spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: getting error: value toDF is not a member of Seq[columns]
Date Thu, 06 Sep 2018 08:56:58 GMT
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



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 Thu, 6 Sep 2018 at 03:38, Manu Zhang <owenzhang1990@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.talebzadeh@gmail.com>
> wrote:
>
>>
>> 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.
>>
>>
>>
>> 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.talebzadeh@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 <deepakmca05@gmail.com>
>>> wrote:
>>>
>>>> Try this:
>>>>
>>>> *import **spark*.implicits._
>>>>
>>>> df.toDF()
>>>>
>>>>
>>>> On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh <
>>>> mich.talebzadeh@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
>>>>> <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 09:49, Mich Talebzadeh <
>>>>> mich.talebzadeh@gmail.com> wrote:
>>>>>
>>>>>> Thanks!
>>>>>>
>>>>>> The spark  is version 2.3.0
>>>>>>
>>>>>> 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 09:41, Jungtaek Lim <kabhwan@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 <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
>>>>>>>>>> <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 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.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>
>>>> --
>>>> Thanks
>>>> Deepak
>>>> www.bigdatabig.com
>>>> www.keosha.net
>>>>
>>>

Mime
View raw message