Thursday Nov 08, 2012

SPARC T4-4 Delivers World Record Performance on Oracle OLAP Perf Version 2 Benchmark

Oracle's SPARC T4-4 server delivered world record performance with subsecond response time on the Oracle OLAP Perf Version 2 benchmark using Oracle Database 11g Release 2 running on Oracle Solaris 11.

  • The SPARC T4-4 server achieved throughput of 430,000 cube-queries/hour with an average response time of 0.85 seconds and the median response time of 0.43 seconds. This was achieved by using only 60% of the available CPU resources leaving plenty of headroom for future growth.

Performance Landscape

Oracle OLAP Perf Version 2 Benchmark
4 Billion Fact Table Rows
System Queries/
hour
Users* Response Time (sec)
Average Median
SPARC T4-4 430,000 7,300 0.85 0.43

* Users - the supported number of users with a given think time of 60 seconds

Configuration Summary and Results

Hardware Configuration:

SPARC T4-4 server with
4 x SPARC T4 processors, 3.0 GHz
1 TB memory
Data Storage
1 x Sun Fire X4275 (using COMSTAR)
2 x Sun Storage F5100 Flash Array (each with 80 FMODs)
Redo Storage
1 x Sun Fire X4275 (using COMSTAR with 8 HDD)

Software Configuration:

Oracle Solaris 11 11/11
Oracle Database 11g Release 2 (11.2.0.3) with Oracle OLAP option

Benchmark Description

The Oracle OLAP Perf Version 2 benchmark is a workload designed to demonstrate and stress the Oracle OLAP product's core features of fast query, fast update, and rich calculations on a multi-dimensional model to support enhanced Data Warehousing.

The bulk of the benchmark entails running a number of concurrent users, each issuing typical multidimensional queries against an Oracle OLAP cube. The cube has four dimensions: time, product, customer, and channel. Each query user issues approximately 150 different queries. One query chain may ask for total sales in a particular region (e.g South America) for a particular time period (e.g. Q4 of 2010) followed by additional queries which drill down into sales for individual countries (e.g. Chile, Peru, etc.) with further queries drilling down into individual stores, etc. Another query chain may ask for yearly comparisons of total sales for some product category (e.g. major household appliances) and then issue further queries drilling down into particular products (e.g. refrigerators, stoves. etc.), particular regions, particular customers, etc.

Results from version 2 of the benchmark are not comparable with version 1. The primary difference is the type of queries along with the query mix.

Key Points and Best Practices

  • Since typical BI users are often likely to issue similar queries, with different constants in the where clauses, setting the init.ora prameter "cursor_sharing" to "force" will provide for additional query throughput and a larger number of potential users. Except for this setting, together with making full use of available memory, out of the box performance for the OLAP Perf workload should provide results similar to what is reported here.

  • For a given number of query users with zero think time, the main measured metrics are the average query response time, the median query response time, and the query throughput. A derived metric is the maximum number of users the system can support achieving the measured response time assuming some non-zero think time. The calculation of the maximum number of users follows from the well-known response-time law

      N = (rt + tt) * tp

    where rt is the average response time, tt is the think time and tp is the measured throughput.

    Setting tt to 60 seconds, rt to 0.85 seconds and tp to 119.44 queries/sec (430,000 queries/hour), the above formula shows that the T4-4 server will support 7,300 concurrent users with a think time of 60 seconds and an average response time of 0.85 seconds.

    For more information see chapter 3 from the book "Quantitative System Performance" cited below.

See Also

Disclosure Statement

Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 11/2/2012.

Monday Oct 03, 2011

SPARC T4-4 Produces World Record Oracle OLAP Capacity

Oracle's SPARC T4-4 server delivered world record capacity on the Oracle OLAP Perf workload.

  • The SPARC T4-4 server was able to operate on a cube with a 3 billion row fact table of sales data containing 4 dimensions which represents as many as 70 quintillion aggregate rows (70 followed by 18 zeros).

  • The SPARC T4-4 server supported 3,500 cube-queries/minute against the Oracle OLAP cube with an average response time of 1.5 seconds and the median response time of 0.15 seconds.

Performance Landscape

Oracle OLAP Perf Benchmark
System Fact Table
Num of Rows
Cube-Queries/
minute
Median Response
seconds
Average Response
seconds
SPARC T4-4 3 Billion 3,500 0.15 1.5

Configuration Summary and Results

Hardware Configuration:

SPARC T4-4 server with
4 x SPARC T4 processors, 3.0 GHz
1 TB main memory
2 x Sun Storage F5100 Flash Array

Software Configuration:

Oracle Solaris 10 8/11
Oracle Database 11g Enterprise Edition with Oracle OLAP option

Benchmark Description

OLAP Perf is a workload designed to demonstrate and stress the Oracle OLAP product's core functionalities of fast query, fast update, and rich calculations on a dimensional model to support Enhanced Data Warehousing. The workload uses a set of realistic business intelligence (BI) queries that run against an OLAP cube.

Key Points and Best Practices

  • The SPARC T4-4 server is estimated to support 2,400 interactive users with this fast response time assuming only 5 seconds between query requests.

See Also

Disclosure Statement

Copyright 2011, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 10/3/2011.

About

BestPerf is the source of Oracle performance expertise. In this blog, Oracle's Strategic Applications Engineering group explores Oracle's performance results and shares best practices learned from working on Enterprise-wide Applications.

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