My problem is related to the need to have all records in a specific column
quoted when writing a CSV. I assumed that by setting the options escapeQuotes
to false in the options, that fields would not have any type of quoting
applied, even when that delimiter exists. Unless I am misunderstandin
Possibly instead of doing the initial grouping, just do a full outer join on
zyzy. This is in scala but should be easily convertible to python.
val data = Array(("john", "red"), ("john", "blue"), ("john", "red"), ("bill",
"blue"), ("bill", "red"), ("sam", "green"))
val distData: DataFra
Maybe something like
var finalDF = spark.sqlContext.emptyDataFrame
for (df <- dfs){
finalDF = finalDF.union(df)
}
Where dfs is a Seq of dataframes.
From: Cesar
Date: Thursday, April 5, 2018 at 2:17 PM
To: user
Subject: Union of multiple data frames
The following code
Hi,
Can someone confirm whether ordering matters between the schema and underlying
JSON string?
Thanks,
Brandon
If you know your json schema you can create a struct and then apply that using
from_json:
val json_schema = StructType(Array(StructField(“x”, StringType, true),
StructField(“y”, StringType, true), StructField(“z”, IntegerType, true)))
.withColumn("_c3", from_json(col("_c3_signals"),json_s
CSV as well. As per your solution, I am creating
SructType only for Json field. So how am I going to mix and match here? i.e. do
type inference for all fields but json field and use custom json_schema for
json field.
On Thu, Aug 30, 2018 at 5:29 PM Brandon Geise wrote:
If you
How about
select unix_timestamp(timestamp2) – unix_timestamp(timestamp1)?
From: Paras Agarwal
Date: Monday, October 15, 2018 at 2:41 AM
To: John Zhuge
Cc: user , dev
Subject: Re: Timestamp Difference/operations
Thanks John,
Actually need full date and time difference not just d
I recently came across this (haven’t tried it out yet) but maybe it can help
guide you to identify the root cause.
https://github.com/groupon/sparklint
From: Vitaliy Pisarev
Date: Thursday, November 15, 2018 at 10:08 AM
To: user
Cc: David Markovitz
Subject: How to address seemingly low
Use .limit on the dataframe followed by .write
On Apr 14, 2019, 5:10 AM, at 5:10 AM, Chetan Khatri
wrote:
>Nuthan,
>
>Thank you for reply. the solution proposed will give everything. for me
>is
>like one Dataframe show(100) in 3000 lines of Scala Spark code.
>However, yarn logs --applicationId
You could use the Hadoop API and check if the file exists.
From: Mich Talebzadeh
Date: Tuesday, May 5, 2020 at 11:25 AM
To: "user @spark"
Subject: Exception handling in Spark
Hi,
As I understand exception handling in Spark only makes sense if one attempts an
action as opposed to lazy
Date: Tuesday, May 5, 2020 at 12:45 PM
To: Brandon Geise
Cc: "user @spark"
Subject: Re: Exception handling in Spark
Thanks Brandon!
i should have remembered that.
basically the code gets out with sys.exit(1) if it cannot find the file
I guess there is no easy way
This is what I had in mind. Can you give this approach a try?
val df = Try(spark.read.csv("")) match {
case Success(df) => df
case Failure(e) => throw new Exception("foo")
}
From: Mich Talebzadeh
Date: Tuesday, May 5, 2020 at 5:17 PM
To: To
Import scala.util.Try
Import scala.util.Success
Import scala.util.Failure
From: Mich Talebzadeh
Date: Tuesday, May 5, 2020 at 6:11 PM
To: Brandon Geise
Cc: Todd Nist , "user @spark"
Subject: Re: Exception handling in Spark
This is what I get
scala> val df =
Try(spar
Match needs to be lower case “match”
From: Mich Talebzadeh
Date: Tuesday, May 5, 2020 at 6:13 PM
To: Brandon Geise
Cc: Todd Nist , "user @spark"
Subject: Re: Exception handling in Spark
scala> import scala.util.{Try, Success, Failure}
import scala.util.{Try, Success, Fa
Sure, just do case Failure(e) => throw e
From: Mich Talebzadeh
Date: Tuesday, May 5, 2020 at 6:36 PM
To: Brandon Geise
Cc: Todd Nist , "user @spark"
Subject: Re: Exception handling in Spark
Hi Brandon.
In dealing with
df case Failure(e) => throw new Exception
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