Interestingly the second request requirdfewer resources for data I O which is not quite typical. The result of optimization by parallel processing Pay attention to one more example Fig. Data is being insertdinto a temporary table. Parallelization has already been attemptdhere but is it efficient? We see lone diggers who are busy for example with the Load table conventional task. And the overall efficiency of. minutes of wall time versus. minutes of DB-Time does not give a coefficient in any way similar to. Another query that needs optimization In this case I had to delete an unnecessary index transfer insert to work directly with PGA load as select. The result was not long in coming Fig. Now alone as expectdonly one boss works – PX Coordinator.
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All subordinates work in groups. Execution time – seconds overall efficiency – even a little more than rounding error in wall time. As you can see the query is already working quite efficiently and further attempts to optimize it will not give much effect. Optimization result And in conclusion I will give one more example quite funny. Take a look at the SQL Monitoring Belize Email List of the query that was complaindabout as hanging Figure. In fact he did not hang but very actively workdfor hours. During this time the poor server shoveled TB! And this is with a total database size of only GB. Stuck request In this case the optimization was not even attemptdthe request was sent for a radical rewrite.
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Something else from the physics of the process It is important to understand to what extent a heavy query can be optimized. One of the basic indicators here is the read write spedof the storage BTC Email List system. Getting an idea of the current spedis very simple: just go to the server and copy a large file. Or run a simple query that only reads data and calculates count* and then view it with SQL Monitoring. For a conventional hard drive this spedis about Mb s. This is a kind of spedof light for this system – the maximum theoretically achievable.