Hello,
I am using spark 1.4.1 with Zeppelin. When using the kryo serializer,
spark.serializer = org.apache.spark.serializer.KryoSerializer
instead of the default Java serializer I am getting the following error. Is
this a known issue?
Thanks,
Piero
java.io.IOException: Failed to connect to
Hello Spark Experts,
I am facing the following issue.
1) I am converting a org.apache.spark.sql.Row into
org.apache.spark.mllib.linalg.Vectors using sparse notation
2) After the parsing proceeds successfully I try to look at the result and
I get the following error:
rows)?
Much appreciated,
Piero Cinquegrana
Marketing Scientist | MarketShare
11150 Santa Monica Blvd, 5th Floor, Los Angeles, CA 90025
P: 310.914.5677 x242 M: 323.377.9197
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val
tv_key
Any tips on how to implement and broadcast left outer join using Scala?
From: Michael Armbrust [mailto:mich...@databricks.com]
Sent: Friday, June 19, 2015 12:40 PM
To: Piero Cinquegrana
Cc: user@spark.apache.org
Subject: Re: SparkSQL: leftOuterJoin is VERY slow!
Broadcast outer joins are on my
each step.
Thanks,
Piero
From: DB Tsai [mailto:dbt...@dbtsai.com]
Sent: Wednesday, June 03, 2015 10:33 PM
To: Piero Cinquegrana
Cc: user@spark.apache.org
Subject: Re: Standard Scaler taking 1.5hrs
Can you do count() before fit to force materialize the RDD? I think something
before fit is slow
algorithm.optimizer.setNumIterations(numIterations)
scala algorithm.optimizer.setStepSize(stepSize)
scala algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
scala val model = algorithm.run(scaledData)
Best,
Piero Cinquegrana
Marketing Scientist | MarketShare
11150 Santa Monica Blvd, 5th Floor
and give us feedback. Thanks.
On Wednesday, June 3, 2015, Piero Cinquegrana
pcinquegr...@marketshare.commailto:pcinquegr...@marketshare.com wrote:
Hello User group,
I have a RDD of LabeledPoint composed of sparse vectors like showing below. In
the next step, I am standardizing the columns
