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Part VI:
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way down through the index layers. If not, the block of data must be located on disk and brought into memory. The same pattern repeats at each level of the index, with computation required to find the particular index entry within the block. Finally, the index has been searched, and the virtual row-id of the correct row has been found. Now the process repeats to locate the actual row of data. The DBMS translates the virtual row-id into a physical location again it s a computationally intensive process. The current blocks of data in the DBMS memory buffers are searched to determine if the required data is already in memory. If so, the DBMS must also check to make sure that the contents of that particular block have not been invalidated by another program s updates. If the block is fresh, processing can proceed; if not, a disk read is required to fetch it into memory. But first the DBMS must choose which block of data currently in memory is to be replaced by this new block again, computation is required to apply the appropriate algorithm to select the stale block. If it has been updated, it must be written to disk first, and then the new block can be retrieved. The DBMS isn t quite finished yet. That 2K or 4K block of data likely contains several rows of PRODUCTS, so the correct row s location must be calculated. Then the DBMS can retrieve the row, copy it into a different memory location where the DBMS can operate on it, and the actual work of accessing and/or modifying the data can proceed. Even if the traditional DBMS doesn t have to perform any actual disk I/O in the process just described, the possibility of disk I/O drives the need to execute hundreds of thousands of CPU instructions (at best) to carry out the requested operation. It is this difference in complexity, even when a traditional DBMS is running with all of its data in memory, that creates a dramatic difference in latency between an in-memory database and a traditional enterprise database.
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The first commercial database products based on in-memory architecture appeared in the late 1990s, following university research earlier in the decade. One of the advantages of these products was their ability to use the same industry-standard SQL as their complex enterprise cousins. A programmer who was already familiar with DB2 or Oracle development could shift to an in-memory database development project and find a comfortable, familiar database language. This advantage, and the flexibility of a SQL-based relational database compared with the rigidity of the earlier proprietary in-memory databases, made the new generation of in-memory databases the popular choice for new telecom and financial trading applications at the beginning of the next decade. By mid-decade, the popularity of in-memory databases had caught the attention of the major enterprise database vendors. One of the market leaders, TimesTen, was acquired by Oracle in 2005. In response, IBM s database division acquired another in-memory database player, Solid Data Systems, about a year and a half later. Although the leading in-memory databases now are offered by major enterprise database vendors, the in-memory products remain a separate offering, firmly targeted at their market niche.
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In many applications that demand the low latency of in-memory databases, the data being accessed is related to the data in a much larger conventional enterprise database. In mobile phone applications, for example, the real-time network information about mobile phones,
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26:
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Specialty Databases
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their locations, and current calling or texting operations is related to data about customers, their billing plans, and historical usage patterns. To get a complete, real-time view of the customer, the information in the in-memory and enterprise databases must be related. In recent years, in-memory databases have evolved to meet this need by being repurposed as a high-performance database cache, front-ending a conventional enterprise database. This caching architecture is shown in Figure 26-1. Real-time database requests are satisfied by the in-memory cache, while more traditional database processing uses the conventional, disk-based back-end database. Both Oracle and IBM have moved to offer these caching configurations after their respective acquisitions of in-memory database vendors. The in-memory database caching currently offered provides consistent SQL access to front-end and back-end data. The cache also offers some level of transparency to the SQL programmer, who does not need to know whether the data being requested resides in the front-end or back-end system. However, today s in-memory caches are not transparent to the database administrator. The administrator must carefully choose which tables or views are to be pulled forward into the in-memory front-end, so that real-time queries can be satisfied without passing the request to the back-end. Those decisions require a careful balance between the performance advantages of caching, and the overhead of keeping the front-end and back-end systems synchronized to ensure data integrity. It s likely that these two-tier caching architectures will gain in popularity to support high-volume web sites and other Internet database processing. As they do, an increased level of transparency and more automatic intelligence in the cache will be important.
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