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The inferred type of the extension to the System.IO.Stream type in Listing 13-8 is as follows: type System.IO.Stream with member ReadAsync: buffer:byte[] * offset:int * count:int -> Async<int> In Listing 13-8, Async.Primitive builds an Async<int> value, where the integer result indicates the number of bytes read from the stream. But what are all these function values As you saw earlier, asynchronous computations work via continuations. This means a primitive step is given two continuation functions, cont and econt, which must be called upon success and/or exceptional failure of the operation. The previous implementation calls BeginRead and passes it a callback that will be invoked when the asynchronous operation returns. Note that the call to BeginRead uses named arguments, covered in 6. The callback calls EndRead to retrieve the result and passes this result to the success continuation cont; the call to EndRead is protected by an exception handler that calls the exception continuation econt should something go wrong. The simple wrapper shown in Listing 13-8 now allows us to use ReadAsync in workflows, such as in the following line of our asynchronous image processor: async { use inStream = File.OpenRead(sprintf "Image%d.tmp" i) let! pixels = inStream.ReadAsync(numPixels) ... } Note that the econt continuation of a primitive step should be called if an exception occurs. The example includes the try/catch handlers required to catch exceptions from EndRead. For more details, see the full implementation of ReadAsync and other similar wrappers in the F# library source code.

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In Oracle9i, the various SGA components must be manually sized by the DBA Starting in Oracle 10g and above, however, there is a new option to consider: automatic SGA memory management, whereby the database instance will allocate and reallocate the various SGA components at runtime in response to workload conditions Moreover, starting in Oracle 11g, there s another new option: automatic memory management, whereby the database instance will not only perform automatic SGA memory management and automatic PGA memory management, it will also decide the optimum size of the SGA and PGA for you reallocating these allotments automatically when deemed reasonable Using the automatic SGA memory management with Oracle 10g and above is simply a matter of setting the SGA_TARGET parameter to the desired SGA size, leaving out the other SGA-related parameters altogether.

The database instance will take it from there, allocating memory to the various pools as needed and even taking memory away from one pool to give to another over time When using automatic memory management with Oracle 11g and above, you simply set the MEMORY_TARGET The database instance will then decide the optimal SGA size and PGA size and those components will be set up appropriately and do their own automatic memory management within their respective boundaries Further, the database can and will resize the SGA and PGA allocations as the workload changes over time Regardless of whether you are using automatic or manual memory management, you ll find that memory is allocated to the various pools in the SGA in units called granules A single granule is an area of memory of 4MB, 8MB, or 16MB in size.

let aggressiveDriver light = dist { match light with | Red -> return! weightedCases [ Stop, 0.9; Drive, 0.1 ] | Yellow -> return! weightedCases [ Stop, 0.1; Drive, 0.9 ] | Green -> return Drive } The following gives the value of the light showing in the other direction: let otherLight light = match light with | Red -> Green | Yellow -> Red | Green -> Red You can now model the probability of a crash between two drivers given a traffic light. Assume there is a 10 percent chance that two drivers going through the intersection will avoid a crash: type CrashResult = Crash | NoCrash let crash(driverOneD,driverTwoD,lightD) = dist { // Sample from the traffic light let! light = lightD // Sample the first driver's behavior given the traffic light let! driverOne = driverOneD light // Sample the second driver's behavior given the traffic light let! driverTwo = driverTwoD (otherLight light) // Work out the probability of a crash match driverOne, driverTwo with | Drive,Drive -> return! weightedCases [ Crash, 0.9; NoCrash, 0.1 ] | _ -> return NoCrash } You can now instantiate the model to a cautious/aggressive driver pair, sample the overall model, and compute the overall expectation of a crash as approximately 3.7 percent: > let model = crash(cautiousDriver,aggressiveDriver,trafficLightD);; val model : Distribution<CrashResult> > model.Sample;; val it : CrashResult = NoCrash ... > model.Sample;; val it : CrashResult = Crash > model.Expectation(function Crash -> 1.0 | NoCrash -> 0.0);; val it : float = 0.0369

The granule is the smallest unit of allocation, so if you ask for a Java pool of 5MB and your granule size is 4MB, Oracle will actually allocate 8MB to the Java pool (8 being the smallest number greater than or equal to 5 that is a multiple of the granule size of 4) The size of a granule is determined by the size of your SGA (this sounds recursive to a degree, as the size of the SGA is dependent on the granule size) You can view the granule sizes used for each pool by querying V$SGA_DYNAMIC_COMPONENTS In fact, we can use this view to see how the total SGA size might affect the size of the granules:.

ops$tkyte%ORA11GR2> show parameter sga_target NAME TYPE VALUE ------------------------------------ ----------- -----------------------------sga_target big integer 256M ops$tkyte%ORA11GR2> select component, granule_size from v$sga_dynamic_components; COMPONENT GRANULE_SIZE ---------------------------------------------------------------- -----------shared pool 4194304 large pool 4194304 java pool 4194304 streams pool 4194304 DEFAULT buffer cache 4194304 KEEP buffer cache 4194304 RECYCLE buffer cache 4194304 DEFAULT 2K buffer cache 4194304 DEFAULT 4K buffer cache 4194304 DEFAULT 8K buffer cache 4194304 DEFAULT 16K buffer cache 4194304 DEFAULT 32K buffer cache 4194304 Shared IO Pool 4194304 ASM Buffer Cache 4194304 14 rows selected.

Note In this section, we showed how to define a simplistic embedded computational probabilistic modeling