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This is used for the mutable portions of a system, or those that change over time. The database is a tier of the application that must usually be scaled up to deal with additional load, as scaling out at the database tier is difficult and complex. When the database reaches its capacity, you can add more memory, pop another CPU into the box, or purchase a more powerful box. A clustering solution can be introduced for failover, but this won t help with the current load on the system. Scaling up is also used for the middle layer of an application when it hasn t been designed to scale out. This is not a happy situation to find your system in.

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n this chapter, we ll look at some issues related to locking in Oracle. In particular, we ll discuss the infamous lost update problem and various ways to address it. Along the way, we ll examine different strategies to implement two solutions to the lost update problem namely, optimistic locking and pessimistic locking. We ll also compare these two solutions and determine when to use each strategy.

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A lock is a mechanism Oracle uses to prevent destructive interaction between transactions accessing the same resource. Here, the resource could be user objects, such as tables; or user data, such as table rows; or internal objects, such as shared data structures in memory. A detailed discussion of locking and concurrency concepts in Oracle is beyond the scope of this book. For that, I refer you to 13 of Oracle Database Concepts Guide (10g Release 1) and 3 of Tom Kyte s Expert One-on-One Oracle (Apress, ISBN: 1-59059-243-3). In this chapter, I will define what lost updates are and how to deal with them when programming in Oracle. Throughout the chapter, unless otherwise stated explicitly, assume a default transaction isolation level of READ COMMITTED (see 4 for a discussion of transaction isolation levels).

This involves adding servers to the system, and balancing the load of traffic on the system between them. It s sometimes called a Web Farm when it s used for web servers, but scaling out can also be used for an application server. When more than one machine is performing work identical to other machines, the IP representing the farm is virtualized. That is, a load balancer is the first to receive all requests into the system, and that load balancer doles out the request to the servers configured in the Farm. Load balancers can be hardware- or softwarebased. How the load is distributed depends on the algorithm used by load balancer in use. Some take a round robin approach, and just rotate between the servers. Some will poll all of the servers in the Farm, and those responding quickly get hit with more traffic (the idea is to approach equilibrium between the servers over time).

Recommendation: Place pipeline operator |> at the start of a line. People often ask how to format pipelines. We recommend this style: let methods = System.AppDomain.CurrentDomain.GetAssemblies |> List.of_array |> List.map (fun assem -> assem.GetTypes()) |> Array.concat Recommendation: Format object expressions using the member syntax. People often ask how to format object expressions. We recommend this style: let thePlayers = { new Organization() with member x.Chief = "Peter Quince" member x.Underlings = [ "Francis Flute"; "Robin Starveling"; "Tom Snout"; "Snug"; "Nick Bottom"] interface IDisposable with member x.Dispose() = () }