Here is a scala version that works fine with Spark 3.2.1 (pre-built) with Hadoop 3.3.1, accessing a S3 bucket from a non AWS machine Make sure the Linux user with reading privileges, before running theįind command to prevent error Permission denied Hint: (Mostly it will be placed in /etc/spark/conf/nf) #make sure jars are added to CLASSPATH spark classpath can be identified by find command below find / -name spark-core*.jar It is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. Hint: We can not directly point the location(It must be in property file) as I want to make an answer generic for distributions and Linux flavors. In Spark, RDD ( resilient distributed dataset) is the first level of the abstraction layer. Into spark classpath which holds all spark jars Hint: If the jar locations are unsure? Running find command as a privileged user can be helpful commands can be find / -name hadoop-aws*.jar I'm writing this answer to access files with S3A from Spark 2.0.1 on Hadoop 2.7.3Ĭopy the AWS jars( hadoop-aws-2.7.3.jar and aws-java-sdk-1.7.4.jar) which shipped with Hadoop by default In addition I've covered all the exception cases I hit along the way and what I believe to be the cause of each and how to fix them. I've detailed this list in more detail on a post I wrote as I worked my way through this process. To use V4 pass these conf in spark-submit and also endpoint (format - s3.) must be specified. And all the new aws region support only V4 protocol. If you are using hadoop 2.7 version with spark then the aws client uses V2 as default auth signature. In spark.properties you probably want some settings that look like this: This JAR contains the class .s3a.S3AFileSystem. You'll also need the hadoop-aws 2.7.1 JAR on the classpath. As far as I can tell using this along with the specific AWS SDK JARs for 1.10.8 hasn't broken anything. You'll need to include what may at first seem to be an out of date AWS SDK library (built in 2014 as version 1.7.4) for versions of Hadoop as late as 2.7.1 (stable): aws-java-sdk 1.7.4. If you use the Spark EC2 setup scripts and maybe missed it, the switch for using something other than 1.0 is to specify -hadoop-major-version 2 (which uses CDH 4.2 as of this writing). Your Spark cluster will need a Hadoop version 2.x or greater. Here are the key parts, as of December 2015: Having experienced first hand the difference between s3a and s3n - 7.9GB of data transferred on s3a was around ~7 minutes while 7.9GB of data on s3n took 73 minutes this is a very important piece of the stack to get correct and it's worth the frustration.
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