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From Jungtaek Lim <kabh...@gmail.com>
Subject Re: getting error: value toDF is not a member of Seq[columns]
Date Wed, 05 Sep 2018 08:41:20 GMT
You may also find below link useful (though it looks far old), since case
class is the thing which Encoder is available, so there may be another
reason which prevent implicit conversion.

https://community.cloudera.com/t5/Advanced-Analytics-Apache-Spark/Spark-Scala-Error-value-toDF-is-not-a-member-of-org-apache/m-p/29994/highlight/true#M973

And which Spark version do you use?


2018년 9월 5일 (수) 오후 5:32, Jungtaek Lim <kabhwan@gmail.com>님이 작성:

> Sorry I guess I pasted another method. the code is...
>
> implicit def localSeqToDatasetHolder[T : Encoder](s: Seq[T]): DatasetHolder[T] = {
>   DatasetHolder(_sqlContext.createDataset(s))
> }
>
>
> 2018년 9월 5일 (수) 오후 5:30, Jungtaek Lim <kabhwan@gmail.com>님이 작성:
>
>> I guess you need to have encoder for the type of result for columns().
>>
>>
>> https://github.com/apache/spark/blob/2119e518d31331e65415e0f817a6f28ff18d2b42/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala#L227-L229
>>
>> implicit def rddToDatasetHolder[T : Encoder](rdd: RDD[T]): DatasetHolder[T] = {
>>   DatasetHolder(_sqlContext.createDataset(rdd))
>> }
>>
>> You can see lots of Encoder implementations in the scala code. If your
>> type doesn't match anything it may not work and you need to provide custom
>> Encoder.
>>
>> -Jungtaek Lim (HeartSaVioR)
>>
>> 2018년 9월 5일 (수) 오후 5:24, Mich Talebzadeh <mich.talebzadeh@gmail.com>님이
>> 작성:
>>
>>> Thanks
>>>
>>> I already do that as below
>>>
>>>     val sqlContext= new org.apache.spark.sql.SQLContext(sparkContext)
>>>   import sqlContext.implicits._
>>>
>>> but still getting the error!
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
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>>>
>>>
>>>
>>> http://talebzadehmich.wordpress.com
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
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>>> arise from relying on this email's technical content is explicitly
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>>>
>>>
>>>
>>> On Wed, 5 Sep 2018 at 09:17, Jungtaek Lim <kabhwan@gmail.com> wrote:
>>>
>>>> You may need to import implicits from your spark session like below:
>>>> (Below code is borrowed from
>>>> https://spark.apache.org/docs/latest/sql-programming-guide.html)
>>>>
>>>> import org.apache.spark.sql.SparkSession
>>>> val spark = SparkSession
>>>>   .builder()
>>>>   .appName("Spark SQL basic example")
>>>>   .config("spark.some.config.option", "some-value")
>>>>   .getOrCreate()
>>>> // For implicit conversions like converting RDDs to DataFramesimport spark.implicits._
>>>>
>>>>
>>>> 2018년 9월 5일 (수) 오후 5:11, Mich Talebzadeh <mich.talebzadeh@gmail.com>님이
>>>> 작성:
>>>>
>>>>> Hi,
>>>>>
>>>>> I have spark streaming that send data and I need to put that data into
>>>>> MongoDB for test purposes. The easiest way is to create a DF from the
>>>>> individual list of columns as below
>>>>>
>>>>> I loop over individual rows in RDD and perform the following
>>>>>
>>>>>     case class columns(KEY: String, TICKER: String, TIMEISSUED:
>>>>> String, PRICE: Float)
>>>>>
>>>>>          for(line <- pricesRDD.collect.toArray)
>>>>>          {
>>>>>             var key = line._2.split(',').view(0).toString
>>>>>            var ticker =  line._2.split(',').view(1).toString
>>>>>            var timeissued = line._2.split(',').view(2).toString
>>>>>            var price = line._2.split(',').view(3).toFloat
>>>>>            val priceToString = line._2.split(',').view(3)
>>>>>            if (price > 90.0)
>>>>>            {
>>>>>              println ("price > 90.0, saving to MongoDB collection!")
>>>>>             // Save prices to mongoDB collection
>>>>>            * var df = Seq(columns(key, ticker, timeissued,
>>>>> price)).toDF*
>>>>>
>>>>> but it fails with message
>>>>>
>>>>>  value toDF is not a member of Seq[columns].
>>>>>
>>>>> What would be the easiest way of resolving this please?
>>>>>
>>>>> thanks
>>>>>
>>>>> Dr Mich Talebzadeh
>>>>>
>>>>>
>>>>>
>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>
>>>>>
>>>>>
>>>>> http://talebzadehmich.wordpress.com
>>>>>
>>>>>
>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>>> any loss, damage or destruction of data or any other property which may
>>>>> arise from relying on this email's technical content is explicitly
>>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>>> arising from such loss, damage or destruction.
>>>>>
>>>>>
>>>>>
>>>>

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