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Status

Current state: [One of "Under Discussion", "Accepted", "Rejected"]

Discussion thread: here

JIRA: here

Released: <Kafka Version>

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

Motivation

Describe the problems you are trying to solve.

Public Interfaces

Briefly list any new interfaces that will be introduced as part of this proposal or any existing interfaces that will be removed or changed. The purpose of this section is to concisely call out the public contract that will come along with this feature.

A public interface is any change to the following:

• Binary log format

• The network protocol and api behavior

• Any class in the public packages under clientsConfiguration, especially client configuration

  • org/apache/kafka/common/serialization

  • org/apache/kafka/common

  • org/apache/kafka/common/errors

  • org/apache/kafka/clients/producer

  • org/apache/kafka/clients/consumer (eventually, once stable)

• Monitoring

• Command line tools and arguments

• Anything else that will likely break existing users in some way when they upgrade

Proposed Changes

Describe the new thing you want to do in appropriate detail. This may be fairly extensive and have large subsections of its own. Or it may be a few sentences. Use judgement based on the scope of the change.

Compatibility, Deprecation, and Migration Plan

• What impact (if any) will there be on existing users?
• If we are changing behavior how will we phase out the older behavior?
• If we need special migration tools, describe them here.
• When will we remove the existing behavior?

Test Plan

Describe in few sentences how the KIP will be tested. We are mostly interested in system tests (since unit-tests are specific to implementation details). How will we know that the implementation works as expected? How will we know nothing broke?

Rejected Alternatives

...

Many stream processing applications need to maintain state across a period of time. Online services, for example, like to understand the behaviour of a typical user. In order to understand this behaviour, the interactions a user has with their website, or streaming service, are grouped together into sessions.

Image Added

Proposed Changes

We will provide a way for developers using the DSL to specify that they want an aggregation to be aggregated into SessionWindows. Three overloaded methods will be added to KGroupedStream:

KTable<Windowed<K>, V> reduce(final Reducer<V> reducer,
                             final SessionWindows sessionWindows,
                             final String storeName);


<T> KTable<Windowed<K>, T> aggregate(final Initializer<T> initializer,
                                   final Aggregator<? super K, ? super V, T> aggregator,
                                   final Merger<? super K, T> sessionMerger,
                                   final SessionWindows sessionWindows,
                                   final Serde<T> aggValueSerde,
                                   final String storeName);
 
KTable<Windowed<K>, Long> count(final SessionWindows sessionWindows, final String storeName);

 A typical aggregation might look like:

stream.groupByKey().aggregate(initializer, 
                             aggregator,
                             merger,  
                             SessionWindows.with(FIVE_MINUTES)
                 .until(ONE_HOUR),
   aggregateValueSerde,
  “session-store”);

Public Interfaces

public class SessionWindows {

    /**
     * Create a new SessionWindows with the specified inactivityGap
     *
     * @param inactivityGap
     * @return
     */
    public static SessionWindows with(final long inactivityGap)


    /**
     * Set the window maintain duration in milliseconds of streams time.
     * This retention time is a guaranteed <i>lower bound</i> for how long 
     * a window will be maintained.
     *
     * @return  itself
     */
    public SessionWindows until(long durationMs)
 
    /**
     * @return the inactivityGap
     */
	public long inactivityGap()
    
   /**
    * @return the minimum amount of time a window will be maintained for.
    */
    public long maintainMs();

}

/**
 * The interface for merging aggregate values with the given key
 *
 * @param <K>   key type
 * @param <T>   aggregate value type
 */
public interface Merger<K, T> {
   /**
    * Compute a new aggregate from the key and two aggregates
    *
    * @param aggKey    the key of the record
    * @param aggOne    the first aggregate
    * @param aggTwo    the second aggregate
    * @return          the new aggregate value
    */
   T apply(K aggKey, T aggOne, T aggTwo);
}

/**
* Interface for storing the aggregated values of sessions
* @param <K>   type of the record keys
* @param <AGG> type of the aggregated values
*/
public interface SessionStore<K, AGG> extends StateStore, ReadOnlySessionStore<K, AGG> {

    /**
     * Find any aggregated session values with the matching key and where the 
     * session’s end time is >= earliestSessionEndTime, i.e, the oldest session to 
     * merge with, and the session’s start time is <= latestSessionStartTime, i.e,
     * the newest session to merge with.
     */
   KeyValueIterator<Windowed<K>, AGG> findSessionsToMerge(final K key, final long earliestSessionEndTime, final long latestSessionStartTime);
 
   /**
    * Remove the aggregated value for the session with the matching session key
    */
   void remove(final Windowed<K> sessionKey);

   /**
    * Write the aggregated result for the given session key
    */	 
   void put(final Windowed<K> sessionKey, AGG aggregate);
}

/**
 * A session store that only supports read operations.
 * Implementations should be thread-safe as concurrent reads and writes
 * are expected.
 *
 * @param <K> the key type
 * @param <V> the value type
 */
@InterfaceStability.Unstable
public interface ReadOnlySessionStore<K, AGG> {

    /**
     * Retrieve all aggregated sessions for the provided key
     * @param    key record key to find aggregated session values for
     * @return   KeyValueIterator containing all session aggregates for the provided key.
     */
    KeyValueIterator<Windowed<K>, AGG> fetch(final K key);
}

/**
 * A {@link QueryableStoreType} that accepts {@link ReadOnlySessionStore}
 * @param <K>   key type of the store
 * @param <V>   value type of the store
 * @return  {@link SessionStoreType}
 */
public static <K, V> QueryableStoreType<ReadOnlySessionStore<K, V>> sessionStore()

KTable<Windowed<K>, V> reduce(final Reducer<V> reducer,
                              final SessionWindows sessionWindows,
                              final String storeName);
 
KTable<Windowed<K>, V> reduce(final Reducer<V> reducer,
                              final SessionWindows sessionWindows,
                              final StateStoreSupplier<SessionStore> storeSupplier);


<T> KTable<Windowed<K>, T> aggregate(final Initializer<T> initializer,
                                     final Aggregator<? super K, ? super V, T> aggregator,
                                     final Merger<? super K, T> sessionMerger,
                                     final SessionWindows sessionWindows,
                                     final Serde<T> aggValueSerde,
                                     final String storeName);
 
<T> KTable<Windowed<K>, T> aggregate(final Initializer<T> initializer,
                                     final Aggregator<? super K, ? super V, T> aggregator,
                                     final Merger<? super K, T> sessionMerger,
                                     final SessionWindows sessionWindows,
                                     final Serde<T> aggValueSerde,
                                     final StateStoreSupplier<SessionStore> storeSupplier);
 
KTable<Windowed<K>, Long> count(final SessionWindows sessionWindows, final String storeName);
 
KTable<Windowed<K>, Long> count(final SessionWindows sessionWindows, final StateStoreSupplier<SessionStore> storeSupplier);

Compatibility, Deprecation, and Migration Plan

• None required as we are introducing new APIs only

Test Plan

The majority of this feature can be tested with unit tests, however we will write integration and/or system tests to cover the end-to-end scenarios.

Rejected Alternatives

• Using the KeyValueStore to store the sessions. It would result in excessive IOs as we’d need to store a List per sessionId. So every lookup for a given sessionId would need to fetch and deserialize the list. Also, punctuate would need to retrieve every session every time it runs in order to determine those that need to be aggregated and forwarded.

• Keeping the list of values in the store. Whilst this would result in us being able to use the existing Windows based API methods on KGroupedStream, it could result in excessive IO operations and possibly excessive storage use.

• Using punctuate to perform the aggregations and forward downstream. This would result in potentially fewer records being forwarded downstream as they would only be forwarded when a session is ‘closed’. However, it doesn’t align with the current approach in Kafka Streams, i.e., to use the cache for de-duplication and forward on commit, flush, or eviction (KIP-63). Further, punctuate currently depends on stream time and is will not be called in the absence of records.