Type Members

1.  class Analyzer extends AnyRef
2.  class Args extends ScallopConf
3.  sealed trait BaseKvRdd extends AnyRef
4.  case class BootstrapInfo(joinConf: api.Join, joinParts: Seq[JoinPartMetadata], externalParts: Seq[ExternalPartMetadata], derivations: Array[StructField], hashToSchema: Map[String, Array[StructField]]) extends Product with Serializable
5.  class ChrononKryoRegistrator extends KryoRegistrator
6.  case class CoveringSet(hashes: Seq[String], rowCount: Long, isCovering: Boolean) extends Product with Serializable
7.  class CpcSketchKryoSerializer extends Serializer[CpcSketch]
8.  sealed trait DataRange extends AnyRef
9.  class DummyExtensions extends (SparkSessionExtensions) ⇒ Unit
10.  case class ExternalPartMetadata(externalPart: ExternalPart, keySchema: Array[StructField], valueSchema: Array[StructField]) extends Product with Serializable
11.  class GroupBy extends Serializable
12.  class GroupByUpload extends Serializable
13.  sealed case class IncompatibleSchemaException(inconsistencies: Seq[(String, DataType, DataType)]) extends Exception with Product with Serializable
14.  class ItemSketchSerializable extends Serializable
15.  class ItemsSketchKryoSerializer extends Serializer[ItemSketchSerializable]
16.  class Join extends JoinBase
17.  abstract class JoinBase extends AnyRef
18.  case class JoinPartMetadata(joinPart: JoinPart, keySchema: Array[StructField], valueSchema: Array[StructField], derivationDependencies: Map[StructField, Seq[StructField]]) extends Product with Serializable
19.  case class KeyWithHash(data: Array[Any], hash: Array[Byte], hashInt: Int) extends Serializable with Product
20.  case class KvRdd(data: RDD[(Array[Any], Array[Any])], keySchema: StructType, valueSchema: StructType)(implicit sparkSession: SparkSession) extends BaseKvRdd with Product with Serializable
21.  class LabelJoin extends AnyRef
22.  class LocalTableExporter extends AnyRef
23.  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)

24.  case class LoggingSchema(keyCodec: AvroCodec, valueCodec: AvroCodec) extends Product with Serializable
25.  case class PartitionRange(start: String, end: String)(implicit tableUtils: TableUtils) extends DataRange with Ordered[PartitionRange] with Product with Serializable
26.  class StagingQuery extends AnyRef
27.  case class TableUtils(sparkSession: SparkSession) extends Product with Serializable
28.  case class TimeRange(start: Long, end: Long)(implicit tableUtils: TableUtils) extends DataRange with Product with Serializable
29.  case class TimedKvRdd(data: RDD[(Array[Any], Array[Any], Long)], keySchema: StructType, valueSchema: StructType, storeSchemasPrefix: Option[String] = None)(implicit sparkSession: SparkSession) extends BaseKvRdd with Product with Serializable