That did resolve my issue.
Thanks a lot.
frakass
n 06/02/2022 17:25, Hannes Bibel wrote:
Hi,
looks like you're packaging your application for Scala 2.13 (should be
specified in your build.sbt) while your Spark installation is built
for Scala 2.12.
Go to
Hello
I wrote this simple job in scala:
$ cat Myjob.scala
import org.apache.spark.sql.SparkSession
object Myjob {
def main(args: Array[String]): Unit = {
val sparkSession = SparkSession.builder.appName("Simple
Application").getOrCreate()
val sparkContext =
Hi,
looks like you're packaging your application for Scala 2.13 (should be
specified in your build.sbt) while your Spark installation is built for
Scala 2.12.
Go to https://spark.apache.org/downloads.html, select under "Choose a
package type" the package type that says "Scala 2.13". With that
Thanks for your input guys! //hinko
On 4 Feb 2022, at 14:58, Sean Owen wrote:
Yes, in the sense that any transformation that can be expressed in the SQL-like
DataFrame API will push down to the JVM, and take advantage of other
optimizations, avoiding the data movement to/from Python and
for example, this work for RDD object:
scala> val li = List(3,2,1,4,0)
li: List[Int] = List(3, 2, 1, 4, 0)
scala> val rdd = sc.parallelize(li)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at
parallelize at :24
scala> rdd.filter(_ > 2).collect()
res0: Array[Int] = Array(3, 4)
DataFrames are a quite different API, more SQL-like in its operations, not
functional. The equivalent would be more like df.filterExpr("value > 2")
On Sun, Feb 6, 2022 at 5:51 AM wrote:
> for example, this work for RDD object:
>
> scala> val li = List(3,2,1,4,0)
> li: List[Int] = List(3, 2, 1,
Basically you are creating a dataframe (a dataframe is a *Dataset* organized
into named columns. It is conceptually equivalent to a table in a
relational database) out of RDD here.
scala> val rdd = sc.parallelize( List(3, 2, 1, 4, 0))
rdd: org.apache.spark.rdd.RDD[Int] =
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Try this:
https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.functions.monotonically_increasing_id.html
On Mon, 7 Feb 2022 at 12:27 pm, wrote:
> For a dataframe object, how to add a column who is auto_increment like
> mysql's behavior?
>
> Thank you.
>
>
For a dataframe object, how to add a column who is auto_increment like
mysql's behavior?
Thank you.
-
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-- Forwarded message -
From: Madhuchaitanya Joshi
Date: Wed, 19 Jan, 2022, 10:51
Subject: (send this email to subscribe)
To:
Hello team,
I am trying to build and compile spark source code using intellij and
eclipse. But I am getting jackson-bind.jar not found error in
Monotonically_increasing_id() will give the same functionality
On Mon, 7 Feb, 2022, 6:57 am , wrote:
> For a dataframe object, how to add a column who is auto_increment like
> mysql's behavior?
>
> Thank you.
>
> -
> To
I am a bit confused why in pyspark this doesn't work?
x = sc.parallelize([3,2,1,4])
x.toDF.show()
Traceback (most recent call last):
File "", line 1, in
AttributeError: 'function' object has no attribute 'show'
Thank you.
rdd = sc.parallelize([3,2,1,4])
rdd.toDF().show()
Traceback (most recent call last):
File "", line 1, in
File "/opt/spark/python/pyspark/sql/session.py", line 66, in toDF
return sparkSession.createDataFrame(self, schema, sampleRatio)
File "/opt/spark/python/pyspark/sql/session.py",
You are passing a list of primitives. It expects something like a list of
tuples, which can each have 1 int if you like.
On Sun, Feb 6, 2022, 10:10 PM wrote:
> >>> rdd = sc.parallelize([3,2,1,4])
> >>> rdd.toDF().show()
> Traceback (most recent call last):
>File "", line 1, in
>File
This is just basic Python - you're missing parentheses on toDF, so you are
not calling a function nor getting its result.
On Sun, Feb 6, 2022 at 9:39 PM wrote:
> I am a bit confused why in pyspark this doesn't work?
>
> >>> x = sc.parallelize([3,2,1,4])
> >>> x.toDF.show()
> Traceback (most
Thanks for the reply.
It looks strange that in scala shell I can implement this translation:
scala> sc.parallelize(List(3,2,1,4)).toDF.show
+-+
|value|
+-+
|3|
|2|
|1|
|4|
+-+
But in pyspark i have to write as:
sc.parallelize([3,2,1,4]).map(lambda x:
Indeed. in spark-shell I ignore the parentheses always,
scala> sc.parallelize(List(3,2,1,4)).toDF.show
+-+
|value|
+-+
|3|
|2|
|1|
|4|
+-+
So I think it would be ok in pyspark.
But this still doesn't work. why?
sc.parallelize([3,2,1,4]).toDF().show()
Traceback
Scala and Python are not the same in this regard. This isn't related to how
spark works.
On Sun, Feb 6, 2022, 10:04 PM wrote:
> Indeed. in spark-shell I ignore the parentheses always,
>
> scala> sc.parallelize(List(3,2,1,4)).toDF.show
> +-+
> |value|
> +-+
> |3|
> |2|
> |1|
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