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Magnetic Disk in .NET Integrated EAN-13 Supplement 5 in .NET Magnetic Disk




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Magnetic Disk use visual studio .net ean13+5 maker toinclude ean13+5 for .net Web application framework 4,000. Solid State Disk Flash Solid State Disk DDR-RAM 1,000. 1,500. 2,000. 2,500. 3,000. 3,500. 4,000. Microseconds FIGURE 22-7 Read latency for SSD compared with magnetic disk. Advanced IO Techniques Table partitioning can org anize data into the three categories. The partition containing current data can be stored on a datafile hosted on DDR-RAM, medium term data partitioned to Flash-based disks, and older data archived to magnetic disk..

When physical disk latency VS .NET EAN-13 Supplement 5 becomes the limiting factor, deploying SSD offers a significant decrease in latency for a significant increase in price. When write latency is at issue, DDR-RAM is preferred over Flash-based SSD.

. THE EXADATA STORAGE SERVER Although the latency of th EAN-13 Supplement 2 for .NET e magnetic disk cannot be completely avoided without abandoning the technology in favor of new technologies such as SSD, getting more throughput out of magnetic disk devices is relatively straightforward: We just use more of them. However, as the number of disk devices increase, the channel between the disks and the database server host, and the capacity of the database server itself, can become the limiting factor.

The Oracle/HP Exadata storage server is a hardware/software solution that addresses this issue by leveraging existing technologies and best practices together with some unique features. The Exadata storage server includes embedded Oracle database code that is capable of performing limited filtering and projections for a data request. For instance, in a normal full table scan every block in the table is transferred from the storage medium into Oracle address space.

Blocks that do not match various WHERE criteria and columns that do not match the SELECT list are then eliminated. With Exadata, at least some of this processing can occur in the storage server; rows and columns not matching the SELECT and WHERE clause are eliminated before being shipped across the channel to the database server. Exadata employs other more conventional techniques to provide optimal performance: High bandwidth InfiniBand interconnect between the storage and the database servers.

Hot storage utilizing the outer 55 percent of each disk. The Inner 45 percent is used for cold storage. Parallel query processing within the storage server.

ASM-based mirroring and striping.. Oracle and HP offer a data base appliance the HP Oracle Database Machine which combines Exadata storage servers and a RAC cluster database in the same physical rack.. 22 A predictable but some EAN-13 Supplement 5 for .NET what misplaced debate has arisen over the competing virtues of Solid State Disk storage versus the Oracle Exadata solution. However, the key technical advantage of SSD is reduced latency whereas the key technical advantage of the Exadata storage is increased throughput.

It s conceivable that SSD technologies and the technologies of Exadata will merge in some future release. For now, they provide solutions for different objectives and database applications..

DATABASE BLOCK SIZE Certain performance debate .net framework European Article Number 13 s seem to polarize the Oracle community. One of the most polarizing has been the issue of changing the Oracle block size to improve performance.

Oracle blocks are the fundamental unit of storage for Oracle: Every IO reads or writes at least one complete block, and it is blocks, not rows or extents, that are held in the buffer cache. Block size is therefore a fundamental characteristic that will impact on logical and physical IO. Advocates of changing the default block size argue one or more of the following: Increasing the block size will reduce the number of physical IOs required to perform table or index scans.

If the block size is higher, the number of blocks that must be read will be lower and hence the number of IOs required will be less. However, Oracle s multiblock read capability often achieves the same result by reading multiple smaller blocks in a single operating system operation. A higher block size will make B*-Tree indexes less deep.

Because each root and branch block can contain more entries, a smaller number of levels will be required. However, this applies only for a narrow range of table sizes, and the maximum improvement might be marginal. That having been said, it is true that for a small number of indexes, a higher block size will reduce the depth of the B*-Tree.

Decreasing the block size will increase the selectivity of blocks in the buffer cache: The bigger the block size, the more wasted rows will be cached. This is definitely theoretically true: If each block contained only one row (that is, if the block size was the size of a single row), every block in the cache would represent a row that had actually been requested, and the buffer cache would be more efficient. This argument is often provided as a reason for not increasing your block size.

Decreasing the block size for bitmap indexes can reduce the number of rows that are locked on DML. For a bitmap index, the number of rows locked on DML is block size dependent; the lower the block size, the fewer rows that will be locked. .

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