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Once a relation has been partitioned among several disks, we can retrieve it in parallel, using all the disks Similarly, when a relation is being partitioned, it can be written to multiple disks in parallel Thus, the transfer rates for reading or writing an entire relation are much faster with I/O parallelism than without it However, reading an entire relation, or scanning a relation, is only one kind of access to data Access to data can be classi ed as follows: 1 Scanning the entire relation 2 Locating a tuple associatively (for example, employee-name = Campbell ); these queries, called point queries, seek tuples that have a speci ed value for a speci c attribute 3 Locating all tuples for which the value of a given attribute lies within a speci ed range (for example, 10000 < salary < 20000); these queries are called range queries The different partitioning techniques support these types of access at different levels of ef ciency: Round-robin The scheme is ideally suited for applications that wish to read the entire relation sequentially for each query With this scheme, both point queries and range queries are complicated to process, since each of the n disks must be used for the search Hash partitioning This scheme is best suited for point queries based on the partitioning attribute For example, if a relation is partitioned on the telephonenumber attribute, then we can answer the query Find the record of the employee with telephone-number = 555-3333 by applying the partitioning hash function to 555-3333 and then searching that disk Directing a query to a single disk saves the startup cost of initiating a query on multiple disks, and leaves the other disks free to process other queries Hash partitioning is also useful for sequential scans of the entire relation If the hash function is a good randomizing function, and the partitioning attributes form a key of the relation, then the number of tuples in each of the disks is approximately the same, without much variance Hence, the time taken to scan the relation is approximately 1/n of the time required to scan the relation in a single disk system The scheme, however, is not well suited for point queries on nonpartitioning attributes Hash-based partitioning is also not well suited for answering range queries, since, typically, hash functions do not preserve proximity within a range Therefore, all the disks need to be scanned for range queries to be answered Range partitioning This scheme is well suited for point and range queries on the partitioning attribute For point queries, we can consult the partitioning vector to locate the disk where the tuple resides For range queries, we consult. pdf417 javascript Java PDF-417 Reader Library to read, scan PDF-417 barcode ...
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15 Mar 2017 ... There is no javascript implementation of ZXing at the moment (Correct me if I'm wrong). But you can use ZXing as a webservice if you have the ... the partitioning vector to nd the range of disks on which the tuples may reside In both cases, the search narrows to exactly those disks that might have any tuples of interest An advantage of this feature is that, if there are only a few tuples in the queried range, then the query is typically sent to one disk, as opposed to all the disks Since other disks can be used to answer other queries, range partitioning results in higher throughput while maintaining good response time On the other hand, if there are many tuples in the queried range (as there are when the queried range is a larger fraction of the domain of the relation), many tuples have to be retrieved from a few disks, resulting in an I/O bottleneck (hot spot) at those disks In this example of execution skew, all processing occurs in one or only a few partitions In contrast, hash partitioning and round-robin partitioning would engage all the disks for such queries, giving a faster response time for approximately the same throughput The type of partitioning also affects other relational operations, such as joins, as we shall see in Section 205 Thus, the choice of partitioning technique also depends on the operations that need to be executed In general, hash partitioning or range partitioning are preferred to round-robin partitioning In a system with many disks, the number of disks across which to partition a relation can be chosen in this way: If a relation contains only a few tuples that will t into a single disk block, then it is better to assign the relation to a single disk Large relations are preferably partitioned across all the available disks If a relation consists of m disk blocks and there are n disks available in the system, then the relation should be allocated min(m, n) disks. pdf417 java open source Building HTML5 Barcode Reader with Pure JavaScript SDK
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