# Kafka Streams Domain Specific Language Topologies in Confluent Platform Naming processors in a Kafka Streams *Domain Specific Language (DSL)* topology controls how internal processor, state store, changelog, and repartition topic names are generated, which directly affects whether you can safely evolve a topology in production. In the Kafka Streams *Processor API (PAPI)*, you must explicitly name each `Processor` and `State Store`, but at the DSL layer, names are auto-generated unless you supply them. At the DSL layer, there are operators. A single DSL operator may compile down to multiple `Processors` and `State Stores`, and if required `repartition topics`. But with the Kafka Streams DSL, all these names are generated for you. There is a relationship between the generated processor names, state store names (hence changelog topic names), and repartition topic names. The names of state stores and changelog/repartition topics are “stateful” while processor names are “stateless”. This distinction of stateful vs. stateless names has important implications when updating your topology. While the internal naming makes creating a topology with the DSL much more straightforward, there are a couple of trade-offs. The first trade-off is what can be considered a readability issue. The other more severe trade-off is the shifting of names due to the relationship between the DSL operator and the generated `Processors`, `State Stores` changelog topics and repartition topics. ## Readability issues The readability trade-off relates to viewing a description of the topology. When you render the string description of your topology via the `Topology#describe()` method, you can see what the processor is, but you don’t have any context for its business purpose. For example, consider the following simple topology: ```none KStream stream = builder.stream("input"); stream.filter((k,v) -> !v.equals("invalid_txn")) .mapValues((v) -> v.substring(0,5)) .to("output") ``` Running `Topology#describe()` yields this string: ```none Topologies: Sub-topology: 0 Source: KSTREAM-SOURCE-0000000000 (topics: [input]) --> KSTREAM-FILTER-0000000001 Processor: KSTREAM-FILTER-0000000001 (stores: []) --> KSTREAM-MAPVALUES-0000000002 <-- KSTREAM-SOURCE-0000000000 Processor: KSTREAM-MAPVALUES-0000000002 (stores: []) --> KSTREAM-SINK-0000000003 <-- KSTREAM-FILTER-0000000001 Sink: KSTREAM-SINK-0000000003 (topic: output) <-- KSTREAM-MAPVALUES-0000000002 ``` From this report, you can see what the different operators are, but what is the broader context here? For example, consider `KSTREAM-FILTER-0000000001`; you can see that it’s a filter operation, which means that records are dropped that don’t match the given predicate. But what is the meaning of the predicate? Additionally, you can see the topic names of the source and sink nodes, but what if the topics aren’t named in a meaningful way? Then you’re left to guess the business purpose behind these topics. Also notice the numbering here: the source node is suffixed with `0000000000` indicating it’s the first processor in the topology. The filter is suffixed with `0000000001`, indicating it’s the second processor in the topology. In Kafka Streams, there are now overloaded methods for both `KStream` and `KTable` that accept a new parameter `Named`. By using the `Named` class DSL, users can provide meaningful names to the processors in their topology. Now, take a look at your topology with all the processors named: ```none KStream stream = builder.stream("input", Consumed.as("Customer_transactions_input_topic")); stream.filter((k,v) -> !v.equals("invalid_txn"), Named.as("filter_out_invalid_txns")) .mapValues((v) -> v.substring(0,5), Named.as("Map_values_to_first_6_characters")) .to("output", Produced.as("Mapped_transactions_output_topic")); ``` ```none Topologies: Sub-topology: 0 Source: Customer_transactions_input_topic (topics: [input]) --> filter_out_invalid_txns Processor: filter_out_invalid_txns (stores: []) --> Map_values_to_first_6_characters <-- Customer_transactions_input_topic Processor: Map_values_to_first_6_characters (stores: []) --> Mapped_transactions_output_topic <-- filter_out_invalid_txns Sink: Mapped_transactions_output_topic (topic: output) <-- Map_values_to_first_6_characters ``` Now you can look at the topology description and easily understand what role each processor plays in the topology. But there’s another reason for naming your processor nodes when you have stateful operators that remain between restarts of your Kafka Streams applications, state stores, changelog topics, and repartition topics, which has to do with potential name shifting of the processor nodes that use generated names. ## Changing names Generated names are numbered where they are built in the topology. The name generation strategy is `KSTREAM|KTABLE--`. The number is a globally incrementing number that represents the operator’s order in the topology. The generated number is prefixed with a varying number of “0”s to create a string that is consistently 10 characters long. This means that if you add/remove or shift the order of operations, the position of the processor shifts, which shifts the name of the processor. Because most processors exist in memory only, this name shifting presents no issue for many topologies. But the name shifting does have implications for topologies with stateful operators or repartition topics. Here’s a different topology with some state: ```none KStream stream = builder.stream("input"); stream.groupByKey() .count() .toStream() .to("output"); ``` This topology description yields the following: ```none Topologies: Sub-topology: 0 Source: KSTREAM-SOURCE-0000000000 (topics: [input]) --> KSTREAM-AGGREGATE-0000000002 Processor: KSTREAM-AGGREGATE-0000000002 (stores: [KSTREAM-AGGREGATE-STATE-STORE-0000000001]) --> KTABLE-TOSTREAM-0000000003 <-- KSTREAM-SOURCE-0000000000 Processor: KTABLE-TOSTREAM-0000000003 (stores: []) --> KSTREAM-SINK-0000000004 <-- KSTREAM-AGGREGATE-0000000002 Sink: KSTREAM-SINK-0000000004 (topic: output) <-- KTABLE-TOSTREAM-0000000003 ``` You can see from the topology description above that the state store is named `KSTREAM-AGGREGATE-STATE-STORE-0000000001`. Here’s what happens when you add a filter to keep some of the records out of the aggregation: ```none KStream stream = builder.stream("input"); stream.filter((k,v)-> v !=null && v.length() >= 6 ) .groupByKey() .count() .toStream() .to("output"); ``` And the corresponding topology: ```none Topologies: Sub-topology: 0 Source: KSTREAM-SOURCE-0000000000 (topics: [input]) --> KSTREAM-FILTER-0000000001 Processor: KSTREAM-FILTER-0000000001 (stores: []) --> KSTREAM-AGGREGATE-0000000003 <-- KSTREAM-SOURCE-0000000000 Processor: KSTREAM-AGGREGATE-0000000003 (stores: [KSTREAM-AGGREGATE-STATE-STORE-0000000002]) --> KTABLE-TOSTREAM-0000000004 <-- KSTREAM-FILTER-0000000001 Processor: KTABLE-TOSTREAM-0000000004 (stores: []) --> KSTREAM-SINK-0000000005 <-- KSTREAM-AGGREGATE-0000000003 Sink: KSTREAM-SINK-0000000005 (topic: output) <-- KTABLE-TOSTREAM-0000000004 ``` Notice that because you’ve added an operation before the `count` operation, the state store (and the changelog topic) names have changed. This name change means you can’t do a rolling re-deployment of your updated topology. Also, you must use the [Streams Reset tool](app-reset-tool.md#streams-developer-guide-app-reset) to re-calculate the aggregations, because the changelog topic has changed on start-up and the new changelog topic contains no data. Fortunately, there’s an easy solution to remedy this situation. Give the state store a user-defined name instead of relying on the generated one, so you don’t have to worry about topology changes shifting the name of the state store. You’ve had the ability to name repartition topics with the `Joined`, `StreamJoined`, and `Grouped` classes, and name state store and changelog topics with `Materialized`. But it’s worth reiterating the importance of naming these DSL topology operations again. Here’s how your DSL code looks now giving a specific name to your state store: ```none KStream stream = builder.stream("input"); stream.filter((k, v) -> v != null && v.length() >= 6) .groupByKey() .count(Materialized.as("Purchase_count_store")) .toStream() .to("output"); ``` And here’s the topology ```none Topologies: Sub-topology: 0 Source: KSTREAM-SOURCE-0000000000 (topics: [input]) --> KSTREAM-FILTER-0000000001 Processor: KSTREAM-FILTER-0000000001 (stores: []) --> KSTREAM-AGGREGATE-0000000002 <-- KSTREAM-SOURCE-0000000000 Processor: KSTREAM-AGGREGATE-0000000002 (stores: [Purchase_count_store]) --> KTABLE-TOSTREAM-0000000003 <-- KSTREAM-FILTER-0000000001 Processor: KTABLE-TOSTREAM-0000000003 (stores: []) --> KSTREAM-SINK-0000000004 <-- KSTREAM-AGGREGATE-0000000002 Sink: KSTREAM-SINK-0000000004 (topic: output) <-- KTABLE-TOSTREAM-0000000003 ``` Now, even though you’ve added processors before your state store, the store name and its changelog topic names don’t change. This makes your topology more robust and resilient to changes made by adding or removing processors. ## Conclusion It is a good practice to name your processing nodes when using the DSL, and it’s even more important to do this when you have “stateful” processors in your application such as repartition topics and state stores (and the accompanying changelog topics). Here are a couple of points to remember when naming your DSL topology: 1. If you have an *existing topology* and you *haven’t* named your state stores (and changelog topics) and repartition topics, you should do so as a best practice. But this is a topology-breaking change, so you must shut down all application instances, make the changes, and run the [Streams Reset tool](app-reset-tool.md#streams-developer-guide-app-reset). Although this may be inconvenient at first, it is worth the effort to protect your application from unexpected errors due to topology changes. 2. If you have a *new topology*, make sure you name the persistent parts of your topology: state stores (changelog topics) and repartition topics. This way, your deployed Kafka Streams application is protected from topology changes that otherwise would break it. If you do not want to add names to stateless processors at first, that’s fine because you can always go back and add the names later. Here’s a quick reference on naming the critical parts of your Kafka Streams application to prevent topology name changes from breaking your application: | Operation | Naming Class | |---------------------------------------------------------|----------------| | Aggregation repartition topics | Grouped | | KStream-KStream Join repartition topics | StreamJoined | | KStream-KTable Join repartition topic | Joined | | KStream-KStream Join state stores | StreamJoined | | State Stores (for aggregations and KTable-KTable joins) | Materialized | | Stream/Table non-stateful operations | Named | To further enforce best practices, Kafka Streams provides a configuration option, `ensure.explicit.internal.resource.naming`: ```java Properties props = new Properties(); props.put(StreamsConfig.ENSURE_EXPLICIT_INTERNAL_RESOURCE_NAMING_CONFIG, true); ``` This parameter ensures that all internal topics, state stores, and changelog topics have explicitly defined names. When this configuration is enabled, a Kafka Streams application does not start if any of these components rely on auto-generated names. This guarantees stability across topology updates, as manually defined names remain unchanged even when new processors or transformations are added. Enforcing explicit naming is particularly important in production environments, where consistency and backward compatibility are essential for maintaining reliable stream processing applications. #### NOTE This website includes content developed at the [Apache Software Foundation](https://www.apache.org/) under the terms of the [Apache License v2](https://www.apache.org/licenses/LICENSE-2.0.html).