Evaluating of Distributed Database on PC Cluster Computers

Transcription

1 Evaluating of Distributed base on PC Cluster Computers SORAPAK PUKDESREE ANON SUKSTRIENWONG VITALWONHYO LACHAROJ School of Science and Technology Bangkok University Rama IV rd., Klongtoey, Bangkok THAILAND Abstract: In modern organization information and data are very importance aspects to be concerned on every application. Most of applications store data and information using database management system. Those data and information typically are stored in a centralized storage that makes easy for administrator to manage and manipulate those data. However this method is limited by the capacity of database server and the way of processing approach. The database server will handle all clients requests that consume performance of CPU power, memory bandwidth, I/O bandwidth, and network bandwidth. Therefore database server should have high capacities in term of CPU power, memory, network bandwidth and large storage that will be costly in budget. Hence there are many researches that propose some other approaches to improve system performance. For example, DBMS such as SQL server and Oracle utilize distributed processing of database using several database servers but the data itself still stored in a centralized storage as shared storage. Currently, MySQL has launched MySQL Cluster DBMS that supports the distribution of database and distributed processing. The data of this methodology will be stored in each storage nodes in the cluster by using hashing function. In this research we would like to evaluate the performance of distributed data using MySQL Cluster DBMS together with our PC Cluster computers. We hope the distribution of data and using cluster technology would improve the performance of database processing. Key-Words: Distributed database, PC Cluster computers, MySQL Cluster. 1 Introduction in a large number of requests, this will cost in high budget of the database server. base Server base Servers Fig.1: Typically base Management Typically, DBMS stores the database on a centralized storage. The database can be shared to several users, and users can access or manipulate the data by making requests to the database server [1]. The database server will handle and processes those requests and sends the results back to each request. This approach makes easy for administrator to manage and to secure the database. But this needs high performance server in term of processing capacity and storage capacity especially Fig.2: Distributed Processing on Centralized base To improve the database processing, there is another approach as distributed processing [2, 4] on the database. In this method there will have more than one database servers in the system, but the data itself stored in a centralized storage which shared for those database servers. In this case, each database server will handle user s requests in parallel approach. Those database servers may

2 access to the centralized database in parallel. Some commercial DBMS such as MS SQL server and Oracle use this approach to improve the system performance. Furthermore, this approach is widely used in web applications that many web servers can access a centralized database as shared storage. Management SQL Node Node Internet Storage Nodes DB Distributed DB Fig.3: Distributed Processing and Distributed In distributed data [2] environment, data will be decomposed into smaller pieces and then distributed to several storage nodes in a cluster of computers. These cluster computers are connected together as a high performance computer. Those pieces of data will be handled by DBMSs. When users would like to access, query or manipulate the data, they make requests to any DBMSs. Users do not need to know where the actual data is stored. The advantages are that the system can deploy distributed processing on distributed of data [4]. Furthermore, system also can be improved I/O bandwidth because each computer in cluster has its own network interface card. Therefore bandwidth of the cluster would increase by the product of number of cluster nodes and bandwidth of each network card. Finally we can apply distributed approach with PC cluster [9] technology, by using typically PC computers; the system budgets can be decreased dramatically. But the distributed data on the network need to be concerned about security management. 2 System Architecture 2.1 Hardware Architecture base Servers Fig.4: Current MySQL Cluster system In the system we have used 10 PC computers that placed in an open computer laboratory. Each PC computer has a single Pentium4 1.8 GHz processor, 256 MB of RAM, 20 GB of HDD, and a 100 Mbps fast Ethernet NIC. Each PC computers is connected by using a 10/100 Mbps Hub/switch. These are all computer that we can use in the system. Even though this does not a high performance PC as the new PC but this reflects our own environment. The capacity of network bandwidth very affects the system performance. The system can be improved by using higher capacity of network bandwidth such as Myrinet or Infiniban. Unfortunately we do not have planed to those high network bandwidths due to the very expensive cost. 2.2 Software Architecture We use Red Hat Enterprise Linux 4 [6] as our system s operating system. RHEL4 has strong potential, more stability, more security and so forth. We also have test the system in some other open source operating system such as fedora but we prefer to RHEL4. Red Hat provides online update kernels or packages that can help administrator to fix or upgrade the system software. The DBMS in the research we have used MySQL Cluster 5.0 [5] which is the latest version at this time. This version gives us more stable than previous version. In previous version all installation files are integrated as only one rpm. But in the new version installation files are separated as many rpm files. This makes easier and more flexible to install because each types of nodes requires different rpm files. We installed only required rpms on different type of nodes. Table in MySQL Cluster is stored in-memory databases in a share-nothing system which makes a great performance because access time of main memory is greatly faster than access time of storage devices. To evaluate the performance of distributed database, we used some open source tools such as Benchw and SQL-Bench. Both MySQL Cluster DBMS and benchmark tools

3 are open source software that we can download binary files or source files. 3 System Implementation The distributed database management system has been implemented on a cluster of personal computers [10] available at the computer lab. We used a pc computer as a MGM node, one pc computer as a SQL node and up to eight pc computers as storage nodes The Operating System used is Red Hat Enterprise Linux 4 instead of using some open source operating system because RHEL4 is more stable, more updated packages and less of bugs. Even though MySQL Cluster 5.0 is a newer and more stable version. During installing and testing the system we have found the application used in system has crashed for a couple times. These may be courses of application s bugs, our miss configurations or our miss operations. But the operating system is working greatly with stability and strong potential. We have tested this approach in some other open source operating systems such as Fedora. 4 System Evaluation To evaluate the performance of distributed database, we used some open source tools such as Benchw and SQL-Bench. Even though there are many databases open source benchmarks but some benchmarks are not working on our system and environment. There are also commercial database benchmarks such as TPC but we picked Benchw and SQL-Bench to evaluate performance of our system. SQL-Bench is a database benchmark provided by MYSQL Cluster. It has nine testing operations but works only eight operations. SQL- Bench operations are alter-table, ALTIS, big-tables, connect, create, insert, select, transactions, and Wisconsin. But only transactions can not be used in our environment due to compatible with database. Finally, SQL-Bench has five testing operations as Q0, Q1, Q2, Q3 and Q4. SQL-Bench has a table named fact0.dat that has ten million records with one gigabytes of space. This table is suitable to test some queries with very large database in the system. The execution time shown in the table is representing the execution time used to process each transaction. The results of SQL-Bench are in Table 1. First, we tested our system using SQL-Bench with all eight operations. We used a MGM node and a SQL node. The result from testing is in table 1. We started testing with a storage node as the same machine as SQL node, then time of 8 operations is seconds. To make a new test, we had to shutdown NDB engine on all of cluster nodes. We had increased the number of storage node one machine for each test. Then we restarted all nodes with two storage nodes. The result with two storage nodes was seconds that degraded system performance 9.05%. The result with three storage nodes was seconds that improved system performance 20.31%. The result with four storage nodes was seconds that improved system performance 18.32%. The result with five storage nodes was seconds that improved system performance 11.04%. The result with six storage nodes was seconds that improved system performance 15.01%. The result with seven storage nodes was seconds that improved system performance 10.60%. Finally, we tested with eight storage nodes. The result was seconds that improved system performance 11.26%. The results showed that when we increased more storage nodes, the time for test operations trends to use less processing time. The maximum performance ratio was 20.31% by using three storage nodes. On the other hand, the worst case from the test was -9.05% by using two storage nodes. Table 1: Results from SQL-Bench benchmark Total Time for 8 All Operation Number of Performance Seconds Storage Nodes Ratio % % % % % % % Finally, we tested our system using Benchw benchmark which is designed to test data loading, index creation and query performance. It provides a large data table named fact0.dat which has ten million records with one gigabytes in size that is very useful and enough for system testing. There are five queries in Benchw which Q0 is a simplest query, and then the query will more complicate in

4 each step such as jointing tables or nested queries. The results of Benchw are in Table 2. We started with Q0 on two storage nodes. The result with two storage nodes was seconds. The result with four storage nodes was seconds that degraded system performance 6.40%. The result with eight storage nodes was seconds that improved system performance 4.39%. On Q1 test, the result with two storage nodes was seconds. The result with four storage nodes was seconds that degraded system performance 13.07%. The result with eight storage nodes was seconds that degraded system performance 14.09%. On Q2 test, the result with two storage nodes was seconds. The result with four storage nodes was seconds that degraded system performance 52.24%. The result with eight storage nodes was seconds that improved system performance 22.06%. On Q3 test, the result with two storage nodes was seconds. The result with four storage nodes was seconds that degraded system performance 19.26%. The result with eight storage nodes was seconds that improved system performance 20.21%. Finally Q4 test, the result with eight storage nodes was seconds. The result with four storage nodes was seconds that degraded system performance 46.60%. The result with eight storage nodes was seconds that improved system performance 24.81%. The results showed that when we increased more storage nodes, the time for test operations trends to use less processing time. The maximum performance ratio was 24.81% by using eight storage nodes on Q4 test. On the other hand, the worst case from the test was % by using four storage nodes on Q2 test. Table 2: Results from Benchw benchmark Number of Storages Type of Query Time (Seconds) Performa nce Ratio 2 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Conclusion and Future work The goal of this research to find some approaches that can improve the performance of database system utilization in term of distributed data and distributed processing. The results from SQL- Bench and Benchw of system s testing show that when we increased number of storage nodes the performance is better than the time of less number of storage nodes. The distributed database approaches trend to improve system performance. The testing might be limited to eight storage nodes due to the availability of PC computers in our laboratory. The results can be used to estimate the suitable number of storage in the system. We have spent too much time in the first phase to set up hardware and software. In our next testing we will increase more storage nodes. Also we are currently used more database benchmarks on the distributed environment such as XMLbench, cplusql ETL, MPI Blast, Spb Benchmark and so forth. However we will still have obstructed in 100 Mbps of network bandwidth. We also are currently applying this distributed database technology with some other distributed technology. 6 Acknowledgements This research is sponsored by First Logic Company Limited and Sun Microsystems (Thailand) which grant us to work on research in high performance computing area. We would like to thank the sponsors and their partners and Bangkok University for their support. References: [1] Advantage base Server: high performance RDBMS, S.l. : Extended Systems, c2001 [2] DISTRIBUTED DATABASE SYSTEM, Bell David Workingham : Addison-Wesley, Pub. Co., 1992

5 [3] D.E. Culler, D.A. Patterson, T.E. Anderson. The Berkeley Network of Workstation (NOW) Project. [4] G.C. Fox. What have we learnt from using real parallel machines to solve real problem? In Proc. Of the 3rd conference on Hypercube concurrent computers and applications, pages Vol.2 pp , Pasadena, CA, January [5] MySQL Cluster [6] Red Hat Enterprise Linux [7] R. Buyya. High Performance Cluster Computing Vol 1. And Vol II, Prentice Hall, [8] SQL-Bench [9] Sterling, J. Becker, Savarese, E. Dorband, A. Ranawak, V. Packer. BEOWULF: A PARALLEL WORKSTATION FOR SCIENTIFIC COMPUTATION. Proceedings, International Conference on Parallel Processing, [10] YOSHIO, MOTOHIKO, KAZUTO, MITSUHISA. COMPaS : A Pentium Pro PCbased SMP Cluster and Its Experience (Special Issue on Parallel Processing). In 1PPS Workshop on PC NOW, pages LNCS, 1998.