Status

Current state: Under Discussion

Discussion thread: TBD 

JIRA: KAFKA-8770 

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

Note: We will now refer to no-ops as idempotent updates in this KIP. No-ops tend to sound generic, so we will use "idempotent updates" as our new terminology.

Motivation

Currently, in Kafka Streams, we support two modes of processing:

• emit on update
• emit on window close

The first one has been present for the majority of Kafka Stream's inception, when results are forwarded regardless . The second option has been added by implementing the so-called "suppression" operator which collects results throughout the window, and extending into the grace period. However, one mode of processing should've been implemented long ago, and that is emit on change. As mentioned in the JIRA, in many cases, the user would process a prior result to yield a new one, yet, this new result is forwarded to a downstream processor even though the operation performed had been effectively a idempotent update. This is problematic in certain circumstances where there is a high number of such operations, leading to an enormous number of useless results being forwarded down the topology.

This KIP will also discuss other possible reporting strategies for Kafka Streams and weigh its pros and cons. To be mindful of the KIP's scope, it is our intention that we restrict any changes to operations being performed on KTables.

Reporting Strategies

Reporting strategies are basically just another word for models of emission. When we emit results, and how we determine if we do it is discussed here.

1. Emit on change: The objective of this KIP is to implement this reporting strategy. The primary benefit of such an emission model is that it will drop idempotent updates, so it will be superior to emit on update in that traffic can be significantly reduced in high idempotent update traffic situations.
2. Emit on update / non-empty content: It has already been implemented by Kafka Streams. Basically, as long as the content or processed result is not empty, we will return the processed result. That could however lead to high idempotent update traffic, which is the primary motivation for adding emit on change.
3. Emit on window close: This has been implemented some time ago with the suppress operator which allows users to combine results in a window, and then we emit only the final result of the active window. 
4. Periodic Emission: This emission model is unique in that it is solely dependent on time to emit a given result. Essentially, every time a fixed time interval has passed, then a result will be emitted. 

For the above emission models, two have already been implemented, and we are aiming to implement another in this KIP. Periodic emission will likely not be added for now, as there has been little user demand for such a feature.

Proposed Behavior Changes

Definition: "idempotent update" is one in which the new result and prior result,  when serialized, are identical byte arrays.

Note: an "update" is a concept that only applies to Table operations, so the concept of an "idempotent update" also only applies to Table operations.
See https://kafka.apache.org/documentation/streams/developer-guide/dsl-api.html#streams_concepts_ktable for more information.

Given that definition, we propose for Streams to drop idempotent updates in any situation where it's possible and convenient to do so. For example, any time we already have both the prior and new results serialized, we may compare them, and drop the update if it is idempotent.

Note that under this proposal, we can implement idempotence checking in the following situations:
1. Any aggregation (for example, KGroupedStream, KGroupedTable, TimeWindowedKStream, and SessionWindowedKStream operations)
2. Any Materialized KTable operation
3. Repartition operations, when we need to send both prior and new results

Metrics

The following metric will be added to record the number of idempotent updates dropped:

dropped-idempotent-updates : (Level 3 - Per Processor Node) DEBUG (rate | total)  

Description: This metric will record the number of idempotent updates (no-ops) that have been dropped.

Note:

• The rate option indicates the ratio of records dropped to actual volume of records passing through the task.
• The total option will just give a raw count of the number of records dropped.

Design Reasoning

With the current default model of emission, we are forwarding the processed results downstream regardless if  it has changed or not. After some discussion, there are a couple of points that I would like to emphasize:

1. There has been some thoughts as to only coupling emit on change with the suppress operator. However, that approach has been abandoned in favor of a more extensive change which would impact almost all KTable operations supported by Kafka Streams. Our justification is that idempotent update changes should be suppressed even before they are forwarded downstream, as the duplication of these useless results has the potential to explode across multiple downstream nodes once it has been forwarded. The expected behavior is the following:
   1. Any operations that have resulted in a idempotent update would be discarded. Exceptions would be made for non-materialized KTables for performance reasons. Some might have concerns about a KTable constructed through aggregations. In this case, if a idempotent update result is produced, how do we determine which timestamp to use? To keep things simple, we have decided that we will forward aggregation results if and only if the timestamp and the value had not changed.
2. About configurations. As noted, we intend that Kafka Streams be as simple as possible when it comes to configurations. In that case, this KIP proposes that instead, emit-on-change becomes the new behavior of Kafka Streams. Since emit-on-change is more efficient (idempotent updates shouldn't really be sent in the first place), there is a strong case for no additional config that allows the user to "opt-out" of this change (and remain with emit-on-update).

The other crucial aspect of our proposal is why we restrict ourselves to dropping idempotent updates for operations that does not have the old result already loaded. To check for idempotence, both the old and new result has to be available. However, when the old result is unavailable, then we will have to procure that result again somehow. Unfortunately, that would exact an extra performance cost (which can be quite expensive). 

Compatibility, Deprecation, and Migration Plan

If the user has logic that involves actively filtering out idempotent update operations, then they will be able to remove this code in a future release once this KIP has been integrated into Kafka Streams. For now, this KIP will aim to add a minimum number of configurations, and avoid adding APIs. If the user has any logic regarding timestamps, there might be a chance that their program will be affected. This is specific however to the individual user, and behavior will have to be changed accordingly.

Rejected Alternatives

Equality Checking using Object#equals()

We have decided to abandon using equals() to check equality for now. There are concerns that this will not be a good way to reliably compare two values without errors. 

Configuration for Opting Out

For configurations, we have decided that an opt-out config is not necessary. Emit-on-change as a whole is intended solely as a performance optimization when it comes to Kafka Streams. The user should have little reason not to want this change, particularly since this KIP should not have a severe impact on performance (it is possible in implementation that we will only check if two values are equal in KTables where prior results has already been loaded). 

Emit-on-change only for Suppress Operator

Also, we have decided that we should not restrict only dropping idempotent updates to the suppress operator. It is better to allow this behavior across most KTable API, rather than just one. This provides both flexibility for the user as well as more granular control over which idempotent updates we can drop and not drop. 

Secondary Approaches

There is a possibility where we can support emit-on-change for all operations.

There is more than one way to yield the prior result. After all, we can obtain it from an upstream processor. For most operations, we can forward downstream both the old and new results of the upstream processor. In this case, the same operation will be performed twice. However, each operation can be very expensive. Performing it twice will in other words has the potential to incur horrendous performance hits. It might be that this is not a serious issue, but it is of significant concern. 

The main bottleneck for emit-on-change for stateless operations is really how to load the prior result. That does not necessarily have to be done. Earlier in our discussion, we have talked about using a hash code as a way of uniquely identifying the results, and then comparing those hash codes. But as noted, hash codes can vary across JVMs, and it is not a requirement that the programs on different runs return the same hash code for the exact same object. Instead, we can consider using some method distinct from Object#hashCode(). There is the possibility here that we can add a configuration for allowing emit-on-change for stateless operations. If emit-on-change is enabled, then we can use some method defined by the user i.e. generateUniqueObjectId(V result)  returning a 32-bit or 64-bit integer as an id – this method which will have stricter constraints than a normal hash code function.  These generated ids would be used as is the hash codes described in the Implementation section below. We store these ids instead, and compare these for equality.

This potentially can work, but the user must implement the provided method correctly. This must be stressed in further documentation. 

Forwarding the Old Result With the New

There had been some considerations, that for stateless operations, we would forward the old result along with the new one. Then, the stateless operation would be performed twice, once on the old, and once on the new. We compare these two new results to check if they are equal. The drawback with this approach is that stateless operations cannot be assumed to be inexpensive. Performing these operations twice would not work (especially since we are working to optimize Kafka Streams).