I am trying to pass data from kafka to spark streaming.
This is what I\'ve done till now:
kafka
and spark
Alternatively, if you want to also specify resources to be allocated at the same time:
spark-submit --deploy-mode cluster --master yarn --num-executors 5 --executor-cores 5 --executor-memory 20g --jars spark-streaming-kafka-assembly_2.10-1.6.0.jar ./spark-kafka.py
If you wanna run your code in a Jupyter-notebook, then this could be helpful:
from __future__ import print_function
import sys
from pyspark.streaming import StreamingContext
from pyspark import SparkContext,SparkConf
from pyspark.streaming.kafka import KafkaUtils
if __name__ == "__main__":
os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars spark-streaming-kafka-assembly_2.10-1.6.0.jar pyspark-shell' #note that the "pyspark-shell" part is very important!!.
#conf = SparkConf().setAppName("Kafka-Spark").setMaster("spark://127.0.0.1:7077")
conf = SparkConf().setAppName("Kafka-Spark")
#sc = SparkContext(appName="KafkaSpark")
sc = SparkContext(conf=conf)
stream=StreamingContext(sc,1)
map1={'spark-kafka':1}
kafkaStream = KafkaUtils.createStream(stream, 'localhost:9092', "name", map1) #tried with localhost:2181 too
print("kafkastream=",kafkaStream)
sc.stop()
Note the introduction of the following line in __main__
:
os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars spark-streaming-kafka-assembly_2.10-1.6.0.jar pyspark-shell'
Sources: https://github.com/jupyter/docker-stacks/issues/154