Florian Funke

The mixed workload CH-benCHmark

While standardized and widely used benchmarks address either operational or real-time Business Intelligence (BI) workloads, the lack of a hybrid benchmark led us to the definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark. This benchmark bridges the gap between the established single-workload suites of TPC-C for OLTP and TPC-H for OLAP, and executes a complex mixed workload: a transactional workload based on the order entry processing of TPC-C and a corresponding TPC-H-equivalent OLAP query suite run in parallel on the same tables in a single database system. As it is derived from these two most widely used TPC benchmarks, the CH-benCHmark produces results highly relevant to both hybrid and classic single-workload systems.

Data Blocks: Hybrid OLTP and OLAP on Compressed Storage using both Vectorization and Compilation

This work aims at reducing the main-memory footprint in high performance hybrid OLTP & OLAP databases, while retaining high query performance and transactional throughput. For this purpose, an innovative compressed columnar storage format for cold data, called Data Blocks is introduced. Data Blocks further incorporate a new light-weight index structure called Positional SMA that narrows scan ranges within Data Blocks even if the entire block cannot be ruled out. To achieve highest OLTP performance, the compression schemes of Data Blocks are very light-weight, such that OLTP transactions can still quickly access individual tuples. This sets our storage scheme apart from those used in specialized analytical databases where data must usually be bit-unpacked. Up to now, high-performance analytical systems use either vectorized query execution or just-in-time (JIT) query compilation. The fine-grained adaptivity of Data Blocks necessitates the integration of the best features of each approach by an interpreted vectorized scan subsystem feeding into JIT-compiled query pipelines. Experimental evaluation of HyPer, our full-fledged hybrid OLTP & OLAP database system, shows that Data Blocks accelerate performance on a variety of query workloads while retaining high transaction throughput.

CPU and cache efficient management of memory-resident databases

Memory-Resident Database Management Systems (MRDBMS) have to be optimized for two resources: CPU cycles and memory bandwidth. To optimize for bandwidth in mixed OLTP/OLAP scenarios, the hybrid or Partially Decomposed Storage Model (PDSM) has been proposed. However, in current implementations, bandwidth savings achieved by partial decomposition come at increased CPU costs. To achieve the aspired bandwidth savings without sacrificing CPU efficiency, we combine partially decomposed storage with Just-in-Time (JiT) compilation of queries, thus eliminating CPU inefficient function calls. Since existing cost based optimization components are not designed for JiT-compiled query execution, we also develop a novel approach to cost modeling and subsequent storage layout optimization. Our evaluation shows that the JiT-based processor maintains the bandwidth savings of previously presented hybrid query processors but outperforms them by two orders of magnitude due to increased CPU efficiency.

Metrics for measuring the performance of the mixed workload CH-benCHmark

Advances in hardware architecture have begun to enable database vendors to process analytical queries directly on operational database systems without impeding the performance of mission-critical transaction processing too much. In order to evaluate such systems, we recently devised the mixed workload CH-benCHmark, which combines transactional load based on TPC-C order processing with decision support load based on TPC-H-like query suite run in parallel on the same tables in a single database system. Just as the data volume of actual enterprises tends to increase over time, an inherent characteristic of this mixed workload benchmark is that data volume increases during benchmark runs, which in turn may increase response times of analytic queries. For purely transactional loads, response times typically do not depend that much on data volume, as the queries used within business transactions are less complex and often indexes are used to answer these queries with point-wise accesses only. But for mixed workloads, the insert throughput metric of the transactional component interferes with the response-time metric of the analytic component. In order to address the problem, in this paper we analyze the characteristics of CH-benCHmark queries and propose normalized metrics which account for data volume growth.

Benchmarking Elastic Query Processing on Big Data

Existing analytical query benchmarks, such as TPC-H, often assess database system performance on on-premises hardware installations. On the other hand, some benchmarks for cloud-based analytics deal with flexible infrastructure, but often focus on simpler queries and semi-structured data. With our benchmark draft we attempt to bridge the gap by challenging analytical platforms to answer complex queries on structured business data while leveraging the elastic infrastructure of the cloud to satisfy performance requirements.