[
https://issues.apache.org/jira/browse/SPARK-19067?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Tathagata Das updated SPARK-19067:
----------------------------------
Description:
Right now the only way to do stateful operations with with Aggregator or UDAF.
However, this does not give users control of emission or expiration of state
making it hard to implement things like sessionization. We should add a more
general construct (probably similar to {{DStream.mapWithState}}) to structured
streaming. Here is the design.
*Requirements*
- Users should be able to specify a function that can do the following
- Access the input row corresponding to a key
- Access the previous state corresponding to a key
- Optionally, update or remove the state
- Output any number of new rows (or none at all)
*Proposed API*
{code}
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V],
State[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K,
Iterator[V], State[S]) => Iterator[U])
// Java friendly
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S,
R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K,
V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
}
// ------------------- New Java-friendly function classes -------------------
public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
R call(K key, Iterator<V> values, state: State<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends
Serializable {
Iterator<R> call(K key, Iterator<V> values, state: GroupState<S>) throws
Exception;
}
// ---------------------- Wrapper class for state data ----------------------
trait GroupState[S] {
def exists(): Boolean
def get(): S // throws Exception is state does not
exist
def getOption(): Option[S]
def update(newState: S): Unit
def remove(): Unit // exists() will be false after this
}
{code}
Key Semantics of the State class
- The state can be null.
- If the state.remove() is called, then state.exists() will return false, and
getOption will returm None.
- After that state.update(newState) is called, then state.exists() will return
true, and getOption will return Some(...).
- None of the operations are thread-safe. This is to avoid memory barriers.
*Usage*
{code}
val stateFunc = (word: String, words: Iterator[String, runningCount:
GroupState[Long]) => {
val newCount = words.size + runningCount.getOption.getOrElse(0L)
runningCount.update(newCount)
(word, newCount)
}
dataset // type is
Dataset[String]
.groupByKey[String](w => w) // generates
KeyValueGroupedDataset[String, String]
.mapGroupsWithState[Long, (String, Long)](stateFunc) // returns
Dataset[(String, Long)]
{code}
*Future Directions*
- Timeout based state expiration (that has not received data for a while) - Done
- General expression based expiration - TODO. Any real usecases that cannot be
done with timeouts?
was:
Right now the only way to do stateful operations with with Aggregator or UDAF.
However, this does not give users control of emission or expiration of state
making it hard to implement things like sessionization. We should add a more
general construct (probably similar to {{DStream.mapWithState}}) to structured
streaming. Here is the design.
*Requirements*
- Users should be able to specify a function that can do the following
- Access the input row corresponding to a key
- Access the previous state corresponding to a key
- Optionally, update or remove the state
- Output any number of new rows (or none at all)
*Proposed API*
{code}
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V],
State[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K,
Iterator[V], State[S]) => Iterator[U])
// Java friendly
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S,
R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K,
V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
}
// ------------------- New Java-friendly function classes -------------------
public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
R call(K key, Iterator<V> values, state: State<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends
Serializable {
Iterator<R> call(K key, Iterator<V> values, state: State<S>) throws Exception;
}
// ---------------------- Wrapper class for state data ----------------------
trait KeyedState[S] {
def exists(): Boolean
def get(): S // throws Exception is state does not
exist
def getOption(): Option[S]
def update(newState: S): Unit
def remove(): Unit // exists() will be false after this
}
{code}
Key Semantics of the State class
- The state can be null.
- If the state.remove() is called, then state.exists() will return false, and
getOption will returm None.
- After that state.update(newState) is called, then state.exists() will return
true, and getOption will return Some(...).
- None of the operations are thread-safe. This is to avoid memory barriers.
*Usage*
{code}
val stateFunc = (word: String, words: Iterator[String, runningCount:
KeyedState[Long]) => {
val newCount = words.size + runningCount.getOption.getOrElse(0L)
runningCount.update(newCount)
(word, newCount)
}
dataset // type is
Dataset[String]
.groupByKey[String](w => w) // generates
KeyValueGroupedDataset[String, String]
.mapGroupsWithState[Long, (String, Long)](stateFunc) // returns
Dataset[(String, Long)]
{code}
*Future Directions*
- Timeout based state expiration (that has not received data for a while)
- General expression based expiration
> mapGroupsWithState - arbitrary stateful operations with Structured Streaming
> (similar to DStream.mapWithState)
> --------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-19067
> URL: https://issues.apache.org/jira/browse/SPARK-19067
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
> Reporter: Michael Armbrust
> Assignee: Tathagata Das
> Priority: Critical
> Fix For: 2.2.0
>
>
> Right now the only way to do stateful operations with with Aggregator or
> UDAF. However, this does not give users control of emission or expiration of
> state making it hard to implement things like sessionization. We should add
> a more general construct (probably similar to {{DStream.mapWithState}}) to
> structured streaming. Here is the design.
> *Requirements*
> - Users should be able to specify a function that can do the following
> - Access the input row corresponding to a key
> - Access the previous state corresponding to a key
> - Optionally, update or remove the state
> - Output any number of new rows (or none at all)
> *Proposed API*
> {code}
> // ------------ New methods on KeyValueGroupedDataset ------------
> class KeyValueGroupedDataset[K, V] {
> // Scala friendly
> def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V],
> State[S]) => U)
> def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K,
> Iterator[V], State[S]) => Iterator[U])
> // Java friendly
> def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S,
> R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
> def flatMapGroupsWithState[S, U](func:
> FlatMapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S],
> resultEncoder: Encoder[U])
> }
> // ------------------- New Java-friendly function classes -------------------
> public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
> R call(K key, Iterator<V> values, state: State<S>) throws Exception;
> }
> public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends
> Serializable {
> Iterator<R> call(K key, Iterator<V> values, state: GroupState<S>) throws
> Exception;
> }
> // ---------------------- Wrapper class for state data ----------------------
> trait GroupState[S] {
> def exists(): Boolean
> def get(): S // throws Exception is state does not
> exist
> def getOption(): Option[S]
> def update(newState: S): Unit
> def remove(): Unit // exists() will be false after this
> }
> {code}
> Key Semantics of the State class
> - The state can be null.
> - If the state.remove() is called, then state.exists() will return false, and
> getOption will returm None.
> - After that state.update(newState) is called, then state.exists() will
> return true, and getOption will return Some(...).
> - None of the operations are thread-safe. This is to avoid memory barriers.
> *Usage*
> {code}
> val stateFunc = (word: String, words: Iterator[String, runningCount:
> GroupState[Long]) => {
> val newCount = words.size + runningCount.getOption.getOrElse(0L)
> runningCount.update(newCount)
> (word, newCount)
> }
> dataset // type
> is Dataset[String]
> .groupByKey[String](w => w) // generates
> KeyValueGroupedDataset[String, String]
> .mapGroupsWithState[Long, (String, Long)](stateFunc) // returns
> Dataset[(String, Long)]
> {code}
> *Future Directions*
> - Timeout based state expiration (that has not received data for a while) -
> Done
> - General expression based expiration - TODO. Any real usecases that cannot
> be done with timeouts?
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