Type Members

1.  class Analyzer extends AnyRef
2.  class Args extends ScallopConf
3.  class ChrononKryoRegistrator extends KryoRegistrator
4.  class CpcSketchKryoSerializer extends Serializer[CpcSketch]
5.  sealed trait DataRange extends AnyRef
6.  class DummyExtensions extends (SparkSessionExtensions) ⇒ Unit
7.  class GroupBy extends Serializable
8.  class GroupByUpload extends Serializable
9.  sealed case class IncompatibleSchemaException(inconsistencies: Seq[(String, DataType, DataType)]) extends Exception with Product with Serializable
10.  class ItemSketchSerializable extends Serializable
11.  class ItemsSketchKryoSerializer extends Serializer[ItemSketchSerializable]
12.  class Join extends AnyRef
13.  case class KeyWithHash(data: Array[Any], hash: Array[Byte], hashInt: Int) extends Serializable with Product
14.  case class KvRdd(data: RDD[(Array[Any], Array[Any])], keySchema: StructType, valueSchema: StructType)(implicit sparkSession: SparkSession) extends Product with Serializable
15.  class LogFlattenerJob extends Serializable

    Purpose of LogFlattenerJob is to unpack serialized Avro data from online requests and flatten each field (both keys and values) into individual columns and save to an offline "flattened" log table.

    Purpose of LogFlattenerJob is to unpack serialized Avro data from online requests and flatten each field (both keys and values) into individual columns and save to an offline "flattened" log table.

    Steps: 1. determine unfilled range and pull raw logs from partitioned log table 2. fetch joinCodecs for all unique schema_hash present in the logs 3. build a merged schema from all schema versions, which will be used as output schema 4. unpack each row and adhere to the output schema 5. save the schema info in the flattened log table properties (cumulatively)

16.  case class PartitionRange(start: String, end: String) extends DataRange with Product with Serializable
17.  class RowWrapper extends Row
18.  class StagingQuery extends AnyRef
19.  case class TableUtils(sparkSession: SparkSession) extends Product with Serializable
20.  case class TimeRange(start: Long, end: Long) extends DataRange with Product with Serializable