Also, notice that the analytical subquery is named "aquery" by the WITH clause at the beginning of the query. Check out the Oracle archive , and catch up on our most recent Oracle tips. Weekly Oracle tips in your inbox TechRepublic's free Oracle newsletter, delivered each Wednesday, covers automating Oracle utilities, generating database alerts, solving directed graph problems, and more. Editor's Picks. It's time to dump Chrome as your default browser on Android. Women and middle managers will lead the Great Resignation into How Windows 11 makes updates so much smaller.
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Show Comments. Subsequent calls to nextval use those cached values, and there is no need to access the sequence. As a consequence, these cached sequence values get lost when the database session ends, leading to gaps:. As with all other objects, changes to sequences are logged to WAL , so that recovery can restore the state from a backup or after a crash.
That means that after recovering from a crash, the sequence may have skipped some values. It is a little-known fact that sequences can also jump backwards. A backwards jump can happen if the WAL record that logs the advancement of the sequence value has not yet been persisted to disk. Because the transaction that contained the call to nextval has not yet committed:.
First off: think twice before you decide to build a gapless sequence. That will deteriorate your data modification performance considerably. You almost never need a gapless sequence. Usually, it is good enough if you know the order of the rows, for example from the current timestamp at the time the row was inserted.
We have modified the code provided to support the ID column, which may reflect different user IDs, machine IDs, or whatever for your particular case. It is important to note that in at least the second case, the sequence numbers must be unique within an ID in order for the solution to work.
The following performance was achieved by these two solutions across 1,, rows, with the index being essential to achieving these results. This basically confirms the findings in the book: solution 3 is faster. In the resources file, you may run the script: Islands Test Harness 1. Of these, there are 3 which seem to perform to relatively the same order of magnitude when it comes to elapsed time. Once again a check of the results confirms that they are identical to those shown in the preceding table for gaps.
Test harness Gaps Test Harness 1. If we first insert two columns and fill the first with one less than the StartSeqNo and the second with one greater than the EndSeqNo , we can circle the numbers that represent the gaps. Each pair of colored, circled numbers in the rightmost table perfectly represents our gaps. These results are:. Now all we have to do is figure out a way to remove the first and last row from the result set! In the two test harness scripts we mentioned previously, the code to generate a large test data set looks like this:.
Using the gaps solutions identified previously, a quick check of the results at 1,, rows delivers these results run Gaps Test Harness 2. If it is possible to convert Islands to Gaps, perhaps it is also possible to convert Gaps to Islands. We need to add a row, and that row will be the first and last sequence number in our series. So now, skipping the intermediate explanatory step as it is quite similar to what we did before, we can arrive at the following code to calculate islands from gaps:.
The SQL MVP Deep Dives Islands solution 3 still wins the elapsed time race but our approach is a respectable showing, being solidly in second place in terms of elapsed time. These results may be reproduced by running Islands Test Harness 2. For the CAV Cross Apply Values solutions for islands to gaps, their ranking appears as 1 for CPU across the board, although it is a slim lead and the convergence at 2M rows suggests that it may start lagging behind above that.
For elapsed time, above 1. However on logical IOs, it is a clear 3 and would probably remain so regardless of the number of rows in the test harness. It also appears that for the CAV gaps to islands solution, we lost the parallelism that resulted in the highly favorable elapsed time result, possibly because SQL had cached a non-parallelized execution plan at the lower row counts.
Ultimately the CAV Cross Apply Values approaches may offer benefits under some conditions but your choice should be made based on a careful analysis of which solution works best for your constraints of data rows, parallelism and the specific resource that you most wish to conserve CPU, elapsed time or logical IOs.
Perhaps in SQL someone can come up with something faster utilizing a window frame.
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