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98% complete, state = (135301852344706746049I, 218922995834555169026I) 99% complete, state = (218922995834555169026I, 354224848179261915075I) 100% complete, state = (354224848179261915075I, 573147844013817084101I) val it : unit = () One difference here is that cancellation and percentage progress reporting are handled automatically based on the iterations of the computation. This is assuming each iteration takes roughly the same amount of time. Other variations on the BackgroundWorker design pattern are possible. For example, reporting percentage completion of fixed tasks such as installation is often performed by timing sample executions of the tasks and adjusting the percentage reports appropriately.

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Questions that come up frequently are "How is this memory allocated " and "What will be the amount of RAM used by my session " These are hard questions to answer for the simple reason that the algorithms for serving out memory under the automatic scheme are not documented and can and will change from release to release. When using things that begin with "A" for automatic you lose a degree of control, as the underlying algorithms decide what to do and how to control things. We can make some observations based on information from MetaLink note 147806.1: The PGA_AGGREGATE_TARGET is a goal of an upper limit. It is not a value that is preallocated when the database is started up. You can observe this by setting the PGA_AGGREGATE_TARGET to a value much higher than the amount of physical memory you have available on your server. You will not see any large allocation of memory as a result. A serial (nonparallel query) session will use a small percentage of the PGA_AGGREGATE_TARGET, typically about 5 percent or less. So, if you ve set the PGA_AGGREGATE_TARGET to 100MB, you d expect to use no more than about 5MB per work area (e.g., the sort or hash work area). You may well have multiple work areas in your session for multiple queries, or more than one sort or hash operation in a single query, but each work area will be about 5 percent or less of the PGA_AGGREGATE_TARGET. Note that this 5 percent is not a hard and fast rule; things change over time, the automatic algorithms can and will change in the database.

As the workload on your server goes up (more concurrent queries, concurrent users), the amount of PGA memory allocated to your work areas will go down. The database will try to keep the sum of all PGA allocations under the threshold set by PGA_AGGREGATE_TARGET. This is analogous to having a DBA sit at a console all day, setting the SORT_AREA_SIZE and HASH_AREA_SIZE parameters based on the amount of work being performed in the database. We will directly observe this behavior shortly in a test. A parallel query may use up to about 30 percent of the PGA_AGGREGATE_TARGET, with each parallel process getting its slice of that 30 percent. That is, each parallel process would be able to use about 0.3 * PGA_AGGREGATE_TARGET / (number of parallel processes).

because its external members are different from those of BackgroundWorker. The .NET documentation recommends you use implementation inheritance for this, but we disagree. Implementation inheritance can only add complexity to the signature of an abstraction and never makes things simpler, whereas an IterativeBackgroundWorker is in many ways simpler than using a BackgroundWorker, despite that it uses an instance of the latter internally. Powerful, compositional, simple abstractions are the primary building blocks of functional programming.

OK, so how can we observe the different work area sizes being allocated to our session By applying the same technique we used earlier in the manual memory management section to observe the memory used by our session and the amount of I/O to temp we performed. I performed the following test on a Red Hat Advanced Server 4.0 Linux machine using Oracle 11.2.0.1 and dedicated server connections. This was a two-CPU Dell PowerEdge with hyperthreading enabled, so it was as if there were four CPUs available. We begin by creating a table to hold the metrics we d like to monitor: create as select from ( select from where and and union select from where and and and group ); table sess_stats name, value, 0 active a.name, b.value v$statname a, v$sesstat b a.statistic# = b.statistic# b.sid = (select sid from v$mystat where rownum=1) (a.name like '%ga %' or a.name like '%direct temp%') all 'total: ' || a.name, sum(b.value) v$statname a, v$sesstat b, v$session c a.statistic# = b.statistic# (a.name like '%ga %' or a.name like '%direct temp%') b.sid = c.sid c.username is not null by 'total: ' || a.name