6 papers
BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams
Sören Henning, Otmar Ertl, Adriano Vogel
Shuffling or repartitioning data streams is an essential operation of state-of-the-art stream processing frameworks to support stateful workloads in a large-scale, distributed sett…
FluxSieve: Unifying Streaming and Analytical Data Planes for Scalable Cloud Observability
Adriano Vogel, Sören Henning, Otmar Ertl
Despite many advances in query optimization, indexing techniques, and data storage, modern data platforms still face difficulties in delivering robust query performance under high…
JumpBackHash: Say Goodbye to the Modulo Operation to Distribute Keys Uniformly to Buckets
Otmar Ertl
Introduction. Distributed data processing and storage systems require efficient methods to distribute keys across buckets. While simple and fast, the traditional modulo-based mappi…
Should I Run My Cloud Benchmark on Black Friday?
Sören Henning, Adriano Vogel, Esteban Perez-Wohlfeil +2
Benchmarks and performance experiments are frequently conducted in cloud environments. However, their results are often treated with caution, as the presumed high variability of pe…
When Should I Run My Application Benchmark?: Studying Cloud Performance Variability for the Case of Stream Processing Applications
Sören Henning, Adriano Vogel, Esteban Perez-Wohlfeil +2
Performance benchmarking is a common practice in software engineering, particularly when building large-scale, distributed, and data-intensive systems. While cloud environments off…
ExaLogLog: Space-Efficient and Practical Approximate Distinct Counting up to the Exa-Scale
Otmar Ertl
This work introduces ExaLogLog, a new data structure for approximate distinct counting, which has the same practical properties as the popular HyperLogLog algorithm. It is commutat…