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
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
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
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
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
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
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
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
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
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
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
Hi,
Can someone confirm whether ordering matters between the schema and underlying
JSON string?
Thanks,
Brandon
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
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
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
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