###### This example shows how to configure a spark application to allocate and utilize GPU in sql computations for Scala

1. Allocate a single GPU for the required project in resource management to run the example.
2. Put the jar file to available location, e.g., maprfs
3. Run the scala-gpu example, check physical plan in the output logs


```shell
== Physical Plan ==
GpuColumnarToRow false
+- GpuFilter NOT (value#2 = 1), true
   +- GpuRowToColumnar targetsize(2147483647)
      +- *(1) SerializeFromObject [input[0, int, false] AS value#2]
         +- Scan[obj#1]
```
3. Disable RAPIDs sql feature by changing the following option in yaml file:
```yaml
spark.conf:
  ...
  spark.rapids.sql.enabled: "false"
  ...
```
4. Restart application and check the physical plan again. Since sql-on-gpu is disabled, now it should look like this:
```shell
== Physical Plan ==
*(1) Filter NOT (value#2 = 1)
+- *(1) SerializeFromObject [input[0, int, false] AS value#2]
   +- Scan[obj#1]
```


To test the same on livy, use the following code:
```scala
import spark.implicits._

val viewName = "df"
val df = sc.parallelize(Seq(1, 2, 3)).toDF("value")
df.createOrReplaceTempView(viewName)

spark.sql(s"SELECT value FROM $viewName WHERE value <>1").explain()
spark.sql(s"SELECT value FROM $viewName WHERE value <>1").show()
```