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.

   :param inputColumn: The column as specified in source.query.selects - on which we need to aggregate with.

   :param operation: The type of aggregation that needs to be performed on the inputColumn.

   :param argMap: Extra arguments that needs to be passed to some of the operations like LAST_K, APPROX_PERCENTILE.

   :param windows: For TEMPORAL case windows are sawtooth. Meaning head slides ahead continuously in time, whereas, the tail only hops ahead, at discrete points in time. Hop is determined by the window size automatically. The maximum hop size is 1/12 of window size. You can specify multiple such windows at once.

   Window > 12 days -> Hop Size = 1 day

   Window > 12 hours -> Hop Size = 1 hr

   Window > 1hr -> Hop Size = 5 minutes

   :param buckets: This is an additional layer of aggregation. You can key a group_by by user, and bucket a "item_view" count by "item_category". This will produce one row per user, with column containing map of "item_category" to "view_count". You can specify multiple such buckets at once.

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

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

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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 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.

   :param snapshotTable: Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table. :param mutationTable: Topic is a kafka table. The table contains all the events historically came through this topic. :param mutationTopic: The logic used to scan both the table and the topic. Contains row level transformations and filtering expressed as Spark SQL statements. :param isCumulative: If each new hive partition contains not just the current day's events but the entire set of events since the begininng. The key property is that the events are not mutated across partitions.

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

   Event source captures data that is essentially immutable - like user clicks, impressions etc.

   Event source captures data that is essentially immutable - like user clicks, impressions etc. It has two parts, an offline table and an online topic. The term fact table from star-schema also maps to this concepts. But lacks a notion of topic.

   :param table: Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table. :param topic: Topic is a kafka table. The table contains all the events historically came through this topic. :param query: The logic used to scan both the table and the topic. Contains row level transformations and filtering expressed as Spark SQL statements. :param isCumulative: If each new hive partition contains not just the current day's events but the entire set of events since the begininng. The key property is that the events are not mutated across partitions.

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

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

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

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

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14.  class Join extends TBase[Join, _Fields] with Serializable with Cloneable with Comparable[Join]

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

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

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

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20.  case class PartitionSpec(format: String, spanMillis: Long) extends Product with Serializable
21.  class Query extends TBase[Query, _Fields] with Serializable with Cloneable with Comparable[Query]

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22.  trait Row extends AnyRef
23.  class Source extends TUnion[Source, _Fields]

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24.  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.

    :param metaData: Contains name, team, output_namespace, execution parameters etc. Things that don't change the semantics of the computation itself. :param query: The spark sql query with date templates. :param startPartition: This is where start_date will be applied from. We expect the query to also produce output hive tables partitioned data starting from startPartition. :param setups: Spark SQL setup statements. Used typically to register UDFs.

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

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

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

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