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Kafka kstream branch example

In a microservices architecture, events drive microservice actions. No event, no shoes, no service. In the most basic scenario, microservices that need to take action on a common stream of events all listen to that stream. When an event lands in that topic, all the microservices receive it in real time and take appropriate action. This paradigm of multiple services acting on the same stream of events is very flexible and extends to numerous domains, as demonstrated in practice through the various examples throughout this blog post:.

This is the simplest way a microservice can subscribe to a stream of events: Within the microservice application, use the Kafka Streams library to build a stream from the data in a Kafka topic, using the method StreamsBuilder stream. Once this stream is created, the application may take any action on the events using the rich Kafka Streams API.

This provides capabilities such as data filtering, transformations and conversions, enrichments with joins, manipulation with scalar functions, analysis with stateful processing, aggregations and windowing operations. Instead of processing all events in the stream, each microservice should take action only on a subset of relevant events.

One way to handle this requirement is to have a microservice that subscribes to the original stream with all the events, examines each record and then takes action only on the events it cares about while discarding the rest.

However, depending on the application, this may be undesirable or resource intensive. A cleaner way is to provide the service with a separate stream that contains only the relevant subset of events that the microservice cares about. To achieve this, a streaming application can branch the original event stream into different substreams using the method KStream branch. This results in new Kafka topics, so then the microservice can subscribe to one of the branched streams directly.

For example, in the finance domain, consider a fraud remediation microservice that should process only the subset of events suspected of being fraudulent. As shown below, the original stream of events is branched into two new streams: one for suspicious events and one for validated events.

This enables the fraud remediation microservice to process just the stream of suspicious events, without ever seeing the validated events.

Branching decisions are based on supplied predicates. These predicates and thus branches must be fixed in number, and they can evaluate data in the event itself. In the example here, there are two predicates:. To use KStream tothe output Kafka topics need to be created before running the application.

This requirement was a deliberate decision discussed within the user community, and it provides two benefits. First, it ensures that the output topics are created with the required configuration settings.

Second, it prevents a buggy or rogue application from creating a bunch of false output topics that would then have to be cleaned up. Consequently, in the above example, the topics defined by the parameters suspiciousTransactionsTopicName and validatedTransactionsTopicName must have already been created ahead of time.

Thus far, the application has assumed advance knowledge of the output topic names. You probably can guess where I am going next—changing the requirement such that either there is no advance knowledge of the output topic names or the output topic names may dynamically change. It used to be the burden on the Kafka Streams application by which, I really mean a burden on the developer to respond to topic name changes.

This entailed reconfiguring and restarting the Kafka Streams application each time there was a topic name change.Consider a topic with events that represent book publications.

Kafka Streams and ksqlDB Compared – How to Choose

In this tutorial, we'll write a program that creates a new topic which only contains the events for a particular author. The function you give it determines whether to pass each event through to the next stage of the topology. Now that an uberjar for the Kafka Streams application has been built, you can launch it locally.

When the console producer starts, it will log some messages and hang, waiting for your input. Type in one line at a time and press enter to send it. Each line represents an event. To send all of the events below, paste the following into the prompt and press enter:.

Leaving your original terminal running, open another to consume the events that have been filtered by your application:. After the consumer starts, you should see the following messages. The prompt will hang, waiting for more events to arrive.

To continue studying the example, send more events through the input terminal prompt. Otherwise, you can Control-C to exit the process. Be sure to fill in the addresses of your production hosts and change any other parameters that make sense for your setup. Finally, launch the container using your preferred container orchestration service.

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If you want to run it locally, you can execute the following:. Instead of running a local Kafka cluster, you may use Confluent Clouda fully-managed Apache Kafka service. First, create your Kafka cluster in Confluent Cloud. Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc. Confluent Developer. How to filter a stream of events. Question: How do I filter messages in a Kafka topic to contain only those that I'm interested in?

Example use case: Consider a topic with events that represent book publications. Try it 1. Initialize the project 2. Get Confluent Platform 3. Configure the project 4. Create a schema for the events 5.Skip navigation links. A KStream is either defined from one or multiple Kafka topics that are consumed message by message or the result of a KStream transformation.

kafka kstream branch example

A KTable can also be converted into a KStream. Topology via process Creates an array of KStream from this stream by branching the records in the original stream based on the supplied predicates. Create a new KStream that consists of all records of this stream which satisfy the given predicate. Create a new KStream that consists all records of this stream which do not satisfy the given predicate. Transform each record of the input stream into zero or more records in the output stream both key and value type can be altered arbitrarily.

Create a new KStream by transforming the value of each record in this stream into zero or more values with the same key in the new stream. Transform the value of each input record into zero or more new values with possibly a new type and emit for each new value a record with the same key of the input record and the value. Group the records of this KStream on a new key that is selected using the provided KeyValueMapper and default serializers and deserializers.

Group the records by their current key into a KGroupedStream while preserving the original values and default serializers and deserializers. Group the records by their current key into a KGroupedStream while preserving the original values and using the serializers as defined by Grouped. Use groupByKey Grouped instead. Join records of this stream with GlobalKTable 's records using non-windowed inner equi join. Join records of this stream with another KStream 's records using windowed inner equi join with default serializers and deserializers.

Join records of this stream with another KStream 's records using windowed inner equi join using the StreamJoined instance for configuration of the key serdethis stream's value serdethe other stream's value serdeand used state stores. Join records of this stream with KTable 's records using non-windowed inner equi join with default serializers and deserializers. Join records of this stream with GlobalKTable 's records using non-windowed left equi join.

Join records of this stream with another KStream 's records using windowed left equi join with default serializers and deserializers. Join records of this stream with another KStream 's records using windowed left equi join using the StreamJoined instance for configuration of the key serdethis stream's value serdethe other stream's value serdeand used state stores.

Join records of this stream with KTable 's records using non-windowed left equi join with default serializers and deserializers.

1. Intro to Streams - Apache Kafka® Streams API

Transform each record of the input stream into a new record in the output stream both key and value type can be altered arbitrarily.Skip navigation links. A KStream is either defined from one or multiple Kafka topics that are consumed message by message or the result of a KStream transformation. A KTable can also be converted into a KStream. Topology via process Creates an array of KStream from this stream by branching the records in the original stream based on the supplied predicates.

Create a new KStream that consists of all records of this stream which satisfy the given predicate.

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Create a new KStream that consists all records of this stream which do not satisfy the given predicate. Transform each record of the input stream into zero or more records in the output stream both key and value type can be altered arbitrarily. Create a new KStream by transforming the value of each record in this stream into zero or more values with the same key in the new stream. Transform the value of each input record into zero or more new values with possibly a new type and emit for each new value a record with the same key of the input record and the value.

Group the records of this KStream on a new key that is selected using the provided KeyValueMapper and default serializers and deserializers. Group the records by their current key into a KGroupedStream while preserving the original values and default serializers and deserializers.

Group the records by their current key into a KGroupedStream while preserving the original values and using the serializers as defined by Grouped. Use groupByKey Grouped instead. Join records of this stream with GlobalKTable 's records using non-windowed inner equi join. Join records of this stream with another KStream 's records using windowed inner equi join with default serializers and deserializers.

Join records of this stream with another KStream 's records using windowed inner equi join using the StreamJoined instance for configuration of the key serdethis stream's value serdethe other stream's value serdeand used state stores. Join records of this stream with KTable 's records using non-windowed inner equi join with default serializers and deserializers. Join records of this stream with GlobalKTable 's records using non-windowed left equi join.

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Join records of this stream with another KStream 's records using windowed left equi join with default serializers and deserializers. Join records of this stream with another KStream 's records using windowed left equi join using the StreamJoined instance for configuration of the key serdethis stream's value serdethe other stream's value serdeand used state stores. Join records of this stream with KTable 's records using non-windowed left equi join with default serializers and deserializers.

Transform each record of the input stream into a new record in the output stream both key and value type can be altered arbitrarily. Transform the value of each input record into a new value with possible new type of the output record. Join records of this stream with another KStream 's records using windowed outer equi join with default serializers and deserializers. Join records of this stream with another KStream 's records using windowed outer equi join using the StreamJoined instance for configuration of the key serdethis stream's value serdethe other stream's value serdeand used state stores.

kafka kstream branch example

Process all records in this stream, one record at a time, by applying a Processor provided by the given ProcessorSupplier. Materialize this stream to a topic and creates a new KStream from the topic using default serializers, deserializers, and producer's DefaultPartitioner.

Materialize this stream to a topic and creates a new KStream from the topic using the Produced instance for configuration of the key serdevalue serdeand StreamPartitioner.

kafka kstream branch example

Materialize this stream to a topic using default serializers specified in the config and producer's DefaultPartitioner. Materialize this stream to a topic using the provided Produced instance.

Dynamically materialize this stream to topics using default serializers specified in the config and producer's DefaultPartitioner. Dynamically materialize this stream to topics using the provided Produced instance. Convert this stream to a KTable. Transform each record of the input stream into zero or one record in the output stream both key and value type can be altered arbitrarily.

Transform the value of each input record into a new value with possibly a new type of the output record.

kafka kstream branch example

All records that do not satisfy the predicate are dropped. This is a stateless record-by-record operation. All records that do satisfy the predicate are dropped.Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. It uses the Alpakka Kafka Connector to write messages in Avro format. Kafka itself is agnostic in regard to the message format, but Avro with a Schema Registry is the preferred solution.

A message format with a schema like e. Avro and a Schema Registry or Protocol buffers compared to a message format without a schema like JSON has the advantage that it is known what the message contains.

Also backwards compatiblity can be maintained when evolving the schema. Using Avro with a Schema Registry has the advantage that the schema definition doesn't has to be added to every single message, but the schema is registered in the Schema Registry and a reference to the schema is stored in the messages. To run the project, you need to have Docker Compose installed. In the root directory of the project you do a docker-compose up and wait until Zookeeper, Kafka and the Schema Registry have been started.

Do a docker ps to verify that you have a running Zookeeper, Kafka, and Schema Registry. After that you can start the producer with sbt "runMain KafkaProducer" and the consumer with sbt "runMain KafkaConsumer".

The sample producer KafkaProducer will create a few instances of SampleEvent and serialize them in Avro format and send them to the Kafka topic mytopic. The sample consumer KafkaConsumer will read from the topic mytopic and deserialize the messages into instances of SampleEvent and log these instances. Skip to content. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit. Git stats 6 commits. Failed to load latest commit information. View code. This project provides an example on how to do this. Running the project To run the project, you need to have Docker Compose installed. In this example project the consumer will automatically stop after 30 seconds. Topics scala kafka avro akka-streams. Releases No releases published. Packages 0 No packages published. Contributors 2. You signed in with another tab or window.As beginner Kafka users, we generally start out with a few compelling reasons to leverage Kafka in our infrastructure.

An initial use case may be implementing Kafka to perform database integration. This is especially helpful when there are tightly coupled yet siloed databases—often the RDBMS and NoSQL variety—which can become single points of failure in mission-critical applications and lead to an unfortunate spaghetti architecture.

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Enter: Kafka! Kafka provides buffering capabilities, persistence, and backpressure, and it decouples these systems because it is a distributed commit log at its architectural core. But wait, there are more benefits as to why we might consider Apache Kafka. It is highly available, fault tolerant, low latency, and foundational for an event-driven architecture for the enterprise. Our initial Kafka use case might even look a little something like change data capture CDCwhere we are capturing the changes derived from a customer table, as well as changes to an order table in our relational store.

We could be doing more—processing and analyzing data as it occurs, and deriving real-time insights by joining streams and enabling actionable logic instead of waiting to process it at a later point in time in a nightly batch. What can we do to enhance this data pipeline? If we expand upon the initial CDC use case presented, we see that we can transform our data once but use it for many applications.

Plus, since this new stream is consumed from Kafka, it still has all the benefits that we listed before. To answer this, we must first understand the stream-table duality concept. Moving from the RDBMS world to the event-driven world—everything begins with events, but we still have to deal with the reality that we have data in tables. With our examples above, we have two separate tables for the customer and order event. These tables are a static view of our data at a point in time.

The concept of streams allows us to read from the Kafka topic in real time and process the data. Understanding how data is converted from a static table into events is a core concept of understanding Kafka Streams and ksqlDB. Due to the stream-table duality, we can convert from table to stream and stream to table with fidelity. When we get our relational data into a Kafka-friendly format, we can start to do more and develop new applications in real time.

There are numerous ways to do stream processing out there, but the two that I am going to focus on here are those which integrate the best with Apache Kafka in terms of security and deployment: Kafka Streams, which is a native component of Apache Kafka, and ksqlDB, which is an event streaming database built and maintained by the original co-creators of Apache Kafka.

As a Java library, Kafka Streams allows you to do stream processing in your Java apps.Discussion thread : here. Voting thread: here. KStream branch method uses varargs to supply predicates and returns array of streams 'Each stream in the result array corresponds position-wise index to the predicate in the supplied predicates'.

This is poor API design that makes building branches very inconvenient because of 'impedance mismatch' between arrays and generics in Java language. Named parameter is needed so one can name the branch operator itself, and then all the branches might get index-suffixed names built from the branch operator name.

Both branch and defaultBranch operations also have overloaded alternatives without the Branched parameter.

Putting Events in Their Place with Dynamic Routing

In many cases we do not need to have a single scope for all the branches, each branch being processed completely independently from others. Then we can use 'consuming' lambdas or method references in Branched parameter:. In other cases we want to combine branches again after splitting. There is also a case when one might need to create branches dynamically, e.

This can be implemented the following way:. This is why 'starting' split operation is necessary and it is better to have it rather than add new branch method to KStream directly. Otherwise we should treat the first iteration separately, and the code for dynamic branching becomes cluttered:.

How to filter a stream of events

Rejected because of violation of method-chaining new auxiliary object is needed. This is critical that KStream consumers in. This is necessary for the case when we need to gather the streams that were defined in separate scopes back into one scope using auxiliary object:.

Evaluate Confluence today. Apache Kafka. Pages Blog. Space shortcuts Retrospectives. Child pages. Kafka Improvement Proposals.


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