![]() |MobileGist |This is super cool!! Great work | We can pick out just the column of interest: ksql > SELECT USER->SCREENNAME, TEXT FROM TWEETS EMIT CHANGES With the stream created, we’re not restricted to only viewing the entire message (and the Twitter payload is not minimal!). If you have JSON or CSV data on your source topic, you just need to modify the above statement to include your schema definition, as shown in these examples. A stream is just a Kafka topic with a schema-and we have the schema already because we’re using Avro, which makes this command nice and simple: ksql > CREATE STREAM TWEETS WITH (KAFKA_TOPIC= 'twitter_01', VALUE_FORMAT= 'Avro') Now we’ll declare a stream on top of the topic. If you want to see the unbounded stream, you can just remove the LIMIT clause from the PRINT statement and ksqlDB will show every message as it arrives until you press Ctrl-C. ksqlDB supports data in JSON, Avro, and CSV (delimited). Notice that ksqlDB detects the serialisation used (in this case Avro) and deserialises the message accordingly. ksql > CREATE SOURCE CONNECTOR SOURCE_TWITTER_01 WITH ( That’s what we’re going to do here with the Twitter connector. KsqlDB can work with data in an existing Apache Kafka ® topic, but it can also create and populate topics using connectors. Ingesting data into ksqlDB from a connector Let’s dive in! As always, you’ll find the full test rig for trying this out yourself on GitHub. Build aggregate materialised views, and use pull queries to directly fetch the state from these.Create a new stream populated only by messages that match a given predicate.Configure the live ingest of a stream of data from an external source (in this case, Twitter).I’m going to show you how to use ksqlDB to do the following: What back then was maybe a baby just starting to bum-shuffle around is now a toddler up and running around and finding its feet in the world of data processing and application development. No more funky \ line continuation characters!. ![]() Better support for existing Apache Avro™️ schemas.Support for flattening an array into rows ( EXPLODE).Native integration with Kafka Connect connectors.Pull query support for directly querying the state store of an aggregate in ksqlDB.This time around, I’m going to revisit the same source of data but with ksqlDB 0.6.Ī lot has been added since KSQL was first released back in 2017, including: Two years later, its successor ksqlDB was born, which we announced this month. When KSQL was released, my first blog post about it showed how to use KSQL with Twitter data.
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