class EventSource extends TBase[EventSource, _Fields] with Serializable with Cloneable with Comparable[EventSource]
Autogenerated by Thrift Compiler (0.13.0)
DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
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Instance Constructors
- new EventSource(other: EventSource)
Performs a deep copy on other.
- new EventSource()
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
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- AnyRef → Any
- final def asInstanceOf[T0]: T0
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- def clear(): Unit
- Definition Classes
- EventSource → TBase
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- @Override()
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def compareTo(other: EventSource): Int
- Definition Classes
- EventSource → Comparable
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- @Override()
- def deepCopy(): EventSource
- Definition Classes
- EventSource → TBase
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(that: EventSource): Boolean
- def equals(that: AnyRef): 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])
- final def getClass(): Class[_ <: AnyRef]
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- AnyRef → Any
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- @native()
- def getFieldValue(field: _Fields): AnyRef
- Definition Classes
- EventSource → TBase
- Annotations
- @Nullable()
- 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()
- 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()
- 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()
- def hashCode(): Int
- Definition Classes
- EventSource → AnyRef → Any
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- @Override()
- 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.
- 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
- def isSetQuery(): Boolean
Returns true if field query is set (has been assigned a value) and false otherwise
- def isSetTable(): Boolean
Returns true if field table is set (has been assigned a value) and false otherwise
- def isSetTopic(): Boolean
Returns true if field topic is set (has been assigned a value) and false otherwise
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @native()
- final def notifyAll(): Unit
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- @native()
- def read(iprot: TProtocol): Unit
- Definition Classes
- EventSource → TSerializable
- def setFieldValue(field: _Fields, value: AnyRef): 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
- 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
- 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
- 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
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- 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
- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
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- @throws(classOf[java.lang.InterruptedException]) @native()
- def write(oprot: TProtocol): Unit
- Definition Classes
- EventSource → TSerializable