ksqlDB

The event streaming database purpose-built for stream processing applications.

Real, real-time

Build applications that respond immediately to events. Craft materialized views over streams. Receive real-time push updates, or pull current state on demand.

Kafka-native

Seamlessly leverage your existing Apache Kafka® infrastructure to deploy stream-processing workloads and bring powerful new capabilities to your applications.

What, not how

Use a familiar, lightweight syntax to pack a powerful punch. Capture, process, and serve queries using only SQL. No other languages or services are required.

Build a complete streaming app with a few SQL statements.

Capture events
CREATE SOURCE CONNECTOR riders WITH (
  'connector.class' = 'JdbcSourceConnector',
  'connection.url'  = 'jdbc:postgresql://...',
  'topic.prefix'    = 'rider',
  'table.whitelist' = 'geoEvents, profiles',
  'key'             = 'profile_id',
  ...);
Perform continuous transformations
CREATE STREAM locations AS
  SELECT rideId, latitude, longitude,
         GEO_DISTANCE(latitude, longitude,
                      dstLatitude, dstLongitude
                      ) AS kmToDst
  FROM geoEvents
  EMIT CHANGES;
Create materialized views
CREATE TABLE activePromotions AS
  SELECT rideId,
         qualifyPromotion(kmToDst) AS promotion
  FROM locations
  GROUP BY rideId
  EMIT CHANGES;
Serve lookups against materialized views
SELECT rideId, promotion
FROM activePromotions
WHERE ROWKEY = '6fd0fcdb';

Simple constructs for building streaming apps.

ksqlDB enables you to build event streaming applications leveraging your familiarity with relational databases. Three categories are foundational to building an application: collections, stream processing, and queries.

Collections

Streams

Streams are immutable, append-only sequences of events. They're useful for representing a series of historical facts.

CREATE STREAM routeWaypoints (
       vehicleId VARCHAR,
       latitude DOUBLE(10, 2),
       longitude DOUBLE(10, 2)
) WITH (
       kafka_topic = 'locations',
       partitions = 3,
       key = 'vehicleId',
       value_format = 'json'
);
Tables

Tables are mutable collections of events. They let you represent the latest version of each value per key.

CREATE TABLE currentCarLocations (
       vehicleId VARCHAR,
       latitude DOUBLE(10, 2),
       longitude DOUBLE(10, 2)
) WITH (
       kafka_topic = 'locations',
       partitions = 3,
       key = 'vehicleId',
       value_format = 'json'
);

Stream processing

Stream processing enables you to execute continuous computations over unbounded streams of events, ad infinitum. Transform, filter, aggregate, and join collections together to derive new collections or materialized views that are incrementally updated in real-time as new events arrive.

Queries

Push

Push queries let you subscribe to a query's result as it changes in real-time. When new events arrive, push queries emit refinements, which allow you to quickly react to new information. They’re a perfect fit for asynchronous application flows.

SELECT vehicleId,
       latitude,
       longitude
FROM currentCarLocations
WHERE ROWKEY = '6fd0fcdb'
EMIT CHANGES;
Pull

Pull queries allow you to fetch the current state of a materialized view. Because materialized views are incrementally updated as new events arrive, pull queries run with predictably low latency. They're a great match for request/response flows.

SELECT vehicleId,
       latitude,
       longitude
FROM currentCarLocations
WHERE ROWKEY = '6fd0fcdb';

One mental model for the entire stack.

Today, nearly all streaming architectures are complex, piecemeal solutions. They comprise multiple subsystems, each with its own mental model.

streaming DBDBAPPAPPAPP DB DB CONNECTOR CONNECTOR CONNECTORSTREAMPROCESSING
streaming DBDBAPPAPPAPP DB EXTRACT LOAD TRANSFORMSTORE STORE

With a lightweight, familiar SQL syntax, ksqlDB presents a single mental model for working with event streams across your entire stack: event capture, continuous event transformations, aggregations, and serving materialized views.

what is ksqldbDBAPPAPPAPPPULLPUSHDBksqlDBStream ProcessingMaterialized ViewsConnectors