Type Members

1.  sealed abstract final class Accuracy extends Enum[Accuracy] with TEnum

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2.  class Aggregation extends TBase[Aggregation, _Fields] with Serializable with Cloneable with Comparable[Aggregation]

   Chronon provides a powerful aggregations primitive - that takes the familiar aggregation operation, via groupBy in SQL and extends it with three things - windowing, bucketing and auto-explode.

   Chronon provides a powerful aggregations primitive - that takes the familiar aggregation operation, via groupBy in SQL and extends it with three things - windowing, bucketing and auto-explode.

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3.  class AggregationPart extends TBase[AggregationPart, _Fields] with Serializable with Cloneable with Comparable[AggregationPart]

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4.  class BootstrapPart extends TBase[BootstrapPart, _Fields] with Serializable with Cloneable with Comparable[BootstrapPart]

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5.  class DataField extends TBase[DataField, _Fields] with Serializable with Cloneable with Comparable[DataField]

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6.  sealed abstract final class DataKind extends Enum[DataKind] with TEnum

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7.  sealed trait DataType extends Serializable
8.  class Derivation extends TBase[Derivation, _Fields] with Serializable with Cloneable with Comparable[Derivation]

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9.  case class EncodeKeyException(requestName: String, message: String) extends IllegalArgumentException with Product with Serializable
10.  class EntitySource extends TBase[EntitySource, _Fields] with Serializable with Cloneable with Comparable[EntitySource]

    Entity Sources represent data that gets mutated over-time - at row-level.

    Entity Sources represent data that gets mutated over-time - at row-level. This is a group of three data elements. snapshotTable, mutationTable and mutationTopic. mutationTable and mutationTopic are only necessary if we are trying to create realtime or point-in-time aggregations over these sources. Entity sources usually map 1:1 with a database tables in your OLTP store that typically serves live application traffic. When mutation data is absent they map 1:1 to dim tables in star schema.

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11.  class EventSource extends TBase[EventSource, _Fields] with Serializable with Cloneable with Comparable[EventSource]

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12.  class ExternalPart extends TBase[ExternalPart, _Fields] with Serializable with Cloneable with Comparable[ExternalPart]

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13.  class ExternalSource extends TBase[ExternalSource, _Fields] with Serializable with Cloneable with Comparable[ExternalSource]

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14.  class FetchException extends IllegalArgumentException
15.  class GroupBy extends TBase[GroupBy, _Fields] with Serializable with Cloneable with Comparable[GroupBy]

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16.  class GroupByServingInfo extends TBase[GroupByServingInfo, _Fields] with Serializable with Cloneable with Comparable[GroupByServingInfo]

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17.  case class InvalidEntityException(requestName: String) extends FetchException with Product with Serializable
18.  class Join extends TBase[Join, _Fields] with Serializable with Cloneable with Comparable[Join]

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19.  class JoinPart extends TBase[JoinPart, _Fields] with Serializable with Cloneable with Comparable[JoinPart]

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20.  class JoinSource extends TBase[JoinSource, _Fields] with Serializable with Cloneable with Comparable[JoinSource]

    Output of a Join can be used as input to downstream computations like GroupBy or a Join.

    Output of a Join can be used as input to downstream computations like GroupBy or a Join. Below is a short description of each of the cases we handle. Case #1: a join's source is another join [TODO]

    • while serving, we expect the keys for the upstream join to be passed in the request. we will query upstream first, and use the result to query downstream
    • while backfill, we will backfill the upstream first, and use the table as the left of the subsequent join
    • this is currently a "to do" because users can achieve this by themselves unlike case 2: Case #2: a join is the source of another GroupBy
    • We will support arbitrarily long transformation chains with this.
    • for batch (Accuracy.SNAPSHOT), we simply backfill the join first and compute groupBy as usual
      • will substitute the joinSource with the resulting table and continue computation
      • we will add a "resolve source" step prior to backfills that will compute the parent join and update the source
    • for realtime (Accuracy.TEMPORAL), we need to do "stream enrichment"
      • we will simply issue "fetchJoin" and create an enriched source. Note the join left should be of type "events".

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21.  case class KeyMissingException(requestName: String, missingKeys: Seq[String], query: Map[String, Any]) extends FetchException with Product with Serializable
22.  class LabelPart extends TBase[LabelPart, _Fields] with Serializable with Cloneable with Comparable[LabelPart]

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23.  case class ListType(elementType: DataType) extends DataType with Product with Serializable
24.  case class MapType(keyType: DataType, valueType: DataType) extends DataType with Product with Serializable
25.  class MetaData extends TBase[MetaData, _Fields] with Serializable with Cloneable with Comparable[MetaData]

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26.  sealed abstract final class Operation extends Enum[Operation] with TEnum

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27.  case class ParametricMacro(value: String, func: (Map[String, String]) ⇒ String) extends Product with Serializable
28.  case class PartitionSpec(format: String, spanMillis: Long) extends Product with Serializable
29.  class Query extends TBase[Query, _Fields] with Serializable with Cloneable with Comparable[Query]

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30.  trait Row extends AnyRef
31.  trait SchemaTraverser[SchemaType] extends AnyRef

    SchemaTraverser aids in the traversal of the given SchemaType.

    SchemaTraverser aids in the traversal of the given SchemaType. In some cases (eg avro), it is more performant to create the top-level schema once and then traverse it top-to-bottom, rather than recreating at each node.

    This helper trait allows the Row.to function to traverse SchemaType without leaking details of the SchemaType structure.

32.  class Source extends TUnion[Source, _Fields]

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33.  class StagingQuery extends TBase[StagingQuery, _Fields] with Serializable with Cloneable with Comparable[StagingQuery]

    Staging Query encapsulates arbitrary spark computation.

    Staging Query encapsulates arbitrary spark computation. One key feature is that the computation follows a "fill-what's-missing" pattern. Basically instead of explicitly specifying dates you specify two macros. {{ start_date }} and {{end_date}}. Chronon will pass in earliest-missing-partition for start_date and execution-date / today for end_date. So the query will compute multiple partitions at once.

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34.  case class StructField(name: String, fieldType: DataType) extends Product with Serializable
35.  case class StructType(name: String, fields: Array[StructField]) extends DataType with Seq[StructField] with Product with Serializable
36.  class TDataType extends TBase[TDataType, _Fields] with Serializable with Cloneable with Comparable[TDataType]

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37.  sealed abstract final class TimeUnit extends Enum[TimeUnit] with TEnum

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38.  case class UnknownType(any: Any) extends DataType with Product with Serializable
39.  class Window extends TBase[Window, _Fields] with Serializable with Cloneable with Comparable[Window]

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