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Oracle has a cost-based optimizer that determines join order, join methods, and access paths Each operation that the optimizer considers has an associated cost function, and the optimizer tries to generate the combination of operations that has the lowest overall cost In estimating the cost of an operation, the optimizer relies on statistics that have been computed for schema objects such as tables and indices The statistics contain information about the size of the object, the cardinality, data distribution of table columns, and so forth For column statistics, Oracle supports height-balanced and frequency histograms To facilitate the collection of optimizer statistics, Oracle can monitor modi cation activity on tables and keep track of those tables that have been subject to enough changes that recalculating the statistics may be appropriate Oracle also tracks what columns are used in where clauses of queries, which make them potential candidates for histogram creation With a single command, a user can tell Oracle to refresh the statistics for those tables that were marked as suf ciently changed Oracle uses sampling to speed up the process of gathering the new statistics and automatically chooses the smallest adequate sample percentage It also determines whether the distribution of the marked columns merit the creation of histograms; if the distribution is close to uniform, Oracle uses a simpler representation of the column statistics Oracle uses both CPU cost and disk I/Os in the optimizer cost model To balance the two components, it stores measures about CPU speed and disk I/O performance as part of the optimizer statistics Oracle s package for gathering optimizer statistics computes these measures For queries involving a nontrivial number of joins, the search space is an issue for a query optimizer Oracle addresses this issue in several ways The optimizer generates an initial join order and then decides on the best join methods and access paths for that join order It then changes the order of the tables and determines the best join methods and access paths for the new join order and so forth, while keeping the best plan that has been found so far Oracle cuts the optimization short if the number of different join orders that have been considered becomes so large that the time spent in the optimizer may be noticeable compared to the time it would take to execute the best plan found so far Since this cutoff depends on the cost estimate for the best plan found so far, nding a good plan early is important so that the optimization can be stopped after a smaller number of join orders, resulting in better response time.

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This next section will focus on configuring the appliance so that the data connections, commonly called security associations (SAs), for an L2L connection can be built to a remote IPSec peer You must complete three tasks: Configure a crypto ACL, which determines what data traffic is to be protected Create a transform set, which determines how the data traffic is to be protected Create a crypto map entry for the remote peer, which brings all the parameters together for the associated peer

The McGraw Hill Companies, 2001

NOTE If you ve configured these items on a Cisco IOS router before, then the syntax used by the appliances is either the same or very similar, making it easy to build IPSec L2L tunnels

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Oracle uses several initial ordering heuristics to increase the likelihood that the rst join order considered is a good one For each join order that is considered, the optimizer may make additional passes over the tables to decide join methods and access paths Such additional passes would target speci c global side effects of the access path selection For instance, a speci c combination of join methods and access paths may eliminate the need to perform an order by sort Since such a global side effect may not be obvious when the costs of the different join methods and access paths are considered locally, a separate pass targeting a speci c side effect is used to nd a possible execution plan with a better overall cost

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Java library for GS1 data structures commonly used in barcodes , such as GTIN, GLN, SSCC ... Provides parsing of element strings used in GS1 - 128 barcodes .
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