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    <p>I'm going to add a somewhat longer and more detailed explanation of the steps to take to solve this problem. I apologize if it's too long.</p> <hr> <p>I'll start out with the base you've given and use it to define a couple of terms that I'll use for the rest of this post. This will be the <strong>base table</strong>:</p> <pre><code>select * from history; +--------+----------+-----------+ | hostid | itemname | itemvalue | +--------+----------+-----------+ | 1 | A | 10 | | 1 | B | 3 | | 2 | A | 9 | | 2 | C | 40 | +--------+----------+-----------+ </code></pre> <p>This will be our goal, the <strong>pretty pivot table</strong>:</p> <pre><code>select * from history_itemvalue_pivot; +--------+------+------+------+ | hostid | A | B | C | +--------+------+------+------+ | 1 | 10 | 3 | 0 | | 2 | 9 | 0 | 40 | +--------+------+------+------+ </code></pre> <p>Values in the <code>history.hostid</code> column will become <strong>y-values</strong> in the pivot table. Values in the <code>history.itemname</code> column will become <strong>x-values</strong> (for obvious reasons).</p> <hr> <p>When I have to solve the problem of creating a pivot table, I tackle it using a three-step process (with an optional fourth step):</p> <ol> <li>select the columns of interest, i.e. <strong>y-values</strong> and <strong>x-values</strong></li> <li>extend the base table with extra columns -- one for each <strong>x-value</strong></li> <li>group and aggregate the extended table -- one group for each <strong>y-value</strong></li> <li>(optional) prettify the aggregated table</li> </ol> <p>Let's apply these steps to your problem and see what we get:</p> <p><strong>Step 1: select columns of interest</strong>. In the desired result, <code>hostid</code> provides the <strong>y-values</strong> and <code>itemname</code> provides the <strong>x-values</strong>.</p> <p><strong>Step 2: extend the base table with extra columns</strong>. We typically need one column per x-value. Recall that our x-value column is <code>itemname</code>: </p> <pre><code>create view history_extended as ( select history.*, case when itemname = "A" then itemvalue end as A, case when itemname = "B" then itemvalue end as B, case when itemname = "C" then itemvalue end as C from history ); select * from history_extended; +--------+----------+-----------+------+------+------+ | hostid | itemname | itemvalue | A | B | C | +--------+----------+-----------+------+------+------+ | 1 | A | 10 | 10 | NULL | NULL | | 1 | B | 3 | NULL | 3 | NULL | | 2 | A | 9 | 9 | NULL | NULL | | 2 | C | 40 | NULL | NULL | 40 | +--------+----------+-----------+------+------+------+ </code></pre> <p>Note that we didn't change the number of rows -- we just added extra columns. Also note the pattern of <code>NULL</code>s -- a row with <code>itemname = "A"</code> has a non-null value for new column <code>A</code>, and null values for the other new columns.</p> <p><strong>Step 3: group and aggregate the extended table</strong>. We need to <code>group by hostid</code>, since it provides the y-values:</p> <pre><code>create view history_itemvalue_pivot as ( select hostid, sum(A) as A, sum(B) as B, sum(C) as C from history_extended group by hostid ); select * from history_itemvalue_pivot; +--------+------+------+------+ | hostid | A | B | C | +--------+------+------+------+ | 1 | 10 | 3 | NULL | | 2 | 9 | NULL | 40 | +--------+------+------+------+ </code></pre> <p><em>(Note that we now have one row per y-value.)</em> Okay, we're almost there! We just need to get rid of those ugly <code>NULL</code>s.</p> <p><strong>Step 4: prettify</strong>. We're just going to replace any null values with zeroes so the result set is nicer to look at:</p> <pre><code>create view history_itemvalue_pivot_pretty as ( select hostid, coalesce(A, 0) as A, coalesce(B, 0) as B, coalesce(C, 0) as C from history_itemvalue_pivot ); select * from history_itemvalue_pivot_pretty; +--------+------+------+------+ | hostid | A | B | C | +--------+------+------+------+ | 1 | 10 | 3 | 0 | | 2 | 9 | 0 | 40 | +--------+------+------+------+ </code></pre> <p>And we're done -- we've built a nice, pretty pivot table using MySQL.</p> <hr> <p>Considerations when applying this procedure:</p> <ul> <li>what value to use in the extra columns. I used <code>itemvalue</code> in this example</li> <li>what "neutral" value to use in the extra columns. I used <code>NULL</code>, but it could also be <code>0</code> or <code>""</code>, depending on your exact situation</li> <li>what aggregate function to use when grouping. I used <code>sum</code>, but <code>count</code> and <code>max</code> are also often used (<code>max</code> is often used when building one-row "objects" that had been spread across many rows)</li> <li>using multiple columns for y-values. This solution isn't limited to using a single column for the y-values -- just plug the extra columns into the <code>group by</code> clause (and don't forget to <code>select</code> them)</li> </ul> <p>Known limitations:</p> <ul> <li>this solution doesn't allow n columns in the pivot table -- each pivot column needs to be manually added when extending the base table. So for 5 or 10 x-values, this solution is nice. For 100, not so nice. There are some solutions with stored procedures generating a query, but they're ugly and difficult to get right. I currently don't know of a good way to solve this problem when the pivot table needs to have lots of columns.</li> </ul>
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    1. CO+1 This is by far the best/most clear explanation of pivot tables/cross tabs in MySQL I have seen
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    2. COExcellent explanation, thanks. Step 4 could be merged into step 3 by using IFNULL(sum(A), 0) AS A, giving you the same result but without the need for creating yet another table
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