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 ozone-s3.properties 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
spark.hadoop.fs.s3a.path.style.access = true
```
note
In the list of configurations, replace the values of access key and secret from the output of ozone s3 getsecret --om-service-id=<ozone service id> and replace the Ozone S3 endpoint URL with the S3 Gateway URL of the Ozone cluster.

The following procedure explains how you can configure Spark access to Ozone using S3A and run a word count program from the Spark shell.

Create an Ozone bucket.
The following example shows how you can create a bucket named sparkbucket:
```
ozone sh bucket create /s3v/sparkbucket
```

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.path.style.access=true -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.path.style.access=true -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -put /tmp/key1 s3a://testbucket/input/key1

Start the Spark shell and wait for the prompt to appear.
```
spark-shell --properties-file <ozone-s3.properties>
```
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")
```
Convert each record in the file to a word.
```
var words = lines.flatMap(_.split(" "))
```
Convert each word to a key-value pair.
```
var wordsKv = words.map((_, 1))
```
Group each key-value pair by key and perform aggregation on each key.
```
var wordCounts = wordsKv.reduceByKey(_ + _ )
```
Save the results of the grouping and aggregation operations to Ozone.
```
wordCounts.saveAsTextFile("s3a://sparkbucket/output")
```

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.path.style.access=true -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.path.style.access=true -Dfs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem -cat s3a://sparkbucket/output/part-00000