There are differences: there are fewer queries in OLAP but they are more complex and require more time to execute. In addition they are often processing parallel. By the way about the waiting time: in OLAP a query that runs for about an hour can be considerdnormal and users are ready to wait for it to finish. The process of obtaining the final data usually requires many hours of server work and includes the stages of loading reloading validation calculation etc. Here the expectations are the same – the earlier the data is receivdthe better but optimization requires the entire critical path as a whole. Simply put users will be happy if they receive ready-made data every day not at : but say at : ideally by the time the working day starts.
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In our figure see Figure an SQL- query is highlight which lies on the critical path and runs for some time. Let’s transform it from a horizontal brick into a vertical one and get a completely different Bhutan Email List picture Fig Rice. Optimization result The total execution time for all four queries has been significantly reducdmovdto the left. This is the main result of OLAP optimization. Interestingly usually after such a flipping the total area of the bricks increases that is the server does more work the overhead of managing parallel processes. But if there are resources for such a request and at this time there is no nedto execute another critical request then the amount of work is not important – less execution time is important.
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Of course it is necessary to act in this way deliberately taking into account the presence of other possible processes at the same time: due to lack of resources they can be blocked. Modification examples Consider an example of such a simple modification Fig. As you can see the initial request took. minutes. A query that needs optimization After simple BTC Email List parallelization into threads the query startdto run for seconds. At the same time his DB Time remaindthe same and is. minutes. In general the ratio sec sec =. and is close to the requirdthreads so the parallelization can be consider successful Fig.
