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Remember, this is an RDD, not a DataFrame or DataSet! Val fRDD = sc.textFile("s3a://markosbucket/folder01")įRDD: .RDD = s3a://markosbucket/folder01 MapPartitionsRDD at textFile at :24 The jars and credentials are read by Spark application now. The value for specific key can also be checked by running (KEY_NAME), as example below shows. The String in the result should, among other parameters, also show values for keys ., ., and .s3a.impl. Once in Spark, the configuration properties can be checked by running
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Spark-shell -properties-file /home/ubuntu/s3-test/s3.properties Once the file is saved, we can test the access by starting spark-shell. Add and adjust the following text in the file. In this case, they are downloaded to /home/ubuntu/s3-test/jarsĬreate a properties file in the project’s home. Download the AWS Java SDK and Hadoop AWS jars. Folder jars is created in the project’s home. The project’s home is /home/ubuntu/s3-test. I have an S3 bucket called markobucket and in folder folder01 I have the test file called SearchLog.tsv. This post assumes there is an S3 bucket with a test file available.
#LZIP FILE SPARK HOW TO#
It describes how to prepare the properties file with AWS credentials, run spark-shell to read the properties, reads a file from S3 and writes from a DataFrame to S3. This example has been tested on Apache Spark 2.0.2 and 2.1.0. Update : Here is a post about how to use Spark, Scala, S3 and sbt in Intellij IDEA to create a JAR application that reads from S3.
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