GitHub user tdas opened a pull request:
https://github.com/apache/spark/pull/16850
[SPARK-19413][SS] MapGroupsWithState for arbitrary stateful operations
## What changes were proposed in this pull request?
`mapGroupsWithState` is a new API for arbitrary stateful operations in
Structured Streaming, similar to `DStream.mapWithState`
*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*
```
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V],
KeyedState[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K,
Iterator[V], KeyedState[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: KeyedState<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends
Serializable {
Iterator<R> call(K key, Iterator<V> values, state: KeyedState<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
}
```
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*
```
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)]
```
## How was this patch tested?
New unit tests.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/tdas/spark mapWithState-branch-2.1
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/16850.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #16850
----
commit 5025cb7511a43e24cb3a181eb7b06c69b024479f
Author: Tathagata Das <[email protected]>
Date: 2017-02-08T04:21:00Z
[SPARK-19413][SS] MapGroupsWithState for arbitrary stateful operations
`mapGroupsWithState` is a new API for arbitrary stateful operations in
Structured Streaming, similar to `DStream.mapWithState`
*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*
```
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V],
KeyedState[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K,
Iterator[V], KeyedState[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: KeyedState<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends
Serializable {
Iterator<R> call(K key, Iterator<V> values, state: KeyedState<S>) throws
Exception;
}
// ---------------------- Wrapper class for state data
----------------------
trait State[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
}
```
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*
```
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)]
```
New unit tests.
Author: Tathagata Das <[email protected]>
Closes #16758 from tdas/mapWithState.
----
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