.sql("SELECT * from table123")
allrdd.flatMap(row=>{
    var recs = Array[Record](); 
    for(col <- (0 to row.length-1)) { 
        row(col) match { 
            case r:Row => recs = recs :+ Record(r.getLong(2),r.getDouble(0),r.getDouble(1)); 
            case _ => ; 
        } 
    }; 
    recs 
})