How to add jdbc drivers to classpath when using 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()