Stop Thinking, Just Do!

Sung-Soo Kim's Blog

Hyper

tagsTags

17 August 2015


Article Source


HyPer – A Hybrid OLTP&OLAP High Performance DBMS

HyPer is a hybrid online transactional processing (OLTP) and online analytical processing (OLAP) high-performance main memory database system that is optimized for modern hardware. HyPer achieves highest performance—compared to state of the art main memory databases—for both, OLTP (> 100,000 single-threaded TPC-C TX/s on modern commodity hardware) and OLAP (best-of-breed response times), operating simultaneously on the same database.

The HyPer prototype demonstrates that it is 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 (ETL)-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 100000 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. We have developed the novel hybrid OLTP & OLAP database system HyPer that is based on snapshotting transactional data via the virtual memory management of the operating system.

In this architecture the OLTP process owns the database and periodically (e.g., in the order of seconds or minutes) forks an OLAP process. This OLAP process constitutes a fresh transaction consistent snapshot of the database. Thereby, we exploit operating systems functionality to create virtual memory snapshots for new, cloned processes. In Unix, for example, this is done by creating a child process of the OLTP process via the fork system call. The forked child process obtains an exact copy of the parent processes address space. This virtual memory snapshot that is created by the fork-operation will be used for executing a session of OLAP queries. These queries can be executed in parallel threads or serially, depending on the system resources or client requirements.

In essence, the virtual memory snapshot mechanism constitutes a OS/hardware supported shadow paging mechanism as proposed decades ago for disk-based database systems. However, the original proposal incurred severe costs as it had to be software-controlled and it destroyed the clustering on disk. Neither of these drawbacks occurs in the virtual memory snapshotting as clustering across RAM pages is not an issue. Furthermore, the sharing of pages and the necessary copy-on-update/write is managed by the operating system with effective hardware support of the MMU (memory management unit) via the page table that translates VM addresses to physical pages and traps necessary replication (copy-on-write) actions. Therefore, the page replication is extremely efficiently done in 2μs as we measured in a micro-benchmark. HyPer’s OLTP throughput is better than VoltDB’s published TPC-C performance and HyPer’s OLAP query response times are superior to MonetDB’s query response times. It should be emphasized that HyPer can match (or beat) these two best- of-breed transaction (VoltDB) and query (MonetDB) processing engines at the same time by performing both workloads in parallel on the same database state.

HyPer’s performance is due to the following design:

  • HyPer relies on in-memory data management without the ballast of traditional database systems caused by DBMS-controlled page structures and buffer management. SQL table definitions are transformed into simple vector-based virtual memory representations – which constitutes a column oriented physical storage scheme.
  • OLAP query processing is separated from mission-critical OLTP transaction processing by forking virtual memory snapshots. Thus, no concurrency control mechanisms are needed – other than the hardware-assisted transparent VM management – to separate the two workload classes.
  • Transactions and queries are specified in SQL or a PL/SQL-like scripting language and are efficiently compiled into efficient LLVM assembly code.
  • HyPer’s transaction processing is fully ACID-compliant. Queries are specified in standard SQL-92 plus some extensions from following standard SQL standards.
  • HyPer relies on logical logging where, in essence, the invocation parameters of the stored (transaction) procedures are logged via a high-speed network.

In-memory Data Management

HyPer relies on in-memory data management without the ballast of traditional database systems caused by DBMS-controlled page structures and buffer management. SQL table definitions are transformed into simple vector-based virtual memory representations – which constitutes a column oriented physical storage scheme.

Efficient Snapshotting

OLAP query processing is separated from mission-critical OLTP transaction processing by forking virtual memory snapshots. Thus, no concurrency control mechanisms are needed – other than the hardware-assisted transparent VM management – to separate the two workload classes.

Data-centric Code Generation

Transactions and queries are specified in SQL or a PL/SQL-like scripting language and are efficiently compiled into efficient LLVM assembly code.

No compromises

HyPer’s transaction processing is fully ACID-compliant. Queries are specified in SQL-92 plus some extensions from subsequent standards.


comments powered by Disqus