Thomas Bodner

Research Assistant, PhD Candidate

Email: thomas.bodner(at)hpi.de
Phone: +49 (331) 5509 - 3934
Address: August-Bebel-Str. 88, 14482 Potsdam
Office: Hasso Plattner Institute, Campus II, V 2.05
Office Hours: Just stop by or mail/call ahead for an appointment
Profiles: DBLP, ResearchGate, LinkedIn, GitHub

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

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

Publications

Goel, A., Pound, J., Auch, N., Bumbulis, P., MacLean, S., Färber, F., Gropengiesser, F., Mathis, C., Bodner, T., Lehner, W.: Towards Scalable Real-time Analytics: An Architecture for Scale-out of OLXP Workloads. Proceedings of the VLDB Endowment. 8, 1716--1727 (2015).

[ Abstract ] [ BibTeX ] [ URL ]

We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.

@article{DBLP:journals/pvldb/GoelPABMFGMBL15, abstract = {We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.}, author = {Goel, Anil and Pound, Jeffrey and Auch, Nathan and Bumbulis, Peter and MacLean, Scott and Färber, Franz and Gropengiesser, Francis and Mathis, Christian and Bodner, Thomas and Lehner, Wolfgang}, journal = {Proceedings of the VLDB Endowment}, keywords = {distributed_database htap}, number = 12, pages = {1716--1727}, title = {Towards Scalable Real-time Analytics: An Architecture for Scale-out of OLXP Workloads}, volume = 8, year = 2015 }
Alexandrov, A., Schiefer, B., Poelman, J., Ewen, S., Bodner, T., Markl, V.: Myriad - Parallel Data Generation on Shared-nothing Architectures. Proceedings of the 1st Workshop on Architectures and Systems for Big Data. (2011).

[ Abstract ] [ BibTeX ] [ URL ]

The need for efficient data generation for the purposes of testing and benchmarking newly developed massively-parallel data processing systems has increased with the emergence of Big Data problems. As synthetic data model specifications evolve over time, the data generator programs implementing these models have to be adapted continuously -- a task that often becomes more tedious as the set of model constraints grows. In this paper we present Myriad - a new parallel data generation toolkit. Data generators created with the toolkit can quickly produce very large datasets in a shared-nothing parallel execution environment, while at the same time preserve with cross-partition dependencies, correlations and distributions in the generated data. In addition, we report on our efforts towards a benchmark suite for large-scale parallel analysis systems that uses Myriad for the generation of OLAP-style relational datasets.

@article{alexandrov2011myriad, abstract = {The need for efficient data generation for the purposes of testing and benchmarking newly developed massively-parallel data processing systems has increased with the emergence of Big Data problems. As synthetic data model specifications evolve over time, the data generator programs implementing these models have to be adapted continuously -- a task that often becomes more tedious as the set of model constraints grows. In this paper we present Myriad - a new parallel data generation toolkit. Data generators created with the toolkit can quickly produce very large datasets in a shared-nothing parallel execution environment, while at the same time preserve with cross-partition dependencies, correlations and distributions in the generated data. In addition, we report on our efforts towards a benchmark suite for large-scale parallel analysis systems that uses Myriad for the generation of OLAP-style relational datasets.}, author = {Alexandrov, Alexander and Schiefer, Berni and Poelman, John and Ewen, Stephan and Bodner, Thomas and Markl, Volker}, journal = {Proceedings of the 1st Workshop on Architectures and Systems for Big Data}, keywords = {benchmarking distributed_database}, title = {Myriad - Parallel Data Generation on Shared-nothing Architectures}, year = 2011 }

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.

Thomas Bodner

Research Assistant, PhD Candidate

Research

Teaching

Supervised Master's Theses

Publications

Patents

Chair of Prof. Hasso Plattner

News

21.11.2019 | Sugar Fall Presentation @ HPI

14.10.2019 | 2019 Bachelor's Project featured in SAP TechEd Keynote

23.07.2019 | Presentation of 2019 Bachelor's Project

12.06.2019 | Global Team-based Innovation Final Presentations

25.02.2019 | Best Paper Award received at ICORES 2019

Open Positions

Literature