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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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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>>
>>
>> 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
>>>
>>>
>>>
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>>>
>>>
>>> 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 *
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>>>>>
>>>>>
>>>>>
>>>>> http://talebzadehmich.wordpress.com
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>>>>>
>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
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>>>>> 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 *
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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 *
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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 *
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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.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>
>>>> --
>>>> Thanks
>>>> Deepak
>>>> www.bigdatabig.com
>>>> www.keosha.net
>>>>
>>>
