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Table of contents:

Object Oriented Database vs Relational Database

• Introduction
• The Wall: Where an RDBMS breaks down
• Programming Time and Cost
• Integrity Loss
• Performance
• Information Model Issues
• Architecture Issues
• Benchmark
• Benchmark Parameters
• Benchmark Execution
• Parts Creation
• Updates
• Deletes
• Selects
• Summary
• Examples
• Cimskil (Manufacturing)
• Exxon (Configuration and FEA)
• CERN (Data Gathering and Analysis)
• Decision•ism (Decision Supportware)
• QUALCOMM (Telecommunications)
• COM21 (Cable Modem)
• Nortel, Sprint (Cell Phone Base Station Management)
• Nousoft (Library Document Management, Web)
• Iridium (Telecommunications)
• BBN (Military Logistics and Control)
• Intraspect (Knowledge Management)
• Fisher-Rosemount (Manufacturing, Process Control)
• A Brief Overview of Our Development
• ODB Selection
• Introduction
• Performance
• Scalability
• Productivity
• Fault Tolerance
• Additional Features and Benefits
• Summary
• Kintetsu (Transportation, Logistics)
• Siemens (Process Control)
• SEH (Semiconductor Manufacturing)
• NEC (Semiconductor Manufacturing)
• American Meter (Process Control)
• Sloan Digital Sky Survey (Geographic Information System)
• LMS (Healthcare, Fetal Monitoring)
• Synthesis (Healthcare, Record Management)
• Daikin (Multi-Media CD-I Authoring)
• How to Avoid the Wall

Object Oriented Database vs Relational Database

Fault Tolerance

Conventional RDBMS deal with fault tolerance issues and high data available with hardware based solutions. An example is the use of RAID technology to guard against media failure. These options are also available to an OODBMS.

OODBMS that are based on peer to peer technology are designed without any single point failures. These systems are setup to degrade gracefully. If part of the distributed information store is not available, then the remaining information may still be accessed. These systems are more resilient to catastrophic failures.

Some systems also offer additional protection by replicating important pieces of data across multiple compute nodes. In this situation, failure of a single node does not result in the loss of data that was replicated and therefore available on some other node.