Relationship to Kafka Streams

ksqlDB is the streaming database for Apache Kafka®. With ksqlDB, you can write event streaming applications by using a lightweight SQL syntax.

Kafka Streams is the Kafka library for writing streaming applications and microservices in Java and Scala.

ksqlDB is built on Kafka Streams, a robust stream processing framework that is part of Kafka.

The Confluent Platform stack, with ksqlDB built on Kafka Streams

ksqlDB gives you a query layer for building event streaming applications on Kafka topics. ksqlDB abstracts away much of the complex programming that's required for real-time operations on streams of data, so that one line of SQL can do the work of a dozen lines of Java or Scala.

For example, to implement simple fraud-detection logic on a Kafka topic named payments, you could write one line of SQL:

CREATE STREAM fraudulent_payments AS
 SELECT fraudProbability(data) FROM payments
 WHERE fraudProbability(data) > 0.8
 EMIT CHANGES;

The equivalent Scala code on Kafka Streams might resemble:

// Example fraud-detection logic using the Kafka Streams API.
object FraudFilteringApplication extends App {

  val builder: StreamsBuilder = new StreamsBuilder()
  val fraudulentPayments: KStream[String, Payment] = builder
    .stream[String, Payment]("payments-kafka-topic")
    .filter((_ ,payment) => payment.fraudProbability > 0.8)
  fraudulentPayments.to("fraudulent-payments-topic")

  val config = new java.util.Properties 
  config.put(StreamsConfig.APPLICATION_ID_CONFIG, "fraud-filtering-app")
  config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")

  val streams: KafkaStreams = new KafkaStreams(builder.build(), config)
  streams.start()
}

ksqlDB is easier to use, and Kafka Streams is more flexible. Which technology you choose for your real-time streaming applications depends on a number of considerations. Keep in mind that you can use both ksqlDB and Kafka Streams together in your implementations.

Differences Between ksqlDB and Kafka Streams

The following table summarizes some of the differences between ksqlDB and Kafka Streams.

Differences ksqlDB Kafka Streams
You write: SQL statements JVM applications
Graphical UI Yes, in Confluent Control Center and Confluent Cloud No
Console Yes No
Data formats Avro, JSON, CSV Any data format, including Avro, JSON, CSV, Protobuf, XML
REST API included Yes No, but you can implement your own
Runtime included Yes, the ksqlDB server Applications run as standard JVM processes
Queryable state No Yes

Developer Workflows

There are different workflows for ksqlDB and Kafka Streams when you develop streaming applications.

  • ksqlDB: You write SQL queries interactively and view the results in real-time, either in the ksqlDB CLI or in Confluent Control Center. You can save a .sql file and deploy it to production as a "headless" application, which runs without a GUI, CLI, or REST interface on ksqlDB servers.
  • Kafka Streams: You write code in Java or Scala, recompile, and run and test the application in an IDE, like IntelliJ. You deploy the application to production as a jar file that runs in a Kafka cluster.

ksqlDB and Kafka Streams: Where to Start?

Use the following table to help you decide between ksqlDB and Kafka Streams as a starting point for your real-time streaming application development.

Start with ksqlDB when... Start with Kafka Streams when...
New to streaming and Kafka Prefer writing and deploying JVM applications like Java and Scala; for example, due to people skills, tech environment
To quicken and broaden the adoption and value of Kafka in your organization Use case is not naturally expressible through SQL, for example, finite state machines
Prefer an interactive experience with UI and CLI Building microservices
Prefer SQL to writing code in Java or Scala Must integrate with external services, or use 3rd-party libraries (but ksqlDB user defined functions(UDFs) may help)
Use cases include enriching data; joining data sources; filtering, transforming, and masking data; identifying anomalous events To customize or fine-tune a use case, for example, with the Kafka Streams Processor API: custom join variants, or probabilistic counting at very large scale with Count-Min Sketch
Use case is naturally expressible by using SQL, with optional help from UDFs Need queryable state, which ksqlDB doesn't support
Want the power of Kafka Streams but you aren't on the JVM: use the ksqlDB REST API from Python, Go, C#, JavaScript, shell

Usually, ksqlDB isn't a good fit for BI reports, ad-hoc querying, or queries with random access patterns, because it's a continuous query system on data streams.

To get started with ksqlDB, try the Tutorials and Examples.

To get started with Kafka Streams, try the Streams Quick Start.

Next Steps

Page last revised on: 2019-12-12


Last update: 2019-12-12