Spark + s3 - error - java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found

Question

I have a spark ec2 cluster where I am submitting a pyspark program from a Zeppelin notebook. I have loaded the hadoop-aws-2.7.3.jar and aws-java-sdk-1.11.179.jar and place them in the /opt/spark/jars directory of the spark instances. I get a java.lang.NoClassDefFoundError: com/amazonaws/AmazonServiceException

Why is spark not seeing the jars? Do I have to have to jars in all the slaves and specify a spark-defaults.conf for the master and slaves? Is there something that needs to be configured in zeppelin to recognize the new jar files?

I have placed jar files /opt/spark/jars on the spark master. I have created a spark-defaults.conf and added the lines

spark.hadoop.fs.s3a.access.key     [ACCESS KEY]
spark.hadoop.fs.s3a.secret.key     [SECRET KEY]
spark.hadoop.fs.s3a.impl           org.apache.hadoop.fs.s3a.S3AFileSystem
spark.driver.extraClassPath        /opt/spark/jars/hadoop-aws-2.7.3.jar:/opt/spark/jars/aws-java-sdk-1.11.179.jar

I have zeppelin interpreter sending a spark submit to the spark master.

I have also placed the jars in the /opt/spark/jars in the slaves too but did not create a spark-deafults.conf.

%spark.pyspark

#importing necessary libaries
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import StringType
from pyspark import SQLContext
from itertools import islice
from pyspark.sql.functions import col

# add aws credentials
sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "[ACCESS KEY]")
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", "[SECRET KEY]")
sc._jsc.hadoopConfiguration().set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")

#creating the context
sqlContext = SQLContext(sc)

#reading the first csv file and store it in an RDD
rdd1= sc.textFile("s3a://filepath/baby-names.csv").map(lambda line: line.split(","))

#removing the first row as it contains the header
rdd1 = rdd1.mapPartitionsWithIndex(
lambda idx, it: islice(it, 1, None) if idx == 0 else it
)

#converting the RDD into a dataframe
df1 = rdd1.toDF(['year','name', 'percent', 'sex'])

#print the dataframe
df1.show()

Error thrown:

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 7, 10.11.93.90, executor 1): java.lang.NoClassDefFoundError: com/amazonaws/AmazonServiceException
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:348)
    at org.apache.hadoop.conf.Configuration.getClassByNameOrNull(Configuration.java:2134)
    at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2099)
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2193)
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2654)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
    at org.apache.hadoop.mapred.LineRecordReader.<init>(LineRecordReader.java:108)
    at org.apache.hadoop.mapred.TextInputFormat.getRecordReader(TextInputFormat.java:67)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.liftedTree1$1(HadoopRDD.scala:267)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:266)
    at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:224)
    at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:95)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ClassNotFoundException: com.amazonaws.AmazonServiceException
    at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    ... 34 more

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:153)
    at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
    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:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NoClassDefFoundError: com/amazonaws/AmazonServiceException
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:348)
    at org.apache.hadoop.conf.Configuration.getClassByNameOrNull(Configuration.java:2134)
    at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2099)
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2193)
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2654)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
    at org.apache.hadoop.mapred.LineRecordReader.<init>(LineRecordReader.java:108)
    at org.apache.hadoop.mapred.TextInputFormat.getRecordReader(TextInputFormat.java:67)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.liftedTree1$1(HadoopRDD.scala:267)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:266)
    at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:224)
    at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:95)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more
Caused by: java.lang.ClassNotFoundException: com.amazonaws.AmazonServiceException
    at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    ... 34 more

Answer 1

I was able to address the above to make sure I had the correct versions of the hadoop aws jar per the version of spark hadoop that I was running, downloading the correct version of aws-java-sdk, and lastly downloading the dependency jets3t library

In the /opt/spark/jars

sudo wget https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk/1.11.30/aws-java-sdk-1.11.30.jar
sudo wget https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.7.3/hadoop-aws-2.7.3.jar
sudo wget https://repo1.maven.org/maven2/net/java/dev/jets3t/jets3t/0.9.4/jets3t-0.9.4.jar

Testing it out

scala> sc.hadoopConfiguration.set("fs.s3n.awsAccessKeyId", [ACCESS KEY ID])
scala> sc.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey", [SECRET ACCESS KEY] )
scala> val myRDD = sc.textFile("s3n://adp-px/baby-names.csv")
scala> myRDD.count()
res2: Long = 49

Answer 2

If S3 access is by assume_role from local cluster then below worked for me.

import boto3
import pyspark as pyspark
from pyspark import SparkContext

session = boto3.session.Session(profile_name='profile_name')
sts_connection = session.client('sts')
response = sts_connection.assume_role(RoleArn='arn:aws:iam:::role/role_name', RoleSessionName='role_name',DurationSeconds=3600)
credentials = response['Credentials']

conf = pyspark.SparkConf()

conf.set('spark.jars.packages', 'org.apache.hadoop:hadoop-aws:3.2.0')  //crosscheck the version. 

sc = SparkContext(conf=conf)
sc._jsc.hadoopConfiguration().set('fs.s3a.aws.credentials.provider', 'org.apache.hadoop.fs.s3a.TemporaryAWSCredentialsProvider')
sc._jsc.hadoopConfiguration().set('fs.s3a.access.key', credentials['AccessKeyId'])
sc._jsc.hadoopConfiguration().set('fs.s3a.secret.key', credentials['SecretAccessKey'])
sc._jsc.hadoopConfiguration().set('fs.s3a.session.token', credentials['SessionToken'])
url = str('s3a://data.csv')

l1 = sc.textFile(url).collect()
for each in l1:
    print(str(each))
    break

keep below proper version of class files also in $SPARK_HOME/jars

1. jets3t
2. aws-java-sdk
3. hadoop-aws

I prefer to delete unwanted jars from ~/.ivy2/jars

Answer 3

Each hadoop version should match aws-java-sdk-...jar, hadoop-aws-...jar.

And every aws-java-sdk version matched with hadoop-aws-..jar (it does not mean the same number).

For example ( aws-java-sdk-bundle-1.11.375.jar, hadoop-aws-3.2.0.jar are pair versions).

Lastly you should enroll the s3 domain in the hive.cnf configuration file.

Answer 4

From the official Hadoop troubleshooting documentation:

   ClassNotFoundException: org.apache.hadoop.fs.s3a.S3AFileSystem

   These are Hadoop filesystem client classes, found in the `hadoop-aws` 
   JAR. An exception reporting this class as missing means that this JAR 
   is not on the classpath.

To solve this problem first need to know what is org.apache.hadoop.fs.s3a:

In the Hadoop website, it explains in detail what Hadoop-AWS module: Integration with Amazon Web Services is. And the prerequisite to use it is having these two jars installed under /Spark/jars directory:

1. hadoop-aws Jar
2. aws-java-sdk-bundle Jar

When downloading these jars, make sure two things:

1. Hadooop version matches with hadoop-aws version, a hadoop-aws-3.xx.jar works for a hadoop-3.xx
2. aws SDK for Java matches the Java version installed. Check this official document from AWS on exact version requirements.

For more troubleshooting, can always refer to the official Hadoop troubleshooting documentation:

Answer 5

Add the following to this file hadoop/etc/hadoop/core-site.xml

<property>
  <name>fs.s3.awsAccessKeyId</name>
  <value>***</value>
</property>
<property>
  <name>fs.s3.awsSecretAccessKey</name>
  <value>***</value>
</property>

Inside the Hadoop installation directory, find aws jars, for MAC installation directory is /usr/local/Cellar/hadoop/

find . -type f -name "*aws*"

sudo cp hadoop/share/hadoop/tools/lib/aws-java-sdk-1.7.4.jar hadoop/share/hadoop/common/lib/
sudo cp hadoop/share/hadoop/tools/lib/hadoop-aws-2.7.5.jar hadoop/share/hadoop/common/lib/

Credit

Answer 6

Following worked for me

My system config:

Ubuntu 16.04.6 LTS python3.7.7 openjdk version 1.8.0_252 spark-2.4.5-bin-hadoop2.7

1. Configure PYSPARK_PYTHON path: add following line in $spark_home/conf/spark-env.sh

   export PYSPARK_PYTHON= python_env_path/bin/python

2. Start pyspark

   pyspark --packages com.amazonaws:aws-java-sdk-pom:1.11.760,org.apache.hadoop:hadoop-aws:2.7.0 --conf spark.hadoop.fs.s3a.endpoint=s3.us-west-2.amazonaws.com

   com.amazonaws:aws-java-sdk-pom:1.11.760 : depends on jdk version hadoop:hadoop-aws:2.7.0: depends on your hadoop version s3.us-west-2.amazonaws.com: depends on your s3 location

3.Read data from s3

df2=spark.read.parquet("s3a://s3location_file_path")

Credits

Answer 7

If nothing works in the above then do a cat and grep for the missing class. High possibility that the Jar is corrupted. For example, if you get class AmazonServiceException not found, then do a grep where the jar is already present as shown below.

grep "AmazonServiceException" *.jar