I'm currently planning the architecture for an application that reads from a Kafka topic and after some conversion puts data to RabbitMq.
I'm kind new for Kafka Streams and they look a good choice for my task. But the problem is that Kafka server is hosted at another vendor's place, so I can't even install Cafka Connector to RabbitMq Sink plugin.
Is it possible to write Kafka steam application that doesn't have any Sink points, but just processes input stream? I can just push to RabbitMQ in foreach operations, but I'm not sure will Stream even work without a sink point.
foreach is a Sink action, so to answer your question directly, no.
However, Kafka Streams should be limited to only Kafka Communication.
Kafka Connect can be installed and ran anywhere, if that is what you wanted to use... You can also use other Apache tools like Camel, Spark, NiFi, Flink, etc to write to RabbitMQ after consuming from Kafka, or write any application in a language of your choice. For example, the Spring Integration or Cloud Streams frameworks allows a single contract between many communication channels
Related
I have a requirement to read messages from a topic, enrich the message based on provided configuration (data required for enrichment is sourced from external systems), and publish the enriched message to an output topic. Messages on both source and output topics should be Avro format.
Is this a good use case for a custom Kafka Connector or should I use Kafka Streams?
Why I am considering Kafka Connect?
Lightweight in terms of code and deployment
Configuration driven
Connection and error handling
Scalability
I like the plugin based approach in Connect. If there is a new type of message that needs to be handled I just deploy a new connector without having to deploy a full scale Java app.
Why I am not sure this is good candidate for Kafka Connect?
Calls to external system
Can Kafka be both source and sink for a connector?
Can we use Avro schemas in connectors?
Performance under load
Cannot do stateful processing (currently there is no requirement)
I have experience with Kafka Streams but not with Connect
Use both?
Use Kafka Connect to source external database into a topic.
Use Kafka Streams to build that topic into a stream/table that can then be manipulated.
Use Kafka Connect to sink back into a database, or other system other than Kafka, as necessary.
Kafka Streams can also be config driven, use plugins (i.e. reflection), is just as scalable, and has no different connection modes (to Kafka). Performance should be the similar. Error handling is really the only complex part. ksqlDB is entirely "config driven" via SQL statements, and can connect to external Connect clusters, or embed its own.
Avro works for both, yes.
Some connectors are temporarily stateful, as they build in-memory batches, such as S3 or JDBC sink connectors
Kafka Streams is good, but I have to do every configuration very manual. Instead Kafka Connect provides its API interface, which is very useful for handling the configuration, as well as Tasks, Workers, etc...
Thus, I'm thinking of using Kafka Connect for my simple data transforming service. Basically, the service will read the data from a topic and send the transformed data to another topic. In order to do that, I have to make a custom Sink Connector to send the transformed data to the kafka topic, however, it seems those interface functions aren't available in SinkConnector. If I can do it, that would be great since I can manage tasks, workers via the REST API and running the tasks under a distributed mode (multiple instances).
There are 2 options in my mind:
Figuring out how to send the message from SinkConnector to a kafka topic
Figuring out how to build a REST interface API like Kafka Connect which wraps up the Kafka Streams app
Any ideas?
Figuring out how to send the message from SinkConnector to a kafka topic
A sink connector consumes data/messages from a Kafka topic. If you want to send data to a Kafka topic you are likely talking about a source connector.
Figuring out how to build a REST interface API like Kafka Connect which wraps up the Kafka Streams app.
using the kafka-connect-archtype you can have a template to create your own Kafka connector (source or sink). In your case that you want to build some stream processing pipeline after the connector, you are mostly talking about a connector of another stream processing engine that is not Kafka-stream. There are connectors for Kafka <-> Spark, Kafka <-> Flink, ...
But you can build your using the template of kafka-connect-archtype if you want. Use the MySourceTask List<SourceRecord> poll() method or the MySinkTask put(Collection<SinkRecord> records) method to process the records as stream. They extend the org.apache.kafka.connect.[source.SourceTask|sink.SinkTask] from Kafka connect.
a REST interface API like Kafka Connect which wraps up the Kafka Streams app
This is exactly what KsqlDB allows you to do
Outside of creating streams and tables with SQL queries, it offers a REST API as well as can interact with Connect endpoints (or embed a Connect worker itself)
https://docs.ksqldb.io/en/latest/concepts/connectors/
I need to push data from database(say Oracle DB) to a kafka topic by calling a stored procedure.I need to do some validations too on message
Should i use a Producer API or Kafka Connector.
You should use Kafka Connect. Either that, or use the producer API and write some code that handles:
Scale-out
Failover
Restarts
Schemas
Serialisation
Transformations
and that integrates with hundreds of other technologies in a standardised manner for ease of portability and management.
At which point, you'll have reinvented Kafka Connect ;-)
To learn more about Kafka Connect see this talk. To learn about the specifics of database->Kafka ingestion see this talk and this blog.
I'm to build a Java based Kafka streaming application that will listen to a topic X continiously, fetch data, perform some basic cleansing and write to a Oracle database. The kafka cluster is outside my domain and have no ability to deploy any code or configurations in it.
What is the best way to design such a solution? I came across Kafka Streams but was confused as to if it can be used for 'Topic > Process > Topic' scenarios?
I came accross Kafka Streams but was confused as to if it can be used for 'Topic > Process > Topic' scenarios?
Absolutely.
For example, excluding the "process" step, it's two lines outside of the configuration setup.
final StreamsBuilder builder = new StreamsBuilder();
builder.stream("streams-plaintext-input").to("streams-pipe-output");
This code is straight from the documentation
If you want to write to any database, you should first check if there is a Kafka Connect plugin to do that for you. Kafka Streams shouldn't really be used to read/write from/to external systems outside of Kafka, as it is latency-sensitive.
In your case, the JDBC Sink Connector would work well.
The kafka cluster is outside my domain and have no ability to deploy any code or configurations in it.
Using either solution above, you don't need to, but you will need some machine with Java installed to run a continous Kafka Streams application and/or Kafka Connect worker.
What are the differences between Apache Beam and Apache Kafka with respect to Stream processing?
I am trying to grasp the technical and programmatic differences as well.
Please help me understand by reporting from your experience.
Beam is an API that uses an underlying stream processing engine like Flink, Storm, etc... in one unified way.
Kafka is mainly an integration platform that offers a messaging system based on topics that standalone applications use to communicate with each other.
On top of this messaging system (and the Producer/Consummer API), Kafka offers an API to perform stream processing using messages as data and topics as input or output. Kafka Stream processing applications are standalone Java applications and act as regular Kafka Consummer and Producer (this is important to understand how these applications are managed and how workload is shared among stream processing application instances).
Shortly said, Kafka Stream processing applications are standalone Java applications that run outside the Kafka Cluster, feed from the Kafka Cluster and export results to the Kafka Cluster. With other stream processing platforms, stream processing applications run inside the cluster engine (and are managed by this engine), feed from somewhere else and export results to somewhere else.
One big difference between Kafka and Beam Stream API is that Beam makes the difference between bounded and unbounded data inside the data stream whereas Kafka does not make that difference. Thereby, handling bounded data with Kafka API has to be done manually using timed/sessionized windows to gather data.
Beam is a programming API but not a system or library you can use. There are multiple Beam runners available that implement the Beam API.
Kafka is a stream processing platform and ships with Kafka Streams (aka Streams API), a Java stream processing library that is build to read data from Kafka topics and write results back to Kafka topics.