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In a real-world migration scenario, the flat file extracts from the source system tend to be the bottleneck and not the network throughput or the data loads The bottleneck is mainly due to the performance limitation of the source database storage system, the CPU limitation on the source platform, or writes to the temporary staging location It is not easy to estimate the time it takes for the initial data load, since multiple components exist in the path of the data and each component will have different throughput It is important to remember that the overall data transfer throughput is limited by the slowest link in the data flow The best way to estimate data transfer times is to measure the actual performance of a test run on the subset of data, under similar network connectivity like it would be in production The initial data migration process will push large amounts of data over the network connecting the source and the destination systems, and in order to get the best possible throughput, it is important to factor in the network throughput The best throughput possible is on the Database Machine side using the InfiniBand network Refer to 4 for the different mechanisms available to connect the Database Machine to the outside world The best practice for extracting data from the source database is to utilize the data dump utilities that come bundled with the source database software installation These utilities are the most efficient and supported methods to pull data out of the source system The loads into Oracle should be performed using the best practices of data loading such as the external table load Initial loads can be performed by using custom extract and load scripts, and by using automated tools such as Oracle Data Integrator, Oracle GoldenGate, or Oracle SQL Developer We will discuss these products and their capabilities in detail in a later section.

FIGURE 5-11

division For both tasks, the most obvious procedures take exponentially long, but with some ingenuity polynomial-time solutions can be found A careful choice of algorithm makes all the difference

Incremental data migration is the process of synchronizing the source and the destination systems by moving only the changed data (instead of the complete set) since the last synchronization process When the incremental data migration happens in real time, it is called real-time replication The key requirement of the incremental data migration is the ability to capture changed data from the source system using an appropriate change

Once configured, we can create the database account that is identified based on the client s (our application server) wallet credentials as follows:

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dbvacctmgr@aos> -- create the account dbvacctmgr@aos> CREATE USER ops$appserver_1 IDENTIFIED EXTERNALLY AS 'CN=appserver_1'; User created dbvacctmgr@aos> -- provide the ability to connect to the database dbvacctmgr@aos> CONNECT / AS SYSDBA Connected sys@aos> GRANT CREATE SESSION TO ops$appserver_1; Grant succeeded

In the cryptosystem we are working toward, it is necessary to compute x y mod N for values of x, y, and N that are several hundred bits long Can this be done quickly The result is some number modulo N and is therefore itself a few hundred bits long However, the raw value of xy could be much, much longer than this Even when x and y are just (219 ) 20-bit numbers, xy is at least (219 ) = 2(19)(524288) , about 10 million bits long! Imagine what happens if y is a 500-bit number! To make sure the numbers we are dealing with never grow too large, we need to perform all intermediate computations modulo N So here s an idea: calculate x y mod N by repeatedly multiplying by x modulo N The resulting sequence of intermediate products, x mod N x2 mod N x3 mod N xy mod N, consists of numbers that are smaller than N , and so the individual multiplications do not take too long But there s a problem: if y is 500 bits long, we need to perform y 1 2 500 multiplications! This algorithm is clearly exponential in the size of y Luckily, we can do better: starting with x and squaring repeatedly modulo N , we get x mod N x2 mod N x4 mod N x8 mod N x2

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