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It uses a sequence-based hi-lo pattern to generate values. Hi/Lo is used in scenarios where an application needs its entities to have an identity prior to persistence. It is a value generation strategy. An alternative to Hi/Lo would be for the application to generate keys as universally unique identifiers (UUID).
A given database management system may offer one or more mechanisms for returning the plan for a given query. Some packages feature tools which will generate a graphical representation of a query plan. Other tools allow a special mode to be set on the connection to cause the DBMS to return a textual description of the query plan.
A surrogate key is frequently a sequential number (e.g. a Sybase or SQL Server "identity column", a PostgreSQL or Informix serial, an Oracle or SQL Server SEQUENCE or a column defined with AUTO_INCREMENT in MySQL). Some databases provide UUID/GUID as a possible data type for surrogate keys (e.g. PostgreSQL UUID [3] or SQL Server ...
In cryptography, a salt is random data fed as an additional input to a one-way function that hashes data, a password or passphrase. [1] Salting helps defend against attacks that use precomputed tables (e.g. rainbow tables), by vastly growing the size of table needed for a successful attack.
Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.
The random nature of standard UUIDs of versions 3, 4, and 5, and the ordering of the fields within standard versions 1 and 2 may create problems with database locality or performance when UUIDs are used as primary keys. For example, in 2002 Jimmy Nilsson reported a significant improvement in performance with Microsoft SQL Server when the ...
For these applications, truly random numbers are ideal, and very high quality pseudo-random numbers are necessary if truly random numbers, such as coming from a hardware random number generator, are unavailable. Truly random numbers are absolutely required to be assured of the theoretical security provided by the one-time pad — the only ...
The generator itself, which once seeded will produce an indefinite quantity of pseudo-random data. The entropy accumulator, which collects genuinely random data from various sources and uses it to reseed the generator when enough new randomness has arrived.