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When you use subqueries, you are giving the DBMS a weak "hint" about processing plans, but the DBMS still has plenty of scope for decision making Each of the possible plans may be best for certain situations so a blanket statement like "always flatten" would be facile But when you know the situation, and you know how to influence the plan, you can do some of the decision making yourself
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When you're defining a column, the first question you should ask is What data is possible or impossible for this column For example, a "phone number" column should accommodate all possible phone numbers but no impossible ones; a "book title" column should accommodate uppercase conversion but not addition Your second question should be Given the answer to the first question, what data type is both efficient and portable for this column For example, an elapsed-minutes column might be defined as INTEGER rather than INTERVAL because INTEGER is smaller and is more easily cast to a C data type This chapter is all about how you can choose the data type and size for a column without growing dependent on any DBMS's foibles After discussing the general questions of column size and size variability, we'll look at the main built-in SQL data types: characters, temporals, numbers, bits, and "large objects" such as BLOBs Then we'll consider factors that cause overhead regardless of data type, such as nullability and column order within a table definition In our discussions, we'll highlight both storage and performance issues
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"Drake's formula looks scientific, but the result can be anywhere from 0% to 100%" Peter D Ward and Donald Brownlee, Rare Earth; Why Complex Life Is Uncommon in the Universe, Copernicus Springer-Verlag, New York These days, disk drives are cheap, and reducing disk space is not in itself a performance issue anymore What is important is the answer to this question If I make every column smaller, will every application get faster, and if so how much Alas, the answer depends so much on the application, we have to hedge and hum Yes, but, if The most important factor in performance is the number of reads and writes the DBMS makes The minimum input/output unit is the page Rows of data never overlap page boundaries (except with InterBase and Oracle), so a 10% reduction in row size won't always cause a 10% reduction in the number of pages Still, if the row size is small and the average is calculated over many tables, that reduction in size should be true Given this, if you reduce row sizes by 10%, a 1,000-page table will be reduced to 900 pages Extrapolating, let's further assume that an index with 1,000 pages will thus be reduced to 910 pages (because there will be more keys in an index page, but the reduction is a bit less than 10% because the ROWID part of an index key never changes in size) The effect on performance is good:
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Indexed scans will read log2(9) = 317% fewer pages, or one less page 317% of the time on average Multiple-row fetches will read fewer rows because the probability of two rows being in the same page is greater A data-change statement (INSERT, UPDATE, or DELETE) will write the same number of pages Fewer pages doesn't translate to fewer writes because a typical OLTP transaction causes a single page write, and that fact is unaffected by the number of rows in a page
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So reducing column sizes might cause the number of I/Os to go down 10%, or 0% But if all four of the preceding scenarios are equally probable, the number of I/Os goes down 6% on average[1]
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We haven't taken caching into account for these calculations because, although the existence of a cache reduces the number of read/writes, the probability that a page is in cache is slightly greater if files are smaller Did you know that "seek" takes seven milliseconds on MS Windows NT even if the disk head doesn't move We did not take no-wait writing into account for similar reasons
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What about non-I/O Let's assume that about 50% of CPU time is devoted to fixed-overhead calculations like stack pushes or conditional jumps The rest of the time is spent doing in-memory copying (which will go down 10%), sorting (which will go down 10% because more keys fit in temporary memory), and operations on columns (which will go down 10% because of the on-chip cache and more than 10% for operations on character columns) In simplified terms, then, let's conclude that for an example scenario where the row size goes down 10%, the CPU time goes down 5% Yes, many assumptions went into this conclusion as we said at the start, hedge hum Yes, but, if! But we can draw this generalization from our conclusion If opportunities exist to reduce row size by 10%, you now have plausible reasons to conjecture that CPU time will correspondingly be reduced by 5% (We ran a very simple 10-character versus 9-character test on the Big Eight and got an average speed gain of 65%) Of course you will have to measure real data afterward to know for sure, but the conjecture will help you decide whether it's worth trying to reduce row size at all
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