Example: Locating and adding JARs to Spark 2 configuration

This example shows how to discover the location of JAR files installed with Spark 2, and add them to the Spark 2 configuration.

# # Using Avro data
# 
# This example shows how to use a JAR file on the local filesystem on
# Spark on Yarn.

from __future__ import print_function
import os,sys
import os.path
from functools import reduce
from pyspark.sql import SparkSession
from pyspark.files import SparkFiles

# Add the data file to HDFS for consumption by the Spark executors.
!hdfs dfs -put resources/users.avro /tmp

# Find the example JARs provided by the Spark parcel. This parcel
# is available on both the driver, which runs in Cloudera AI, and the 
# executors, which run on Yarn.
exampleDir = os.path.join(os.environ["SPARK_HOME"], "examples/jars")
exampleJars = [os.path.join(exampleDir, x) for x in os.listdir(exampleDir)]

# Add the Spark JARs to the Spark configuration to make them available for use.
spark = SparkSession\
    .builder\
    .config("spark.jars", ",".join(exampleJars))\
    .appName("AvroKeyInputFormat")\
    .getOrCreate()
sc = spark.sparkContext

# Read the schema.
schema = open("resources/user.avsc").read()
conf = {"avro.schema.input.key": schema }

avro_rdd = sc.newAPIHadoopFile(
    "/tmp/users.avro", # This is an HDFS path!
    "org.apache.avro.mapreduce.AvroKeyInputFormat",
    "org.apache.avro.mapred.AvroKey",
    "org.apache.hadoop.io.NullWritable",
    keyConverter="org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter",
    conf=conf)
output = avro_rdd.map(lambda x: x[0]).collect()
for k in output:
    print(k)
spark.stop()