White Papers & Collateral
STAC-M3 Kanaga Audited Report
STAC recently performed the volume- and user-scaling suite of STAC-M3™ Benchmarks (Kanaga) on a stack involving McObject’s eXtremeDB Financial Edition 7.0 hosted on an IBM Power System S824L server with two 12-core IBM POWER8 CPUs connected to a 12 x 5.7 TB IBM FlashSystem 900 storage system. This report documents the benchmark results. The McObject code used to execute these benchmarks is available from the STAC Vault for inspection and use by qualified STAC Benchmark Council members.
IBM White Paper: Powering the Financial Industry
STAC testing confirms that deploying McObject’s eXtremeDB in an environment of Power Systems and IBM FlashSystem can provide financial institutions with the application performance they need to capture and keep competitive advantage, increase profitability and move confidently into what IBM calls the “Cognitive Era.”
Pipelining Vector-Based Statistical Functions for In-Memory Analytics
Columnar data handling accelerates time series analysis (including market data analysis) by maximizing the proportion of relevant data brought into the CPU cache with each fetch. As explained in this white paper, McObject’s eXtremeDB Financial Edition delivers columnar data handling, and builds on it with pipelining technology that enables multiple vector-based statistical functions to work on a given data sequence (time series) within the CPU cache, without the need to “materialize” interim results as output in main memory. This eliminates the latency caused by back-and-forth transfers between CPU cache and memory.
Database Persistence, Without the Performance Penalty
Some applications require higher data durability than in-memory data storage provides. What if DRAM could be made persistent? AgigA Tech’s AGIGARAM non-volatile DIMM (NVDIMM) delivers that capability. McObject benchmarked eXtremeDB Financial Edition IMDS using AGIGARAM as main memory storage, including “pulling the plug” mid-execution, and comparing the NVDIMM to transaction logging as solutions to provide durability/recoverability. This paper presents the benchmark tests and results.
Terabyte-Plus In-Memory Database System (IMDS) Benchmark – Complete Report
(22-page full report)
Terabyte-Plus In-Memory Database System (IMDS) Benchmark – Brief
In-memory database systems (IMDSs) hold out the promise of breakthrough performance for time-sensitive, data-intensive tasks. This benchmark examines the performance of McObject’s 64-bit eXtremeDB IMDS as it creates and manipulates a 1.17 Terabyte, 15.54 billion row database on a 160-core Linux-based SGI® Altix® 4700 server.
In-Memory Database Systems: Myths and Facts
In-memory database system vendors claim to provide breakthrough performance by storing all record in main memory. But database systems already employ caching, “memory tables”, solid state drive (SSD) storage, and the ability to run in-memory on RAM-disks. Do IMDSs offer anything new? In fact, the distinctions can be critical to project success. This report explains why.
Benchmarking In-Memory & On-Disk Databases With Hard-Disk, SSD and Memory-Tier NAND Flash
Eliminating latency, including data management latency, within the trade cycle makes a huge difference for trading success. In-memory database systems (IMDSs) deliver unparalleled speed, via volatile RAM-based storage. To add data durability, IMDSs offer transaction logging. Are they still significantly faster than “traditional” (on-disk) DBMSs? McObject’s benchmark research answers this question and measures the performance impact of different storage options (HDD, SSD and state-of-the-art memory-tier flash device), to inform your decisions on data management, storage, low-latency and volatility in trading system design.
In-Memory vs. RAM-Disk Databases: A Linux-based Benchmark
A new type of DBMS, the in-memory database system (IMDS), claims breakthrough performance and availability via memory-only processing. But doesn’t database caching, or using a RAM-disk, achieve the same result with a traditional (disk-based) database? This benchmark tests eXtremeDB against a widely used embedded database, in both disk-based and RAM-disk modes. Deployment on RAM-disk boosts the traditional database by as much as 74 percent, but it still lags the IMDS substantially. Read about the architectural reasons for this disparity.