Research

Towards Cloud-based Enterprise Applications on Large Shared Datasets

Enterprises are moving their applications to public cloud environments to benefit from the resource elasticity and cost efficiency that their infrastructures provide. The resulting collocation of applications brings an opportunity to share application data within and across organizations for integrated analytics. Current database systems, however, do not exhibit either the in-memory performance for real-time analytics on data inside an organization, or the elasticity for efficient ad-hoc combination with large external data. In this work, we discuss a database architecture that exploits modern cloud infrastructure to combine both. Specifically, we make three contributions:

First, we present a main memory storage engine that is centered around a columnar, serialization-free, and interoperable data format. This storage engine enables efficient data exchange between application stacks by eliminating costly data transformations. Data can be accessed in-place over RDMA or as database checkpoints on remote shared cloud storage. The performance for both transactional and analytical data processing is kept up through auxiliary data structures, such as MVCC version chains, indexes, and filters.

Second, we propose a massively-parallel query engine that runs the relational operators as short-lived and stateless cloud functions against shared storage. The cloud functions are specified in C++ to utilize scarce per-function resources efficiently, e.g., via SIMD capabilities and tailored memory management. The query engine thereby embraces the fine-grained compute resource consumption model of current cloud platforms.

Third, we design a query plan optimizer that targets our execution engine. It takes into account the particularities of the cloud platform resources, i.e., strict time and space limits on compute units and multi-tiered storage with various price-performance points. It further respects the cloud provider's resource pricing to produce fast yet budget-conscious query plans.

Teaching

Winter 2019/20

• Seminar - Develop your own Database

Summer 2019

• Bachelor's Project - Usage Mining for the Enterprise, results presented at SAP TechEd 2019 Keynote
• Lecture - Trends and Concepts in the Software Industry I

Supervised Master's Theses

Open

• A Storage Engine for HTAP Workloads on Standard Columnar Data Formats
• Supporting Efficient Analytics on Remote Shared Database Snapshots
• A Query Compiler for Modern Cloud Infrastructure

Ongoing

• Elastic Query Execution via Short-lived and Stateless Cloud Functions
  Jan Mensch

Completed

• Network Request Handling in Database Systems
  Toni Stachewicz, 2019
• Data-dependent Implicit Authorizations for Fine-grained Database Access Control
  Dennis Hempfing, 2018
• Pushing Down User-defined Functionality in Distributed Log-centric Big Data Stacks
  Josephine Rückert (Technische Universität Ilmenau), 2017

Patents

Bumbulis, P., Pound, J., Auch, N., Goel, A., Ringwald, M., Bodner, T., MacLean, S.:
An Algorithm for Consistent Replication of Log-Structured Data.
US 2016 and EU 2017.