Configuring Spark access for S3A

You must configure specific properties for client applications such as Spark to access the Ozone data store using S3A.

  • You must import the CA certificate to run Ozone S3 Gateway from the S3A filesystem.
  • You must create an file with the following configuration to run the Spark word count program:
    spark.hadoop.fs.s3a.impl = org.apache.hadoop.fs.s3a.S3AFileSystem
    spark.hadoop.fs.s3a.access.key = <access key>
    spark.hadoop.fs.s3a.secret.key = <secret>
    spark.hadoop.fs.s3a.endpoint = <Ozone S3 endpoint url>
    spark.hadoop.fs.s3a.bucket.probe = 0
    spark.hadoop.fs.s3a.change.detection.version.required = false
    spark.hadoop.fs.s3a.change.detection.mode = none = true
The following procedure explains how you can configure Spark access to Ozone using S3A and run a word count program from the Spark shell.
  1. Create an Ozone bucket.
    The following example shows how you can create a bucket named sparkbucket:
    ozone sh bucket create /s3v/sparkbucket
  2. Add data to the bucket.
    The following example shows how you can add data to the sparkbucket bucket:
    hadoop fs  -Dfs.s3a.bucket.probe=0 -Dfs.s3a.change.detection.version.required=false -Dfs.s3a.change.detection.mode=none -Dfs.s3a.access.key=<accesskey> -Dfs.s3a.secret.key=<secret> -Dfs.s3a.endpoint=<s3 endpoint url> -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -mkdir -p s3a://sparkbucket/input
    hadoop fs  -Dfs.s3a.bucket.probe=0 -Dfs.s3a.change.detection.version.required=false -Dfs.s3a.change.detection.mode=none -Dfs.s3a.access.key=<accesskey> -Dfs.s3a.secret.key=<secret> -Dfs.s3a.endpoint=<s3 endpoint url> -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -put /tmp/key1 s3a://testbucket/input/key1
  3. Start the Spark shell and wait for the prompt to appear.
    spark-shell --properties-file <>
  4. Create a Resilient Distributed Dataset (RDD) from an Ozone file and enter the specified command on the Spark shell.
    var lines = sc.textFile("s3a://sparkbucket/input/key1")
  5. Convert each record in the file to a word.
    var words = lines.flatMap(_.split(" "))
  6. Convert each word to a key-value pair.
    var wordsKv =, 1))
  7. Group each key-value pair by key and perform aggregation on each key.
    var wordCounts = wordsKv.reduceByKey(_ + _ )
  8. Save the results of the grouping and aggregation operations to Ozone.
  9. Exit the spark shell and view the results through S3A.
    hadoop fs  -Dfs.s3a.bucket.probe=0 -Dfs.s3a.change.detection.version.required=false -Dfs.s3a.change.detection.mode=none -Dfs.s3a.access.key=<accesskey> -Dfs.s3a.secret.key=<secret> -Dfs.s3a.endpoint=<ozone s3 endpoint url> -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -ls -R s3a://sparkbucket/
    hadoop fs  -Dfs.s3a.bucket.probe=0 -Dfs.s3a.change.detection.version.required=false -Dfs.s3a.change.detection.mode=none -Dfs.s3a.access.key=<accesskey> -Dfs.s3a.secret.key=<secret> -Dfs.s3a.endpoint=<ozone s3 endpoint url> -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -cat s3a://sparkbucket/output/part-00000