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Note: This is from an introductory database systems course at my university so I'm not entirely sure if it matters, but for what it's worth we are being taught MySQL.


One homework question I'm doing right now is to analyze some query plans for a query with two predicates, specifically:

SELECT *
FROM Student
WHERE wam > 75 AND faculty = 'Arts';

however the plans provided assume an index only on one of the predicates, such as an unclustered B+ tree index on (wam).

One example part of the question is as follows:

Compute the estimated cost of the best plan assuming that an unclustered B+ tree index on (wam) is the only index available. Suppose there are 60 index pages. Discuss and calculate alternative plans

How would I go about calculating the cost of a plan like this? Would I have to calculate costs for unclustered/clustered B+ tree and hash indexes for the other predicate, faculty, as well? I am aware that one alternative plan is also simply a full table scan.

"Student" has 800 pages, 64000 tuples. "faculty" can take 10 unique values. "wam" can take 100 unique values.

Thank you, if I'm not being specific enough feel free to point out anything lacking in this post.

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The optimal index for that query is INDEX(faculty, wam), in that order.

MySQL does not (in most situations) use two different indexes. However, it will use a 'composite' index to good use in some situations, such as this.

Without knowing how many rows have Arts and home many have > 75, you cannot accurately conclude which of INDEX(wam) or INDEX(faculty) would be better. Nor can the Optimizer, since it's statistics gathering is not very good. SHOW INDEXES and EXPLAIN SELECT provide numbers that come from the same statistics, so you won't necessarily come to the optimal conclusion.

A "Hash" index (which InnoDB does not have) is essentially no better than a BTree index. A Hash index is useless for a range (eg, > 75). (Hence, MySQL uses BTrees, and does not provide Hash indexes.)

Another point: If more than a small percentage of the table has wam > 75, INDEX(wam) will be shunned; scanning the entire table is likely to be faster than bouncing between the index's BTree and the data BTree. Again > 75 and 60 pages and 10 and 100 give no good clues of which path the Optimizer should take. The answer to the quoted question is "it depends" or "not enough info".

You could tackle the question by presenting two answers: One where very few rows have wam > 75, and one where most meet that predicate.

Has your instructor had any experience using databases, or only textbooks?

More discussion on devising optimal indexes: http://mysql.rjweb.org/doc.php/index_cookbook_mysql

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    "Nor can the Optimizer, since it's statistics gathering is not very good." I have to disagree (a bit), atleast since MySQL 8 is it possible to make histogram statistics with ANALYZE TABLE for better statistics gathering still it would require to completly sample the table for the best results.. But still pretty useless if you don't have enough RAM to sample large tables, MySQL devs should really extend this feature that ram to ssd swapping can be used :-) – Raymond Nijland Oct 21 '19 at 18:38
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    @RaymondNijland - Good points. However, I doubt if the histogram generator really wants to fill up RAM. And the Histogram is done once, not every time the query is run, correct? Therefore, the cost of making it is not relevant. – Rick James Oct 21 '19 at 18:47
  • "However, I doubt if the histogram generator really wants to fill up RAM. And the Histogram is done once, not every time the query is run, correct?" Correct.. But the size of the "sample" is controled by histogram_generation_max_mem_size meaning to less RAM meaning your are not able to 100% scan the table if the table is larger then that setting.. – Raymond Nijland Oct 21 '19 at 18:50
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    I hope (but do not know for a fact) that the Histogram generator does not try to keep all the data in RAM. One rarely needs a histogram with more than a hundred items. The entire table could be sorted (on disk), then probed to generate the buckets. That setting implies to me that they may have not worked hard enough on the algorithm. – Rick James Oct 21 '19 at 19:14
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    @RaymondNijland - Thanks for the link. – Rick James Oct 21 '19 at 22:11

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