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[Minor] document edge case of updateFunc usage #18088

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[Minor] document edge case of updateFunc usage #18088

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[Minor] document edge case of updateFunc usage
emilyselwood May 24, 2017
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fix-permissions
emilyselwood May 25, 2017
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7 changes: 7 additions & 0 deletions streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
100644 → 100755
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Original file line number Diff line number Diff line change
Expand Up @@ -389,6 +389,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
Expand All @@ -403,6 +404,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* Hash partitioning is used to generate the RDDs with `numPartitions` partitions.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
Expand All @@ -419,6 +421,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of the key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
Expand All @@ -440,6 +443,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
* @param updateFunc State update function. Note, that this function may generate a different
* tuple with a different key than the input key. Therefore keys may be removed
Expand All @@ -464,6 +468,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of the key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
Expand All @@ -487,6 +492,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. Note, that this function may generate a different
* tuple with a different key than the input key. Therefore keys may be removed
Expand All @@ -513,6 +519,7 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of the key.
* In every batch the updateFunc will be called for each state even if there are no new values.
* org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
Expand Down