InnoDB offers:

ACID transactions
row-level locking
foreign key constraints
automatic crash recovery
table compression (read/write)
spatial data types (no spatial indexes)

在InnoDB中，一行中除TEXT和BLOB外的所有数据最多占用8000字节。InnoDB没有全文索引。在InnoDB中，COUNT(*)s(当WHERE, GROUP BY或JOIN不使用时)执行速度比MyISAM慢，因为行数没有存储在内部。InnoDB将数据和索引存储在一个文件中。InnoDB使用缓冲池来缓存数据和索引。

MyISAM提供：

fast COUNT(*)s (when WHERE, GROUP BY, or JOIN is not used)
full text indexing
smaller disk footprint
very high table compression (read only)
spatial data types and indexes (R-tree)

MyISAM has table-level locking, but no row-level locking. No transactions. No automatic crash recovery, but it does offer repair table functionality. No foreign key constraints. MyISAM tables are generally more compact in size on disk when compared to InnoDB tables. MyISAM tables could be further highly reduced in size by compressing with myisampack if needed, but become read-only. MyISAM stores indexes in one file and data in another. MyISAM uses key buffers for caching indexes and leaves the data caching management to the operating system.

总的来说，我推荐InnoDB用于大多数用途，MyISAM仅用于特殊用途。InnoDB现在是MySQL新版本的默认引擎。

InnoDB offers:

ACID transactions
row-level locking
foreign key constraints
automatic crash recovery
table compression (read/write)
spatial data types (no spatial indexes)

在InnoDB中，一行中除TEXT和BLOB外的所有数据最多占用8000字节。InnoDB没有全文索引。在InnoDB中，COUNT(*)s(当WHERE, GROUP BY或JOIN不使用时)执行速度比MyISAM慢，因为行数没有存储在内部。InnoDB将数据和索引存储在一个文件中。InnoDB使用缓冲池来缓存数据和索引。

MyISAM提供：

fast COUNT(*)s (when WHERE, GROUP BY, or JOIN is not used)
full text indexing
smaller disk footprint
very high table compression (read only)
spatial data types and indexes (R-tree)

MyISAM has table-level locking, but no row-level locking. No transactions. No automatic crash recovery, but it does offer repair table functionality. No foreign key constraints. MyISAM tables are generally more compact in size on disk when compared to InnoDB tables. MyISAM tables could be further highly reduced in size by compressing with myisampack if needed, but become read-only. MyISAM stores indexes in one file and data in another. MyISAM uses key buffers for caching indexes and leaves the data caching management to the operating system.

总的来说，我推荐InnoDB用于大多数用途，MyISAM仅用于特殊用途。InnoDB现在是MySQL新版本的默认引擎。

对于一个有更多写和读的负载，你将受益于InnoDB。因为InnoDB提供的是行锁而不是表锁，所以你的select可以是并发的，不仅仅是彼此之间的select，还有许多insert。但是，除非你打算使用SQL事务，否则将InnoDB提交刷新设置为2 (innodb_flush_log_at_trx_commit)。这将为您提供大量原始性能，否则将表从MyISAM转移到InnoDB时会损失这些性能。

Also, consider adding replication. This gives you some read scaling and since you stated your reads don't have to be up-to-date, you can let the replication fall behind a little. Just be sure that it can catch up under anything but the heaviest traffic or it will always be behind and will never catch up. If you go this way, however, I strongly recommend you isolate reading from the slaves and replication lag management to your database handler. It is so much simpler if the application code does not know about this.

最后，要注意不同的表负载。您不会在所有表上都有相同的读/写比率。一些接近100%读取的小表可以负担得起MyISAM。同样地，如果你有一些接近100%写的表，你可能会受益于INSERT DELAYED，但这只在MyISAM中支持(对于InnoDB表，DELAYED子句会被忽略)。

但基准是肯定的。

为了增加广泛的选择，这里涵盖了两个发动机之间的机械差异，我提出了一个经验速度比较研究。

就纯粹的速度而言，MyISAM并不总是比InnoDB快，但根据我的经验，在pure READ工作环境中，MyISAM往往快2.0-2.5倍。显然，这并不适用于所有环境——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，使用timeit库进行时间比较。出于兴趣，我还包括了内存引擎，这提供了最好的性能，尽管它只适用于较小的表(当您超过MySQL内存限制时，您会不断遇到表'tbl'已满)。我研究的四种选择类型是:

香草选择 计数 有条件的选择 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

在第二和第三个表中用“MyISAM”替换“InnoDB”和“memory”。

 

1)香草选择

查询:SELECT * FROM tbl WHERE index_col = xx

结果:画

它们的速度基本上是相同的，并且正如预期的那样，与要选择的列数成线性关系。InnoDB似乎比MyISAM快一点，但这真的是微不足道的。

代码:

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)计算

查询:SELECT count(*) FROM tbl

结果:MyISAM获胜

这个说明了MyISAM和InnoDB之间的一个很大的不同——MyISAM(和内存)跟踪表中的记录数量，所以这个事务是快速的，O(1)。在我调查的范围内，InnoDB计数所需的时间随着表的大小超线性增加。我怀疑在实践中观察到的许多MyISAM查询的加速都是由于类似的效果。

代码:

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)有条件选择

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，对于更大的表，它比InnoDB高出50%左右。在这类查询中，MyISAM的好处似乎得到了最大化。

代码:

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

结果:InnoDB胜出

对于这个查询，我为子选择创建了一组额外的表。每个都是简单的两列bigint，一列有主键索引，另一列没有任何索引。由于表的大小很大，我没有测试内存引擎。SQL表创建命令为

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，'MyISAM'再次替换'InnoDB'。

在这个查询中，我将选择表的大小保留为1000000，而是改变子选择列的大小。

在这一点上，InnoDB很容易获胜。在我们得到一个合理的大小表后，两个引擎都线性缩放子选择的大小。索引加快了MyISAM命令的速度，但有趣的是，它对InnoDB的速度几乎没有影响。 subSelect.png

代码:

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) )

我认为所有这些的关键信息是，如果你真的关心速度，你需要对你正在执行的查询进行基准测试，而不是假设哪个引擎更适合。

有点晚了…但这是我几个月前写的一篇相当全面的文章，详细介绍了MYISAM和InnoDB之间的主要区别。喝杯茶(或者一块饼干)，好好享受吧。

---

MyISAM和InnoDB之间的主要区别在于引用完整性和事务。还有其他区别，如锁定、回滚和全文搜索。

参照完整性

Referential integrity ensures that relationships between tables remains consistent. More specifically, this means when a table (e.g. Listings) has a foreign key (e.g. Product ID) pointing to a different table (e.g. Products), when updates or deletes occur to the pointed-to table, these changes are cascaded to the linking table. In our example, if a product is renamed, the linking table’s foreign keys will also update; if a product is deleted from the ‘Products’ table, any listings which point to the deleted entry will also be deleted. Furthermore, any new listing must have that foreign key pointing to a valid, existing entry.

InnoDB是一个关系DBMS (RDBMS)，因此具有引用完整性，而MyISAM没有。

事务和原子性

表中的数据使用数据操作语言(DML)语句进行管理，例如SELECT、INSERT、UPDATE和DELETE。事务将两个或多个DML语句组合到一个工作单元中，因此可以应用整个工作单元，也可以不应用任何工作单元。

MyISAM不支持事务，而InnoDB支持。

如果在使用MyISAM表时操作中断，操作将立即中止，受影响的行(甚至是每行中的数据)仍然受到影响，即使操作没有完成。

如果一个操作在使用InnoDB表时被中断，因为它使用事务，具有原子性，任何没有完成的事务都不会生效，因为没有提交。

表锁vs行锁

当对MyISAM表执行查询时，所查询的整个表将被锁定。这意味着后续查询只会在当前查询完成后执行。如果您正在读取一个大的表，并且/或者有频繁的读和写操作，这可能意味着大量的查询积压。

当对InnoDB表执行查询时，只有涉及的行被锁定，表的其余部分仍可用于CRUD操作。这意味着查询可以在同一个表上同时运行，前提是它们不使用同一行。

这个特性在InnoDB中被称为并发。尽管并发性很好，但对于选择的表范围有一个主要缺点，即在内核线程之间切换时会产生开销，您应该对内核线程设置一个限制，以防止服务器停止。

事务和回滚

当你在MyISAM中运行一个操作时，更改被设置;在InnoDB中，这些更改可以回滚。用于控制事务的最常用命令是COMMIT、ROLLBACK和SAVEPOINT。1. COMMIT -你可以写多个DML操作，但是只有在COMMIT时更改才会被保存。ROLLBACK -你可以放弃任何尚未提交的操作。SAVEPOINT—设置ROLLBACK操作可以回滚到的操作列表中的一个点

可靠性

MyISAM不提供数据完整性——硬件故障、不干净的关机和取消的操作都可能导致数据损坏。这将需要完全修复或重新构建索引和表。

另一方面，InnoDB使用事务日志、双写缓冲区和自动校验和验证来防止损坏。在InnoDB做任何更改之前，它会将事务之前的数据记录到一个名为ibdata1的系统表空间文件中。如果出现崩溃，InnoDB会通过重放这些日志自动恢复。

全文索引

InnoDB直到MySQL 5.6.4版本才支持FULLTEXT索引。在撰写本文时，许多共享主机提供商的MySQL版本仍然低于5.6.4，这意味着InnoDB表不支持FULLTEXT索引。

然而，这并不是使用MyISAM的正当理由。最好换一个支持最新版本MySQL的主机提供商。并不是说使用FULLTEXT索引的MyISAM表不能转换为InnoDB表。

结论

总之，InnoDB应该是您的默认存储引擎选择。在满足特定需求时选择MyISAM或其他数据类型。

这个问题和大部分答案都已经过时了。

是的，MyISAM比InnoDB快是无稽之谈。注意问题的日期:2008年;现在已经过去了近十年。从那时起，InnoDB在性能上取得了显著的进步。

戏剧性的图表是MyISAM获胜的一种情况:没有where子句的COUNT(*)。但这真的是你花时间做的事情吗?

如果你运行并发测试，InnoDB很可能会赢，即使是对MEMORY。

如果在对select进行基准测试时执行任何写入操作，MyISAM和MEMORY可能会因为表级锁定而丢失。

事实上，Oracle非常确定InnoDB更好，以至于他们几乎从8.0中删除了MyISAM。

这个问题写于5.1的早期。从那时起，这些主要版本被标记为“一般可用性”:

2010: 5.5(。12月8日) 2013: 5.6(。2月10日) 2015年:5.7(。10月9日) 2018年:8.0(。四月十一日)

底线:不要使用MyISAM