// Cache sample table range5 using pure SQL
// That registers range5 to contain the output of range(5) function
spark.sql("CACHE TABLE range5 AS SELECT * FROM range(5)")
val q1 = spark.sql("SELECT * FROM range5")
scala> q1.explain
== Physical Plan ==
InMemoryTableScan [id#0L]
   +- InMemoryRelation [id#0L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `range5`
         +- *Range (0, 5, step=1, splits=8)

// you could also use optimizedPlan to see InMemoryRelation
scala> println(q1.queryExecution.optimizedPlan.numberedTreeString)
00 InMemoryRelation [id#0L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `range5`
01    +- *Range (0, 5, step=1, splits=8)

// Use Dataset's cache
val q2 = spark.range(10).groupBy('id % 5).count.cache
scala> println(q2.queryExecution.optimizedPlan.numberedTreeString)
00 InMemoryRelation [(id % 5)#84L, count#83L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
01    +- *HashAggregate(keys=[(id#77L % 5)#88L], functions=[count(1)], output=[(id % 5)#84L, count#83L])
02       +- Exchange hashpartitioning((id#77L % 5)#88L, 200)
03          +- *HashAggregate(keys=[(id#77L % 5) AS (id#77L % 5)#88L], functions=[partial_count(1)], output=[(id#77L % 5)#88L, count#90L])
04             +- *Range (0, 10, step=1, splits=8)

// Cache a Dataset
val q = spark.range(10).cache

// Make sure that q Dataset is cached
val cache = spark.sharedState.cacheManager
scala> cache.lookupCachedData(q.queryExecution.logical).isDefined
res0: Boolean = true

scala> q.explain
== Physical Plan ==
InMemoryTableScan [id#122L]
   +- InMemoryRelation [id#122L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
         +- *Range (0, 10, step=1, splits=8)

val qCrossJoined = q.crossJoin(q)
scala> println(qCrossJoined.queryExecution.optimizedPlan.numberedTreeString)
00 Join Cross
01 :- InMemoryRelation [id#122L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
02 :     +- *Range (0, 10, step=1, splits=8)
03 +- InMemoryRelation [id#170L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
04       +- *Range (0, 10, step=1, splits=8)

// Use sameResult for comparison
// since the plans use different output attributes
// and have to be canonicalized internally
import org.apache.spark.sql.execution.columnar.InMemoryRelation
val optimizedPlan = qCrossJoined.queryExecution.optimizedPlan
scala> optimizedPlan.children(0).sameResult(optimizedPlan.children(1))
res1: Boolean = true