Sounds like the same root cause as SPARK-14948 or SPARK-10925.
A workaround is to "clone" df3 like this:
val df3clone = df3.toDF(df.schema.fieldNames:_*)
Then use df3clone in place of df3 in the second join.
On Wed, Jul 11, 2018 at 2:52 PM Nirav Patel wrote:
> I am trying to joind df1 with
I am trying to joind df1 with df2 and result of which to again with df2.
df is a common dataframe.
val df3 = df1
.join(*df2*,
df1("PARTICIPANT_ID") === df2("PARTICIPANT_ID") and
df1("BUSINESS_ID") === df2("BUSINESS_ID"))
.drop(df1("BUSINESS_ID")) //dropping duplica
Severity: Medium
Vendor: The Apache Software Foundation
Versions Affected:
Spark versions through 2.1.2
Spark 2.2.0 through 2.2.1
Spark 2.3.0
Description:
In Apache Spark up to and including 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, it's
possible for a malicious user to construct a URL pointing to a Spa
Severity: High
Vendor: The Apache Software Foundation
Versions affected:
Spark versions through 2.1.2
Spark 2.2.0 to 2.2.1
Spark 2.3.0
Description:
In Apache Spark up to and including 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when
using PySpark or SparkR, it's possible for a different local user to
conn
Hi,
does anybody if (and how) it's possible to get a (dev-local) Spark
installation to talk to fakes3 for s3[n|a]:// URLs?
I have managed to connect to AWS S3 from my local installation by adding
hadoop-aws and aws-java-sdk to jars, using s3:// URLs as arguments for
SparkContext#textFile(), but I
Oh, sorry, i missed that you use spark without dynamic allocation. Anyway,
i don't know does this parameters works without dynamic allocation.
On Wed, Jul 11, 2018 at 5:11 PM Thodoris Zois wrote:
> Hello,
>
> Yeah you are right, but I think that works only if you use Spark dynamic
> allocation.
Hello,
Yeah you are right, but I think that works only if you use Spark dynamic
allocation. Am I wrong?
-Thodoris
> On 11 Jul 2018, at 17:09, Pavel Plotnikov
> wrote:
>
> Hi, Thodoris
> You can configure resources per executor and manipulate with number of
> executers instead using spark.ma
Hi, Thodoris
You can configure resources per executor and manipulate with number of
executers instead using spark.max.cores. I think
spark.dynamicAllocation.minExecutors
and spark.dynamicAllocation.maxExecutors configuration values can help you.
On Tue, Jul 10, 2018 at 5:07 PM Thodoris Zois wrote
Arrays need to be a single type, I think you're looking for a Struct
column. See:
https://medium.com/@mrpowers/adding-structtype-columns-to-spark-dataframes-b44125409803
On Wed, Jul 11, 2018 at 6:37 AM, dimitris plakas
wrote:
> Hello everyone,
>
> I am new to Pyspark and i would like to ask if t
Thanks for your suggestion.
I have been checking Spark-jobserver. Just a off-topic question about this
project: Does Apache Spark project have any support/connection to this
Spark-jobserver project? I noticed that they do not have release for the
newest version of Spark (e.g., 2.3.1).
As you men
Hello everyone,
I am new to Pyspark and i would like to ask if there is any way to have a
Dataframe column which is ArrayType and have a different DataType for each
elemnt of the ArrayType. For example
to have something like :
StructType([StructField("Column_Name", ArrayType(ArrayType(FloatType()
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