Stopped the FT services. Copied the FTDATA contents over to new drive from existing drive. Deatched the database and attached it back by specifying new FTDATA location for FT Catalogues.
Registry changes for default FT path and noisexxx.txt and tsxxx.xml files:
Default path:
FullTextDefaultPath -- HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Microsoft SQL Server\MSSQL.1\MSSQLServer
Seperate keys for each language, so we had to change about 17 keys here:
NoiseFiles -- HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Microsoft SQL Server\MSSQL.1\MSSearch\Language -- seperate keys for each language.
Seperate keys for each language, so we had to change about 17 keys here:
TsaurusFiles - HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Microsoft SQL Server\MSSQL.1\MSSearch\Language\ -- seperate keys for each language.
Restarted the FT service.
Make sure that the FT catalogues are online; (query sys.database_files under teh same database) because there is a possibility that they go offline. We had few files offline and we rebuilt them and they came up OK.
Wednesday, December 22, 2010
Tuesday, December 21, 2010
Oracle Cluster Registry Utilites
E Managing the Oracle Cluster Registry
http://download.oracle.com/docs/cd/B28359_01/rac.111/b28255/ocrsyntax.htm
http://download.oracle.com/docs/cd/B28359_01/rac.111/b28255/ocrsyntax.htm
Oracle Cluster Design
Design for scalability
It is often advised to focus system design on hardware scalability rather than on capacity. It is typically cheaper to add a new node to a system in order to achieve improved performance than to partake in performance tuning to improve the capacity that each node can handle. But this approach can have diminishing returns (as discussed in performance engineering). For example: suppose 70% of a program can be sped up if parallelized and run on multiple CPUs instead of one. If α is the fraction of a calculation that is sequential, and 1 − α is the fraction that can be parallelized, then the maximum speedup that can be achieved by using P processors is given according to Amdahl's Law: . Substituting the value for this example, using 4 processors we get . If we double the compute power to 8 processors we get . Doubling the processing power has only improved the speedup by roughly one-fifth. If the whole problem was parallelizable, we would, of course, expect the speed up to double also. Therefore, throwing in more hardware is not necessarily the optimal approach.
[edit] Weak versus strong scaling
In the context of high performance computing there are two common notions of scalability. The first is strong scaling, which is defined as how the solution time varies with the number of processors for a fixed total problem size[6]. The second is weak scaling, which is defined as how the solution time varies with the number of processors for a fixed problem size per processor.
It is often advised to focus system design on hardware scalability rather than on capacity. It is typically cheaper to add a new node to a system in order to achieve improved performance than to partake in performance tuning to improve the capacity that each node can handle. But this approach can have diminishing returns (as discussed in performance engineering). For example: suppose 70% of a program can be sped up if parallelized and run on multiple CPUs instead of one. If α is the fraction of a calculation that is sequential, and 1 − α is the fraction that can be parallelized, then the maximum speedup that can be achieved by using P processors is given according to Amdahl's Law: . Substituting the value for this example, using 4 processors we get . If we double the compute power to 8 processors we get . Doubling the processing power has only improved the speedup by roughly one-fifth. If the whole problem was parallelizable, we would, of course, expect the speed up to double also. Therefore, throwing in more hardware is not necessarily the optimal approach.
[edit] Weak versus strong scaling
In the context of high performance computing there are two common notions of scalability. The first is strong scaling, which is defined as how the solution time varies with the number of processors for a fixed total problem size[6]. The second is weak scaling, which is defined as how the solution time varies with the number of processors for a fixed problem size per processor.
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