thanks if you define columns class as below
scala> case class columns(KEY: String, TICKER: String, TIMEISSUED:
String, *PRICE:
Double)*
defined class columns
scala> var df = Seq(columns("key", "ticker", "timeissued", 1.23f)).toDF
df: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
more fields]
scala> df.printSchema
root
|-- KEY: string (nullable = true)
|-- TICKER: string (nullable = true)
|-- TIMEISSUED: string (nullable = true)
|-- PRICE: double (nullable = false)
looks better
Dr Mich Talebzadeh
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On Thu, 6 Sep 2018 at 10:10, Jungtaek Lim <kabh...@gmail.com> wrote:
> This code works with Spark 2.3.0 via spark-shell.
>
> scala> case class columns(KEY: String, TICKER: String, TIMEISSUED: String,
> PRICE: Float)
> defined class columns
>
> scala> import spark.implicits._
> import spark.implicits._
>
> scala> var df = Seq(columns("key", "ticker", "timeissued", 1.23f)).toDF
> 18/09/06 18:02:23 WARN ObjectStore: Failed to get database global_temp,
> returning NoSuchObjectException
> df: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
> more fields]
>
> scala> df
> res0: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
> more fields]
>
> Maybe need to know about actual type of key, ticker, timeissued, price
> from your variables.
>
> Jungtaek Lim (HeartSaVioR)
>
> 2018년 9월 6일 (목) 오후 5:57, Mich Talebzadeh <mich.talebza...@gmail.com>님이 작성:
>
>> I am trying to understand why spark cannot convert a simple comma
>> separated columns as DF.
>>
>> I did a test
>>
>> I took one line of print and stored it as a one liner csv file as below
>>
>> var allInOne = key+","+ticker+","+timeissued+","+price
>> println(allInOne)
>>
>> cat crap.csv
>> 6e84b11d-cb03-44c0-aab6-37e06e06c996,MRW,2018-09-06T09:35:53,275.45
>>
>> Then after storing it in HDFS, I read that file as below
>>
>> import org.apache.spark.sql.functions._
>> val location="hdfs://rhes75:9000/tmp/crap.csv"
>> val df1 = spark.read.option("header", false).csv(location)
>> case class columns(KEY: String, TICKER: String, TIMEISSUED: String,
>> PRICE: Double)
>> val df2 = df1.map(p => columns(p(0).toString,p(1).toString,
>> p(2).toString,p(3).toString.toDouble))
>> df2.printSchema
>>
>> This is the result I get
>>
>> df1: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 2
>> more fields]
>> defined class columns
>> df2: org.apache.spark.sql.Dataset[columns] = [KEY: string, TICKER: string
>> ... 2 more fields]
>> root
>> |-- KEY: string (nullable = true)
>> |-- TICKER: string (nullable = true)
>> |-- TIMEISSUED: string (nullable = true)
>> |-- PRICE: double (nullable = false)
>>
>> So in my case the only difference is that that comma separated line is
>> stored in a String as opposed to csv.
>>
>> How can I achieve this simple transformation?
>>
>> Thanks
>>
>> Dr Mich Talebzadeh
>>
>>
>>
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>>
>>
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>>
>>
>> *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
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>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Thu, 6 Sep 2018 at 03:38, Manu Zhang <owenzhang1...@gmail.com> wrote:
>>
>>> Have you tried adding Encoder for columns as suggested by Jungtaek Lim ？
>>>
>>> On Thu, Sep 6, 2018 at 6:24 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
>>>>
>>>> Dr Mich Talebzadeh
>>>>
>>>>
>>>>
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>>>>
>>>>
>>>>
>>>> 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.
>>>>
>>>>
>>>>
>>>> I can rebuild the comma separated list as follows:
>>>>
>>>>
>>>> case class columns(KEY: String, TICKER: String, TIMEISSUED: String,
>>>> PRICE: Float)
>>>> val sqlContext= new org.apache.spark.sql.SQLContext(sparkContext)
>>>> import sqlContext.implicits._
>>>>
>>>>
>>>> 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
>>>> var allInOne = key+","+ticker+","+timeissued+","+price
>>>> println(allInOne)
>>>>
>>>> and the print shows the columns separated by ","
>>>>
>>>>
>>>> 34e07d9f-829a-446a-93ab-8b93aa8eda41,SAP,2018-09-05T23:22:34,56.89
>>>>
>>>> So I just need to convert that line of rowinto a DataFrame
>>>>
>>>> I try this conversion to DF to write to MongoDB document with
>>>> MongoSpark.save(df,
>>>> writeConfig)
>>>>
>>>> var df = sparkContext.parallelize(Seq(columns(key, ticker, timeissued,
>>>> price))).toDF
>>>>
>>>> [error]
>>>> /data6/hduser/scala/md_streaming_mongoDB/src/main/scala/myPackage/md_streaming_mongoDB.scala:235:
>>>> value toDF is not a member of org.apache.spark.rdd.RDD[columns]
>>>> [error] var df = sparkContext.parallelize(Seq(columns(key,
>>>> ticker, timeissued, price))).toDF
>>>> [
>>>>
>>>>
>>>> frustrating!
>>>>
>>>> has anyone come across this?
>>>>
>>>> thanks
>>>>
>>>> On Wed, 5 Sep 2018 at 13:30, Mich Talebzadeh <mich.talebza...@gmail.com>
>>>> wrote:
>>>>
>>>>> yep already tried it and it did not work.
>>>>>
>>>>> 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.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Wed, 5 Sep 2018 at 10:10, Deepak Sharma <deepakmc...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Try this:
>>>>>>
>>>>>> *import **spark*.implicits._
>>>>>>
>>>>>> df.toDF()
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh <
>>>>>> mich.talebza...@gmail.com> wrote:
>>>>>>
>>>>>>> With the following
>>>>>>>
>>>>>>> case class columns(KEY: String, TICKER: String, TIMEISSUED: String,
>>>>>>> PRICE: Float)
>>>>>>>
>>>>>>> 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
>>>>>>>
>>>>>>> var df = Seq(columns(key, ticker, timeissued, price))
>>>>>>> println(df)
>>>>>>>
>>>>>>> I get
>>>>>>>
>>>>>>>
>>>>>>> List(columns(ac11a78d-82df-4b37-bf58-7e3388aa64cd,MKS,2018-09-05T10:10:15,676.5))
>>>>>>>
>>>>>>> So just need to convert that list to DF
>>>>>>>
>>>>>>> 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 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.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Wed, 5 Sep 2018 at 09:49, Mich Talebzadeh <
>>>>>>> mich.talebza...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Thanks!
>>>>>>>>
>>>>>>>> The spark is version 2.3.0
>>>>>>>>
>>>>>>>> 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 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.
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Wed, 5 Sep 2018 at 09:41, Jungtaek Lim <kabh...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> 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 <kabh...@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 <kabh...@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.talebza...@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 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.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, 5 Sep 2018 at 09:17, Jungtaek Lim <kabh...@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.talebza...@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.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>
>>>>>> --
>>>>>> Thanks
>>>>>> Deepak
>>>>>> www.bigdatabig.com
>>>>>> www.keosha.net
>>>>>>
>>>>>
