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Even in a well-designed transaction processing system, each transaction usually has to do at least a few I/O operations, if the data required by the transaction is on disk An important factor in tuning a transaction processing system is to make sure that the disk subsystem can handle the rate at which I/O operations are required For instance, disks today have an access time of about 10 milliseconds, and transfer times of 20 MB per second, which gives about 100 random access I/O operations of 1 KB each If each transaction requires just 2 I/O operations, a single disk would support at most 50 transactions per second The only way to support more transactions per second is to increase the number of disks If the system needs to support n transactions per second, each performing 2 I/O operations, data must be striped (or otherwise partitioned) across n/50 disks (ignoring skew) Notice here that the limiting factor is not the capacity of the disk, but the speed at which random data can be accessed (limited in turn by the speed at which the disk arm can move) The number of I/O operations per transaction can be reduced by storing more data in memory If all data are in memory, there will be no disk I/O except for writes Keeping frequently used data in memory reduces the number of disk I/Os, and is worth the extra cost of memory Keeping very infrequently used data in memory would be a waste, since memory is much more expensive than disk The question is, for a given amount of money available for spending on disks or memory, what is the best way to spend the money to achieve maximum number of
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BIRT ยป Creating a state matrix in a report need help or example ...
I've got the matrix and some sub reports working but now I need to get ... I have a
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The McGraw Hill Companies, 2001
transactions per second A reduction of 1 I/O per second saves (price per disk drive) / (access per second per disk) Thus, if a particular page is accessed n times per second, the saving due to keeping it in memory is n times the above value Storing a page in memory costs (price per MB of memory) / (pages per MB of memory) Thus, the break-even point is n price per MB of memory price per disk drive = access per second per disk pages per MB of memory
2 VTH (125)2 = = 009 W 4RTH 4(42)
CHAPTER 8:
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Eclipse Birt Barcode Component - J4L Components
The J4L Barcodes are integrated in Eclipse Birt 4.3 or later. The components
support 1D barcodes, PDF417, Datamatrix , QRCode, Azteccode and Maxicode.