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Exception: Java gateway process exited before sending its port number with pyspark

I am working with python and pyspark in a jupyter notebook. I am trying to read several parquet files from an aws s3 bucket and convert them into a single json file.
This is what I have:
from functools import reduce
from pyspark.sql import DataFrame
bucket = s3.Bucket(name='mybucket')
keys =[]
for key in bucket.objects.all():
keys.append(key.key)
print(keys[0])
from pyspark.sql import SparkSession
# initialise sparkContext
spark = SparkSession.builder \
.master('local') \
.appName('myAppName') \
.config('spark.executor.memory', '5gb') \
.config("spark.cores.max", "6") \
.getOrCreate()
sc = spark.sparkContext
But I am getting:
Exception: Java gateway process exited before sending its port number with pyspark
I am not sure how to fix this, thank you!

Your getting this error because your pyspark is not able to communicate with your cluster. you need to set the value of some global variable like this.
import os
import findspark
findspark.init()
os.environ['PYSPARK_SUBMIT_ARGS'] = """--name job_name --master yarn / local
--conf spark.dynamicAllocation.enabled=true
pyspark-shell"""
os.environ['PYSPARK_PYTHON'] = "python3.6" # what ever version of python your using
os.environ['python'] = "python3.6"
findspark package is optional but it's good to use in case of pyspark.

Why my simple Spark application works so slow?

I am trying to count the frequent item sets generated by FP growth of mllib using Spark API. My Spark is version 1.5.1. The following is my code:
#!/usr/bin/python
from pyspark.mllib.fpm import FPGrowth
from pyspark import SparkContext,SparkConf
from pyspark import HiveContext
import os
os.environ['PYSPARK_PYTHON']='/usr/bin/python'
appName = "FP_growth"
sc = SparkContext()
sql_context = HiveContext(sc)
def read_spu(prod):#prod_code):
sql = """
select
t.orderno_nosplit,
t.prod_code,
t.item_code,
sum(t.item_qty) as item_qty
from ioc_fdm.fdm_dwr_ioc_fcs_pk_spu_item_f_chain t
where t.prod_code='%s'
group by t.prod_code, t.orderno_nosplit, t.item_code """%prod
spu_result = sql_context.sql(sql)
return spu_result.cache()
if __name__ == '__main__':
spu=read_spu('6727780')
conf=0.7
trans=spu.rdd.repartition(100).map(lambda x: (x[0],x[2])).groupByKey().mapValues(list).values().cache()
model = FPGrowth.train(trans, 0.01, 100)
freq_count = model.freqItemsets().count()
print 'freq_count:',freq_count
sc.stop()
The input data are read from Hadoop, and the data is not very large, only about 20000 rows. However, the script works very very slow in the stage of .count. I don't know why. From the performance, it seems it is because of data skew. But the output data is not large(only about 100KB per task ).
The cluster has 8 nodes of 320 cores and 1.56 T total memory (not only one user). My spark-submit script is spark-submit --master yarn-cluster --executor-memory 30g --num-executors 20 --executor-cores 5 FP_growth.py
The attachments are the screen prints of performance when running:
Resource used
Active Stages
Tasks

how to load --jars with pyspark with spark standalone on client mode

I am using python 2.7 with spark standalone cluster on client mode.
I want to use jdbc for mysql and found that i need to load it using --jars argument, I have the jdbc on my local, and manage to load it with pyspark console like here
When I write a python script inside my ide, using pyspark, I don't manage to load the additional jar mysql-connector-java-5.1.26.jar and keep get
no suitable driver
error
How can I load additional jar files when running a python script in client mode, using a standalone cluster on client mode and refering to a remote master?
edit: added some code #########################################################################
this is the basic code that i am using, i use pyspark with spark context in python e.g i do not use spark submit directly and don't understand how to use spark submit parameters in this case...
def createSparkContext(masterAdress = algoMaster):
"""
:return: return a spark context that is suitable for my configs
note the ip for the master
app name is not that important, just to show off
"""
from pyspark.mllib.util import MLUtils
from pyspark import SparkConf
from pyspark import SparkContext
import os
SUBMIT_ARGS = "--driver-class-path /var/nfs/general/mysql-connector-java-5.1.43 pyspark-shell"
#SUBMIT_ARGS = "--packages com.databricks:spark-csv_2.11:1.2.0 pyspark-shell"
os.environ["PYSPARK_SUBMIT_ARGS"] = SUBMIT_ARGS
conf = SparkConf()
#conf.set("spark.driver.extraClassPath", "var/nfs/general/mysql-connector-java-5.1.43")
conf.setMaster(masterAdress)
conf.setAppName('spark-basic')
conf.set("spark.executor.memory", "2G")
#conf.set("spark.executor.cores", "4")
conf.set("spark.driver.memory", "3G")
conf.set("spark.driver.cores", "3")
#conf.set("spark.driver.extraClassPath", "/var/nfs/general/mysql-connector-java-5.1.43")
sc = SparkContext(conf=conf)
print sc._conf.get("spark.executor.extraClassPath")
return sc
sql = SQLContext(sc)
df = sql.read.format('jdbc').options(url='jdbc:mysql://ip:port?user=user&password=pass', dbtable='(select * from tablename limit 100) as tablename').load()
print df.head()
Thanks

Your SUBMIT_ARGS is going to be passed to the spark-submit when creating a sparkContext from python. You should use --jars instead of --driver-class-path.
EDIT
Your problem is actually a lot simpler than it seems: you're missing the parameter driver in the options:
sql = SQLContext(sc)
df = sql.read.format('jdbc').options(
url='jdbc:mysql://ip:port',
user='user',
password='pass',
driver="com.mysql.jdbc.Driver",
dbtable='(select * from tablename limit 100) as tablename'
).load()
You can also put userand password in separate arguments.

load external libraries inside pyspark code

I have a spark cluster I use in local mode. I want to read a csv with the databricks external library spark.csv. I start my app as follows:
import os
import sys
os.environ["SPARK_HOME"] = "/home/mebuddy/Programs/spark-1.6.0-bin-hadoop2.6"
spark_home = os.environ.get('SPARK_HOME', None)
sys.path.insert(0, spark_home + "/python")
sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.8.2.1-src.zip'))
from pyspark import SparkContext, SparkConf, SQLContext
try:
sc
except NameError:
print('initializing SparkContext...')
sc=SparkContext()
sq = SQLContext(sc)
df = sq.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load("/my/path/to/my/file.csv")
When I run it, I get the following error:
java.lang.ClassNotFoundException: Failed to load class for data source: com.databricks.spark.csv.
My question: how can I load the databricks.spark.csv library INSIDE my python code. I don't want to load it from outside (using --packages) from instance.
I tried to add the following lines but it did not work:
os.environ["SPARK_CLASSPATH"] = '/home/mebuddy/Programs/spark_lib/spark-csv_2.11-1.3.0.jar'

If you create SparkContext from scratch you can for example set PYSPARK_SUBMIT_ARGS before SparkContext is intialized:
os.environ["PYSPARK_SUBMIT_ARGS"] = (
"--packages com.databricks:spark-csv_2.11:1.3.0 pyspark-shell"
)
sc = SparkContext()
If for some reason you expect that SparkContext has been already initialized, as it is suggested by your code, this won't work. In local mode you could try to use Py4J gateway and URLClassLoader but it doesn't look like a good idea and won't work in a cluster mode.

spark python script not writing to hbase

I am trying to run the script from this blog
import sys
import json
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
def SaveRecord(rdd):
host = 'sparkmaster.example.com'
table = 'cats'
keyConv = "org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter"
valueConv = "org.apache.spark.examples.pythonconverters.StringListToPutConverter"
conf = {"hbase.zookeeper.quorum": host,
"hbase.mapred.outputtable": table,
"mapreduce.outputformat.class": "org.apache.hadoop.hbase.mapreduce.TableOutputFormat",
"mapreduce.job.output.key.class": "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
"mapreduce.job.output.value.class": "org.apache.hadoop.io.Writable"}
datamap = rdd.map(lambda x: (str(json.loads(x)["id"]),[str(json.loads(x)["id"]),"cfamily","cats_json",x]))
datamap.saveAsNewAPIHadoopDataset(conf=conf,keyConverter=keyConv,valueConverter=valueConv)
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: StreamCatsToHBase.py <hostname> <port>")
exit(-1)
sc = SparkContext(appName="StreamCatsToHBase")
ssc = StreamingContext(sc, 1)
lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
lines.foreachRDD(SaveRecord)
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
I am unable to run it. I have tried three different command line options but none is producing the output nor writing the data to hbase table
Here are the command line options that i tried
spark-submit --jars /usr/local/spark/lib/spark-examples-1.5.2-hadoop2.4.0.jar --jars /usr/local/hbase/lib/hbase-examples-1.1.2.jar sp_json.py localhost 2389 > sp_json.log
spark-submit --driver-class-path /usr/local/spark/lib/spark-examples-1.5.2-hadoop2.4.0.jar sp_json.py localhost 2389 > sp_json.log
spark-submit --driver-class-path /usr/local/spark/lib/spark-examples-1.5.2-hadoop2.4.0.jar --jars /usr/local/hbase/lib/hbase-examples-1.1.2.jar sp_json.py localhost 2389 > sp_json.log
Here is the logfile. It is too verbose. It is one of the reasons that debugging is difficult in Apache spark because it spits out too much information.

Finally got it working using the following command syntaxspark-submit --jars /usr/local/spark/lib/spark-examples-1.5.2-hadoop2.4.0.jar,/usr/local/hbase/lib/hbase-examples-1.1.2.jar sp_json.py localhost 2399 > sp_json.log