ksqlDB vs Kafka streams – Data streams are all the rage right now. A technique to move and process huge amounts of data simultaneously without caching it.
Table of Contents
What is Apache Kafka?
With the messagebroker Kafka, the data can be stored resource-efficiently in so-called topics as so-called logs. These topics can then be subscribed to and rewritten by any number of clients, primarily microservices.
The metadata information is stored externally in a schemaregistry and assigned to the data again via an ID when it is read. In this way, each microservice can be developed independently of technology and programming languages. The data structure remains the same.
However, if a microservice wants to access the data streams from two or more topics and these arrive with different frequencies, then the correct allocation of the data is often difficult. The so-called data stream position can be controlled with event streaming databases.
What is ksqlDB?
Especially for Apache Kafka, ksqlDB allows easy transformation of data within Kafka’s data pipelines.
The following figure shows how a software architecture with Apache Kafka and ksqlDB could look like. It is still possible to subscribe to the data streams from the messagebroker, or indirectly via ksqlDB using pulls and pushs. The communication between table and kafka is done directly via the eventstreaming platform Confluent.
It can be used to materialize views asynchronously using interactive SQL queries.
So with this, microservices can enrich the data and transform it in real time.
This enables anomaly detection, real-time monitoring, and real-time data format conversion.
ksqlDB is an event streaming database. Thus, it is based on continuous streams of structured event data that can be published to multiple applications in real time. The following figure shows such an event stream schematically.
Each individual record always consists of an event and a unique key for identification.
These event streams can be combined with streaming analytics and is a way to offload work to back-end processing applications. If you want to know more about messaging patterns and how a message is transmitted between sender and receiver, read our article.
Window-based Query Processing
ksqlDB allows continuous stream queries. These are based on window-based aggregation of events.
Windows are polling intervals that are continuously executed over the data streams. These windows can be expanded and moved as needed to handle new incoming data items.
Several window types are shown in the figure below. They differ in their composition to each other.
The “Tumbling” type repeats a non-overlapping interval, while the “Bouncing” type allows overlaps. In a “Session” the elements are grouped by activity sessions without allowing overlaps. The session is terminated when no elements are received for a certain time.
In addition to continuous queries through window-based aggregation of events, ksqlDB offers many other features that are helpful in dealing with streams. For example, the last value of a column can be tracked when aggregating events from a stream into a table.
Multiple streams can be merged by real-time joins or transformed in real-time. In doing so, the database is Distributed, Fault Tolerant and Scalable.
The Kafka Connect connectors can be executed and controlled directly.
Push and pull queries are applicable to the flows. Thus, subscribers get the constantly updated results of a query, or can retrieve data in request/response flows at a specific time.
With Confluent’s event streaming database ksqlDB, a service is provided that offers an absolutely compatible solution for real-time data stream processing with Kafka. Kafka in particular lends itself as a central element in a microservice-based software architecture. Microservices run as separate processes and consume in parallel from the message broker. Aligning these processes remains a challenge. However, ksqlDB ensures real-time stream processing within the services.