MISTRAL Performance of TPC-D Benchmark and Datawarehouses

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MISTRAL Performance of TPC-D Benchmark and Datawarehouses

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1999 FORWISS. FORWISS. MISTRAL. Performance of TPC-D Benchmark and Datawarehouses ... Dept. of Computer Science, Technical University Munich ... – PowerPoint PPT presentation

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Title: MISTRAL Performance of TPC-D Benchmark and Datawarehouses


1
MISTRALPerformance of TPC-D Benchmark and
Datawarehouses
  • Prof. R. Bayer, Ph.D.
  • Dr. Volker Markl
  • Dept. of Computer Science, Technical University
    Munich
  • and Bavarian Research Center for Knowledgebased
    Systems
  • (FORWISS)

2
Test Bed for Performance Measurements
  • Hardware
  • Compaq Proliant 5000
  • 4 Pentium II 200 MHz
  • 512 MB RAM
  • hard disk 7 4 GB 28 GB
  • Operating System
  • Windows NT 4.0
  • RDBMS
  • Oracle 8
  • 8kB pages
  • Access Methods
  • Tetris Algorithm for UB-Trees
  • Oracle IOT (clustering B-Tree)
  • Oracle FTS (full table scan)

3
TPC-D Schema
4
Shipping Priority Query (Q3)
SELECT L_ORDERKEY, SUM(L_EXTENDEDPRICE(1-L
_DISCOUNT)) AS REVENUE, O_ORDERDATE,
O_SHIPPRIORITYFROM CUSTOMER, ORDER,
LINEITEMWHERE C_MKTSEGMENT 'FOOD'
AND C_CUSTKEY O_CUSTKEY AND L_ORDERKEY
O_ORDERKEY AND O_ORDERDATE lt DATE 1.5.98 AND
L_SHIPDATE gt DATE 1.6.98GROUP BY L_ORDERKEY,
O_ORDERDATE, O_SHIPPRIORITYORDER BY REVENUE
DESC, O_ORDERDATE
5
Tetris algorithm Q3
6
Response times 50 LINEITEM (Q3)
7
Temporary Storage 50 LINEITEM (Q3)
8
Sorting 50 of LINEITEM
9
Forecasting Revenue Change Query (Q6)
SUM(L_EXTENDEDPRICEL_DISCOUNT) AS REVENUEFROM
LINEITEMWHERE L_SHIPDATE gt date
AND L_SHIPDATE lt date INVERVAL 1 YEAR
AND L_DISCOUNT BETWEEN discount -0.01 AND
discount 0.01 AND L_QUANTITY lt quantity
10
Forecasting Revenue Change Query (Q6)
11
Performance of Q6
12
Retrieving 3,3 of LINEITEM
13
GFKSnowflake Schema
14
TETRIS MHC
15
Performance Measurements GFK
  • DBMS
  • TransBase (covering, clustering compound
    B-Trees)
  • UB/API on top of TransBase (UB-Tree, two ESQL
    Statements are optimized and processed per
    UB-Tree page access)
  • TransBase Hypercube (UB-Tree inside the DBMS
    Kernel)
  • Database
  • real world data warehouse from GFK
  • 3D Snowflake Schema
  • Time (3 years 18 MP)
  • Segment (10500 outlets)
  • Product ( 500000 items in 604 product groups)
  • 42 Mio fact tuples ( 4 GB fact table size)
  • Computer
  • Sun ULTRA 1 Workstation (64 MB Main Memory)

16
Indexes
  • MHC to encode hierarchies
  • TIME_CS (5 bits)
  • SEGMENT_CS (24 bits)
  • PRODUCT_CS (29 bits)
  • Compound on (PRODUCT_CS, TIME_CS, SEGMENT_CS)
    or(TIME_CS, SEGMENT_CS, PRODUCT_CS)
  • UB-Tree (UB/API) onTIME_CS, PRODUCT_CS,
    SEGMENT_CS

17
GFK Datawarehouse Reports
? selectivity ltlt 1
18
Compound fixed 2MP, varying PG
19
UB-Tree fixed 2MP, varying PG
20
Response Time Result Set Size
21
Clustering of UB-Trees
Ø 0.85
s / dc
clustering factor
22
Clustering depending on Result Set Size
23
Summary UB-Tree
  • Excellent performance on large real DBs, gt factor
    10
  • Very low storage requirement
  • 1st answer extremely fast, interactive use!!
  • Response time proportional to size of answer
  • Wide applicability all DBs are
    multidimensional!!
  • Easy integration into DBMS, simple DDL extension
  • Very useful as middleware
  • Patent applications
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