By taking with your advice flatMap, now I can convert result from
RDD[Iterable[MyCaseClass]] to RDD[MyCaseClass]. Basically just to perform
flatMap in the end before starting to convert RDD object back to DF (i.e.
SparkSession.createDataFrame(rddRecordsOfMyCaseClass)). For instance,
df.map { ... }.filter{ ... }.flatMap { records => records.flatMap { record =>
Seq(record) } }
Not smart code, but it works for my case.
Thanks for the advice!
‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Saturday, December 1, 2018 12:17 PM, Chris Teoh <chris.t...@gmail.com> wrote:
> Hi James,
>
> Try flatMap (_.toList). See below example:-
>
> scala> case class MyClass(i:Int)
> defined class MyClass
>
> scala> val r = 1 to 100
> r: scala.collection.immutable.Range.Inclusive = Range(1, 2, 3, 4, 5, 6, 7, 8,
> 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
> 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
> 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
> 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
> 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100)
>
> scala> val r2 = 101 to 200
> r2: scala.collection.immutable.Range.Inclusive = Range(101, 102, 103, 104,
> 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
> 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
> 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
> 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
> 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,
> 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
> 195, 196, 197, 198, 199, 200)
>
> scala> val c1 = r.map(MyClass(_)).toIterable
> c1: Iterable[MyClass] = Vector(MyClass(1), MyClass(2), MyClass(3),
> MyClass(4), MyClass(5), MyClass(6), MyClass(7), MyClass(8), MyClass(9),
> MyClass(10), MyClass(11), MyClass(12), MyClass(13), MyClass(14), MyClass(15),
> MyClass(16), MyClass(17), MyClass(18), MyClass(19), MyClass(20), MyClass(21),
> MyClass(22), MyClass(23), MyClass(24), MyClass(25), MyClass(26), MyClass(27),
> MyClass(28), MyClass(29), MyClass(30), MyClass(31), MyClass(32), MyClass(33),
> MyClass(34), MyClass(35), MyClass(36), MyClass(37), MyClass(38), MyClass(39),
> MyClass(40), MyClass(41), MyClass(42), MyClass(43), MyClass(44), MyClass(45),
> MyClass(46), MyClass(47), MyClass(48), MyClass(49), MyClass(50), MyClass(51),
> MyClass(52), MyClass(53), MyClass(54), MyClass(55), MyClass(56), MyClass(57),
> MyClass(58), MyClass(59), MyClass(...
>
> scala> val c2 = r2.map(MyClass(_)).toIterable
> c2: Iterable[MyClass] = Vector(MyClass(101), MyClass(102), MyClass(103),
> MyClass(104), MyClass(105), MyClass(106), MyClass(107), MyClass(108),
> MyClass(109), MyClass(110), MyClass(111), MyClass(112), MyClass(113),
> MyClass(114), MyClass(115), MyClass(116), MyClass(117), MyClass(118),
> MyClass(119), MyClass(120), MyClass(121), MyClass(122), MyClass(123),
> MyClass(124), MyClass(125), MyClass(126), MyClass(127), MyClass(128),
> MyClass(129), MyClass(130), MyClass(131), MyClass(132), MyClass(133),
> MyClass(134), MyClass(135), MyClass(136), MyClass(137), MyClass(138),
> MyClass(139), MyClass(140), MyClass(141), MyClass(142), MyClass(143),
> MyClass(144), MyClass(145), MyClass(146), MyClass(147), MyClass(148),
> MyClass(149), MyClass(150), MyClass(151), MyClass(152), MyClass(153),
> MyClass(154), MyClass(15...
> scala> val rddIt = sc.parallelize(Seq(c1,c2))
> rddIt: org.apache.spark.rdd.RDD[Iterable[MyClass]] = ParallelCollectionRDD[2]
> at parallelize at <console>:28
>
> scala> rddIt.flatMap(_.toList)
> res4: org.apache.spark.rdd.RDD[MyClass] = MapPartitionsRDD[3] at flatMap at
> <console>:26
>
> res4 is what you're looking for.
>
> On Sat, 1 Dec 2018 at 21:09, Chris Teoh <chris.t...@gmail.com> wrote:
>
>> Do you have the full code example?
>>
>> I think this would be similar to the mapPartitions code flow, something like
>> flatMap( _ => _.toList )
>>
>> I haven't yet tested this out but this is how I'd first try.
>>
>> On Sat, 1 Dec 2018 at 01:02, James Starks <suse...@protonmail.com.invalid>
>> wrote:
>>
>>> When processing data, I create an instance of RDD[Iterable[MyCaseClass]]
>>> and I want to convert it to RDD[MyCaseClass] so that it can be further
>>> converted to dataset or dataframe with toDS() function. But I encounter a
>>> problem that SparkContext can not be instantiated within SparkSession.map
>>> function because it already exists, even with allowMultipleContexts set to
>>> true.
>>>
>>> val sc = new SparkConf()
>>> sc.set("spark.driver.allowMultipleContexts", "true")
>>> new SparkContext(sc).parallelize(seq)
>>>
>>> How can I fix this?
>>>
>>> Thanks.
>>
>> --
>> Chris
>
> --
> Chris
