Hi,
I wonder when to enable spark's off heap settings. Shouldn't tungsten enable
these automatically in 2.1?
http://stackoverflow.com/questions/43330902/spark-off-heap-memory-config-and-tungsten
Regards,
Georg
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http://stackoverflow.com/questions/36648128/how-to-store-custom-objects-in-a-dataset
describes the Problem. Actually, I have the same Problem. Is there a simple
way to build such an Encoder which serializes into multiple fields? I would
not want to replicate the Whole JTS geometry class hierarchy o
Starting out with graph frames I would like to understand stateful motifs
better.
There is a nice example in the documentation.
How can I explicitly return the counts?
How could it be extended to count
- the friends of each vertex with age > 30
- the percentage of friendsGreater30 / allFriends
I optimized a spark sql script but have come to the conclusion that the sql
api is not ideal as the tasks which are generated are slow and require too
much shuffling.
So the script should be converted to rdd
http://stackoverflow.com/q/41445571/2587904
How can I formulate this more efficient usi
it up here
http://stackoverflow.com/questions/41298550/spark-threadpoolexecutor-very-often-called-in-tasks
as well with a minimal example of
https://github.com/geoHeil/sparkContrastCoding
Looking forward to any input to speed up this spark job.
cheers,
Georg
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To force spark to use kryo serialization I set
spark.kryo.registrationRequired to true.
Now spark complains that: Class is not registered:
org.apache.spark.sql.types.DataType[] is not registered.
How can I fix this? So far I could not successfully register this class.
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Hi
I am curious how to dynamically generate spark sql in the scala api.
http://stackoverflow.com/q/41102347/2587904
>From this list val columnsFactor = Seq("bar", "baz")
I want to generate multiple withColumn statements
dfWithNewLabels.withColumn("replace", lit(null: String))
.withColu
Hi,
I am writing my first own spark pipeline components with persistence and
have troubles debugging them.
https://github.com/geoHeil/sparkCustomEstimatorPersistenceProblem holds a
minimal example where
`sbt run` and `sbt test` result in "different" errors.
When I tried to debug it in
I am facing a strange issue when trying to correct some errors in my raw data
The problem is reported here:
https://issues.apache.org/jira/browse/SPARK-18532
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Sent from
How can I fill nan values with the last (good) value?
For me, it would be enough to fill it with the previous value of a window
function. So far I could it not get to work as my window function only
returns nan values.
Here is code for a minimal example:
http://stackoverflow.com/questions/40592207
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