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How to load sql file in spark using python

My pySpark version is 2.4 and python version is 2.7. I have multiple line sql file which needs to run in spark. Instead of running line by line, is it possible to keep the sql file in python (which initialize spark) and execute it using spark-submit ? I am trying to write a generic script in python so that we just need to replace sql file from hdfs folder later. Below is my code snippet.
import sys
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
args = str(sys.argv[1]).split(',')
fd = args[0]
ld = args[1]
sd = args[2]
#Below line does not work
df = open("test.sql")
query = df.read().format(fd,ld,sd)
#Initiating SparkSession.
spark = SparkSession.builder.appName("PC").enableHiveSupport().getOrCreate()
#Below line works fine
df_s=spark.sql("""select * from test_tbl where batch_date='2021-08-01'""")
#Execute the sql (Does not work now)
df_s1=spark.sql(query)
spark-submit throws following error for the above code.
Exception in thread "main" org.apache.spark.SparkException:
Application application_1643050700073_7491 finished with failed status
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1158)
at org.apache.spark.deploy.yarn.YarnClusterApplication.start(Client.scala:1606)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:847)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:161)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:184)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:922)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:931)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) 22/02/10 01:24:52 INFO util.ShutdownHookManager: Shutdown hook called
I am relatively new in pyspark. Can anyone please guide me what I am missing here ?

You cant run pyspark on your local directory. If you want to do an sql statement on a File in HDFS, you have to put your file from HDFS, first on your local directory.
Referred to spark 2.4.0 Spark Documentation, you can simply use the pyspark API.
from os.path import expanduser, join, abspath
from pyspark.sql import SparkSession
from pyspark.sql import Row
spark.sql("YOUR QUERY").show()
or query files directly with:
df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")

Run kafkastream with JAR artifact in Jupyter

I am working on a simple python script to stream messages from Kafka using pyspark, and I'm doing so using jupyter.
I get an error message saying Spark Streaming's Kafka libraries not found in class path (more details below). I included the solution suggested by #tshilidzi-mudau in a previous post (and confirmed in the docs) to avoid this problem. What should I do to fix the bug?
Following what suggested in the error prompt, I downloaded the JAR of the artifact, stored it in $SPARK_HOME/jars and included the reference in the code.
Here is the code:
import os
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-0-10-assembly_2.10-2.2.2.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")
try:
sc.stop()
except:
pass
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()
And this is the error:
Spark Streaming's Kafka libraries not found in class path. Try one of the following.
1. Include the Kafka library and its dependencies with in the
spark-submit command as
$ bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8:2.2.2 ...
2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
Group Id = org.apache.spark, Artifact Id = spark-streaming-kafka-0-8-assembly, Version = 2.2.2.
Then, include the jar in the spark-submit command as
$ bin/spark-submit --jars <spark-streaming-kafka-0-8-assembly.jar> ...
TypeError Traceback (most recent call last)
<ipython-input-9-34de7dbdfc7c> in <module>()
13 ssc = StreamingContext(sc,1)
14 broker = "<my_broker_ip>"
---> 15 directKafkaStream = KafkaUtils.createDirectStream(ssc, ["test1"], {"metadata.broker.list": broker})
16 directKafkaStream.pprint()
17 ssc.start()
/opt/spark/python/pyspark/streaming/kafka.pyc in createDirectStream(ssc, topics, kafkaParams, fromOffsets, keyDecoder, valueDecoder, messageHandler)
120 return messageHandler(m)
121
--> 122 helper = KafkaUtils._get_helper(ssc._sc)
123
124 jfromOffsets = dict([(k._jTopicAndPartition(helper),
/opt/spark/python/pyspark/streaming/kafka.pyc in _get_helper(sc)
193 def _get_helper(sc):
194 try:
--> 195 return sc._jvm.org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper()
196 except TypeError as e:
197 if str(e) == "'JavaPackage' object is not callable":
TypeError: 'JavaPackage' object is not callable

zeppelin pyspark how to connect remote spark?

My zeppelin is using local spark now.
Got ValueError: Cannot run multiple SparkContexts at once when I tried to create a remote SparkContext .
Follow
multiple SparkContexts error in tutorial
write below code:
from pyspark import SparkConf, SparkContext
sc.stop()
conf = SparkConf().setAppName('train_etl').setMaster('spark://xxxx:7077')
sc = SparkContext(conf=conf)
Got another error:
Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-6681108227268089746.py", line 363, in <module>
sc.setJobGroup(jobGroup, jobDesc)
File "/usr/local/spark/python/lib/pyspark.zip/pyspark/context.py", line 944, in setJobGroup
self._jsc.setJobGroup(groupId, description, interruptOnCancel)
AttributeError: 'NoneType' object has no attribute 'setJobGroup'
What should I do?

by default, Spark automatically creates the SparkContext object named sc, when the PySpark application started.you have to use below line in your code which
sc = SparkContext.getOrCreate()
Get the singleton SQLContext if it exists or create a new one using the given SparkContext.
This function can be used to create a singleton SQLContext object that can be shared across the JVM.
If there is an active SQLContext for current thread, it will be returned instead of the global one.

Enter http://zeppelin_host:zeppelin_port/#/interpreter
config parameter master of spark interpreter (which use for pyspark) to spark://xxxx:7077

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.