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Re: Help improving query performance




On Wed, Feb 4, 2015 at 3:25 PM, shawn l.green <shawn.l.green@xxxxxxxxxx> wrote:
> Hi Larry,
>
>
> On 2/4/2015 3:18 PM, Larry Martell wrote:
>>
>> On Wed, Feb 4, 2015 at 2:56 PM, shawn l.green <shawn.l.green@xxxxxxxxxx>
>> wrote:
>>>
>>> Hi Larry,
>>>
>>>
>>> On 2/1/2015 4:49 PM, Larry Martell wrote:
>>>>
>>>>
>>>> I have 2 queries. One takes 4 hours to run and returns 21 rows, and
>>>> the other, which has 1 additional where clause, takes 3 minutes and
>>>> returns 20 rows. The main table being selected from is largish
>>>> (37,247,884 rows with 282 columns). Caching is off for my testing, so
>>>> it's not related to that. To short circuit anyone asking, these
>>>> queries are generated by python code, which is why there's an IN
>>>> clause with 1 value, as oppose to an =.
>>>>
>>>> Here are the queries and their explains. The significant difference is
>>>> that the faster query has "Using
>>>> intersect(data_cst_bbccbce0,data_cst_fba12377)" in the query plan -
>>>> those 2 indexes are on the 2 columns in the where clause, so that's
>>>> why the second one is faster. But I am wondering what I can do to make
>>>> the first one faster.
>>>>
>>>>
>>>> 4 hour query:
>>>>
>>>> SELECT MIN(data_tool.name) as tool,
>>>>          MIN(data_cst.date_time) "start",
>>>>          MAX(data_cst.date_time) "end",
>>>>          MIN(data_target.name) as target,
>>>>          MIN(data_lot.name) as lot,
>>>>          MIN(data_wafer.name) as wafer,
>>>>          MIN(measname) as measname,
>>>>          MIN(data_recipe.name) as recipe
>>>> FROM data_cst
>>>> INNER JOIN data_tool ON data_tool.id = data_cst.tool_id
>>>> INNER JOIN data_target ON data_target.id = data_cst.target_name_id
>>>> INNER JOIN data_lot ON data_lot.id = data_cst.lot_id
>>>> INNER JOIN data_wafer ON data_wafer.id = data_cst.wafer_id
>>>> INNER JOIN data_measparams ON data_measparams.id =
>>>> data_cst.meas_params_name_id
>>>> INNER JOIN data_recipe ON data_recipe.id = data_cst.recipe_id
>>>> WHERE data_target.id IN (172) AND
>>>>         data_cst.date_time BETWEEN '2015-01-26 00:00:00' AND '2015-01-26
>>>> 23:59:59'
>>>> GROUP BY wafer_id, data_cst.lot_id, target_name_id
>>>>
>>>
>>> ... snipped ...
>>>
>>>>
>>>>
>>>> Faster query:
>>>>
>>>> SELECT MIN(data_tool.name) as tool,
>>>>          MIN(data_cst.date_time) "start",
>>>>          MAX(data_cst.date_time) "end",
>>>>          MIN(data_target.name) as target,
>>>>          MIN(data_lot.name) as lot,
>>>>          MIN(data_wafer.name) as wafer,
>>>>          MIN(measname) as measname,
>>>>          MIN(data_recipe.name) as recipe
>>>> FROM data_cst
>>>> INNER JOIN data_tool ON data_tool.id = data_cst.tool_id
>>>> INNER JOIN data_target ON data_target.id = data_cst.target_name_id
>>>> INNER JOIN data_lot ON data_lot.id = data_cst.lot_id
>>>> INNER JOIN data_wafer ON data_wafer.id = data_cst.wafer_id
>>>> INNER JOIN data_measparams ON data_measparams.id =
>>>> data_cst.meas_params_name_id
>>>> INNER JOIN data_recipe ON data_recipe.id = data_cst.recipe_id
>>>> WHERE data_target.id IN (172) AND
>>>>         data_recipe.id IN (148) AND
>>>>         data_cst.date_time BETWEEN '2015-01-26 00:00:00' AND '2015-01-26
>>>> 23:59:59'
>>>> GROUP BY wafer_id, data_cst.lot_id, target_name_id
>>>>
>>> ... snip ...
>>>>
>>>>
>>>>
>>>> Thanks for taking the time to read this, and for any help or pointers
>>>> you can give me.
>>>>
>>>
>>> The biggest difference is the added selectivity generated by the WHERE
>>> term
>>> against the data_recipe table.
>>>
>>> Compare the two EXPLAINS, in the faster query you see that data_recipe is
>>> listed second. This allows the additional term a chance to reduce the
>>> number
>>> of row combinations for the entire query.
>>>
>>> To really get at the logic behind how the Optimizer chooses its execution
>>> plan, get an optimizer trace. Look at the "cost" estimates for each phase
>>> being considered.
>>> http://dev.mysql.com/doc/refman/5.6/en/optimizer-trace-table.html
>>> http://dev.mysql.com/doc/internals/en/optimizer-tracing.html
>>
>>
>> Thanks very much Shawn for the reply and the links. I will check those
>> out and I'm sure I will find them very useful.
>>
>> Meanwhile I changed the query to select from data_cst using the where
>> clause into a temp table and then I join the temp table with the other
>> tables. That has improved the slow query from 4 hours to 10 seconds
>> (!)
>>
>
> Did you also add an index to the temporary table for the JOIN condition? It
> might make it even faster

No, I didn't. I (and the users) were so shocked and happy with the
massive improvement I moved on to make similar changes to other
queries.

This is a django app, and it's a one-shot deal - i.e. there's just the
one query run and the response is sent back to the browser and that's
the end of the session and the temp table. So I'm thinking it's
probably not worth it.

As an aside this change has messed up all my unit tests - they send
multiple requests, but they're all in the same session. So only the
first succeeds and the next one fails because the temp table already
exists. I haven't figured out how to get it run each request in its
own session. I guess I'm going to have to drop the temp table after I
join with it before I sent the response back.

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