object AggregateMessages extends Logging with Serializable
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
MSG_COL_NAME: String
Column name for aggregated messages, used in AggregateMessages.msg
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
dst: Column
Reference for destination column, used for specifying messages
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def
edge: Column
Reference for edge column, used for specifying messages
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
getCachedDataFrame(df: DataFrame): DataFrame
Create a new cached copy of a DataFrame.
Create a new cached copy of a DataFrame. For iterative DataFrame-based algorithms.
WARNING: This is NOT the same as
DataFrame.cache()
. The original DataFrame will NOT be cached.This is a workaround for SPARK-13346, which makes it difficult to use DataFrames in iterative algorithms. This workaround converts the DataFrame to an RDD, caches the RDD, and creates a new DataFrame. This is important for avoiding the creation of extremely complex DataFrame query plans when using DataFrames in iterative algorithms.
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
logDebug(s: ⇒ String): Unit
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def
logInfo(s: ⇒ String): Unit
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def
logTrace(s: ⇒ String): Unit
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def
logWarn(s: ⇒ String): Unit
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def
msg: Column
Reference for message column, used for specifying aggregation function
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
src: Column
Reference for source column, used for specifying messages
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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final
def
wait(): Unit
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