![]() An error in Python might be either an error in the syntax of an expression or a Python exception. When a Python program meets an error, it stops the execution of the rest of the program. See my Cookbook for creating an optimal index, given a SELECT.Python Tutorial Python Features Python History Python Applications Python Install Python Example Python Variables Python Data Types Python Keywords Python Literals Python Operators Python Comments Python If else Python Loops Python For Loop Python While Loop Python Break Python Continue Python Pass Python Strings Python Lists Python Tuples Python List Vs Tuple Python Sets Python Dictionary Python Functions Python Built-in Functions Python Lambda Functions Python Files I/O Python Modules Python Exceptions Python Date Python Regex Python Sending Email Read CSV File Write CSV File Read Excel File Write Excel File Python Assert Python List Comprehension Python Collection Module Python Math Module Python OS Module Python Random Module Python Statistics Module Python Sys Module Python IDEs Python Arrays Command Line Arguments Python Magic Method Python Stack & Queue PySpark MLlib Python Decorator Python Generators Web Scraping Using Python Python JSON Python Itertools Python Multiprocessing How to Calculate Distance between Two Points using GEOPY Gmail API in Python How to Plot the Google Map using folium package in Python Grid Search in Python Python High Order Function nsetools in Python Python program to find the nth Fibonacci Number Python OpenCV object detection Python SimpleImputer module Second Largest Number in Python Do you have a simple test case that demonstrates such? I doubt if the definition order of indexes matters.The Optimizer does not have good statistics on composite keys this probably leads to some of what you are seeing.An index will be shunned if more than about 20% of the rows would be touched - a table scan is likely to be faster. ![]() Don't hide an indexed column inside a function it can't use the index.= is the easiest to optimize in some cases IN and LIKE turn into =. =, IS NULL, !=, IN, LIKE and various "range" predicates are handled differently.I don't think 'cardinality' of individual columns is used for picking an index.Meanwhile, please do not use LEFT unless you really expect missing rows in the 'righthand' table it confuses the reader. There are cases where the Optimizer validly ignores LEFT don't count on that to force a different query plan.But it does not yet take into consideration SSD vs HDD, nor what is cached already. The Optimizer has changed multiple times recently - the "Cost model" is improving constantly.5 columns in a composite index is about the limit for 'practicality'.Hence DROP the smaller index when it is an exact prefix of the larger. New comment: When you have both INDEX(a) and INDEX(a,b), the Optimizer sometimes picks the shorter (1-col) on even though the larger (2-col) would be better.In general, you should not have both only have the larger index. INDEX(a,b) is bulkier than INDEX(a) a possible rational for using the latter is that it might entail fewer index blocks.Without the SELECT to look at, all I can do is hand-waving. ![]() I would expect the optimizer to choose i2 over author_id, but if I get it right it depends on the order they are defined.Īre these observations true in general or does it only happen for small tables? With the following indexes and a query where predicates involve author_id, country, org, publish_date, price: KEY `author_id` (`author_id`), Now all the sudden index i2 were chosen: table type possible_keys keyĭo I miss something or does the optimizer pick the first declared index that it benefits from even though there are better indexes declared later on? ![]() Why did it not choose i2 over i0 when it did choose author_id before? I got even more puzzled when I switched places in the table for i0 and i2: PRIMARY KEY (`id`), I was a bit surprised to see the new plan: table type possible_keys key So I removed the index author_id and created a new index like: KEY `i2` (`author_id`, `country`, `org`, `publish_date`, `price`) table type possible_keys keyīook ref author_id,publish_date,i0,i1 author_id However, that did not help, the execution plan remained the same. In an answer given the query were rewritten as a LEFT JOIN, and that was the same thought that I had as well. The OP wanted to know if there were a way to optimize his query. Optimize a query with two range conditions
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