class EventSource extends TBase[EventSource, _Fields] with Serializable with Cloneable with Comparable[EventSource]
Autogenerated by Thrift Compiler (0.13.0)
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Instance Constructors
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new
EventSource(other: EventSource)
Performs a deep copy on other.
- new EventSource()
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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final
def
asInstanceOf[T0]: T0
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- Any
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def
clear(): Unit
- Definition Classes
- EventSource → TBase
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- @Override()
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def
clone(): AnyRef
- Attributes
- protected[lang]
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- @throws( ... ) @native()
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def
compareTo(other: EventSource): Int
- Definition Classes
- EventSource → Comparable
- Annotations
- @Override()
-
def
deepCopy(): EventSource
- Definition Classes
- EventSource → TBase
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(that: EventSource): Boolean
-
def
equals(that: Any): Boolean
- Definition Classes
- EventSource → AnyRef → Any
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- @Override()
-
def
fieldForId(fieldId: Int): _Fields
- Definition Classes
- EventSource → TBase
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- @Nullable()
-
def
finalize(): Unit
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- protected[lang]
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- @throws( classOf[java.lang.Throwable] )
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final
def
getClass(): Class[_]
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- @native()
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def
getFieldValue(field: _Fields): AnyRef
- Definition Classes
- EventSource → TBase
- Annotations
- @Nullable()
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def
getQuery(): Query
The logic used to scan both the table and the topic.
The logic used to scan both the table and the topic. Contains row level transformations and filtering expressed as Spark SQL statements.
- Annotations
- @Nullable()
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def
getTable(): String
Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table.
Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table. Table names can contain subpartition specs, example db.table/system=mobile/currency=USD
- Annotations
- @Nullable()
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def
getTopic(): String
Topic is a kafka table.
Topic is a kafka table. The table contains all the events historically came through this topic.
- Annotations
- @Nullable()
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def
hashCode(): Int
- Definition Classes
- EventSource → AnyRef → Any
- Annotations
- @Override()
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
isIsCumulative(): Boolean
If each new hive partition contains not just the current day's events but the entire set of events since the begininng.
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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def
isSet(field: _Fields): Boolean
Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise
Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise
- Definition Classes
- EventSource → TBase
-
def
isSetIsCumulative(): Boolean
Returns true if field isCumulative is set (has been assigned a value) and false otherwise
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def
isSetQuery(): Boolean
Returns true if field query is set (has been assigned a value) and false otherwise
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def
isSetTable(): Boolean
Returns true if field table is set (has been assigned a value) and false otherwise
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def
isSetTopic(): Boolean
Returns true if field topic is set (has been assigned a value) and false otherwise
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
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- @native()
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final
def
notifyAll(): Unit
- Definition Classes
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- @native()
-
def
read(iprot: TProtocol): Unit
- Definition Classes
- EventSource → TSerializable
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def
setFieldValue(field: _Fields, value: Any): Unit
- Definition Classes
- EventSource → TBase
-
def
setIsCumulative(isCumulative: Boolean): EventSource
If each new hive partition contains not just the current day's events but the entire set of events since the begininng.
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.
- def setIsCumulativeIsSet(value: Boolean): Unit
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def
setQuery(query: Query): EventSource
The logic used to scan both the table and the topic.
The logic used to scan both the table and the topic. Contains row level transformations and filtering expressed as Spark SQL statements.
- def setQueryIsSet(value: Boolean): Unit
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def
setTable(table: String): EventSource
Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table.
Table currently needs to be a 'ds' (date string - yyyy-MM-dd) partitioned hive table. Table names can contain subpartition specs, example db.table/system=mobile/currency=USD
- def setTableIsSet(value: Boolean): Unit
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def
setTopic(topic: String): EventSource
Topic is a kafka table.
Topic is a kafka table. The table contains all the events historically came through this topic.
- def setTopicIsSet(value: Boolean): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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-
def
toString(): String
- Definition Classes
- EventSource → AnyRef → Any
- Annotations
- @Override()
- def unsetIsCumulative(): Unit
- def unsetQuery(): Unit
- def unsetTable(): Unit
- def unsetTopic(): Unit
- def validate(): Unit
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final
def
wait(): Unit
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- @throws( ... )
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
- Definition Classes
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- @throws( ... ) @native()
-
def
write(oprot: TProtocol): Unit
- Definition Classes
- EventSource → TSerializable