Event streaming is an increasingly critical infrastructure service used in many industries and there is growing demand for cloud-native solutions. Confluent Cloud provides a massive scale event streaming platform built on top of Apache Kafka with tens of thousands of clusters running in 70+ regions across AWS, Google Cloud, and Azure. This paper introduces Kora , the cloud-native platform for Apache Kafka at the core of Confluent Cloud. We describe Kora's design that enables it to meet its cloud-native goals, such as reliability, elasticity, and cost efficiency. We discuss Kora's abstractions which allow users to think in terms of their workload requirements and not the underlying infrastructure, and we discuss how Kora is designed to provide consistent, predictable performance across cloud environments with diverse capabilities.

R is a popular data analysis language, but there is scant experimental data characterizing the run-time profile of R programs. This paper addresses this limitation by systematically cataloging where time is spent when running R programs. Our evaluation using four different workloads shows that when analyzing large datasets, R programs a) spend more than 85% of their time in processor stalls, which leads to slower execution times, b) trigger the garbage collector frequently, which leads to higher memory stalls, and c) create a large number of unnecessary temporary objects that causes R to swap to disk quickly even for datasets that are far smaller than the available main memory. Addressing these issues should allow R programs to run faster than they do today, and allow R to be used for analyzing even larger datasets. As outlined in this paper, the results presented in this paper motivate a number of future research investigations in the database, architecture, and programming language communities. All data and code that is used in this paper (which includes the R programs, and changes to the R source code for instrumentation) can be found at: http://quickstep.cs.wisc.edu/dissecting-R/.