D1 Solutions AGa Netcetera Company

Real Life Performance of In-Memory Database Systems for BI10th European TDWI Conference

Munich, June 2010

10th European TDWI Conference

Munich, June 2010

Authors:

Dr. Andreas Hauenstein Dr. Simon Hefti Dr. Andrej Vckovski

In-Memory Database Systems

Buzzwords: Column-Orientation, In-Memory, Shared Nothing

Meaning: Looks like Oracle/DB2/SQLServer from the outside,just much faster

We are talking about relational systems, queryable in SQL

We are not talking about client side caching (Microstrategy or QlikView do this)

There is a new generation of DB systems, for example MonetDB, Exasol, Greenplum, LucidDB

Business Intelligence Data Warehouse

We are not looking at transactional systems

Any DB of an online shop or any DB driving a web site is transactional

Typically BI applications are driven by a non-transactional data store that is bulk loaded in intervals by an ETL process. This is called a data warehouse.

Next generation DB systems also exist for transactional systems. An example is Oracle TimesTen. This is a different subject.

General Purpose DB Systems (e.g. Oracle, SQL Server)

DB Systems Specialized for Analytics(e.g. Teradata)

DB Systems Spezialized for Transactions(e.g. TimesTen)

Business Intelligence Generated SQL

Tools with a GUI that generate SQL statements

Examples: Business Objects, OBIEE, Microstrategy, Cognos

No SQL tuning possible

Bad SQL

Non-technical users

Frequently changing queries

Lots of averages and sums, groupings, consolidation

Real Life Problem (1)

Consolidation of numbers along a hierarchy

Use a Parent-Child Table with a bridge table to do this in a relational DB

Real Life Problem (2)

Every company has this sort of problem

The most important people (CEO) experience the worst performance

OLAP tools exist because this sort of query is traditionally slow on relational systems

At a customer, 6 GB of data resulted in a 20 minute waitfor the CEO

Even Pre-Calculating all reports over night became difficult

The Data Model

levels12

leaves4096

nodes8191

Bridge Table

400 K Rows 300 K Rows

500 K Rows

Size of the Data

34438011875DIM_PRODUCT50111DIM_TIME

300177DIM_TRANS815DIM_UNIT

18110DIM_BUSINESSTYPE45339229819DIM_CLIENT

53248118DIM_ORG_FLAT

16019518723739T_FACTS

816DIM_MEASURE

5320679'780DIM_ACCOUNTING

8916123DIM _ORG

17415366775561

RowsBlocks

Quite small data volume

Bad performance on several platforms

Realistic scenario

775561 blocks * 8192 Bytes = 6 GB

Data Generation

One function call creates complete dimension table dim_org

Generates id column, parent pointer, bridge table dim_org_flat

Generated from a helper table with just integers and random numbers

Similar function to generate fact table

Started out as PL/SQL, now a Perl script that works with any DB

It is easy to model any scenario with this tool

create_dim( p_bf => 2, p_depth => 12, p_name => 'org', p_cols => 'org01,org02,org03,org04,org05,org06,org07,org08,org09,org10', p_types => 't10,t10,t10,t10,t10,t10,t10,t10,t10,t10);

The Test Query

Generated by BI tool

Initial Tests on Oracle and SQL Server

All the same order of magnitude

Adding RAM does not help a traditional DB

PCs are better than you think

Aggregated Fact Rows

Home PC159 sec205 sec1023 secOracle 10 GWindows 2003 ServerDell Dimension E521 4GB RAM

293 sec699 sec741 secMS SQL Server2005

Windows 2003 ServerDell Dimension E521 4 GB RAM

Expensive ProductionServer

167 sec168 sec1200 secOracle 10GAIXIBM 9117-570 8 GB RAM 1.9 GHt 4 CPUs

Linux with little RAM386 sec413 sec1432 secOracle 10 GRed Hat LinuxHP DL 380 Proliant Server 0.5 GB RAM Intel Xeon 3.2 GHz

16 Mio 1 Mio 3500OS DescriptionDBMSMachine

A New Generation DB System

Im memory DB factor 30-50 faster

Thats the speed of sound relative to a bicycle

With generic Intel hardware

Worth looking at several of these new systems

Aggregated Fact Rows

Home PC159 sec205 sec1023 secOracle 10 GWindows 2003 ServerDell Dimension E521 4GB RAM

293 sec699 sec741 secMS SQL Server2005

Windows 2003 ServerDell Dimension E521 4 GB RAM

Expensive Production Server167 sec168 sec1200 secOracle 10GAIXIBM 9117-570 8 GB RAM 1.9 GHt 4 CPUs

Linux with little RAM386 sec413 sec1432 secOracle 10 GRed Hat LinuxHP DL 380 Proliant Server 0.5 GB RAM Intel Xeon 3.2 GHz

In Memory DB0 sec2 sec22 secExasolExacluster

(Linux Microkernel)

Exasol Test System 2 Quad Core Intel CPU 32 GB RAM 2 nodes

16 Mio 1 Mio 3500OS DescriptionDBMSMachine

A New Generation DB System

Im memory DB factor 30-50 faster

Thats the speed of sound relative to a bicycle

With generic Intel hardware

Worth looking at several of these new systems

0

200

400

600

800

1000

1200

1400

1600

DD SQL DD CRA HP IBM Exa

The Contenders

Oracle 11 G

MySQL

MonetDB

LucidDB

Greenplum (their own hardware)

Exasol (their own hardware)

The Test Server

Intel Dual Xeon E 5205

16 GB RAM

2 x 250 GB SATA Disk

64 Bit Debian Linux

Interesting DB Systems That Were Not Tested

Teradata

Oracle ExaData

Netezza

Vertica

Infobright

Kognitio

The field is very active and new products and approaches keep entering the market.

MonetDB

Origin: Result of research at CWI in the Netherlands

Open Source: Yes

Free of Charge: Yes

Remarks:o Recent publicity through a paper in Communications of the ACM:

Breaking the Memory Wall in MonetDBo Constantly changing as research progresseso Easy to get into direct contact with the developers

Quote from the website:MonetDB is a open-source database system for high-performance Applications in data mining, OLAP, GIS, XMLQuery, text and multimediaretrieval.

LucidDB

Origin: Formerly part of LucidEra in San Mateo, California

Open Source: Yes

Free of Charge: Yes

Remarks:o Emphasizes ease of configuration and maintenance o Mostly written in Java

Quote from the website:LucidDB is the first and only open-source RDBMS purpose-built entirely fordata warehousing and business intelligence. It is based on architecturalcornerstones such as column-store, bitmap indexing, hash join/aggregation,and page-level multiversioning.

Greenplum

Origin: Located in San Mateo, California. Postgres based.

Open Source: Based on Open Source Technology

Free of Charge: No

Remarks:o Based on similiar hardware architecture as Exasolo Highly configurable and tunable, lots of featureso Column store is an option, default is row store

Quote from the website:Greenplum Database utilizes a shared-nothing MPP (massively parallel processing) architecture that has been designed from the ground up for BI and analytical processing using commodity hardware. In this architecture, data is automatically partitioned across multiple 'segment' servers, and each 'segment' owns and manages a distinct portion of the overall data.All communication is via a network interconnect -- there is no disk-levelsharing or contention to be concerned with (i.e. it is a 'shared-nothingarchitecture).

Exasol

Origin: Developed from scratch in Nrnberg, Germany

Open Source: No

Free of Charge: No

Remarks:o Based on similiar hardware architecture as Greenplumo Pure column store DBo Emphasizes ease of administrationo No need to create indexes or gather statisticso Imitates some Oracle-isms for compatibility

Quote from the website:The database has been specially developed for analysis and is being used successfully for data warehousing, Web analytics, data mining applications and more. In contrast with universal databases, this specialization means that the data to be analyzed can be made available to analysis tools virtually in real time.

Typical Shared Nothing Node

Combine many of these, connected by GB Ethernet

Results With 16 Mio Rows in the Fact Table

Oracle on a new 64 Bit box is 4 times faster than on an average 32 bit box

Both Oracle and LucidDB were twice as fast after dropping all indexes on the fact table (those are the times in the chart)

We did not manage to tune MySQL to get acceptable performance for a free system, LucidDB has good performance and little hassle

MonetDB needed a fix in the optimizer before coping with the query

Next generation in memory DBs are at least one order of magnitude faster

226

2280

460

31 13 100

500

1000

1500

2000

2500

Oracle MySQL LucidDB MonetDB Greenplum Exasol

183

364

105

210

3

54

97

13

133

288

26

60

50

100

150

200

250

300

350

400

16 160 320

Exasol [sec](public demo system)

Exasol [sec](untuned comparablehardware) Exasol [sec](local dimensionscomparable hardware ) Greenplum[sec]

Performance Scaling

Both systems scale linearly It is possible to query at least ten times the data

volume efficiently The vendors claim unlimited linear scaling by adding

commodity hardware

Conclusion

Big Lessons Database technology is in upheaval at the moment

By adopting the new technologies, you can totally revolutionize the way you access your data

Prices will fall rapidly. This is like the PC revolution.

Small Lessons If you have an Oracle on a 32 Bit system, move to a 64 Bit architecture. It will give

you a factor 4 without any pain

If your table scans are slow, drop all indexes

If you move to a new technology, you will get a factor 50

The commercial systems are worth their money. Their SQL is more compatible, and they are more stable