2011Proceedings of the VLDB EndowmentRequires access

HyPer-sonic combined transaction and query processing

Florian Funke, Alfons Kemper, Thomas Neumann

Open publisher page 7 citations

Abstract

In this demo we will prove that it is - against common belief - indeed possible to build a main-memory database system that achieves world-record transaction processing throughput and best-of-breed OLAP query response times in one system in parallel on the same database state. The two workloads of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, users with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load -data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer , that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the full ACID properties for OLTP transactions and executes OLAP query sessions (multiple queries) on arbitrarily current and consistent snapshots. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy-on-write) yields both at the same time: unprecedentedly high transaction rates as high as 100,000+ transactions per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.

About this research paper

What this paper is about

In this demo we will prove that it is - against common belief - indeed possible to build a main-memory database system that achieves world-record transaction processing throughput and best-of-breed OLAP query response times in one system in parallel on the same database state. The two workloads of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, users with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load -data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer , that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the full ACID properties for OLTP transactions and executes OLAP query sessions (multiple queries) on arbitrarily current and consistent snapshots. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy-on-write) yields both at the same time: unprecedentedly high transaction rates as high as 100,000+ transactions per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.

Why it matters

OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In this demo we will prove that it is - against common belief - indeed possible to build a main-memory database system that achieves world-record transaction processing throughput and best-of-breed OLAP query response times in one system in parallel on the same database state. The two workloads of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, users with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load -data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer , that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the full ACID properties for OLTP transactions and executes OLAP query sessions (multiple queries) on arbitrarily current and consistent snapshots. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy-on-write) yields both at the same time: unprecedentedly high transaction rates as high as 100,000+ transactions per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.

Key concepts: Online transaction processing, Online analytical processing, Computer science, Database, Transaction processing, Database transaction, Distributed transaction, Data warehouse

Related papers

Back to paper searchBrowse research topicsOriginal source
HyPer-sonic combined transaction and query processing — Research Paper | ScholarLens