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KsqlDB: Event streaming database for stream processing applications, Hacker News





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.




Seamlessly leverage your existingApache 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.


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









Streamsare 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' );








Tablesare 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 processingenables 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.             













Push querieslet 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 queriesallow 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 DB)(DB) (APP)APPAPPDB(EXTR) *************************************************************************************************************************************** (A) (C) (T)L (O)ADTR ANSFORM(S) TORE(S) ************************************************************************************************************************************************* (T) (ORE)





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






what is ksqldb(DB)APPAPPAPPPUL (L)PUSH(DB)ksqlDBST(r)eam Pr(O)Cessing(Ma) teriali(Z)ed(Vi) (e)WS(C)onneCTor(s)




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