Well, I’m facing a huge interesting case. I’m working at Wallbox where we need to deal with billions of rows every day. Now we need to use Spark for some Kafka filtering and publish the results into different topics according to some rules. I won’t dig deep into the logic except for performance-related stuff, let’s try to increase the processing speed. When reading from Kafka you usually get 1 task per partition, so if you have 6 partitions and 48 cores you are not using 87.5 percent of your cluster. That could be adjusted with the following property **minPartitions.** Read more

After spending several weeks working on a ksql solution to filter billions of events and determine their destination topic, I was disappointed to find that it did not live up to my expectations. I had hoped for a more robust product that would align with our needs. Previously, we utilized a similar filter in Spark, incurring traffic costs for both Confluent and AWS. With kSQL, the advantage was that we could avoid paying for AWS traffic. Read more