Peer-to-Peer—Database Checkout

The ArcGIS 9.2 software incorporates support for incremental updates between ArcSDE geodatabase environments.

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Geodatabase Multigeneration Replication: The ArcGIS disconnected editing functionality will be expanded in future ArcGIS 9 releases to support loosely coupled ArcSDE distributed database environments. Figure 6-17 presents an overview of the future loosely coupled ArcSDE distributed database concept.

Figure 6-17

Distributed Geodatabase Architecture

Multigeneration replication supports a single ArcSDE geodatabase distributed over multiple platform environments. The child checkout versions of the parent database supports an unlimited number of update transactions without losing local version edits or requiring a new checkout. Updates are passed between parent and child database environments through simple datagrams that can be transmitted over standard WAN communications. This new geodatabase architecture supports distributed database environments over multiple sites connected by limited bandwidth communications (only the reconciled changes are transmitted between sites to support database synchronization).

6.6 Data Management Overview

Support for distributed database solutions has traditionally introduced high-risk operations, with potential for data corruption and use of stale data sources in GIS operations. There are organizations that support successful distributed solutions. Their success is based on careful planning and detailed attention to their administrative processes that support the distributed data sites. More successful GIS implementations support central consolidated database environments with effective remote user performance and support. Future distributed database management solutions may significantly reduce the risk of supporting distributed environments.

Whether centralized or distributed, the success of enterprise GIS solutions will depend heavily on the administrative team that keeps the system operational and provides an architecture solution that supports user access needs.

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7 Performance Fundamentals

Computer platforms must be configured properly to support system performance requirements. There are many factors that contribute to user performance and productivity. Enterprise GIS solutions include distributed processing environments where user performance can be the product of contributions from several hardware platform environments. Many of these platform resources are shared with other users. Understanding

distributed processing technology provides a fundamental framework for deploying a successful enterprise GIS.

The importance of working together to understand the technology is illustrated in figure 7-1.

Figure 7-1

Understanding the Technology

Technology is changing very rapidly, and we all see and try to understand what these changes mean with the help of our own experience. It is important to listen to the experience of others and incorporate their experience with our own as we move technology forward. I have found that we all have information to contribute and the questions we have can help others understand the technology in a better and more complete way. Modeling our experience, and joining these models with the experience of others, can facilitate our learning process.

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7.1 Understanding the Technology

ESRI has implemented distributed GIS solutions since the late 1980s. For many years, distributed processing environments were not well understood, and customers relied on the experience of technical experts to identify hardware requirements to support their implementation needs. Each technical expert had a different perspective on what hardware infrastructure might be required to support a successful implementation, and

recommendations were not consistent. Many hardware decisions were made based on the size of the project budget rather than a clear understanding of user requirements and the associated hardware technology.

System performance models were developed in the early 1990s to document what was understood about distributed processing systems. These system performance models have been used by ESRI consultants to support distributed computing hardware solutions since 1992. These same performance models have also been used to identify potential performance problems with existing computing environments.

The initial performance models were developed to support desktop GIS applications with file and GIS database data sources. UNIX and Windows application computer servers were used to provide remote terminal access to GIS applications supported in centralized data centers. A simple concurrent user model was used to support capacity planning.

Web mapping services were introduced in the late 1990s, and transaction-based sizing models were developed to support capacity planning and proper hardware selection. Transaction rates were identified in terms of map displays per hour. The transaction-based capacity planning models proved to be much more accurate and measurable than the previous concurrent user models, although in many cases customers were more

comfortable identifying sizing requirements in terms of peak concurrent user load than using peak map requests per hour.

The release of ArcGIS Server 9.2 in 2006 introduced some new challenges for the traditional sizing models, and an effort to review lessons learned and take a close look at the road ahead was in order. The result is a new approach to capacity planning that incorporates the best of the traditional client/server and Web services sizing models and provides an adaptive sizing methodology to support future enterprise GIS operations. The new capacity planning methodology is much easier to use and provides metrics to manage performance compliance during development, initial system implementation, and delivery.

This new capacity planning model was developed and shared with the objective of helping software developers, business partners, technical marketing specialists, and ESRI distributors better understand the performance and scalability of ESRI technology to provide customers with the best possible GIS solutions to support their enterprise GIS operations.

This section presents a basic overview of the system performance fundamentals. The terms and relationships introduced in this section describe fundamental relationships we can all use to better understand performance.

The following sections on Software and Platform performance provide additional insight on the processing demands GIS software can place on our system environment, and identify the processing capabilities of current vendor hardware technology. An understanding of these performance fundamentals can provide a framework for building and maintaining more effective real-world GIS operations.

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7.1.1 What Is Capacity Planning?

Figure 7-2 identifies some key factors that contribute to overall system performance. Proper hardware and architecture selection is one primary component of the overall system performance equation. There are many other performance factors that contribute to overall user productivity.

Figure 7-2

System Performance Factors

Capacity planning is selecting the right software and hardware to meet user workflow performance needs.

Enhancements in any of the system performance factors can improve user productivity and impact total system capacity. Performance cannot be guaranteed by proper hardware selection alone. The performance

fundamentals described in this section can help identify appropriate hardware selection based on customer business requirements. Our understanding of GIS processing requirements and how this workload is supported by vendor platform technology is based on more than 20 years of experience helping customers deploy ESRI GIS technology. A balanced hardware investment, based on projected peak user workflow loads, supports system performance requirements and saves money and time through properly targeted hardware purchases.

7.1.2 What Is System Performance?

Computer platforms are supported by several component technologies. Each component technology is

important - the weakest component will limit overall platform performance. Some software applications require lots of data transfer and dynamic graphics display, while others require heavy computing. Hardware vendors build computers with a balance of component resources that optimize performance for a broad range of customers. Compute intensive software, like GIS, find the hardware platform processor core the computer technology which limits server performance.

In much the same way, distributed computing solutions (enterprise computing environments) are supported by several hardware platforms that contribute to overall system performance. Each hardware component

contributes - the weakest component will limit overall system performance. Hardware platforms supporting a computing environment must be carefully selected to satisfy peak processing needs.