How to add jdbc drivers to classpath when using PySpark? - pyspark

Question

How / where do I install the jdbc drivers for spark sql? I'm running the all-spark-notebook docker image, and am trying to pull some data directly from a sql database into spark.
From what I can tell I can tell I need to include the drivers in my Classpath, I'm just not sure how to do that from pyspark?
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.master("local") \
.appName("Python Spark SQL basic example") \
.getOrCreate()
jdbcDF = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql:dbserver") \
.option("dbtable", "jdbc:postgresql:dbserver") \
.load()
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-2-f3b08ff6d117> in <module>()
2 spark = SparkSession .builder .master("local") .appName("Python Spark SQL basic example") .getOrCreate()
3
----> 4 jdbcDF = spark.read .format("jdbc") .option("url", "jdbc:postgresql:dbserver") .option("dbtable", "jdbc:postgresql:dbserver") .load()
/usr/local/spark/python/pyspark/sql/readwriter.py in load(self, path, format, schema, **options)
163 return self._df(self._jreader.load(self._spark._sc._jvm.PythonUtils.toSeq(path)))
164 else:
--> 165 return self._df(self._jreader.load())
166
167 #since(1.4)
/usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
/usr/local/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o36.load.
: java.sql.SQLException: No suitable driver
at java.sql.DriverManager.getDriver(DriverManager.java:315)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:84)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:84)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:83)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:34)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:32)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:306)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:146)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)

Answer 1

In order to include the driver for postgresql you can do the following:
from pyspark.conf import SparkConf
conf = SparkConf() # create the configuration
conf.set("spark.jars", "/path/to/postgresql-connector-java-someversion-bin.jar") # set the spark.jars
...
spark = SparkSession.builder \
.config(conf=conf) \ # feed it to the session here
.master("local") \
.appName("Python Spark SQL basic example") \
.getOrCreate()
Now, since you are using Docker, I guess you have to mount the folder that has the driver jar and refer to the mounted folder. (e.g.: How to mount a host directory in a Docker container)
Hope this helps, good luck!
Edit: A diffferent way would be to give the --driver-class-path argument when using spark-submit like this:
spark-submit --driver-class-path=path/to/postgresql-connector-java-someversion-bin.jar file_to_run.py
but I'm guessing this is not how you will run this.

Answer 2

Putting the driver into the pyspark path works but the correct way to do it is to add something line this:
conf = pyspark.SparkConf().setAll([('spark.executor.id', 'driver'),
('spark.app.id', 'local-1631738601802'),
('spark.app.name', 'PySparkShell'),
('spark.driver.port', '32877'),
('spark.sql.warehouse.dir', 'file:/home/data_analysis_tool/spark-warehouse'),
('spark.driver.host', 'localhost'),
('spark.sql.catalogImplementation', 'hive'),
('spark.rdd.compress', 'True'),
('spark.driver.bindAddress', 'localhost'),
('spark.serializer.objectStreamReset', '100'),
('spark.master', 'local[*]'),
('spark.submit.pyFiles', ''),
('spark.app.startTime', '1631738600836'),
('spark.submit.deployMode', 'client'),
('spark.ui.showConsoleProgress', 'true'),
('spark.driver.extraClassPath','/tmp/postgresql-42.2.23.jar')])
note the line:
('spark.driver.extraClassPath','/tmp/postgresql-42.2.23.jar')
Here is the whole code:
import psycopg2
import pandas as pd
import pyspark
from pyspark.sql import SparkSession
from sqlalchemy import create_engine
import qgrid
#appName = "PySpark PostgreSQL Example - via psycopg2"
#master = "local"
#spark = SparkSession.builder.master(master).appName(appName).getOrCreate()
conf = pyspark.SparkConf().setAll([('spark.executor.id', 'driver'),
('spark.app.id', 'local-1631738601802'),
('spark.app.name', 'PySparkShell'),
('spark.driver.port', '32877'),
('spark.sql.warehouse.dir', 'file:/home/data_analysis_tool/spark-warehouse'),
('spark.driver.host', 'localhost'),
('spark.sql.catalogImplementation', 'hive'),
('spark.rdd.compress', 'True'),
('spark.driver.bindAddress', 'localhost'),
('spark.serializer.objectStreamReset', '100'),
('spark.master', 'local[*]'),
('spark.submit.pyFiles', ''),
('spark.app.startTime', '1631738600836'),
('spark.submit.deployMode', 'client'),
('spark.ui.showConsoleProgress', 'true'),
('spark.driver.extraClassPath','/tmp/postgresql-42.2.23.jar')])
sc = pyspark.SparkContext(conf=conf)
sc.getConf().getAll()
sparkSession = SparkSession (sc)
sparkDataFrame = sparkSession.read.format("jdbc") \
.options(
url="jdbc:postgresql://localhost:5432/Database",
dbtable="test_features_3",
user="database_user",
password="Pa$$word").load()
print (sparkDataFrame.count())
sc.stop()