Status

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

We propose to add a new type of state that can be used to create simpler and more efficient implementations of the operators that either maintain a MapState<?, List<?>>, and/or that require range operations. This includes temporal joins, operators that sort their input streams by timestamp, and a few other cases:

...

Several of these operators are using MapState<Long, List<T>> to store lists of records associated with timestamps. Updating those lists is expensive, especially when they are stored in RocksDB, as that requires deserializing and reserializing the lists when appending new list entries.

With this proposal, the common case of MapState<Long, List<T>> becomes BinarySortedState<Long, T>. We can use the timestamps that were the MapState’s keys as The implementation can exploit namespaces in the state backends for representing the keys (timestamps, in this case), and the MapState’s values can be stored as lists using each state backend’s native ListState implementation.

Another opportunity for optimization is to take advantage of the fact that RocksDB stores its key/value pairs in order, sorted by key. We can use this to implement efficient range scans.

This FLIP also makes it possible for users to create custom time windows that are implemented as keyed process functions that achieve performance similar to that of the built-in window operators. This isn’t currently possible because the window operators take advantage of the state backends’ namespaces to optimize state access in a way that can’t be done with Flink’s public APIs. By exposing these namespaces in this limited, controlled way, users will be able to achieve comparable performance.

But rather than limiting the scope to using Longs as the keys in BinarySortedState<UK, UV>, we propose instead to support any type for which a lexicographically ordered type serializer can be implemented. This means that when comparing any two keys, a and b, their binary, serialized representations will be ordered in exactly the same way as the original objects, a and b. We can then take advantage of the fact that RocksDB stores its key/value pairs in order, sorted by the serialized representations of the keys, to implement efficient range scans.

This FLIP also makes it possible for users to create custom time windows that are implemented as keyed process functions that achieve performance similar to that of the built-in window operators. This isn’t currently possible because the window operators take advantage of the state backends’ namespaces to optimize state access as outlined above, something that can’t be done with Flink’s public APIs. By exposing these namespaces in this limited, controlled way, users will be able to achieve comparable performance.

An implementation is underway. Benchmarking has shown a ~130% increase in throughput for sorting, An implementation is underway. POC benchmarking has shown ~130% speedup for sorting, and ~60% for temporal joins.

Public Interfaces

The complete proposed interface for BinarySortedState<UK, UV> is shown below. In summary, it includes:

...

• clear()

/**@PublicEvolving
public *interface {@link State} interface for partitioned key-value state. The key-value pair can be added, updated
 * and retrieved.
 *
 * <p>The state is accessed and modified by user functions, and checkpointed consistently by the
 * system as part of the distributed snapshots.
 *
 * <p>The state is only accessible by functions applied on a {@code KeyedStream}. The key is
 * automatically supplied by the system, so the function always sees the value mapped to the key of
 * the current element. That way, the system can handle stream and state partitioning consistently
 * together.
 *
 * @param <UK> Type of the keys in the state.
 * @param <UV> Type of the values in the state.
 */
@PublicEvolving
public interface BinarySortedState<UK, UV> extends State {

    /**BinarySortedState<UK, UV> extends State {

    /**
     * Returns the current values associated with the given key.
     *
     * @param key The key of the mapping
     * @return The values of the mapping with the given key (or <tt>null</tt> if there is none)
     * @throws Exception Thrown if the system cannot access the state.
     */
    @Nullable
    Iterable<UV> valuesAt(UK key) throws Exception;

    /**
     * Returns the current values associated with the first key in state, based on the state's
     * ordering function.
     *
     * Returns@return theThe currententry valuesof associated with the givenfirst key.
 (or <tt>null</tt> if  *
     * @param key The key of the mapping
     * @return The values of the mapping with the given key (or <tt>null</tt> if there there is none)
     * @throws Exception Thrown if the system cannot access the state.
     */
    @Nullable
    Iterable<UV> valuesAt(UK keyMap.Entry<UK, Iterable<UV>> firstEntry() throws Exception;

    /**
     * Returns the current values associated with the first key in state at or after {@code
     * fromKey}, based on the state's ordering function.
     *
     ordering* function.
@param fromKey The query key *(inclusive)
     * @return The entry of the first key after {@code fromKey} (or <tt>null</tt> if there is none)
     * @throws Exception Thrown if the system cannot access the state.
     */
    @Nullable
    Map.Entry<UK, Iterable<UV>> firstEntry(UK fromKey) throws Exception;

    /**
     * Returns the current values associated with the firstlast key in state, atbased oron after {@codethe state's
     * fromKey}, based on the state's ordering function.
     *
     * @param fromKey The query key (inclusive)
     * @return The entry of the firstlast key after {@code fromKey} (or <tt>null</tt> if there is none)
     * @throws Exception Thrown if the system cannot access the state.
     */
    @Nullable
    Map.Entry<UK, Iterable<UV>> firstEntrylastEntry(UK fromKey) throws Exception;

    /**
     * Returns the current values associated with the last key in state, at basedor onbefore the state's{@code toKey},
     * ordering based on the state's ordering function.
     *
     * @param toKey The query key (inclusive)
     * @return The entry of the last key at or before {@code toKey} (or <tt>null</tt> if there is
     *     none)
     * @throws Exception Thrown if the system cannot access the state.
     */
    @Nullable
    Map.Entry<UK, Iterable<UV>> lastEntry(UK toKey) throws Exception;

    /**
     * Returns the currentAssociates a new set of values associated with the lastgiven key in(overwrites statethe at or before {@code toKey},previous mapping).
     *
 based  on the state's* ordering@param function.
key The key of the *mapping
     * @param toKeyvalues The new queryvalues of keythe (inclusive)mapping
     * @return@throws TheException entryThrown ofif the lastsystem keycannot at or before {@code toKey} (or <tt>null</tt> if there is
     *     none)access the state.
     */
    void put(UK key, Iterable<UV> values) throws Exception;

    /**
     * Adds @throwsa Exceptionnew Thrownvalue ifto the systemmapping cannotwith accessthe thegiven statekey.
     */
    @Nullable
 * @param key  Map.Entry<UK, Iterable<UV>> lastEntry(UK toKey) throws Exception;

    /**
     * Associates a new set of values with the given key (overwrites the previous mapping)The key of the mapping
     * @param value The new value to add to the mapping
     * @throws Exception Thrown if the system cannot access the state.
     */
    void *add(UK @param key, TheUV keyvalue) of the mappingthrows Exception;

    /**
 * @param values The new* valuesReturns ofall the mapping
mappings in the   * @throws Exception Thrown if the system cannot access the statestate for the given range of keys.
     */
    void put(UK key, Iterable<UV> values) throws Exception;

    /*** @param fromKey The first key of the mappings to return (inclusive)
     * Adds@param toKey aThe newlast valuekey toof the mappingmappings with the given key.to return
     *
 @param inclusiveToKey Whether {@code *toKey} @paramshould keybe Theinclusive key of the mappingor not
     * @param value The new value to add to the mapping@return An iterable view of all the state's key-values pairs in the given range.
     * @throws Exception Thrown if the system cannot access the state.
     */
    Iterable<Map.Entry<UK, voidIterable<UV>>> addreadRange(UK fromKey, UK keytoKey, UVboolean valueinclusiveToKey)
            throws Exception;

    /**
     * Returns all the mappings in the state for the given range of keys.
the smallest key until the *given key.
     * @param fromKey The first key of the mappings to return (inclusive)
     * @param toKey The last key of the mappings to return
     * @param inclusiveToKey Whether {@code toKey} should be inclusive or not
     * @return An iterable view of all the state's key-values pairs in the given range.
     * @throws Exception Thrown if the system cannot access the state.
     */
    Iterable<Map.Entry<UK, Iterable<UV>>> readRangereadRangeUntil(UK fromKey, UK toKey, boolean inclusiveToKey)
            throws Exception;

    /**
     * Returns all the mappings in the state for the range of the smallestgiven key (inclusive) until the given
     * largest key.
     *
     * @param toKeyfromKey The lastfirst key of the mappings to return
     * @param@return inclusiveToKeyAn Whetheriterable {@codeview toKey} should be inclusive or not
     * @return An iterable view of all of all the state's key-values pairs in the given range.
     * @throws Exception Thrown if the system cannot access the state.
     */
    Iterable<Map.Entry<UK, Iterable<UV>>> readRangeUntilreadRangeFrom(UK toKey, boolean inclusiveToKey)
            fromKey) throws Exception;

    /**
     * ReturnsDeletes all the mappings in the state values for the rangemapping ofwith the given key.
 (inclusive) until the
     * largest key.
     *
     * @param fromKeykey The first key of the mappings to return
     * @return An iterable view of all the state's key-values pairs in the given range.mapping
     * @throws Exception Thrown if the system cannot access the state.
     */
    Iterable<Map.Entry<UK, Iterable<UV>>> readRangeFromvoid clearEntryAt(UK fromKeykey) throws Exception;

    /**
     * Deletes all values forin the mappinggiven withrange theof given keykeys.
     *
     * @param keyfromKey The first key of the mapping mappings to remove (inclusive)
     * @throws@param ExceptiontoKey ThrownThe iflast thekey systemof cannotthe accessmappings theto state.remove
     */
 @param inclusiveToKey Whether void clearEntryAt(UK key) throws Exception;

    /**{@code toKey} should be inclusive or not
     * Deletes@throws allException valuesThrown inif the system givencannot rangeaccess ofthe keysstate.
     */
    void * @param fromKey The first key of the mappings to remove (inclusive)clearRange(UK fromKey, UK toKey, boolean inclusiveToKey) throws Exception;

    /**
     * Deletes all values in the range of the smallest key until the given key.
     *
     * @param toKey The last key of the mappings to remove
     * @param inclusiveToKey Whether {@code toKey} should be inclusive or not
     * @throws Exception Thrown if the system cannot access the state.
     */
    void clearRangeclearRangeUntil(UK fromKey, UK toKey, boolean inclusiveToKey) throws Exception;

    /**
     * Deletes all values in the range of the smallestgiven key (inclusive) until the givenlargest key.
     *
     * @param toKeyfromKey The lastfirst key of the mappings to remove
     * @param@throws inclusiveToKeyException WhetherThrown {@code toKey} should be inclusive or not
     * @throws Exception Thrown if if the system cannot access the state.
     */
    void clearRangeUntilclearRangeFrom(UK toKey, boolean inclusiveToKeyfromKey) throws Exception;

    /**
     * Deletes all values infrom the range of the given key (inclusive) until the largest key.
 state. */
    void clear();
}

Under this proposal, a new state descriptor type is added:

@PublicEvolving
public class BinarySortedStateDescriptor<UK, UV> extends ListStateDescriptor<UV> {

    /**
     * @paramCreate fromKeya Thenew first{@code keyBinarySortedStateDescriptor} ofwith the mappings to removegiven name and the given type
     * @throwsserializers.
 Exception Thrown if the system*
 cannot access the state.
 * @param name The */
name of the  void clearRangeFrom(UK fromKey) throws Exception;

    /** Deletes all values from{@code MapStateDescriptor}.
     * @param keySerializer The type serializer for the keys in the state.
  */
   * void clear();
}

Under this proposal, RuntimeContext is extended with methods for registering BinarySortedState.

public interface RuntimeContext {      

    @PublicEvolving@param valueSerializer The type serializer for the values in the state.
     */
    <UK, UV> BinarySortedState<UK, UV> getBinarySortedState(
public BinarySortedStateDescriptor(
            String BinarySortedStateDescriptor<UKname,
  UV> stateProperties);
}

public interface KeyedStateStore {

    <T> TemporalValueState<T> getTemporalValueState(ValueStateDescriptor<T> stateProperties          LexicographicalTypeSerializer<UK> keySerializer,
            TypeSerializer<UV> valueSerializer);

    <T>  TemporalListState<T> getTemporalListState(ListStateDescriptor<T> stateProperties);
}

Proposed Changes

There are many places in Flink SQL, for example, where something like this is used:

private transient MapState<Long, List<RowData>> dataState;

Under this proposal, this will come

private transient TemporalListState<RowData> dataState;

Having the getAtOrBefore and getAtOrAfter methods available for these temporal state types makes it much easier to implement operations such as temporal joins and temporal sorting. By providing optimized implementations of these methods for each state backend, the simpler implementations of these temporal operations can also be more performant.

EmbeddedRocksDBStateBackend: Add a LexicographicLongSerializer

In order to have an efficient implementation of the getAtOrBefore and getAtOrAfter methods for the RocksDB state backend, we want to leverage the iterators available in RocksDB. 

For this purpose, we have implemented a new LexicographicLongSerializer that serializes the timestamps, which are longs, with an unsigned big-endian encoding so that their binary sort order matches the numerical sort order. 

HashMapStateBackend

Our intended implementation is to lazily create a sorted list of the timestamps for the current key in response to the first call to getAtOrBefore or getAtOrBefore, and then reuse that list until it becomes invalid.

Operators that can potentially be improved by using Temporal State

• TemporalRowTimeJoinOperator
• RowTimeSortOperator
• CepOperator
• IntervalJoinOperator
• AbstractStreamArrowPythonOverWindowAggregateFunctionOperator

• The various over/rank window operators

New Examples

We have implemented two new examples: a temporal join, and a temporal sort.

New Documentation

In addition to javadocs, state#using-keyed-state should be updated to include TemporalValueState and TemporalListState.

Updated Training

docs/learn-flink/event_driven/#example should be updated.

Compatibility, Deprecation, and Migration Plan

Deprecation

While this FLIP does open the door for eventually deprecating some of the existing (and often abused) mechanisms for customizing DataStream windows, we are not proposing to deprecate anything as part of this FLIP.

Compatibility/Migration

Any operators that are reworked to use temporal state will thereafter use state in an incompatible way, compared to their current implementations.

To support upgrades to any SQL operator that is reworked to use temporal state, the mechanisms of FLIP-190 for handling operator migrations need to be followed. This involves annotating the ExecNode of the operator with an updated version number, and then modifying the operator so that it works regardless of which version of the state it finds available. 

These operators can then either migrate the state, or directly support both the old and new versions of the state. In the context of this FLIP, migrating from any MapState<Long, List<T>> that is found to TemporalListState<T> will be simpler than continuing to maintain the old state as MapState, so that is the preferred approach.

Test Plan

We have implemented unit tests for the new state types that are tested against both state backends.

We should verify that any operators that are updated to use temporal state have sufficient test coverage. Tests will need to be added for state migration.

Rejected Alternatives

Directly expose the state backend namespaces

By itself, this wouldn’t accomplish much, and the interfaces proposed here satisfy the objectives without exposing this internal API.

Provide iterators over the per-key timestamps

Many use cases only need to fetch a single value, and don’t need iteration. So while providing users with iterators might offer a slight performance increase in some cases (by keeping low-level/native pointers into the iteration), the added complexity doesn’t seem to be justified.

Add support for TemporalMapState

We haven’t found a motivating use case.

SortedMapState

 /**
     * Create a new {@code BinarySortedStateDescriptor} with the given name and the given type
     * information.
     *
     * @param name The name of the {@code MapStateDescriptor}.
     * @param keySerializer The type serializer for the keys in the state.
     * @param valueTypeInfo The type information for the values in the state.
     */
    public BinarySortedStateDescriptor(
            String name,
            LexicographicalTypeSerializer<UK> keySerializer,
            TypeInformation<UV> valueTypeInfo);

    /**
     * Create a new {@code BinarySortedStateDescriptor} with the given name and the given type
     * information.
     *
     * <p>If this constructor fails (because it is not possible to describe the type via a class),
     * consider using the {@link #BinarySortedStateDescriptor(String, LexicographicalTypeSerializer,
     * TypeInformation)} constructor.
     *
     * @param name The name of the {@code MapStateDescriptor}.
     * @param keySerializer The type serializer for the keys in the state.
     * @param valueType The class of the type of values in the state.
     */
    public BinarySortedStateDescriptor(
            String name, LexicographicalTypeSerializer<UK> keySerializer, Class<UV> valueType);

    /**
     * Gets the serializer for the keys in the state.
     *
     * @return The serializer for the keys in the state.
     */
    public LexicographicalTypeSerializer<UK> getKeySerializer();
}

Here the LexicographicalTypeSerializer<T> is a special TypeSerializer that provides a well-defined binary sort order that we will use in the operations above (ideally the same that you would expect from the original Java objects). For better performance with heap-based state-backends, however, you can also provide an efficient Java object comparator based on the data type's binary sort order. This way, heap-based backends don't have to serialize keys in order to sort them. The proposal is to initially provide only an internal implementation of a LexicographicLongSerializer in order to support BinarySortedState<Long, T> out of the box. Users will be able to extend LexicographicalTypeSerializer if they want to use other types as the keys for ordering the state.

@PublicEvolving
public abstract class LexicographicalTypeSerializer<T> extends TypeSerializer<T> {

    /**
     * Provides an efficient Java object comparator based on this data type's binary sort order.
     *
     * @return If empty, implementations can compare based on serialized form and using {@link
     *     BinarySortOrderComparator#INSTANCE}, otherwise use the provided (efficient) comparator.
     */
    public Optional<Comparator<T>> findComparator() {}

    public static final class BinarySortOrderComparator implements Comparator<byte[]> {

        public static final BinarySortOrderComparator INSTANCE = new BinarySortOrderComparator();

        @Override
        public int compare(byte[] first, byte[] second) {}

        /**
         * Variant of {@link #compare(byte[], byte[])} that starts the comparison from a given
         * position. This can be used if you know that the first <code>from</code> bytes are equal.
         *
         * @param first the first object to be compared.
         * @param second the second object to be compared.
         * @param from array position to start the comparison at
         * @return a negative integer, zero, or a positive integer as the first argument is less
         *     than, equal to, or greater than the second.
         * @see #compare(byte[], byte[])
         */
        public int compare(byte[] first, byte[] second, int from) {}
    }
}

Here's an example of how these pieces fit together:

private transient BinarySortedState<Long, Long> events;

BinarySortedStateDescriptor<Long, Long> desc =
    new BinarySortedStateDescriptor<>(
        "Events",
        LexicographicLongSerializer.INSTANCE,
        Types.LONG);

events = getRuntimeContext()
    .getBinarySortedState(desc);

As illustrated above, RuntimeContext (and KeyedStateStore) need to be extended with a method for registering BinarySortedState:

    @PublicEvolving
    <UK, UV> BinarySortedState<UK, UV> getBinarySortedState(
        BinarySortedStateDescriptor<UK, UV> stateProperties);

Proposed Changes

EmbeddedRocksDBStateBackend

The RocksDB state backend is extended to use the state descriptor's LexicographicalTypeSerializer as the namespace serializer.

A RocksDBBinarySortedNavigator is added to support the various lookup and range operations. It takes advantage of RocksDB's native iterators.

HashMapStateBackend

A HeapBinarySortedNavigator is needed to provide the same lookup and range operations for the heap-based state backend. The proposed implementation uses a

HashMap<FK, TreeSet<UKI>> to keep track of the namespaces that need to be iterated over.

Operators that can potentially be improved by using BinarySortedState

As outlined in the Motivation section, many of Flink's operators can benefit by being updated to leverage this new state type. 

However, in a first release, we plan to limit the scope, and only include the new state type.

New Documentation

In addition to javadocs, state#using-keyed-state needs to be updated to include BinarySortedState.

Compatibility, Deprecation, and Migration Plan

Deprecation

While this FLIP does open the door for eventually deprecating some of the existing (and often abused) mechanisms for customizing DataStream windows, we are not proposing to deprecate anything as part of this FLIP.

Compatibility/Migration

Any operators that are reworked to use binary sorted state will thereafter use state in an incompatible way, compared to their current implementations.

To support upgrades to any SQL operator that is reworked to use binary sorted state, the mechanisms of FLIP-190 for handling operator migrations need to be followed. This involves annotating the ExecNode of the operator with an updated version number, and then modifying the operator so that it works regardless of which version of the state it finds available. 

These operators can then either migrate the state, or directly support both the old and new versions of the state. In the context of this FLIP, migrating from any MapState<Long, List<T>> that is found to BinarySortedState<Long, T> will be simpler than continuing to maintain the old state as MapState, so that is the preferred approach.

Test Plan

We will include unit tests for the new state types that are tested against both state backends.

We should verify that any operators that are updated to use temporal state have sufficient test coverage. Tests will need to be added for state migration.

Rejected Alternatives

Directly expose the state backend namespaces

By itself, this wouldn’t accomplish much, and the interfaces proposed here satisfy the objectives without exposing this internal API.

Limit the scope to only offering TemporalState

An earlier draft of this FLIP proposed adding TemporalValueState and TemporalListState types, roughly equivalent to BinarySortedState<Long, ?>. After some discussion on the mailing list, this was determined to be too limiting. The idea here would be to support MapState where the key type could be any type that implements Comparable. This would be a generalization of TemporalState, which is similar, but limited to only supporting Longs as the keys. This was rejected as being overly difficult to implement in a way that would cleanly leverage RocksDB’s iterators, and we didn’t have a motivating use case that would benefit from this generalization.