barcode generator in vb.net 2005 Anatomy of an In-Memory Database in Software

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Anatomy of an In-Memory Database
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In-memory databases take the traditional architecture of an enterprise database system and radically change some of the core assumptions. In a conventional database, the active database is stored on mass storage (disks), and only a small fraction of the data is held in the computer system s memory at any one time. In an in-memory database, the entire active database is stored in memory (hence the name). Every database access or update can be satisfied by memory access; you never need to retrieve data from mass storage. The database system may still maintain a log of transactions on disk, or store a copy of the database on disk for error recovery (most commercial systems do both of these things), but the disk access is not in the direct path to complete the database operation.
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The simple change in assumption from a disk-centric to a memory-centric design has broad-reaching implications for the way the DBMS operates: In a conventional database, the data in memory is a copy of the real data on disk, and the copy can become out-of-date as other CPUs or systems update the database. The DBMS must take measures to ensure that the in-memory data is fresh, and refresh it from the disk if it is not. For an in-memory database, the data in memory is the active copy; it is always fresh. In a conventional database, the arrangement of the data on disk can dramatically impact performance. The DBA must take measures to ensure that the storage system (SAN or NAS along with associated disk drives) is configured and optimized such that frequently accessed data can be retrieved and updated with minimal disk input and/or output (I/O), which is much slower than memory access. In an in-memory database, the physical storage configuration of data is irrelevant the speed to access different random locations in the computer s memory is, for most purposes, the same. Because the physical disk configuration is important, a conventional database is constantly rearranging data locations, splitting data stored in a single disk block into multiple disk blocks, and updating its internal data structures to keep track of the locations. An in-memory database has no need to move data around in memory. Because the physical location of a database row on disk can change, a conventional database keeps track of the row based on a virtual address or row-id. Every time the row must be physically accessed, the database performs an internal lookup to find the actual current physical location. In conventional databases, DBAs and database users consider fault tolerance a given, because conventional DBMSs are designed to write data updates to the log file first, and logs are typically mirrored to more than one physical device. This is not at all the case with in-memory databases, where a sudden power failure or server failure can easily lose changes made since the last write to disk. Fault tolerance can be achieved with in-memory databases through technology such as redundant database copies, uninterruptible power systems, and nonvolatile memory, but it takes careful planning and design to implement these features. The practical impact of these differences is illustrated by considering a simple database operation finding the row pointed to by a foreign key. In the sample database, this is a move from a specific row in the ORDERS table to the corresponding row in the PRODUCTS table for the product being ordered, for example. In an in-memory database, this is a very simple operation. The foreign key will be represented within the database by a direct pointer to the memory location of the corresponding PRODUCTS row. Following this link takes only a few CPU instructions, and the time required is measured in millionths or even billionths of a second. For the conventional database, the situation is far more complex. The foreign key is probably represented by the data values of the manufacturer and product IDs. The database must use its indexing scheme to locate the corresponding PRODUCTS row. For a larger database of tens of thousands of products, the index may be several layers deep. If the disk block containing the top level of the index is in memory, the DBMS can start working its
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