rdlc data matrix

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rdlc data matrix

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By performing regular exports of the database, these problems can be identified in advance of their appearance on the production system Unlike tables where all rows are written to the export file, only the definitions of indexes are included in the export Unfortunately, the export does not perform any validation of blocks in index segments, and therefore, index corruption is difficult to detect If you are performing R A backups, there is little value in performing additional exports MN However, if you have sufficient resources, then exporting the database once a week can do no harm Remember, however, that the export file will occupy a significant amount of space on disk It can, of course, be compressed as it is written out to disk, thereby reducing the amount of space required.

If space is at a premium, then in our opinion, make R A backup copies of the database rather than MN using the same disk space for an export Your backup and recovery strategy will also be limited by the availability and speed of tape devices The optimum solution is to have separate networked tape devices that can be shared by many systems Some users install additional network cards in each server, so that the backup can be written directly to the tape device over a dedicated network to prevent the backup interfering with other network traffic Whether you use networked tape devices or directly attached tape devices, you should have a minimum of two to eliminate another potential single point of failure, as well as to increase the overall speed of the backup..

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To demonstrate the use of FParsec we re going to look at implementing the same arithmetic language we implemented with fslex and fsyacc. But first, you will start by looking at a few simple examples. FParsec comes with a number of predefined parsers, so the simplest usage is to just use these. The following example shows the use of the pfloat parser, which parses a float and returns a string. open FParsec let pi = CharParsers.run CharParsers.pfloat "3.1416" printfn "Result: %A" pi The result of this example when compiled and run is as follows: Result: Success: 3.1416 Even in this simple, three line example there s a few important things to note. We ve already mentioned the parser, pfloat, this one of many predefined parsers that can be used as building blocks. Table 13-2 summarizes the important ones. The function run runs the parser on a string. There are a number of other functions to allow you to run a parser on just about any other input type. Table 13-3 lists all of these functions along with the type of input they should be run on. Finally, notice that the result is printed out as "Success: 3.1416". This is because not just the floating point number is returned. The result is actually a discriminating union which indicates success or failure of the parsing and also contains information about the position of the parser as well as the result of the parser, in this case a floating point number. You ll look at the result in more detail in the next example. Table 13-2. Useful Predefined Parsers

99% 99.9% 99.99% 99.999% 99.9999% 99.99999%

The background processes are started as necessary by the rtcpm, which provides a faulttolerant environment by restarting them if necessary. Fault tolerance for rtcpm itself is provided through the standard OPMN mechanisms. The command-line utility for managing Real-Time Collaboration is rtcctl, which is located in the ORACLE_HOME/imeeting/bin directory. This is in fact a shell script that launches a Java process. A point that can cause confusion is that in earlier releases it was called imtctl. When searching through web sites and documentation for technical assistance with RealTime Collaboration, it may be worth searching for both names. In the current Linux release, imtctl does in fact still exist; it is byte-for-byte identical to rtcctl.

For a standalone Oracle RAC cluster, a 99% level of availability is probably the highest attainable target A target closer to 98% is probably more reasonable In fact, it is highly likely that the level of availability achieved for a clustered database will be less than that achieved for a single-instance database, because there are more components in the product stack for a clustered database This means that there are potentially more drivers to be patched or updated In addition, all components in the stack must be compatible It is therefore often necessary to patch more than one level in order to maintain dependencies However, the target level of availability does not differentiate between planned and unplanned downtime While there will inevitably be more planned downtime on a clustered database, there should, in theory, be less unplanned downtime For many customers, this is entirely sufficient.

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