I am not able to stop Spark-streaming job.
Let me explain briefly
* getting data from Kafka topic
* splitting data to create a JavaRDD
* mapping the JavaRDD to JavaPairRDD to do a reduceByKey transformation
* writing the JavaPairRDD into the C* DB // something going wrong here
the message
(this was also posted to stackoverflow on 03/10)
I am setting up a very simple logistic regression problem in scikit-learn
and in spark.ml, and the results diverge: the models they learn are
different, but I can't figure out why (data is the same, model type is the
same, regularization is the
I am not an expert on this but here is what I think:
Catalyst maintains information on whether a plan node is ordered. If your
dataframe is a result of a order by, catalyst will skip the sorting when it
does merge sort join. If you dataframe is created from storage, for
instance. ParquetRelation,
Yeoul,
I think a you can run an microbench for pyspark
serialization/deserialization would be to run a withColumn + a python udf
that returns a constant and compare that with similar code in
Scala.
I am not sure if there is way to measure just the serialization code,
because pyspark API only
Hi,
The error message indicates that a Streaming Context object end up in the
fields of the closure that Spark tries to serialize.
Could you show us the enclosing function and component ?
The workarounds proposed in the following stack overflow reply might help
you to fix the problem:
Spark thriff server hiveStatement.getQueryLog return empty?
