from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.load(source="jdbc", url="jdbc:postgresql://host/dbname", dbtable="schema.tablename")

os.environ['SPARK_CLASSPATH'] = "C:UsersACERNEW3DesktopSparkspark-1.3.0-bin-hadoop2.4postgresql-9.2-1002.jdbc3.jar"

pyspark --conf spark.executor.extraClassPath=<jdbc.jar> --driver-class-path <jdbc.jar> --jars <jdbc.jar> --master <master-URL>

from pyspark import SparkContext, SparkConf
from pyspark.sql import DataFrameReader, SQLContext
import os

sparkClassPath = os.getenv('SPARK_CLASSPATH', '/path/to/connector/postgresql-42.1.4.jar')

# Populate configuration
conf = SparkConf()
conf.setAppName('application')
conf.set('spark.jars', 'file:%s' % sparkClassPath)
conf.set('spark.executor.extraClassPath', sparkClassPath)
conf.set('spark.driver.extraClassPath', sparkClassPath)
# Uncomment line below and modify ip address if you need to use cluster on different IP address
#conf.set('spark.master', 'spark://127.0.0.1:7077')

sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)

url = 'postgresql://127.0.0.1:5432/postgresql'
properties = {'user':'username', 'password':'password'}

df = DataFrameReader(sqlContext).jdbc(url='jdbc:%s' % url, table='tablename', properties=properties)

df.printSchema()
df.show()