如何启用从Cassandra到Spark的流媒体？

Question

I have the following spark job :

from __future__ import print_function

import os
import sys
import time
from random import random
from operator import add
from pyspark.streaming import StreamingContext
from pyspark import SparkContext,SparkConf
from pyspark.streaming.kafka import KafkaUtils
from pyspark.sql import SQLContext, Row
from pyspark.streaming import StreamingContext
from pyspark_cassandra import streaming,CassandraSparkContext

if __name__ == "__main__":

    conf = SparkConf().setAppName("PySpark Cassandra Test")
    sc = CassandraSparkContext(conf=conf)
    stream = StreamingContext(sc, 2)

    rdd=sc.cassandraTable("keyspace2","users").collect()
    #print rdd
    stream.start()
    stream.awaitTermination()
    sc.stop() 

When I run this, it gives me the following error :

ERROR StreamingContext: Error starting the context, marking it as stopped
java.lang.IllegalArgumentException: requirement failed: \
No output operations registered, so nothing to execute

the shell script I run:

./bin/spark-submit --packages TargetHolding:pyspark-cassandra:0.2.4 example
s/src/main/python/test/reading-cassandra.py

Comparing spark streaming with kafka, I have this line missing from the above code:

kafkaStream = KafkaUtils.createStream(stream, 'localhost:2181', "name", {'topic':1})

where I'm actually using createStream but for cassandra, I can't see anything like this on the docs. How do I start the streaming between spark streaming and cassandra?

Versions :

Cassandra v2.1.12
Spark v1.4.1
Scala 2.10

Answer 1

To create DStream out of a Cassandra table, you can use a ConstantInputDStream providing the RDD created out of the Cassandra table as input. This will result in the RDD being materialized on each DStream interval.

Be warned that large tables or tables that continuously grow in size will negatively impact performance of your Streaming job.

See also: Reading from Cassandra using Spark Streaming for an example.