clear steps for installation of spark, cassandra and cassandra connector to run on spyder 2.3.7 using python 3.5 and anaconda 2.4 ipython 4.0

could u send me documents and links to satisfy all above requirements of installation of spark, cassandra and cassandra connector to run on spyder 2.3.7 using python 3.5 and anaconda 2.4 ipython 4.0

Failed to open native connection to Cassandra at

how to solve this problem below py4j.protocol.Py4JJavaError: An error occurred while calling o33.load. : java.io.IOException: Failed to open native connection to Cassandra at {127.0.1.1}:9042

cassandra can not accessed via pyspark or spark-shell but it is accessible using cqlsh. what is the problem.

i have tried all possible examples on internet to access cassandra table via pypsark or spark shell. however, all of trials resulted in fails related to java gateway. what is the main problem?

true conf for sparkconf().set().setMaster() to connect to cassandra

cassandra 3.7 is compatible with datastax Spark Cassandra Connector 2.0?

best versions for cassandra spark connection

hi in order to connect pyspark to cassandra which versions of items for conection must be installed. i think cassandra 3.7 is not compatible with spark 2.0 and datastax pyspark-cassandra connector 2.0, please give me the correct version and steps to connect them

cassandra.yaml configuration for cassandra spark connection

how to configure cassandra.yaml configuration file for datastax cassandra spark connection

is there any bug for the configuration of spark 2.0 cassandra spark connector 2.0 and cassandra 3.0.8

please tell me the configuration including the most recent version of cassandra, spark and cassandra spark connector

cassandra and spark can be built and worked on the same computer?

can we connect to cassandra from spark using spark-cassandra-connector which all three are built on the same computer? what kind of problems this configuration leads to?

unresolved dependency: datastax#spark-cassandra-connector;2.0.0-s_2.11-M3-20-g75719df: not found

while i run the spark-shell as below spark-shell --jars '/home/ktuser/spark-cassandra-connector/target/scala-2.11/root_2.11-2.0.0-M3-20-g75719df.jar' --packages datastax:spark-cassandra-connector:2.0.0-s_2.11-M3-20-g75719df --conf spark.cassandra.connection.host=localhost i get the error unres

ERROR StandaloneSchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up.

i tried to connect cassandra via spark-cassandra-conenctor2.0.0 on pyspark but i get the error below i think it s related to pyspark/context.py but i dont know how?

how to find NaN values of each row of spark dataframe to decide whether the rows is dropeed or not

is there any way to do this directly. if its not, is there any todo this indirectly using another datastrcutures of spark

how to decide which part of process use spark dataframe and pandas dataframe?

is there a clear guide to decide the above?

rdd and dataframe columns dtype

how can i set columns dtype of rdd

tutorial for access elements of dataframe columns and column values of a specific rows?

From: muhammet pakyürek Sent: Monday, October 17, 2016 11:51 AM To: user@spark.apache.org Subject: rdd and dataframe columns dtype how can i set columns dtype of rdd

how to see spark class variable values on variable explorer of spyder for python?

is there any way to to see spark class variable values on variable explorer of spyder for python?

pyspark dataframe codes for lead lag to column

is there pyspark dataframe codes for lead lag to column? lead/lag column is something 1 lag -1lead 2 213 324 435 54 -1

sql.functions partitionby AttributeError: 'NoneType' object has no attribute '_jvm'

i work with partitioonby for lead lag functions i get the errror above and here is the explanation jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))

i get the error of Py4JJavaError: An error occurred while calling o177.showString while running code below

i used spark 2.0.1 and work pypsaprk.sql dataframe lower = arguments["lower"] lower_udf = udf(lambda x: lower if x