DatasetSuiteBaseLike

abstract def assert[U](message: String, expected: U, actual: U)(implicit CT: ClassTag[U]): Unit

abstract def assert[U](expected: U, actual: U)(implicit CT: ClassTag[U]): Unit

abstract def assertEmpty[U](arr: Array[U])(implicit CT: ClassTag[U]): Unit

abstract def assertTrue(expected: Boolean): Unit

abstract def fail(message: String): Unit

abstract def sc: SparkContext

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def appID: String

def appName: String

def approxEquals(r1: Row, r2: Row, tol: Double, tolTimestamp: Duration): Boolean

def approxEquals(r1: Row, r2: Row, tolTimestamp: Duration): Boolean

def approxEquals(r1: Row, r2: Row, tol: Double): Boolean

final def asInstanceOf[T0]: T0

def assertDataFrameApproximateEquals(expected: DataFrame, result: DataFrame, tol: Double, tolTimestamp: Duration, customShow: (DataFrame) ⇒ Unit = _.show()): Unit

Compares if two DataFrames are equal, checks that the schemas are the same.

def assertDataFrameDataEquals(expected: DataFrame, result: DataFrame): Unit

Compares if two DataFrames are equal without caring about order of rows, by finding elements in one DataFrame that is not in the other.

def assertDataFrameEquals(expected: DataFrame, result: DataFrame, customShow: (DataFrame) ⇒ Unit = _.show()): Unit

Compares if two DataFrames are equal, checks the schema and then if that matches checks if the rows are equal.

def assertDataFrameNoOrderEquals(expected: DataFrame, result: DataFrame): Unit

Compares if two DataFrames are equal without caring about order of rows, by finding elements in one DataFrame that is not in the other.

def assertDatasetApproximateEquals[U](expected: Dataset[U], result: Dataset[U], tol: Double, tolTimestamp: Duration, customShow: (DataFrame) ⇒ Unit = _.show())(implicit UCT: ClassTag[U]): Unit

Compares if two Datasets are equal, Datasets should have the same type.

def assertDatasetEquals[U](expected: Dataset[U], result: Dataset[U])(implicit UCT: ClassTag[U]): Unit

Check if two Datasets are equals, Datasets should have the same type.

def assertSchemasEqual(expected: StructType, result: StructType): Unit

Compare if two schemas are equal, ignoring autoGeneratedAlias magic

def assertSmallDataFrameDataEquals(expected: DataFrame, result: DataFrame): Unit

Compares if two DataFrames are equal without caring about order of rows, by finding elements in one DataFrame that is not in the other.

def builder(): Builder

Constructs a configuration for hive or iceberg, where the metastore is located in a temp directory.

def clone(): AnyRef

def conf: SparkConf

def enableHiveSupport: Boolean

def enableIcebergSupport: Boolean

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

final def getClass(): Class[_]

def hashCode(): Int

val icebergWarehouse: String

implicit def impSqlContext: SQLContext

final def isInstanceOf[T0]: Boolean

lazy val localMetastorePath: String

lazy val localWarehousePath: String

val maxUnequalRowsToShow: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setup(sc: SparkContext): Unit

Setup work to be called when creating a new SparkContext.

lazy val spark: SparkSession

def sqlBeforeAllTestCases(): Unit

lazy val sqlContext: SQLContext

final def synchronized[T0](arg0: ⇒ T0): T0

lazy val tempDir: File

def toString(): String

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

final def wait(): Unit

def assertDataFrameApproximateEquals(expected: DataFrame, result: DataFrame, tol: Double): Unit

Compares if two DataFrames are equal, checks that the schemas are the same.

def assertDatasetApproximateEquals[U](expected: Dataset[U], result: Dataset[U], tol: Double)(implicit UCT: ClassTag[U]): Unit

Compares if two Datasets are equal, Datasets should have the same type.

def finalize(): Unit