如果这样，MyISAM如何比InnoDB“更快”

• MyISAM需要对数据进行磁盘读取吗？
• InnoDB使用缓冲池存储索引和数据，而MyISAM仅用于索引？

在这种独特情况下，MyISAM可以使InnoDB更快的唯一方法

我的ISAM

读取时，可以从.MYI文件读取一次MyISAM表的索引，并将其加载到MyISAM密钥缓存中（由key_buffer_size调整大小）。如何使MyISAM表的.MYD读起来更快？有了这个：

ALTER TABLE mytable ROW_FORMAT=Fixed;

我在过去的帖子中写了这个

• MyISAM和InnoDB的精华（请先阅读本篇）
• 在固定大小的字段上使用CHAR vs VARCHAR会对性能产生什么影响？（交易＃2）
• 针对高端和繁忙服务器优化了my.cnf（在复制标题下）
• 哪种DBMS适合超快速读取和简单的数据结构？（第3段）

创新数据库

好的，InnoDB呢？InnoDB是否进行任何磁盘I / O查询？令人惊讶的是，是的！您可能以为我为之疯狂，但这是绝对正确的，即使对于SELECT查询也是如此。此时，您可能想知道“ InnoDB在世界上如何进行磁盘I / O查询？”

这一切都可以回溯到InnoDB，它是一个具有ACID投诉事务存储引擎。为了使InnoDB具有事务性，它必须支持Iin ACID，即隔离。维护事务隔离的技术是通过MVCC（Multiversion并发控制）完成的。简单来说，InnoDB会在事务尝试更改数据之前记录其数据。在哪里记录？在系统表空间文件中，更好地称为ibdata1。这需要磁盘I / O。

比较

既然InnoDB和MyISAM都执行磁盘I / O，那么哪些随机因素决定了谁更快？

• 列大小
• 列格式
• 字符集
• 数值范围（需要足够大的INT）
• 行跨块拆分（行链接）
• 由DELETEs和引起的数据碎片UPDATEs
• 主键的大小（InnoDB具有聚集索引，需要两次键查找）
• 索引条目的大小
• 清单继续...

因此，在繁重的读取环境中，如果有足够的数据被写入ibdata1中包含的撤消日志中以支持事务行为，则具有固定行格式的MyISAM表有可能胜过InnoDB从InnoDB缓冲池中读出的数据施加在InnoDB数据上。

结论

仔细计划您的数据类型，查询和存储引擎。数据增长后，移动数据可能会变得非常困难。只是问问Facebook ...

在一个简单的世界中，MyISAM的读取速度更快，InnoDB的写入速度更快。

一旦开始引入混合读/写，由于其行锁定机制，InnoDB的读取速度也会更快。

几年前，我写了一个MySQL存储引擎的比较， 直到今天仍然如此，概述了MyISAM和InnoDB之间的独特区别。

以我的经验，应该将InnoDB用于除读取大量缓存表以外的所有内容，在缓存表中，由于损坏而丢失数据并不那么重要。

为了增加此处涉及两个引擎之间机械差异的响应，我提出了一个经验速度比较研究。

就纯速度而言，MyISAM并不总是比InnoDB快，但以我的经验，在PURE READ工作环境中，它往往要快2.0到2.5倍。显然，这并不适合所有环境-正如其他人所写的那样，MyISAM缺少事务和外键之类的东西。

我在下面做了一些基准测试-我使用python进行循环，并使用timeit库进行时间比较。出于兴趣，我还包括了内存引擎，尽管它仅适用于较小的表（The table 'tbl' is full超过MySQL内存限制时，您会不断遇到），但它可提供最佳的整体性能。我查看的四种选择是：

1. 香草选择
2. 计数
3. 条件选择
4. 索引和非索引子选择

首先，我使用以下SQL创建了三个表

CREATE TABLE
    data_interrogation.test_table_myisam
    (
        index_col BIGINT NOT NULL AUTO_INCREMENT,
        value1 DOUBLE,
        value2 DOUBLE,
        value3 DOUBLE,
        value4 DOUBLE,
        PRIMARY KEY (index_col)
    )
    ENGINE=MyISAM DEFAULT CHARSET=utf8

在第二个和第三个表中用“ MyISAM”代替“ InnoDB”和“内存”。

1）香草选择

查询： SELECT * FROM tbl WHERE index_col = xx

结果：平局

它们的速度大致相同，并且所期望的要选择的列数是线性的。InnoDB的似乎稍微比的MyISAM快但这的确是微不足道的。

码：

import timeit
import MySQLdb
import MySQLdb.cursors
import random
from random import randint

db = MySQLdb.connect(host="...", user="...", passwd="...", db="...", cursorclass=MySQLdb.cursors.DictCursor)
cur = db.cursor()

lengthOfTable = 100000

# Fill up the tables with random data
for x in xrange(lengthOfTable):
    rand1 = random.random()
    rand2 = random.random()
    rand3 = random.random()
    rand4 = random.random()

    insertString = "INSERT INTO test_table_innodb (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
    insertString2 = "INSERT INTO test_table_myisam (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
    insertString3 = "INSERT INTO test_table_memory (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"

    cur.execute(insertString)
    cur.execute(insertString2)
    cur.execute(insertString3)

db.commit()

# Define a function to pull a certain number of records from these tables
def selectRandomRecords(testTable,numberOfRecords):

    for x in xrange(numberOfRecords):
        rand1 = randint(0,lengthOfTable)

        selectString = "SELECT * FROM " + testTable + " WHERE index_col = " + str(rand1)
        cur.execute(selectString)

setupString = "from __main__ import selectRandomRecords"

# Test time taken using timeit
myisam_times = []
innodb_times = []
memory_times = []

for theLength in [3,10,30,100,300,1000,3000,10000]:

    innodb_times.append( timeit.timeit('selectRandomRecords("test_table_innodb",' + str(theLength) + ')', number=100, setup=setupString) )
    myisam_times.append( timeit.timeit('selectRandomRecords("test_table_myisam",' + str(theLength) + ')', number=100, setup=setupString) )
    memory_times.append( timeit.timeit('selectRandomRecords("test_table_memory",' + str(theLength) + ')', number=100, setup=setupString) )

2）计数

查询： SELECT count(*) FROM tbl

结果：MyISAM获胜

这说明了MyISAM和InnoDB之间的巨大区别-MyISAM（和内存）跟踪表中的记录数，因此此事务速度很快，且O（1）。在我研究的范围内，InnoDB计数所需的时间随着表的大小而呈超线性增加。我怀疑在实践中观察到的许多MyISAM查询的提速是由于类似的影响。

码：

myisam_times = []
innodb_times = []
memory_times = []

# Define a function to count the records
def countRecords(testTable):

    selectString = "SELECT count(*) FROM " + testTable
    cur.execute(selectString)

setupString = "from __main__ import countRecords"

# Truncate the tables and re-fill with a set amount of data
for theLength in [3,10,30,100,300,1000,3000,10000,30000,100000]:

    truncateString = "TRUNCATE test_table_innodb"
    truncateString2 = "TRUNCATE test_table_myisam"
    truncateString3 = "TRUNCATE test_table_memory"

    cur.execute(truncateString)
    cur.execute(truncateString2)
    cur.execute(truncateString3)

    for x in xrange(theLength):
        rand1 = random.random()
        rand2 = random.random()
        rand3 = random.random()
        rand4 = random.random()

        insertString = "INSERT INTO test_table_innodb (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
        insertString2 = "INSERT INTO test_table_myisam (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
        insertString3 = "INSERT INTO test_table_memory (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"

        cur.execute(insertString)
        cur.execute(insertString2)
        cur.execute(insertString3)

    db.commit()

    # Count and time the query
    innodb_times.append( timeit.timeit('countRecords("test_table_innodb")', number=100, setup=setupString) )
    myisam_times.append( timeit.timeit('countRecords("test_table_myisam")', number=100, setup=setupString) )
    memory_times.append( timeit.timeit('countRecords("test_table_memory")', number=100, setup=setupString) )

3）条件选择

查询： SELECT * FROM tbl WHERE value1<0.5 AND value2<0.5 AND value3<0.5 AND value4<0.5

结果：MyISAM获胜

在这里，MyISAM和内存的性能大致相同，对于较大的表，它们比InnoDB快50％。这种查询似乎使MyISAM的好处最大化。

码：

myisam_times = []
innodb_times = []
memory_times = []

# Define a function to perform conditional selects
def conditionalSelect(testTable):
    selectString = "SELECT * FROM " + testTable + " WHERE value1 < 0.5 AND value2 < 0.5 AND value3 < 0.5 AND value4 < 0.5"
    cur.execute(selectString)

setupString = "from __main__ import conditionalSelect"

# Truncate the tables and re-fill with a set amount of data
for theLength in [3,10,30,100,300,1000,3000,10000,30000,100000]:

    truncateString = "TRUNCATE test_table_innodb"
    truncateString2 = "TRUNCATE test_table_myisam"
    truncateString3 = "TRUNCATE test_table_memory"

    cur.execute(truncateString)
    cur.execute(truncateString2)
    cur.execute(truncateString3)

    for x in xrange(theLength):
        rand1 = random.random()
        rand2 = random.random()
        rand3 = random.random()
        rand4 = random.random()

        insertString = "INSERT INTO test_table_innodb (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
        insertString2 = "INSERT INTO test_table_myisam (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
        insertString3 = "INSERT INTO test_table_memory (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"

        cur.execute(insertString)
        cur.execute(insertString2)
        cur.execute(insertString3)

    db.commit()

    # Count and time the query
    innodb_times.append( timeit.timeit('conditionalSelect("test_table_innodb")', number=100, setup=setupString) )
    myisam_times.append( timeit.timeit('conditionalSelect("test_table_myisam")', number=100, setup=setupString) )
    memory_times.append( timeit.timeit('conditionalSelect("test_table_memory")', number=100, setup=setupString) )

4）子选择

结果：InnoDB获胜

对于此查询，我为子选择创建了一组附加表。每行仅是两列BIGINT，一列具有主键索引，一列不具有任何索引。由于表很大，因此我没有测试内存引擎。SQL表创建命令是

CREATE TABLE
    subselect_myisam
    (
        index_col bigint NOT NULL,
        non_index_col bigint,
        PRIMARY KEY (index_col)
    )
    ENGINE=MyISAM DEFAULT CHARSET=utf8;

在第二个表中，再次用“ MyISAM”代替“ InnoDB”。

在此查询中，我将选择表的大小保留为1000000，而是更改了子选择列的大小。

在这里，InnoDB轻松获胜。到达合理的尺寸表后，两个引擎都随子选择的尺寸线性缩放。索引加快了MyISAM命令的速度，但有趣的是对InnoDB的速度影响很小。subSelect.png

码：

myisam_times = []
innodb_times = []
myisam_times_2 = []
innodb_times_2 = []

def subSelectRecordsIndexed(testTable,testSubSelect):
    selectString = "SELECT * FROM " + testTable + " WHERE index_col in ( SELECT index_col FROM " + testSubSelect + " )"
    cur.execute(selectString)

setupString = "from __main__ import subSelectRecordsIndexed"

def subSelectRecordsNotIndexed(testTable,testSubSelect):
    selectString = "SELECT * FROM " + testTable + " WHERE index_col in ( SELECT non_index_col FROM " + testSubSelect + " )"
    cur.execute(selectString)

setupString2 = "from __main__ import subSelectRecordsNotIndexed"

# Truncate the old tables, and re-fill with 1000000 records
truncateString = "TRUNCATE test_table_innodb"
truncateString2 = "TRUNCATE test_table_myisam"

cur.execute(truncateString)
cur.execute(truncateString2)

lengthOfTable = 1000000

# Fill up the tables with random data
for x in xrange(lengthOfTable):
    rand1 = random.random()
    rand2 = random.random()
    rand3 = random.random()
    rand4 = random.random()

    insertString = "INSERT INTO test_table_innodb (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"
    insertString2 = "INSERT INTO test_table_myisam (value1,value2,value3,value4) VALUES (" + str(rand1) + "," + str(rand2) + "," + str(rand3) + "," + str(rand4) + ")"

    cur.execute(insertString)
    cur.execute(insertString2)

for theLength in [3,10,30,100,300,1000,3000,10000,30000,100000]:

    truncateString = "TRUNCATE subselect_innodb"
    truncateString2 = "TRUNCATE subselect_myisam"

    cur.execute(truncateString)
    cur.execute(truncateString2)

    # For each length, empty the table and re-fill it with random data
    rand_sample = sorted(random.sample(xrange(lengthOfTable), theLength))
    rand_sample_2 = random.sample(xrange(lengthOfTable), theLength)

    for (the_value_1,the_value_2) in zip(rand_sample,rand_sample_2):
        insertString = "INSERT INTO subselect_innodb (index_col,non_index_col) VALUES (" + str(the_value_1) + "," + str(the_value_2) + ")"
        insertString2 = "INSERT INTO subselect_myisam (index_col,non_index_col) VALUES (" + str(the_value_1) + "," + str(the_value_2) + ")"

        cur.execute(insertString)
        cur.execute(insertString2)

    db.commit()

    # Finally, time the queries
    innodb_times.append( timeit.timeit('subSelectRecordsIndexed("test_table_innodb","subselect_innodb")', number=100, setup=setupString) )
    myisam_times.append( timeit.timeit('subSelectRecordsIndexed("test_table_myisam","subselect_myisam")', number=100, setup=setupString) )

    innodb_times_2.append( timeit.timeit('subSelectRecordsNotIndexed("test_table_innodb","subselect_innodb")', number=100, setup=setupString2) )
    myisam_times_2.append( timeit.timeit('subSelectRecordsNotIndexed("test_table_myisam","subselect_myisam")', number=100, setup=setupString2) )

我认为所有这一切的基本含义是，如果您真正关心速度，则需要对正在执行的查询进行基准测试，而不是对哪种引擎更合适做任何假设。

哪个更快？两者可能更快。YMMV。

您应该使用哪个？InnoDB-崩溃安全等