我想知道以下内容在性能上是否有区别
SELECT ... FROM ... WHERE someFIELD IN(1,2,3,4)
SELECT ... FROM ... WHERE someFIELD between 0 AND 5
SELECT ... FROM ... WHERE someFIELD = 1 OR someFIELD = 2 OR someFIELD = 3 ...
或者MySQL会像编译器优化代码一样优化SQL吗?
EDIT
根据评论中说明的原因，将AND改为OR。
当前回答
公认的答案并不能解释原因。
下面引用自高性能MySQL，第三版。
在许多数据库服务器中，In()只是多个OR子句的同义词，因为这两个子句在逻辑上是等价的。在MySQL中不是这样，它对in()列表中的值进行排序，并使用快速的二进制搜索来查看一个值是否在列表中。这是列表大小的O(Log n)，而等效的OR子句系列的列表大小是O(n)(即，对于大列表来说要慢得多)
其他回答
这取决于你在做什么;范围有多宽，数据类型是什么(我知道你的例子使用数字数据类型，但你的问题也可以适用于许多不同的数据类型)。
这是一个你想以两种方式编写查询的实例;让它工作，然后使用EXPLAIN来找出执行差异。
我相信这个问题有一个具体的答案，但这就是我如何，实际上，找出我给定问题的答案。
这可能会有一些帮助:http://forge.mysql.com/wiki/Top10SQLPerformanceTips
问候, 弗兰克
就在你以为安全的时候…
你的eq_range_index_dive_limit值是多少?特别地，你在In子句中有更多还是更少的条目?
这将不包括基准测试，但将深入了解内部工作原理。让我们使用一个工具来查看发生了什么——Optimizer Trace。
查询:SELECT * FROM canada WHERE id…
使用3个OR值，部分跟踪看起来像这样:
"condition_processing": {
"condition": "WHERE",
"original_condition": "((`canada`.`id` = 296172) or (`canada`.`id` = 295093) or (`canada`.`id` = 293626))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(multiple equal(296172, `canada`.`id`) or multiple equal(295093, `canada`.`id`) or multiple equal(293626, `canada`.`id`))"
},
...
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "id",
"ranges": [
"293626 <= id <= 293626",
"295093 <= id <= 295093",
"296172 <= id <= 296172"
],
"index_dives_for_eq_ranges": true,
"chosen": true
...
"refine_plan": [
{
"table": "`canada`",
"pushed_index_condition": "((`canada`.`id` = 296172) or (`canada`.`id` = 295093) or (`canada`.`id` = 293626))",
"table_condition_attached": null,
"access_type": "range"
}
]
注意颅内压是如何给予ORs的。这意味着OR没有转换成IN, InnoDB将通过ICP执行一堆=测试。(我觉得不值得考虑MyISAM。)
(这是Percona的5.6.22-71.0-log;Id是二级索引。)
现在来看带有几个值的IN()
Eq_range_index_dive_limit = 10;有8个值。
"condition_processing": {
"condition": "WHERE",
"original_condition": "(`canada`.`id` in (296172,295093,293626,295573,297148,296127,295588,295810))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(`canada`.`id` in (296172,295093,293626,295573,297148,296127,295588,295810))"
},
...
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "id",
"ranges": [
"293626 <= id <= 293626",
"295093 <= id <= 295093",
"295573 <= id <= 295573",
"295588 <= id <= 295588",
"295810 <= id <= 295810",
"296127 <= id <= 296127",
"296172 <= id <= 296172",
"297148 <= id <= 297148"
],
"index_dives_for_eq_ranges": true,
"chosen": true
...
"refine_plan": [
{
"table": "`canada`",
"pushed_index_condition": "(`canada`.`id` in (296172,295093,293626,295573,297148,296127,295588,295810))",
"table_condition_attached": null,
"access_type": "range"
}
]
注意，IN似乎没有被转换成OR。
附注:注意，常数值是排序的。这在两个方面是有益的:
通过更少的跳跃，可能会有更好的缓存，更少的I/O来获取所有的值。 如果两个类似的查询来自不同的连接，并且它们位于事务中，则更有可能导致延迟，而不是由于重叠列表而导致死锁。
最后，有很多值的IN()
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "(`canada`.`id` in (293831,292259,292881,293440,292558,295792,292293,292593,294337,295430,295034,297060,293811,295587,294651,295559,293213,295742,292605,296018,294529,296711,293919,294732,294689,295540,293000,296916,294433,297112,293815,292522,296816,293320,293232,295369,291894,293700,291839,293049,292738,294895,294473,294023,294173,293019,291976,294923,294797,296958,294075,293450,296952,297185,295351,295736,296312,294330,292717,294638,294713,297176,295896,295137,296573,292236,294966,296642,296073,295903,293057,294628,292639,293803,294470,295353,297196,291752,296118,296964,296185,295338,295956,296064,295039,297201,297136,295206,295986,292172,294803,294480,294706,296975,296604,294493,293181,292526,293354,292374,292344,293744,294165,295082,296203,291918,295211,294289,294877,293120,295387))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(`canada`.`id` in (293831,292259,292881,293440,292558,295792,292293,292593,294337,295430,295034,297060,293811,295587,294651,295559,293213,295742,292605,296018,294529,296711,293919,294732,294689,295540,293000,296916,294433,297112,293815,292522,296816,293320,293232,295369,291894,293700,291839,293049,292738,294895,294473,294023,294173,293019,291976,294923,294797,296958,294075,293450,296952,297185,295351,295736,296312,294330,292717,294638,294713,297176,295896,295137,296573,292236,294966,296642,296073,295903,293057,294628,292639,293803,294470,295353,297196,291752,296118,296964,296185,295338,295956,296064,295039,297201,297136,295206,295986,292172,294803,294480,294706,296975,296604,294493,293181,292526,293354,292374,292344,293744,294165,295082,296203,291918,295211,294289,294877,293120,295387))"
},
...
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "id",
"ranges": [
"291752 <= id <= 291752",
"291839 <= id <= 291839",
...
"297196 <= id <= 297196",
"297201 <= id <= 297201"
],
"index_dives_for_eq_ranges": false,
"rows": 111,
"chosen": true
...
"refine_plan": [
{
"table": "`canada`",
"pushed_index_condition": "(`canada`.`id` in (293831,292259,292881,293440,292558,295792,292293,292593,294337,295430,295034,297060,293811,295587,294651,295559,293213,295742,292605,296018,294529,296711,293919,294732,294689,295540,293000,296916,294433,297112,293815,292522,296816,293320,293232,295369,291894,293700,291839,293049,292738,294895,294473,294023,294173,293019,291976,294923,294797,296958,294075,293450,296952,297185,295351,295736,296312,294330,292717,294638,294713,297176,295896,295137,296573,292236,294966,296642,296073,295903,293057,294628,292639,293803,294470,295353,297196,291752,296118,296964,296185,295338,295956,296064,295039,297201,297136,295206,295986,292172,294803,294480,294706,296975,296604,294493,293181,292526,293354,292374,292344,293744,294165,295082,296203,291918,295211,294289,294877,293120,295387))",
"table_condition_attached": null,
"access_type": "range"
}
]
旁注:我需要这个，由于跟踪的体积:
@@global.optimizer_trace_max_mem_size = 32222;
我敢打赌它们是一样的，你可以通过执行以下操作来运行测试:
循环遍历“in(1,2,3,4)”500次，看看需要多长时间。循环“=1 or= 2 or=3…”版本500次，看看它运行了多长时间。
你也可以尝试一个连接的方式，如果someField是一个索引，你的表很大，它可以更快…
SELECT ...
FROM ...
INNER JOIN (SELECT 1 as newField UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4) dt ON someFIELD =newField
我在我的SQL Server上尝试了上面的join方法，它几乎与in(1,2,3,4)相同，它们都导致了一个聚集索引查找。我不确定MySQL将如何处理它们。
我认为BETWEEN会更快，因为它应该转换为:
Field >= 0 AND Field <= 5
我的理解是，不管怎样，一个IN语句都会被转换成一堆OR语句。IN的价值是易用性。(节省了必须多次键入每个列名，也使它更容易与现有逻辑一起使用-您不必担心and /OR优先级，因为IN是一个语句。对于一堆OR语句，你必须确保用圆括号将它们括起来，以确保它们作为一个条件进行计算。)
对你的问题唯一真正的答案是分析你的查询。然后你就会知道在你的特定情况下什么是最好的。
我需要确定这一点，所以我对这两种方法都进行了基准测试。我始终发现IN比OR要快得多。
不要相信那些发表“意见”的人，科学就是测试和证据。
我运行了1000倍的等效查询循环(为了一致性，我使用sql_no_cache):
: 2.34969592094秒
或者:5.83781504631秒
更新: (我没有原始测试的源代码，因为它是6年前的，尽管它返回的结果与此测试相同)
为了要求一些示例代码来测试这一点，这里是最简单的可能用例。使用Eloquent来简化语法，原始的SQL等价执行相同的操作。
$t = microtime(true);
for($i=0; $i<10000; $i++):
$q = DB::table('users')->where('id',1)
->orWhere('id',2)
->orWhere('id',3)
->orWhere('id',4)
->orWhere('id',5)
->orWhere('id',6)
->orWhere('id',7)
->orWhere('id',8)
->orWhere('id',9)
->orWhere('id',10)
->orWhere('id',11)
->orWhere('id',12)
->orWhere('id',13)
->orWhere('id',14)
->orWhere('id',15)
->orWhere('id',16)
->orWhere('id',17)
->orWhere('id',18)
->orWhere('id',19)
->orWhere('id',20)->get();
endfor;
$t2 = microtime(true);
echo $t."\n".$t2."\n".($t2-$t)."\n";
1482080514.3635 1482080517.3713 3.0078368186951
$t = microtime(true);
for($i=0; $i<10000; $i++):
$q = DB::table('users')->whereIn('id',[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])->get();
endfor;
$t2 = microtime(true);
echo $t."\n".$t2."\n".($t2-$t)."\n";
1482080534.0185 1482080536.178 2.1595389842987
