Heads up tomorrows Friday review is going to be at 8:30 am instead of 9:30
am because I had to move some flights around.
On Fri, Jul 13, 2018 at 12:03 PM, Holden Karau wrote:
> This afternoon @ 3pm pacific I'll be looking at review tooling for Spark &
> Beam https://www.youtube.com/watch?v=ff8_j
Hi Patrick,
It looks like it's failing in Scala before it even gets to Python to
execute your udf, which is why it doesn't seem to matter what's in your
udf. Since you are doing a grouped map udf maybe your group sizes are too
big or skewed? Could you try to reduce the size of your groups by addin
One workaround is to rename the fid column for each df before joining.
On Thu, Jul 19, 2018 at 9:50 PM wrote:
> Spark 2.3.0 has this problem upgrade it to 2.3.1
>
> Sent from my iPhone
>
> On Jul 19, 2018, at 2:13 PM, Nirav Patel wrote:
>
> corrected subject line. It's missing attribute error
Spark 2.3.0 has this problem upgrade it to 2.3.1
Sent from my iPhone
> On Jul 19, 2018, at 2:13 PM, Nirav Patel wrote:
>
> corrected subject line. It's missing attribute error not ambiguous reference
> error.
>
>> On Thu, Jul 19, 2018 at 2:11 PM, Nirav Patel wrote:
>> I am getting attribute
We do have two big tables each includes 5 billion of rows, so my question here
is should we partition /sort the data and convert it to Parquet before doing
any join?
Best Regards ... Amin
Mohebbi PhD candidate in Software Engineering at unive
corrected subject line. It's missing attribute error not ambiguous
reference error.
On Thu, Jul 19, 2018 at 2:11 PM, Nirav Patel wrote:
> I am getting attribute missing error after joining dataframe 'df2' twice .
>
> Exception in thread "main" org.apache.spark.sql.AnalysisException:
> resolved a
I am getting attribute missing error after joining dataframe 'df2' twice .
Exception in thread "main" org.apache.spark.sql.AnalysisException: resolved
attribute(s) *fid#49 *missing from
value#14,value#126,mgrId#15,name#16,d31#109,df2Id#125,df2Id#47,d4#130,d3#129,df1Id#13,name#128,
*fId#127* in ope
Hi Gerard,
First, I like to thank you for your fast reply and for directing my question to
the proper mailinglist!
I established the JDBC connection in the context of the state function
(flatMapGroupsWithState). The JDBC connection is made by using the read in the
SparkSession. Like below:
spar
Hi Team - Any good calculator/Excel to estimate compute and storage
requirements for the new spark jobs to be developed.
Capacity planning based on:-
Job, Data type etc
Thanks,
Deepu Raj
PySpark 2.3.1 on YARN, Python 3.6, PyArrow 0.8.
I'm trying to run a pandas UDF, but I seem to get nonsensical exceptions in
the last stage of the job regardless of my output type.
The problem I'm trying to solve:
I have a column of scalar values, and each value on the same row has a
sorted vecto
Hello All!
I am using spark 2.3.1 on kubernetes to run a structured streaming spark
job which read stream from Kafka , perform some window aggregation and
output sink to Kafka.
After job running few hours(5-6 hours), the executor pods is getting
crashed which is caused by "Too many open files in
Hi Chris,
Could you show the code you are using? When you mention "I like to use a
static datasource (JDBC) in the state function" do you refer to a DataFrame
from a JDBC source or an independent JDBC connection?
The key point to consider is that the flatMapGroupsWithState function must
be serial
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