PySpark Processing Stream data and saving processed data to file - python-2.7

Question

I am trying to replicate a device that is streaming it's location's coordinates, then process the data and save it to a text file.
I am using Kafka and Spark streaming (on pyspark),this is my architecture:
1-Kafka producer emits data to a topic named test in the following string format :
"LG float LT float" example : LG 8100.25191107 LT 8406.43141483
Producer code :
from kafka import KafkaProducer
import random
producer = KafkaProducer(bootstrap_servers='localhost:9092')
for i in range(0,10000):
lg_value = str(random.uniform(5000, 10000))
lt_value = str(random.uniform(5000, 10000))
producer.send('test', 'LG '+lg_value+' LT '+lt_value)
producer.flush()
The producer works fine and i get the streamed data in the consumer(and even in spark)
2- Spark streaming is receiving this stream,i can even pprint() it
Spark streaming processing code
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
ssc = StreamingContext(sc, 1)
kvs = KafkaUtils.createDirectStream(ssc, ["test"], {"bootstrap.servers": "localhost:9092"})
lines = kvs.map(lambda x: x[1])
words = lines.flatMap(lambda line: line.split(" "))
words.pprint()
word_pairs = words.map(lambda word: (word, 1))
counts = word_pairs.reduceByKey(lambda a, b: a+b)
results = counts.foreachRDD(lambda word: word.saveAsTextFile("C:\path\spark_test.txt"))
//I tried this kvs.saveAsTextFiles('C:\path\spark_test.txt')
// to copy all stream and it works fine
ssc.start()
ssc.awaitTermination()
As an error i get :
16/12/26 00:51:53 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
org.apache.spark.SparkException: Python worker did not connect back in time
And other exceptions.
What i actually want is to save each entry "LG float LT float" as a JSON format in a file,but first i want to simply save the coordinates in a file,i cant seem to make that happen.Any ideas?
I can provide with the full stack trace if needed

Answer 1

I solved this like this, so i made a function to save each RDD, in the file ,this is the code that solved my problem :
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
ssc = StreamingContext(sc, 1)
kvs = KafkaUtils.createDirectStream(ssc, ["test"], {"bootstrap.servers": "localhost:9092"})
lines = kvs.map(lambda x: x[1])
coords = lines.map(lambda line: line)
def saveCoord(rdd):
rdd.foreach(lambda rec: open("C:\path\spark_test.txt", "a").write(
"{"+rec.split(" ")[0]+":"+rec.split(" ")[1]+","+rec.split(" ")[2]+":"+rec.split(" ")[3]+"},\n"))
coords.foreachRDD(saveCoord)
coords.pprint()
ssc.start()
ssc.awaitTermination()