Spark Streaming - updateStateByKey and caching data - apache-spark

Question

I have a problem with using updateStateByKey function and caching some big data at the same time. Here is a example.
Lets say I get data (lastname,age) from kafka. I want to keep actual age for every person so I use updateStateByKey. Also I want to know name of every person so I join output with external table (lastname,name) e.g. from Hive. Lets assume it's really big table, so I don't want to load it in every batch. And there's a problem.
All works well, when I load table in every batch, but when I try to cache table, StreamingContext doesn't start. I also tried to use registerTempTable and later join data with sql but i got the same error.
Seems like the problem is checkpoint needed by updateStateByKey. When I remove updateStateByKey and leave checkpoint i got error, but when I remove both it works.
Error I'm getting: pastebin
Here is code:
import sys
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext, HiveContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
# function to keep actual state
def updateFunc(channel, actualChannel):
if (actualChannel is None or not channel is None):
try:
actualChannel = channel[-1]
except Exception:
pass
if channel is None:
channel = actualChannel
return actualChannel
def splitFunc(row):
row = row.strip()
lname,age = row.split()
return (lname,age)
def createContext(brokers,topics):
# some conf
conf = SparkConf().setAppName(appName).set("spark.streaming.stopGracefullyOnShutdown","true").set("spark.dynamicAllocation.enabled","false").\
set("spark.serializer","org.apache.spark.serializer.KryoSerializer").set("spark.sql.shuffle.partitions",'100')
# create SparkContext
sc = SparkContext(conf=conf)
# create HiveContext
sqlContext = HiveContext(sc)
# create Streaming Context
ssc = StreamingContext(sc, 5)
# read big_df and cache (not work, Streaming Context not start)
big_df = sqlContext.sql('select lastname,name from `default`.`names`')
big_df.cache().show(10)
# join table
def joinTable(time,rdd):
if rdd.isEmpty()==False:
df = HiveContext.getOrCreate(SparkContext.getOrCreate()).createDataFrame(rdd,['lname','age'])
# read big_df (work)
#big_df = HiveContext.getOrCreate(SparkContext.getOrCreate()).sql('select lastname,name from `default`.`names`')
# join DMS
df2 = df.join(big_df,df.lname == big_df.lastname,"left_outer")
return df2.map(lambda row:row)
# streaming
kvs = KafkaUtils.createDirectStream(ssc, [topics], {'metadata.broker.list': brokers})
kvs.map(lambda (k,v): splitFunc(v)).updateStateByKey(updateFunc).transform(joinTable).pprint()
return ssc
if __name__ == "__main__":
appName="SparkCheckpointUpdateSate"
if len(sys.argv) != 3:
print("Usage: SparkCheckpointUpdateSate.py <broker_list> <topic>")
exit(-1)
brokers, topics = sys.argv[1:]
# getOrCreate Context
checkpoint = 'SparkCheckpoint/checkpoint'
ssc = StreamingContext.getOrCreate(checkpoint,lambda: createContext(brokers,topics))
# start streaming
ssc.start()
ssc.awaitTermination()
Can you tell me how to properly cache data when checkpoint is enabled? Maybe there is some workaround I don't know.
Spark ver. 1.6

Answer 1

I get this working using lazily instantiated global instance of big_df. Something like that is done in recoverable_network_wordcount.py
.
def getBigDf():
if ('bigdf' not in globals()):
globals()['bigdf'] = HiveContext.getOrCreate(SparkContext.getOrCreate()).sql('select lastname,name from `default`.`names`')
return globals()['bigdf']
def createContext(brokers,topics):
...
def joinTable(time,rdd):
...
# read big_df (work)
big_df = getBigDF()
# join DMS
df2 = df.join(big_df,df.lname == big_df.lastname,"left_outer")
return df2.map(lambda row:row)
...
Seems like in streaming all data must be cached inside streaming processing, not before.