1. OLAP Query Processing in Grids
DMG 2007
OLAP Query Processing in Grids
Nelson Kotowski
Federal University of Rio de Janeiro, Brazil
Alexandre A. B. Lima
University of Grande Rio, Brazil
Esther Pacitti, Patrick Valduriez
INRIA and University of Nantes, France
Marta Mattoso

2. Agenda OLAP in Grids Database clusters GParGRES
Preliminary experimental results
Conclusion

3. Figure thanks to Peter Kacsuk and Gergely Sipos
OLAP using Grids
Problem
How to fulfill OLAP needs within current grid software infrastructure ?
Grid Services ?
Adapting database cluster techniques to grids ?
Grid
Figure thanks to Peter Kacsuk and Gergely Sipos

4. Using Database Clusters in Grids
Middleware
DBMS
PC Cluster
Clients
A sequential “black-box” DBMS runs at each node
It is based on database replication
The middleware coordinates parallel query execution
Applications and databases are easily migrated from sequential environments
Both inter and intra-query parallelism can be explored

5. Inter-query Parallelism
Improves overall system throughput
Good for OLTP applications
Not adequate for OLAP
DBMS
Node 1
DBMS Q1
Node 2 Q2 Q3
DBMS
Node 3 Q4
São duas as técnicas de paralelismo utilizadas pelos DBCs para aumentar a vazão do sistemas ou reduzir o tempo de processamento das consultas.
Inter-c - capacidade de processamento de mais de uma consulta simultamente. Bastante empregada em aplicações OLTP, características por atender a muitos usuários simultaneamente.
Quatro consultas são recebidas e enviadas ao mesmo tempo para quatro SGBDs distintos
DBMS
Node 4

6. Intra-query Parallelism
Reduces individual query execution time
Required for high-performance OLAP
DBMS
Node 1 Q1
Q11
DBMS
Node 2
Q12
Virtual
Partitioning
Q13
Q14
DBMS
Node 3 Q2 Q3
DBMS
Node 4 Q4

7. ParGRES Database cluster middleware developed by our research group
Optimized for OLAP support
Provides inter and intra-query parallelism
Offers high-performance for heavy-weight query processing over large databases
using non-expensive components
in a non-intrusive way
Making no changes to database applications
Keeping the same DBMS
Keeping the same logical database schema
Shows super-linear speedup

8. GParGRES

9. GParGRES: a Database Grid Middleware
Middleware that provides
Transparent access to distributed databases in a grid
Intra-query parallelism during heavy-weight query processing
Based on ParGRES
Assumes that grid nodes are PC clusters running ParGRES instances
Intra-query parallelism is achieved through virtual partitioning
Two levels of query splitting
Grid-level splitting: implemented by GParGRES
Node-level splitting: implemented by ParGRES

10. GParGRES: Architecture

11. GParGRES: Architecture
Concentrates metadata concerning GParGRES services, such as the state of each FS and DQS instance, and ParGRES execution in the nodes

12. GParGRES: Architecture
GParGRES entry point, responsible for creating new instances of DQS

13. GParGRES: Architecture
Manages global query execution. Receives the query and splits it into subqueries by using virtual partitioning to implement intra-query parallelism. It also performs final result composition

14. GParGRES: Architecture
Grid Local Query Service (GLQS) – local component responsible for receiving subqueries from DQS and passing them to the local ParGRES instance

15. GParGRES: Architecture

16. GParGRES: a Database Grid Middleware

17. GParGRES: a Database Grid Middleware

18. GParGRES: a Database Grid Middleware

19. GParGRES: a Database Grid Middleware

20. GParGRES: a Database Grid Middleware
select o_orderpriority,
count(*)
from orders
where o_orderdate >=
date ' '
group by o_orderpriority;

21. GParGRES: a Database Grid Middleware
create table temp_result_1
( o_orderpriority varchar(2),
order_count integer);

22. GParGRES: a Database Grid Middleware
select o_orderpriority,
count(*)
from orders
where o_orderdate >=
date ' '
and o_orderkey >= ? and o_orderkey < ?
group by o_orderpriority;

23. GParGRES: a Database Grid Middleware

24. GParGRES: a Database Grid Middleware

25. GParGRES: a Database Grid Middleware

26. GParGRES: a Database Grid Middleware
insert into temp_result_1
values (?,?);

27. GParGRES: a Database Grid Middleware
select o_orderpriority,
sum(order_count) from temp_result_1
group by o_orderpriority;

28. GParGRES: a Database Grid Middleware

29. GParGRES: Preliminary Experimental Results
A preliminary GParGRES prototype has been implemented in Java
Simple versions of DQS and GLQS (using ParGRES components) were implemented
Experimental Setup
Two clusters from Grid’5000
Parasol cluster: 64 nodes, each with 2 Opteron 2.2GHz CPUs, 2GB RAM and 73 GB HD
Paraquad cluster: 64 nodes, each with 2 Dual Core Xeon 2.33GHz CPUs, 4GB RAM and 160GB HD
Kadeploy
Generate customized images of operating systems and applications
PostgreSQL 8.2.4
ParGRES
TPC-H database and queries
SF = 1

30. GParGRES: Preliminary Experimental Results (cont.)
Two kinds of experiments
Isolated clusters
Mixed Configuration

31. GParGRES: Preliminary Experimental Results (cont.)
Isolated cluster - Parasol

32. GParGRES: Preliminary Experimental Results (cont.)
Isolated cluster - Paraquad

33. GParGRES: Preliminary Experimental Results (cont.)
Mixed Configuration

34. GParGRES – Implementation Issues
Goals
To implement all components as grid services
WSRF-compliant components: RS, FS and GLQS
When running in a grid managed by Globus Toolkit 4, RS can be implemented by Web Service Monitoring and Discovery Service (WS MDS)
Techniques employed in OGSA-DAI will help implementing some components (e.g. FS)

35. Related Work OGSA-DAI OGSA-DQP New data models for grid warehouses
Open Grid Services Architecture - Data Access and Integration
OGSA-DQP
Open Grid Services Architecture - Distributed Query Processing
New data models for grid warehouses
Wehrle et al. propose a data model for distributing and querying a data warehouse in computing grids
The warehouse is formed by data “chunks”
Special structures are needed (e.g. X-Tree)
Recursos de Dados
– Qualquer objeto que pode ser uma fonte de dados
– Em geral o foco está nos bancos de dados
Serviços de Dados
– Interface comum aos recursos de dados
– Expõe as capacidades dos recursos de dados
– Consultas em SQL, Consultas em X-Path
– Transformações de dados

36. Conclusion GParGRES is a grid service for OLAP query processing
It provides transparent inter and intra-query processing with
No need for application migration
No need for database schema migration
DBMS independence
GParGRES explore successful techniques implemented in ParGRES
Two levels of query splitting
Grid-level splitting: implemented by GParGRES
Node-level splitting: implemented by ParGRES
Components are WSRF-compliant, easing the compatibility with existing grid solutions
Preliminary results obtained in Grid’5000 show good performance

37. Future Work Integration with OGSA-DAI
Support for partial database replication
Support for top-k queries
Extension of best position algorithms

38. A different view of the Grid
DMG 2007
Thanks!
A different view of the Grid
Kandinsky
the Grid, 1923
Albertina Museum
Vienna