Status

Current state: Under Discussion

Discussion thread: here 

JIRA: here

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

Motivation

Kafka Streams has grown to augment KTables (e.g., changelogs) with state stores. These state stores can be seen as a way to materialize the KTables. State stores can be queried through the Interactive Queries (IQ for short) APIs, as introduced in KIP-67. This led to some confusions and inconsistencies, e.g., :

• Several KTables must be materialized, e.g., those that are created as a result of aggregation (because the aggregated data must be stored somewhere). We force users to provide a state store name because of this requirement.

• In some cases, it is not a must that the KTables are materialized, e.g., in KStreamBuilder.table().

• In some other cases, the KTables are never materialized, because the user does not have the option of materializing them, for example KTables resulting from operations like filter(). Thus user unfortunately cannot query these KTables through interactive queries. For example, the second KTable below cannot be materialized:

     KTable table1 = builder.table(..., topic, stateStoreName); // materialized

     KTable table2 = table1.filter(...); // not materialized, and not queryable by IQ

• KTables resulting from join() operators are not materialized, because the user does not have the option of materializing them. Thus they cannot be queried.

• Certain operations on KTables, such as print() and foreach(), are often confusing and redundant, since users can already print the values of a materialized view. Furthermore, users also have the option to use the KStream equivalent functions after converting a KTable to a KStream. So they have 3 ways of printing and iterating through a KTable.

Proposal

We propose to clean up the KTable API and make the KTable semantics clearer and consistent through API improvements and associated JavaDoc improvements.

In a nutshell the approach is as follows:

• Decouple the notion of materialization from the notion of querying. Materialization is an internal streams decision. Querying is a user-facing decision.
• We will overload each method that creates a KTable with a store name. If the user provides that name, the user can subsequently query that store with that provided name.
• The above guarantee says that a user can query the store. It doesn't say anything about how we implement that feature internally. We could be materializing the store (e.g., backing it with a RocksDb store). Or we could be providing a read-only view of the store (e.g., by computing the result on the fly). The filter example above illustrates the two options:

                                                               KTable table2 = table1.filter() <----- user does not provide a name, no guarantee table2 is queryable

                                                               KTable table2 = table1.filter("filterStoreName")      <----- user provides a name, we guarantee table2 is queryable based on that name. Internally we could be writing each filtered value to a RocksDb store, or computing the filter result on the fly each time the store is queried.

Interfaces / API Changes (DSL only)

2 overloads for all APIs that create KTables: with and without store name

All API that create KTables will have 2 overloaded methods, one with the store name, and one without. Note that a null store name does not indicate that a KTable would not be materialized, but only that it will not be used for interactive queries. An internal name will be generated in cases materialization must happen.

These APIs include the ones below and any of their existing overloads. We do not list the overloads here to keep the list uncluttered. Each API will have one version with no store name, and one version with a store name.

In KTable.java overload each of the following APIs by adding store name:

• KTable<K, V> filter(final Predicate<? super K, ? super V> predicate);

• KTable<K, V> filterNot(final Predicate<? super K, ? super V> predicate);

• <VR> KTable<K, VR> mapValues(final ValueMapper<? super V, ? extends VR> mapper);

• KTable<K, V> through(final String topic) 

• <VO, VR> KTable<K, VR> join(final KTable<K, VO> other, final ValueJoiner<? super V, ? super VO, ? extends VR> joiner);

• <VO, VR> KTable<K, VR> leftJoin(final KTable<K, VO> other,  final ValueJoiner<? super V, ? super VO, ? extends VR> joiner);

• <VO, VR> KTable<K, VR> outerJoin(final KTable<K, VO> other, final ValueJoiner<? super V, ? super VO, ? extends VR> joiner);

In KStreamBuilder.java overload each of the following APIs by adding store name:

•  public <K, V> KTable<K, V> table(final String topic);

• public <K, V> GlobalKTable<K, V> globalTable(final String topic)

• public <K, V> GlobalKTable<K, V> globalTable(final Serde<K> keySerde, final Serde<V> valSerde, final String topic)

In KGroupedTable.java overload each of the following APIs by adding store name:

• KTable<K, Long> count(final String storeName);

• KTable<K, V> reduce(final Reducer<V> adder, final Reducer<V> subtractor);

• <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer, final Aggregator<? super K, ? super V, VR> adder, final Aggregator<? super K, ? super V, VR> subtractor)

In KGroupedStream.java:

• KTable<K, Long> count(final String storeName);

• KTable<K, V> reduce(final Reducer<V> reducer);

• <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer, final Aggregator<? super K, ? super V, VR> aggregator, final Serde<VR> aggValueSerde);

Remove unnecessary methods

• Depreciate the following KTable methods: print(), writeAsText(), foreach() and any of their overloads.

Implementation plan

One implementation detail that is important is how the Kafka Streams internals decides whether to materialize a KTable. Note that the above APIs provide a way for Interactive Queries to query a state store. They do not dictate whether the state store itself if a real, materialized one, or a view on top of a real, materialized store. Going back to the first example in the motivation:

KTable table1 = builder.table(..., topic, stateStoreName); // materialized

KTable table2 = table1.filter(final String stateStoreName2)

The store with name "stateStoreName2" could be a view on top of the store with name "stateStoreName", in which case each time we query stateStoreName2, we would, on the fly, compute the result of the filter on values actually stored in the first store. Alternatively, "stateStoreName2" could in itself be materialised, i.e., contain all the results obtained from the filtering. Materialising all Ktables with a state store name could be expensive, however it is a straightforward to implement and could be a good V1. A subsequent JIRA could do an optimization in V2.

Compatibility, Deprecation, and Migration Plan

• Will depreciate the KTable methods print(), writeAsText(), foreach() and any of their overloads. 

• Code will be backwards compatible.

Rejected Alternatives

Change the name of KTable. We discussed potentially changing the name of a KTable to something like KChangelogStream, but this doesn’t solve the main problem this KIP addresses, which is API inconsistency.

Use .materialize(String storeName) instead of overloads. In this proposal, we remove all state store parameters in KTable methods, and add a .materialize() method on the KTable. This gets us to a place where we are forced to make the DSL less declarative (see “What is not in scope” above) and forces us to have an orthogonal discussion to this KIP.

Have the KTable be the materialized view. In this alternative, the KTable would no longer be a changelog stream, it would be a materialized view. So we would collapse two things (the existing KTable and state store) into one abstraction. All KTable methods would need to take a state store name.

• There are some performance implications of doing this, e.g., each KTable would now always be materialized and that is expensive. However, we could consider mitigating the performance penalty by introducing “virtual” stores, where the data is not written to a topic, but is computed on the fly. For example, the KTable obtained from filter() would not have a changelog topic, but could still have a virtual store queryable through Interactive Queries.

• It is not clear that collapsing 2 abstractions helps. In particular, a KTable models a changelog. That itself is a useful abstraction. A state store is a materialized view. That’s a distinct abstraction with parallels in the database world.