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Two recurring topics in asynchronous programming are exceptions and cancellation. Let s first explore some of the behavior of asynchronous programs with regard to exceptions. > let failingTask = async { do failwith "fail" };; val failingTask: Async<unit> > Async.Run failingTask;; Microsoft.FSharp.Core.FailureException: fail stopped due to error > let failingTasks = [ async { do failwith "fail A" }; async { do failwith "fail B" }; ];; val failingTasks: Async<unit> > Async.Run (Async.Parallel failingTasks);; Microsoft.FSharp.Core.FailureException: fail A stopped due to error > Async.Run (Async.Parallel failingTasks);; Microsoft.FSharp.Core.FailureException: fail B stopped due to error From this you can see the following: Tasks fail only when they are actually executed. The construction of a task using the async { ... } syntax will never fail. Tasks run using Async.Run report any failure back to the controlling thread as an exception. It is nondeterministic which task will fail first. Tasks composed using Async.Parallel report the first failure from amongst the collected set of tasks. An attempt is made to cancel other tasks by setting the cancellation flag for the group of tasks, and any further failures are ignored. You can wrap a task using the Async.Catch combinator. This has the following type:

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For simplicity, assume that there s just a single default pool. Because the other pools are managed in the same way, we need only discuss one of them. The blocks in the buffer cache are basically managed in a single place with two different lists pointing at them: The list of dirty blocks that need to be written by the database block writer (DBWn; we'll take a look at that process a little later) A list of nondirty blocks

The list of nondirty blocks used to be a Least Recently Used (LRU) list in Oracle 8.0 and before. Blocks were listed in order of use. The algorithm has been modified slightly in Oracle8i and in later versions. Instead of maintaining the list of blocks in some physical order, Oracle employs a touch count algorithm, which effectively increments a counter associated with a block as you hit it in the cache. This count is not incremented every time you hit the block, but about once every three seconds if you hit it continuously. You can see this algorithm at work in one of the truly magic sets of tables: the X$

For example: > Async.Run (Async.Catch failingTask);; val it : Choice<unit,exn> = Choice2_2 (FailureException ()) You can also handle errors by using try/finally in an async { ... } workflow.

tables. The X$ tables are wholly undocumented by Oracle, but information about them leaks out from time to time.

Note I am using a user connected as SYSDBA in the following examples, because the X$ tables are by default

A distinction is often made between shared-memory concurrency and message passing concurrency. The former is often more efficient on local machines and is covered in the section Using Shared-Memory Concurrency later in this chapter. The latter scales to systems where there is no shared memory, for example, distributed systems, and can also be used to avoid performance problems associated with shared memory. Asynchronous message passing and processing is a common foundation for concurrent programming, and in this section we look at some simple examples of message-passing programs.

visible only to that account. You shouldn t in practice use the SYSDBA account to run queries. The need to query for information about blocks in the buffer cache is a rare exception to that rule.