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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.

Sessions usually represent some period of user activity separated by a defined gap of inactivity. As events are processed they may create new sessions or be merged into existing sessions. Any sessions that are merged will usually extend the time window the session covers. Thus, session windows are dynamic in nature, i.e, they don’t fit into fixed and aligned time windows, but rather are varying-sized time windows that can't be pre-computed and are driven by timestamps of the data.

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<K, V, T> aggregator,
                                   final SessionMerger<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.inactivityGap(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 inactivityGap(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)

}

/**
 * The interface for merging aggregate values for {@link SessionWindows} with the given key
 *
 * @param <K>   key type
 * @param <T>   aggregate value type
 */
public interface SessionMerger<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 {

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

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

/**
 * Represents the key for a Session Window
 */
public class SessionKey<K> {
    // record key
    private final K key;
    // session start time
    private final long start;
    // session end time
    private final long end; 
}

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.