Joins With Kafka

Florian Trossbach and Matthias J Sax show the various sorts of joins offered in Kafka, both streams and tables:

Apache Kafka’s Streams API provides a very sophisticated API for joins that can handle many use cases in a scalable way. However, some join semantics might be surprising to developers as streaming join semantics differ from SQL semantics. Furthermore, the semantics of changelog streams and tombstone messages (that are used for deletes) are a new concept in stream processing.

Kafka’s journey from Pub/Sub broker to distributed streaming platform is well underway, and our times as engineers are very exciting!

I didn’t know you could join streams together in Kafka, so that’s really cool.

Related Posts

Hyperparameter Tuning with MLflow

Joseph Bradley shows how you can perform hyperparameter tuning of an MLlib model with MLflow: Apache Spark MLlib users often tune hyperparameters using MLlib’s built-in tools CrossValidator and TrainValidationSplit.  These use grid search to try out a user-specified set of hyperparameter values; see the Spark docs on tuning for more info. Databricks Runtime 5.3 and 5.3 ML and above support […]

Read More

TensorFrames: Spark Plus TensorFlow

Adi Polak gives us an introduction to TensorFrames: In all TensorFrames functionality, the DataFrame is sent together with the computations graph. The DataFrame represents the distributed data, meaning in every machine there is a chunk of the data that will go through the graph operations/ transformations. This will happen in every machine with the relevant […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930