Pyspark - read zip file from s3 to an RDD [duplicate]

Question

Trying to read a file located in S3 using spark-shell:

scala> val myRdd = sc.textFile("s3n://myBucket/myFile1.log")
lyrics: org.apache.spark.rdd.RDD[String] = s3n://myBucket/myFile1.log MappedRDD[55] at textFile at <console>:12

scala> myRdd.count
java.io.IOException: No FileSystem for scheme: s3n
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2607)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2614)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91)
    ... etc ...

The IOException: No FileSystem for scheme: s3n error occurred with:

• Spark 1.31 or 1.40 on dev machine (no Hadoop libs)
• Running from the Hortonworks Sandbox HDP v2.2.4 (Hadoop 2.60) which integrates Spark 1.2.1 out of the box
• Using s3:// or s3n:// scheme

What is the cause of this error? Missing dependency, Missing configuration, or mis-use of sc.textFile()?

Or may be this is due to a bug that affects Spark build specific to Hadoop 2.60 as this post seems to suggest. I am going to try Spark for Hadoop 2.40 to see if this solves the issue.

Answer 1

Confirmed that this is related to the Spark build against Hadoop 2.60. Just installed Spark 1.4.0 "Pre built for Hadoop 2.4 and later" (instead of Hadoop 2.6). And the code now works OK.

sc.textFile("s3n://bucketname/Filename") now raises another error:

java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access Key must be specified as the username or password (respectively) of a s3n URL, or by setting the fs.s3n.awsAccessKeyId or fs.s3n.awsSecretAccessKey properties (respectively).

The code below uses the S3 URL format to show that Spark can read S3 file. Using dev machine (no Hadoop libs).

scala> val lyrics = sc.textFile("s3n://MyAccessKeyID:MySecretKey@zpub01/SafeAndSound_Lyrics.txt")
lyrics: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[3] at textFile at <console>:21

scala> lyrics.count
res1: Long = 9

Even Better: the code above with AWS credentials inline in the S3N URI will break if the AWS Secret Key has a forward "/". Configuring AWS Credentials in SparkContext will fix it. Code works whether the S3 file is public or private.

sc.hadoopConfiguration.set("fs.s3n.awsAccessKeyId", "BLABLA")
sc.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey", "....") // can contain "/"
val myRDD = sc.textFile("s3n://myBucket/MyFilePattern")
myRDD.count

Answer 2

Despite that this question has already an accepted answer, I think that the exact details of why this is happening are still missing. So I think there might be a place for one more answer.

If you add the required hadoop-aws dependency, your code should work.

Starting Hadoop 2.6.0, s3 FS connector has been moved to a separate library called hadoop-aws. There is also a Jira for that: Move s3-related FS connector code to hadoop-aws.

This means that any version of spark, that has been built against Hadoop 2.6.0 or newer will have to use another external dependency to be able to connect to the S3 File System.
Here is an sbt example that I have tried and is working as expected using Apache Spark 1.6.2 built against Hadoop 2.6.0:

libraryDependencies += "org.apache.hadoop" % "hadoop-aws" % "2.6.0"

In my case, I encountered some dependencies issues, so I resolved by adding exclusion:

libraryDependencies += "org.apache.hadoop" % "hadoop-aws" % "2.6.0" exclude("tomcat", "jasper-compiler") excludeAll ExclusionRule(organization = "javax.servlet")

On other related note, I have yet to try it, but that it is recommended to use "s3a" and not "s3n" filesystem starting Hadoop 2.6.0.

The third generation, s3a: filesystem. Designed to be a switch in replacement for s3n:, this filesystem binding supports larger files and promises higher performance.