[Spark2.X] SparkStreaming to Cassandra performance problem

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[Spark2.X] SparkStreaming to Cassandra performance problem

Saulo Sobreiro
Hi all,

I am implementing a use case where I read some sensor data from Kafka with SparkStreaming interface (KafkaUtils.createDirectStream) and, after some transformations, write the output (RDD) to Cassandra.

Everything is working properly but I am having some trouble with the performance. My kafka topic receives around 2000 messages per second. For a 4 min. test, the SparkStreaming app takes 6~7 min. to process and write to Cassandra, which is not acceptable for longer runs.

I am running this application in a "sandbox" with 12GB of RAM, 2 cores and 30GB SSD space. HDP:
Spark 2.1

I would like to know you have some suggestion to improve performance (other than getting more resources :) ). 

My code (pyspark) is posted in the end of this email so you can take a look.

Thank you in advance,

Best Regards,

=============== # CODE # =================================
# run command:
# spark2-submit --packages org.apache.spark:spark-streaming-kafka_2.11:1.6.3,anguenot:pyspark-cassandra:0.7.0,org.apache.spark:spark-core_2.11:1.5.2  --conf spark.cassandra.connection.host='localhost' --num-executors 2 --executor-cores 2 SensorDataStreamHandler.py localhost:6667 test_topic2

# Run Spark imports
from pyspark import SparkConf # SparkContext, SparkConf
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils

# Run Cassandra imports
import pyspark_cassandra
from pyspark_cassandra import CassandraSparkContext, saveToCassandra

def recordHandler(record):
    (mid, tt, in_tt, sid, mv) = parseData( record )
    return processMetrics(mid, tt, in_tt, sid, mv)

def process(time, rdd):
    rdd2 = rdd.map( lambda w: recordHandler(w[1]) )
    if rdd2.count() > 0:
        return rdd2

def casssave(time, rdd):
    rdd.saveToCassandra( "test_hdpkns", "measurement" )

# ...
brokers, topic = sys.argv[1:]

# ...

sconf = SparkConf() \
        .setAppName("SensorDataStreamHandler") \
        .setMaster("local[*]") \
        .set("spark.default.parallelism", "2")

sc = CassandraSparkContext(conf = sconf)
batchIntervalSeconds = 2
ssc = StreamingContext(sc, batchIntervalSeconds)

kafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers})

kafkaStream \
    .transform(process) \