case class GraphDataGen(numVertices: Int, input: Option[String] = None, output: Option[String], saveMode: String, mu: Double = 4.0, sigma: Double = 1.3, seed: Long = 1, numPartitions: Int = 0) extends Workload with Product with Serializable
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Instance Constructors
- new GraphDataGen(numVertices: Int, input: Option[String] = None, output: Option[String], saveMode: String, mu: Double = 4.0, sigma: Double = 1.3, seed: Long = 1, numPartitions: Int = 0)
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def
doWorkload(df: Option[DataFrame] = None, spark: SparkSession): DataFrame
Actually run the workload.
Actually run the workload. Takes an optional DataFrame as input if the user supplies an inputDir, and returns the generated results DataFrame.
- Definition Classes
- GraphDataGen → Workload
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val
input: Option[String]
- Definition Classes
- GraphDataGen → Workload
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final
def
isInstanceOf[T0]: Boolean
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- val mu: Double
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def
notifyAll(): Unit
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- val numPartitions: Int
- val numVertices: Int
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val
output: Option[String]
- Definition Classes
- GraphDataGen → Workload
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def
reconcileSchema(dataFrame: DataFrame): DataFrame
Validate that the data set has a correct schema and fix if necessary.
Validate that the data set has a correct schema and fix if necessary. This is to solve issues such as the KMeans load-from-disk pathway returning a DataFrame with all the rows as StringType instead of DoubleType.
- Definition Classes
- Workload
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def
run(spark: SparkSession): DataFrame
- Definition Classes
- Workload
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val
saveMode: String
- Definition Classes
- GraphDataGen → Workload
- val seed: Long
- val sigma: Double
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final
def
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def
toMap: Map[String, Any]
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