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CHAPTER 13 PARTITIONING
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The CLERK data is in all of the partitions, and the fact that three of the tablespaces are offline does affect us. This is unavoidable unless we had partitioned on JOB, but then we would have had the same issues with queries that needed data by LOC. Anytime you need to access the data from many different keys, you will have this issue. Oracle will give you the data whenever it can. Note, however, that if the query can be answered from the index, avoiding the TABLE ACCESS BY ROWID, the fact that the data is unavailable is not as meaningful: ops$tkyte@ORA11GR2> select count(*) from emp where job = 'CLERK'; COUNT(*) ---------4 Since Oracle didn t need the table in this case, the fact that most of the partitions were offline doesn t affect this query (assuming the index isn t in one of the offline tablespaces of course!). As this type of optimization (i.e., answer the query using just the index) is common in an OLTP system, there will be many applications that are not affected by the data that is offline. All we need to do now is make the offline data available as fast as possible (restore it and recover it).
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Partitioning and Performance, Revisited
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Many times I hear people say, I m very disappointed in partitioning. We partitioned our largest table and it went much slower. So much for partitioning being a performance increasing feature! Partitioning can do one of the following three things to overall query performance: Make your queries go faster Not impact the performance of your queries at all Make your queries go much slower and use many time the resources as the nonpartitioned implementation
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In a data warehouse, with an understanding of the questions being asked of the data, the first bullet point is very much achievable. Partitioning can positively impact queries that frequently full scan large database tables by eliminating large sections of data from consideration. Suppose you have a table with 1 billion rows in it. There is a timestamp attribute. Your query is going to retrieve one years worth of data from this table (and it has 10 years of data). Your query uses a full table scan to retrieve this data. Had it been partitioned by this timestamp entry say, a partition per month then you could have full scanned one-tenth the data (assuming a uniform distribution of data over the years). Partition elimination would have removed the other 90 percent of the data from consideration. Your query would likely run faster. Now, take a similar table in an OLTP system. You would never retrieve 10 percent of a 1 billion row table in that type of application. Therefore, the massive increase in speed seen by the data warehouse just would not be achievable in a transactional system. You are not doing the same sort of work, and the same possible improvements are just not realistic. Therefore, in general, in your OLTP system the first bullet point is not achievable, and you won t be applying partitioning predominantly for increased performance. Increased availability absolutely. Administrative ease of use very much so. But in an OLTP system, I say you have to work hard to make sure you achieve the second point: that you do not impact the performance of your queries at all, negatively or positively. Many times, your goal is to apply partitioning without affecting query response time. On many occasions, I ve seen that the implementation team will see they have a medium-sized table, say of 10 million rows. Now, 10 million sounds like an incredibly large number (and five or ten years ago, it would have been, but time changes all things).So the team decides to partition the data. But
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