Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

Ok somehow this worked!

 // Save prices to mongoDB collection
 val document = sparkContext.parallelize((1 to 1).
map(i =>
Document.parse(s"{key:'$key',ticker:'$ticker',timeissued:'$timeissued',price:$price,CURRENCY:'$CURRENCY',op_type:$op_type,op_time:'$op_time'}")))
 //
 // Writing document to Mongo collection
 //
 MongoSpark.save(document, writeConfig)

Note that all non numeric columns are enclosed with '$column'

I just created a dummy map with one single mapping (1 to 1)

These are the results in MongoDB document

{
"_id" : ObjectId("5b915796d3c6071e82fdca2b"),
"key" : "23c39917-08a9-4845-ba74-51997707d374",
"ticker" : "IBM",
"timeissued" : "2018-09-06T17:51:21",
"price" : 207.23,
"CURRENCY" : "GBP",
"op_type" : NumberInt(1),
"op_time" : "1536251798114"
}
{
"_id" : ObjectId("5b915796d3c6071e82fdca2c"),
"key" : "22f353f9-9b28-463c-9f1c-64213ded7cd5",
"ticker" : "TSCO",
"timeissued" : "2018-09-06T17:51:21",
"price" : 179.52,
"CURRENCY" : "GBP",
"op_type" : NumberInt(1),
"op_time" : "1536251798162"
}


Dr Mich Talebzadeh



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On Thu, 6 Sep 2018 at 10:24, Mich Talebzadeh 
wrote:

> 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
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
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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 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 10:10, Jungtaek Lim  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 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 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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  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 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
>>
>>
>>
>> LinkedIn * 
>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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  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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 arising from such loss, damage or destruction.



 I can rebuild the comma separated list as follows:


case class columns(KEY: String, TICKER: String, 

Re: getting error: value toDF is not a member of Seq[columns]

[Jungtaek Lim]

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 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
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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  wrote:
>
>> Have you tried adding Encoder for columns as suggested by Jungtaek Lim ？
>>
>> On Thu, Sep 6, 2018 at 6:24 AM Mich Talebzadeh 
>> wrote:
>>
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
>>> LinkedIn * 
>>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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
>>> [
>>>
>>>
>>> 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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 * 
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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  wrote:

> Have you tried adding Encoder for columns as suggested by Jungtaek Lim ？
>
> On Thu, Sep 6, 2018 at 6:24 AM Mich Talebzadeh 
> wrote:
>
>>
>> Dr Mich Talebzadeh
>>
>>
>>
>> LinkedIn * 
>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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 
>> wrote:
>>
>>> yep already tried it and it did not work.
>>>
>>> thanks
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
>>> LinkedIn * 
>>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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 
>>> wrote:
>>>
 Try this:

 

Re: getting error: value toDF is not a member of Seq[columns]

[Manu Zhang]

Have you tried adding Encoder for columns as suggested by Jungtaek Lim ？

On Thu, Sep 6, 2018 at 6:24 AM Mich Talebzadeh 
wrote:

>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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 
> wrote:
>
>> yep already tried it and it did not work.
>>
>> thanks
>>
>> Dr Mich Talebzadeh
>>
>>
>>
>> LinkedIn * 
>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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  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



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 On Wed, 5 Sep 2018 at 09:49, Mich Talebzadeh 
 wrote:

> Thanks!
>
> The spark  is version 2.3.0
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
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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
> arise 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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 
wrote:

> yep already tried it and it did not work.
>
> thanks
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
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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 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  wrote:
>
>> Try this:
>>
>> *import **spark*.implicits._
>>
>> df.toDF()
>>
>>
>> On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh 
>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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 
>>> wrote:
>>>
 Thanks!

 The spark  is version 2.3.0

 Dr Mich Talebzadeh



 LinkedIn * 
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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
 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  wrote:

> You may also find below link useful (though it looks far old), since
> case class is the thing 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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  wrote:

> Try this:
>
> *import **spark*.implicits._
>
> df.toDF()
>
>
> On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh 
> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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 
>> wrote:
>>
>>> Thanks!
>>>
>>> The spark  is version 2.3.0
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
>>> LinkedIn * 
>>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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  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 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 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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> http://talebzadehmich.wordpress.com
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility
>>> for any loss, damage or 

Re: getting error: value toDF is not a member of Seq[columns]

[Deepak Sharma]

Try this:

*import **spark*.implicits._

df.toDF()


On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh 
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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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 
> wrote:
>
>> Thanks!
>>
>> The spark  is version 2.3.0
>>
>> Dr Mich Talebzadeh
>>
>>
>>
>> LinkedIn * 
>> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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  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 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 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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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  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 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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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*



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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 
wrote:

> Thanks!
>
> The spark  is version 2.3.0
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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  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 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 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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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  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 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 

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

Thanks!

The spark  is version 2.3.0

Dr Mich Talebzadeh



LinkedIn * 
https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
*



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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 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  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 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 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 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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 *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  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 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 * 
>> 

Re: getting error: value toDF is not a member of Seq[columns]

[Jungtaek Lim]

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 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 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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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  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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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.
>
>
>

Re: getting error: value toDF is not a member of Seq[columns]

[Jungtaek Lim]

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 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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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  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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
 *



 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.



>>>

Re: getting error: value toDF is not a member of Seq[columns]

[Jungtaek Lim]

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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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  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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> *
>>>
>>>
>>>
>>> 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.
>>>
>>>
>>>
>>

Re: getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
*



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  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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> *
>>
>>
>>
>> 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.
>>
>>
>>
>

Re: getting error: value toDF is not a member of Seq[columns]

[Jungtaek Lim]

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 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> *
>
>
>
> 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.
>
>
>

getting error: value toDF is not a member of Seq[columns]

[Mich Talebzadeh]

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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
*



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.