MySQL is very different from other database servers, and its architectural characteristics make it useful for a wide range of purposes as well as making it a poor choice for others. MySQL is not perfect, but it is flexible enough to work well in very demanding environments, such as web applications. At the same time, MySQL can power embedded applications, data warehouses, content indexing and delivery software, highly available redundant systems, online transaction processing (OLTP), and much more.
To get the most from MySQL, you need to understand its design so that you can work with it, not against it. MySQL is flexible in many ways. For example, you can configure it to run well on a wide range of hardware, and it supports a variety of data types. However, MySQL’s most unusual and important feature is its storage-engine architecture, whose design separates query processing and other server tasks from data storage and retrieval. This separation of concerns lets you choose how your data is stored and what performance, features, and other characteristics you want.
This chapter provides a high-level overview of the MySQL server architecture, the major differences between the storage engines, and why those differences are important. We’ll finish with some historical context and benchmarks. We’ve tried to explain MySQL by simplifying the details and showing examples. This discussion will be useful for those new to database servers as well as readers who are experts with other database servers.
A good mental picture of how MySQL’s components work together will help you understand the server. Figure 1-1 shows a logical view of MySQL’s architecture.
The topmost layer contains the services that aren’t unique to MySQL. They’re services most network-based client/server tools or servers need: connection handling, authentication, security, and so forth.
The second layer is where things get interesting. Much of MySQL’s brains are here, including the code for query parsing, analysis, optimization, caching, and all the built-in functions (e.g., dates, times, math, and encryption). Any functionality provided across storage engines lives at this level: stored procedures, triggers, and views, for example.
The third layer contains the storage engines. They are responsible for storing and retrieving all data stored “in” MySQL. Like the various filesystems available for GNU/Linux, each storage engine has its own benefits and drawbacks. The server communicates with them through the storage engine API. This interface hides differences between storage engines and makes them largely transparent at the query layer. The API contains a couple of dozen low-level functions that perform operations such as “begin a transaction” or “fetch the row that has this primary key.” The storage engines don’t parse SQL or communicate with each other; they simply respond to requests from the server.
Each client connection gets its own thread within the server process. The connection’s queries execute within that single thread, which in turn resides on one core or CPU. The server caches threads, so they don’t need to be created and destroyed for each new connection.
When clients (applications) connect to the MySQL server, the
server needs to authenticate them. Authentication is based on username,
originating host, and password. X.509 certificates
can also be used across an SSL (Secure Sockets Layer) connection. Once a
client has connected, the server verifies whether the client has
privileges for each query it issues (e.g., whether the client is allowed
to issue a
SELECT statement that
Country table in the
MySQL parses queries to create an internal structure (the parse tree), and then applies a variety of optimizations. These can include rewriting the query, determining the order in which it will read tables, choosing which indexes to use, and so on. You can pass hints to the optimizer through special keywords in the query, affecting its decision-making process. You can also ask the server to explain various aspects of optimization. This lets you know what decisions the server is making and gives you a reference point for reworking queries, schemas, and settings to make everything run as efficiently as possible. We discuss the optimizer in much more detail in Chapter 6.
The optimizer does not really care what storage engine a particular table uses, but the storage engine does affect how the server optimizes the query. The optimizer asks the storage engine about some of its capabilities and the cost of certain operations, and for statistics on the table data. For instance, some storage engines support index types that can be helpful to certain queries. You can read more about indexing and schema optimization in Chapter 4 and Chapter 5.
Before even parsing the query, though, the server consults the
query cache, which can store only
SELECT statements, along with their result
sets. If anyone issues a query that’s identical to one already in the
cache, the server doesn’t need to parse, optimize, or execute the query
at all—it can simply pass back the stored result set. We write more
about that in Chapter 7.
Anytime more than one query needs to change data at the same time, the problem of concurrency control arises. For our purposes in this chapter, MySQL has to do this at two levels: the server level and the storage engine level. Concurrency control is a big topic to which a large body of theoretical literature is devoted, so we will just give you a simplified overview of how MySQL deals with concurrent readers and writers, so you have the context you need for the rest of this chapter.
We’ll use an email box on a Unix system as an example. The classic mbox file format is very simple. All the messages in an mbox mailbox are concatenated together, one after another. This makes it very easy to read and parse mail messages. It also makes mail delivery easy: just append a new message to the end of the file.
But what happens when two processes try to deliver messages at the same time to the same mailbox? Clearly that could corrupt the mailbox, leaving two interleaved messages at the end of the mailbox file. Well-behaved mail delivery systems use locking to prevent corruption. If a client attempts a second delivery while the mailbox is locked, it must wait to acquire the lock itself before delivering its message.
This scheme works reasonably well in practice, but it gives no support for concurrency. Because only a single process can change the mailbox at any given time, this approach becomes problematic with a high-volume mailbox.
Reading from the mailbox isn’t as troublesome. There’s nothing wrong with multiple clients reading the same mailbox simultaneously; because they aren’t making changes, nothing is likely to go wrong. But what happens if someone tries to delete message number 25 while programs are reading the mailbox? It depends, but a reader could come away with a corrupted or inconsistent view of the mailbox. So, to be safe, even reading from a mailbox requires special care.
If you think of the mailbox as a database table and each mail message as a row, it’s easy to see that the problem is the same in this context. In many ways, a mailbox is really just a simple database table. Modifying rows in a database table is very similar to removing or changing the content of messages in a mailbox file.
The solution to this classic problem of concurrency control is rather simple. Systems that deal with concurrent read/write access typically implement a locking system that consists of two lock types. These locks are usually known as shared locks and exclusive locks, or read locks and write locks.
Without worrying about the actual locking technology, we can describe the concept as follows. Read locks on a resource are shared, or mutually nonblocking: many clients can read from a resource at the same time and not interfere with each other. Write locks, on the other hand, are exclusive—i.e., they block both read locks and other write locks—because the only safe policy is to have a single client writing to the resource at a given time and to prevent all reads when a client is writing.
In the database world, locking happens all the time: MySQL has to prevent one client from reading a piece of data while another is changing it. It performs this lock management internally in a way that is transparent much of the time.
One way to improve the concurrency of a shared resource is to be more selective about what you lock. Rather than locking the entire resource, lock only the part that contains the data you need to change. Better yet, lock only the exact piece of data you plan to change. Minimizing the amount of data that you lock at any one time lets changes to a given resource occur simultaneously, as long as they don’t conflict with each other.
The problem is locks consume resources. Every lock operation—getting a lock, checking to see whether a lock is free, releasing a lock, and so on—has overhead. If the system spends too much time managing locks instead of storing and retrieving data, performance can suffer.
A locking strategy is a compromise between lock overhead and data safety, and that compromise affects performance. Most commercial database servers don’t give you much choice: you get what is known as row-level locking in your tables, with a variety of often complex ways to give good performance with many locks.
MySQL, on the other hand, does offer choices. Its storage engines can implement their own locking policies and lock granularities. Lock management is a very important decision in storage engine design; fixing the granularity at a certain level can give better performance for certain uses, yet make that engine less suited for other purposes. Because MySQL offers multiple storage engines, it doesn’t require a single general-purpose solution. Let’s have a look at the two most important lock strategies.
The most basic locking strategy available in MySQL, and the one with the lowest overhead, is table locks. A table lock is analogous to the mailbox locks described earlier: it locks the entire table. When a client wishes to write to a table (insert, delete, update, etc.), it acquires a write lock. This keeps all other read and write operations at bay. When nobody is writing, readers can obtain read locks, which don’t conflict with other read locks.
Table locks have variations for good performance in specific
situations. For example,
table locks allow some types of concurrent write operations. Write
locks also have a higher priority than read locks, so a request for a
write lock will advance to the front of the lock queue even if readers
are already in the queue (write locks can advance past read locks in
the queue, but read locks cannot advance past write locks).
Although storage engines can manage their own locks, MySQL
itself also uses a variety of locks that are effectively table-level
for various purposes. For instance, the server uses a table-level lock
for statements such as
regardless of the storage engine.
The locking style that offers the greatest concurrency (and carries the greatest overhead) is the use of row locks. Row-level locking, as this strategy is commonly known, is available in the InnoDB and XtraDB storage engines, among others. Row locks are implemented in the storage engine, not the server (refer back to the logical architecture diagram if you need to). The server is completely unaware of locks implemented in the storage engines, and as you’ll see later in this chapter and throughout the book, the storage engines all implement locking in their own ways.
You can’t examine the more advanced features of a database system for very long before transactions enter the mix. A transaction is a group of SQL queries that are treated atomically, as a single unit of work. If the database engine can apply the entire group of queries to a database, it does so, but if any of them can’t be done because of a crash or other reason, none of them is applied. It’s all or nothing.
Little of this section is specific to MySQL. If you’re already familiar with ACID transactions, feel free to skip ahead to Transactions in MySQL.
A banking application is the classic example of why transactions are
necessary. Imagine a bank’s database with two tables:
savings. To move $200 from Jane’s checking
account to her savings account, you need to perform at least three
Make sure her checking account balance is greater than $200.
Subtract $200 from her checking account balance.
Add $200 to her savings account balance.
The entire operation should be wrapped in a transaction so that if any one of the steps fails, any completed steps can be rolled back.
You start a transaction with the
TRANSACTION statement and then either make its changes permanent
COMMIT or discard the changes with
ROLLBACK. So, the SQL for our sample
transaction might look like this:
1 START TRANSACTION; 2 SELECT balance FROM checking WHERE customer_id = 10233276; 3 UPDATE checking SET balance = balance - 200.00 WHERE customer_id = 10233276; 4 UPDATE savings SET balance = balance + 200.00 WHERE customer_id = 10233276; 5 COMMIT;
But transactions alone aren’t the whole story. What happens if the database server crashes while performing line 4? Who knows? The customer probably just lost $200. And what if another process comes along between lines 3 and 4 and removes the entire checking account balance? The bank has given the customer a $200 credit without even knowing it.
Transactions aren’t enough unless the system passes the ACID test. ACID stands for Atomicity, Consistency, Isolation, and Durability. These are tightly related criteria that a well-behaved transaction processing system must meet:
A transaction must function as a single indivisible unit of work so that the entire transaction is either applied or rolled back. When transactions are atomic, there is no such thing as a partially completed transaction: it’s all or nothing.
The database should always move from one consistent state to the next. In our example, consistency ensures that a crash between lines 3 and 4 doesn’t result in $200 disappearing from the checking account. Because the transaction is never committed, none of the transaction’s changes are ever reflected in the database.
The results of a transaction are usually invisible to other transactions until the transaction is complete. This ensures that if a bank account summary runs after line 3 but before line 4 in our example, it will still see the $200 in the checking account. When we discuss isolation levels, you’ll understand why we said usually invisible.
Once committed, a transaction’s changes are permanent. This means the changes must be recorded such that data won’t be lost in a system crash. Durability is a slightly fuzzy concept, however, because there are actually many levels. Some durability strategies provide a stronger safety guarantee than others, and nothing is ever 100% durable (if the database itself were truly durable, then how could backups increase durability?). We discuss what durability really means in MySQL in later chapters.
ACID transactions ensure that banks don’t lose your money. It is generally extremely difficult or impossible to do this with application logic. An ACID-compliant database server has to do all sorts of complicated things you might not realize to provide ACID guarantees.
Just as with increased lock granularity, the downside of this extra
security is that the database server has to do more work. A database
server with ACID transactions also generally requires more CPU power,
memory, and disk space than one without them. As we’ve said several times,
this is where MySQL’s storage engine architecture works to your advantage.
You can decide whether your application needs transactions. If you don’t
really need them, you might be able to get higher performance with a
nontransactional storage engine for some kinds of queries. You might be
able to use
LOCK TABLES to give the
level of protection you need without transactions. It’s all up to
Isolation is more complex than it looks. The SQL standard defines four isolation levels, with specific rules for which changes are and aren’t visible inside and outside a transaction. Lower isolation levels typically allow higher concurrency and have lower overhead.
Each storage engine implements isolation levels slightly differently, and they don’t necessarily match what you might expect if you’re used to another database product (thus, we won’t go into exhaustive detail in this section). You should read the manuals for whichever storage engines you decide to use.
Let’s take a quick look at the four isolation levels:
UNCOMMITTED isolation level, transactions can view the
results of uncommitted transactions. At this level, many problems
can occur unless you really, really know what you are doing and
have a good reason for doing it. This level is rarely used in
practice, because its performance isn’t much better than the other
levels, which have many advantages. Reading uncommitted data is
also known as a dirty read.
The default isolation level for most database
systems (but not MySQL!) is
COMMITTED. It satisfies the simple definition of
isolation used earlier: a transaction will see only those changes
made by transactions that were already committed when it began,
and its changes won’t be visible to others until it has committed.
This level still allows what’s known as a nonrepeatable read. This means
you can run the same statement twice and see different
solves the problems that
UNCOMMITTED allows. It guarantees that any rows a
transaction reads will “look the same” in subsequent reads within
the same transaction, but in theory it still allows another tricky
problem: phantom reads. Simply put, a
phantom read can happen when you select some range of rows,
another transaction inserts a new row into the range, and then you
select the same range again; you will then see the new “phantom”
row. InnoDB and XtraDB solve the phantom read problem with
multiversion concurrency control, which we explain later in this
REPEATABLE READ is
MySQL’s default transaction isolation level.
The highest level of isolation,
SERIALIZABLE, solves the phantom read
problem by forcing transactions to be ordered so that they can’t
possibly conflict. In a nutshell,
SERIALIZABLE places a lock on every row
it reads. At this level, a lot of timeouts and lock contention can
occur. We’ve rarely seen people use this isolation level, but your
application’s needs might force you to accept the decreased
concurrency in favor of the data stability that results.
Table 1-1 summarizes the various isolation levels and the drawbacks associated with each one.
A deadlock is when two or more
transactions are mutually holding and requesting locks on the same
resources, creating a cycle of dependencies. Deadlocks occur when
transactions try to lock resources in a different order. They can happen
whenever multiple transactions lock the same resources. For example,
consider these two transactions running against the
START TRANSACTION; UPDATE StockPrice SET close = 45.50 WHERE stock_id = 4 and date = '2002-05-01'; UPDATE StockPrice SET close = 19.80 WHERE stock_id = 3 and date = '2002-05-02'; COMMIT;
START TRANSACTION; UPDATE StockPrice SET high = 20.12 WHERE stock_id = 3 and date = '2002-05-02'; UPDATE StockPrice SET high = 47.20 WHERE stock_id = 4 and date = '2002-05-01'; COMMIT;
If you’re unlucky, each transaction will execute its first query and update a row of data, locking it in the process. Each transaction will then attempt to update its second row, only to find that it is already locked. The two transactions will wait forever for each other to complete, unless something intervenes to break the deadlock.
To combat this problem, database systems implement various forms of deadlock detection and timeouts. The more sophisticated systems, such as the InnoDB storage engine, will notice circular dependencies and return an error instantly. This can be a good thing—otherwise, deadlocks would manifest themselves as very slow queries. Others will give up after the query exceeds a lock wait timeout, which is not always good. The way InnoDB currently handles deadlocks is to roll back the transaction that has the fewest exclusive row locks (an approximate metric for which will be the easiest to roll back).
Lock behavior and order are storage engine–specific, so some storage engines might deadlock on a certain sequence of statements even though others won’t. Deadlocks have a dual nature: some are unavoidable because of true data conflicts, and some are caused by how a storage engine works.
Deadlocks cannot be broken without rolling back one of the transactions, either partially or wholly. They are a fact of life in transactional systems, and your applications should be designed to handle them. Many applications can simply retry their transactions from the beginning.
Transaction logging helps make transactions more efficient. Instead of updating the tables on disk each time a change occurs, the storage engine can change its in-memory copy of the data. This is very fast. The storage engine can then write a record of the change to the transaction log, which is on disk and therefore durable. This is also a relatively fast operation, because appending log events involves sequential I/O in one small area of the disk instead of random I/O in many places. Then, at some later time, a process can update the table on disk. Thus, most storage engines that use this technique (known as write-ahead logging) end up writing the changes to disk twice.
If there’s a crash after the update is written to the transaction log but before the changes are made to the data itself, the storage engine can still recover the changes upon restart. The recovery method varies between storage engines.
MySQL provides two transactional storage engines: InnoDB and NDB Cluster. Several third-party engines are also available; the best-known engines right now are XtraDB and PBXT. We discuss some specific properties of each engine in the next section.
MySQL operates in
AUTOCOMMIT mode by
default. This means that unless you’ve explicitly begun a transaction,
it automatically executes each query in a separate transaction. You
can enable or disable
for the current connection by setting a variable:
SHOW VARIABLES LIKE 'AUTOCOMMIT';+---------------+-------+ | Variable_name | Value | +---------------+-------+ | autocommit | ON | +---------------+-------+ 1 row in set (0.00 sec) mysql>
SET AUTOCOMMIT = 1;
are equivalent, as are
OFF. When you run with
=0, you are
always in a transaction, until you issue a
ROLLBACK. MySQL then starts a new
transaction immediately. Changing the value of
AUTOCOMMIT has no effect on nontransactional
tables, such as MyISAM or Memory tables, which have no notion of
committing or rolling back changes.
Certain commands, when issued during an open transaction, cause
MySQL to commit the transaction before they execute. These are
typically Data Definition Language (DDL) commands that make
significant changes, such as
ALTER TABLE, but
LOCK TABLES and some
other statements also have this effect. Check your version’s
documentation for the full list of commands that automatically commit
MySQL lets you set the isolation level using the
SET TRANSACTION ISOLATION
LEVEL command, which takes effect when the next transaction
starts. You can set the isolation level for the whole server in the
configuration file, or just for your session:
SET SESSION TRANSACTION ISOLATION LEVEL READ COMMITTED;
MySQL recognizes all four ANSI standard isolation levels, and InnoDB supports all of them.
MySQL doesn’t manage transactions at the server level. Instead, the underlying storage engines implement transactions themselves. This means you can’t reliably mix different engines in a single transaction.
If you mix transactional and nontransactional tables (for instance, InnoDB and MyISAM tables) in a transaction, the transaction will work properly if all goes well.
However, if a rollback is required, the changes to the nontransactional table can’t be undone. This leaves the database in an inconsistent state from which it might be difficult to recover and renders the entire point of transactions moot. This is why it is really important to pick the right storage engine for each table.
MySQL will not usually warn you or raise errors if you do transactional operations on a nontransactional table. Sometimes rolling back a transaction will generate the warning “Some nontransactional changed tables couldn’t be rolled back,” but most of the time, you’ll have no indication you’re working with nontransactional tables.
InnoDB uses a two-phase locking protocol. It can acquire
locks at any time during a transaction, but it does not release them
ROLLBACK. It releases all the locks at the
same time. The locking mechanisms described earlier are all implicit.
InnoDB handles locks
automatically, according to your isolation level.
However, InnoDB also supports explicit locking, which the SQL standard does not mention at all:
SELECT ... LOCK IN SHARE
SELECT ... FOR
MySQL also supports the
commands, which are implemented in the server, not in the storage
engines. These have their uses, but they are not a substitute for
transactions. If you need transactions, use a transactional storage
We often see applications that have been converted from MyISAM
to InnoDB but are still using
TABLES. This is no longer necessary because of row-level locking, and it can cause severe performance
The interaction between
TABLES and transactions is complex, and there are
unexpected behaviors in some server versions. Therefore, we
recommend that you never use
TABLES unless you are in a transaction and
AUTOCOMMIT is disabled, no matter what
storage engine you are using.
Most of MySQL’s transactional storage engines don’t use a simple row-locking mechanism. Instead, they use row-level locking in conjunction with a technique for increasing concurrency known as multiversion concurrency control (MVCC). MVCC is not unique to MySQL: Oracle, PostgreSQL, and some other database systems use it too, although there are significant differences because there is no standard for how MVCC should work.
You can think of MVCC as a twist on row-level locking; it avoids the need for locking at all in many cases and can have much lower overhead. Depending on how it is implemented, it can allow nonlocking reads, while locking only the necessary rows during write operations.
MVCC works by keeping a snapshot of the data as it existed at some point in time. This means transactions can see a consistent view of the data, no matter how long they run. It also means different transactions can see different data in the same tables at the same time! If you’ve never experienced this before, it might be confusing, but it will become easier to understand with familiarity.
Each storage engine implements MVCC differently. Some of the variations include optimistic and pessimistic concurrency control. We’ll illustrate one way MVCC works by explaining a simplified version of InnoDB’s behavior.
InnoDB implements MVCC by storing with each row two additional,
hidden values that record when the row was created and when it was expired
(or deleted). Rather than storing the actual times at which these events
occurred, the row stores the system version number at the time each event
occurred. This is a number that increments each time a transaction begins.
Each transaction keeps its own record of the current system version, as of
the time it began. Each query has to check each row’s version numbers
against the transaction’s version. Let’s see how this applies to
particular operations when the transaction isolation level is set to
InnoDB must find a version of the row that is at least as old as the transaction (i.e., its version must be less than or equal to the transaction’s version). This ensures that either the row existed before the transaction began, or the transaction created or altered the row.
The row’s deletion version must be undefined or greater than the transaction’s version. This ensures that the row wasn’t deleted before the transaction began.
Rows that pass both tests may be returned as the query’s result.
InnoDB records the current system version number with the new row.
InnoDB records the current system version number as the row’s deletion ID.
InnoDB writes a new copy of the row, using the system version number for the new row’s version. It also writes the system version number as the old row’s deletion version.
The result of all this extra record keeping is that most read queries never acquire locks. They simply read data as fast as they can, making sure to select only rows that meet the criteria. The drawbacks are that the storage engine has to store more data with each row, do more work when examining rows, and handle some additional housekeeping operations.
MVCC works only with the
READ COMMITTED isolation
READ UNCOMMITTED isn’t
MVCC-compatible because queries don’t read the row version that’s appropriate for their transaction
version; they read the newest version, no matter what.
MVCC-compatible because reads lock every row they return.
This section gives an overview of MySQL’s storage engines. We won’t go into great detail here, because we discuss storage engines and their particular behaviors throughout the book. Even this book, though, isn’t a complete source of documentation; you should read the MySQL manuals for the storage engines you decide to use.
MySQL stores each database (also called a schema) as a subdirectory of its data
directory in the underlying filesystem. When you create a table, MySQL
stores the table definition in a .frm file with the same name as the
table. Thus, when you create a table named
MyTable, MySQL stores the table definition in
MyTable.frm. Because MySQL uses the filesystem to
store database names and table definitions, case sensitivity depends on
the platform. On a Windows MySQL instance, table and database names are
case insensitive; on Unix-like
systems, they are case sensitive. Each storage engine stores the table’s
data and indexes differently, but the server itself handles the table
You can use the
SHOW TABLE STATUS command
(or in MySQL 5.0 and newer versions, query the
tables) to display information about tables. For example, to examine the
user table in the
mysql database, execute the following:
SHOW TABLE STATUS LIKE 'user' \G*************************** 1. row *************************** Name: user Engine: MyISAM Row_format: Dynamic Rows: 6 Avg_row_length: 59 Data_length: 356 Max_data_length: 4294967295 Index_length: 2048 Data_free: 0 Auto_increment: NULL Create_time: 2002-01-24 18:07:17 Update_time: 2002-01-24 21:56:29 Check_time: NULL Collation: utf8_bin Checksum: NULL Create_options: Comment: Users and global privileges 1 row in set (0.00 sec)
The output shows that this is a MyISAM table. You might also notice a lot of other information and statistics in the output. Let’s look briefly at what each line means:
The table’s name.
The table’s storage engine. In old versions of MySQL, this
column was named
The row format. For a MyISAM table, this can be
Compressed. Dynamic rows vary in length
because they contain variable-length fields such as
BLOB. Fixed rows, which are always the
same size, are made up of fields that don’t vary in length, such as
INTEGER. Compressed rows exist only in
compressed tables; see Compressed MyISAM tables.
The number of rows in the table. For MyISAM and most other engines, this number is always accurate. For InnoDB, it is an estimate.
How many bytes the average row contains.
How much data (in bytes) the entire table contains.
The maximum amount of data this table can hold. This is engine-specific.
How much disk space the index data consumes.
For a MyISAM table, the amount of space that is allocated but
currently unused. This space holds previously deleted rows and can
be reclaimed by future
When the table was first created.
When data in the table last changed.
When the table was last checked using
CHECK TABLE or
The default character set and collation for character columns in this table.
A live checksum of the entire table’s contents, if enabled.
Any other options that were specified when the table was created.
This field contains a variety of extra information. For a MyISAM table, it contains the comments, if any, that were set when the table was created. If the table uses the InnoDB storage engine, the amount of free space in the InnoDB tablespace appears here. If the table is a view, the comment contains the text “VIEW.”
InnoDB is the default transactional storage engine for MySQL and the most important and broadly useful engine overall. It was designed for processing many short-lived transactions that usually complete rather than being rolled back. Its performance and automatic crash recovery make it popular for nontransactional storage needs, too. You should use InnoDB for your tables unless you have a compelling need to use a different engine. If you want to study storage engines, it is also well worth your time to study InnoDB in depth to learn as much as you can about it, rather than studying all storage engines equally.
InnoDB has a complex release history, but it’s very helpful to understand it. In 2008, the so-called InnoDB plugin was released for MySQL 5.1. This was the next generation of InnoDB created by Oracle, which at that time owned InnoDB but not MySQL. For various reasons that are great to discuss over beers, MySQL continued shipping the older version of InnoDB, compiled into the server. But you could disable this and install the newer, better-performing, more scalable InnoDB plugin if you wished. Eventually, Oracle acquired Sun Microsystems and thus MySQL, and removed the older codebase, replacing it with the “plugin” by default in MySQL 5.5. (Yes, this means that now the “plugin” is actually compiled in, not installed as a plugin. Old terminology dies hard.)
The modern version of InnoDB, introduced as the InnoDB plugin in
MySQL 5.1, sports new features such as building indexes by sorting,
the ability to drop and add indexes without rebuilding the whole
table, and a new storage format that offers compression, a new way to
store large values such as
columns, and file format management. Many people who use MySQL 5.1
don’t use the plugin, sometimes because they aren’t aware of it. If
you’re using MySQL 5.1, please ensure that you’re using the InnoDB
plugin. It’s much better than the older version of InnoDB.
InnoDB is such an important engine that many people and companies have invested in developing it, not just Oracle’s team. Notable contributions have come from Google, Yasufumi Kinoshita, Percona, and Facebook, among others. Some of these improvements have been included into the official InnoDB source code, and many others have been reimplemented in slightly different ways by the InnoDB team. In general, InnoDB’s development has accelerated greatly in the last few years, with major improvements to instrumentation, scalability, configurability, performance, features, and support for Windows, among other notable items. MySQL 5.6 lab previews and milestone releases include a remarkable palette of new features for InnoDB, too.
Oracle is investing tremendous resources in improving InnoDB performance, and doing a great job of it (a considerable amount of external contribution has helped with this, too). In the second edition of this book, we noted that InnoDB failed pretty miserably beyond four CPU cores. It now scales well to 24 CPU cores, and arguably up to 32 or even more cores depending on the scenario. Many improvements are slated for the upcoming 5.6 release, but there are still opportunities for enhancement.
InnoDB stores its data in a series of one or more data files that are collectively known as a tablespace. A tablespace is essentially a black box that InnoDB manages all by itself. In MySQL 4.1 and newer versions, InnoDB can store each table’s data and indexes in separate files. InnoDB can also use raw disk partitions for building its tablespace, but modern filesystems make this unnecessary.
InnoDB uses MVCC to achieve high concurrency, and it implements
all four SQL standard isolation levels. It defaults to the
REPEATABLE READ isolation level, and it has
locking strategy that prevents phantom reads in
this isolation level: rather than locking only the rows you’ve touched
in a query, InnoDB locks gaps in the index structure as well,
preventing phantoms from being inserted.
InnoDB tables are built on a clustered index, which we will cover in detail in later chapters. InnoDB’s index structures are very different from those of most other MySQL storage engines. As a result, it provides very fast primary key lookups. However, secondary indexes (indexes that aren’t the primary key) contain the primary key columns, so if your primary key is large, other indexes will also be large. You should strive for a small primary key if you’ll have many indexes on a table. The storage format is platform-neutral, meaning you can copy the data and index files from an Intel-based server to a PowerPC or Sun SPARC without any trouble.
InnoDB has a variety of internal optimizations. These include predictive read-ahead for prefetching data from disk, an adaptive hash index that automatically builds hash indexes in memory for very fast lookups, and an insert buffer to speed inserts. We cover these later in this book.
InnoDB’s behavior is very intricate, and we highly recommend reading the “InnoDB Transaction Model and Locking” section of the MySQL manual if you’re using InnoDB. There are many subtleties you should be aware of before building an application with InnoDB, because of its MVCC architecture. Working with a storage engine that maintains consistent views of the data for all users, even when some users are changing data, can be complex.
As a transactional storage engine, InnoDB supports truly “hot” online backups through a variety of mechanisms, including Oracle’s proprietary MySQL Enterprise Backup and the open source Percona XtraBackup. MySQL’s other storage engines can’t take hot backups—to get a consistent backup, you have to halt all writes to the table, which in a mixed read/write workload usually ends up halting reads too.
As MySQL’s default storage engine in versions 5.1 and older, MyISAM provides a large list of features, such as full-text indexing, compression, and spatial (GIS) functions. MyISAM doesn’t support transactions or row-level locks. Its biggest weakness is undoubtedly the fact that it isn’t even remotely crash-safe. MyISAM is why MySQL still has the reputation of being a nontransactional database management system, more than a decade after it gained transactions! Still, MyISAM isn’t all that bad for a nontransactional, non-crash-safe storage engine. If you need read-only data, or if your tables aren’t large and won’t be painful to repair, it isn’t out of the question to use it. (But please, don’t use it by default. Use InnoDB instead.)
MyISAM typically stores each table in two files: a data file and an index file. The two files bear .MYD and .MYI extensions, respectively. MyISAM tables can contain either dynamic or static (fixed-length) rows. MySQL decides which format to use based on the table definition. The number of rows a MyISAM table can hold is limited primarily by the available disk space on your database server and the largest file your operating system will let you create.
MyISAM tables created in MySQL 5.0 with variable-length rows are
configured by default to handle 256 TB of data, using 6-byte pointers
to the data records. Earlier MySQL versions defaulted to 4-byte
pointers, for up to 4 GB of data. All MySQL versions can handle a
pointer size of up to 8 bytes. To change the pointer size on a MyISAM
table (either up or down), you must alter the table with new values
AVG_ROW_LENGTH options that represent
ballpark figures for the amount of space you need. This will cause the
entire table and all of its indexes to be rewritten, which might take
a long time.
As one of the oldest storage engines included in MySQL, MyISAM has many features that have been developed over years of use to fill niche needs:
MyISAM locks entire tables, not rows. Readers obtain shared (read) locks on all tables they need to read. Writers obtain exclusive (write) locks. However, you can insert new rows into the table while select queries are running against it (concurrent inserts).
MySQL supports manual and automatic checking and
repairing of MyISAM tables, but don’t confuse this with
transactions or crash recovery. After repairing a table, you’ll
likely find that some data is simply gone. Repairing is slow,
too. You can use the
mytable commands to check a table for errors and
repair them. You can also use the myisamchk
command-line tool to check and repair tables when the server is
You can create indexes on the first 500 characters of
TEXT columns in MyISAM tables. MyISAM
supports full-text indexes, which index individual words for
complex search operations. For more information on indexing, see
MyISAM tables marked with the
DELAY_KEY_WRITE create option don’t
write changed index data to disk at the end of a query. Instead,
MyISAM buffers the changes in the in-memory key buffer. It
flushes index blocks to disk when it prunes the buffer or closes
the table. This can boost performance, but after a server or
system crash, the indexes will definitely be corrupted and will
need repair. You can configure delayed key writes globally, as
well as for individual tables.
You can compress (or “pack”) tables with the myisampack utility. You can’t modify compressed tables (although you can uncompress, modify, and recompress tables if you need to), but they generally use less space on disk. As a result, they offer faster performance, because their smaller size requires fewer disk seeks to find records. Compressed MyISAM tables can have indexes, but they’re read-only.
The overhead of decompressing the data to read it is insignificant for most applications on modern hardware, where the real gain is in reducing disk I/O. The rows are compressed individually, so MySQL doesn’t need to unpack an entire table (or even a page) just to fetch a single row.
Because of its compact data storage and low overhead due to its simpler design, MyISAM can provide good performance for some uses. It does have some severe scalability limitations, including mutexes on key caches. MariaDB offers a segmented key cache that avoids this problem. The most common MyISAM performance problem we see, however, is table locking. If your queries are all getting stuck in the “Locked” status, you’re suffering from table-level locking.
MySQL has a variety of special-purpose storage engines. Many of them are somewhat deprecated in newer versions, for various reasons. Some of these are still available in the server, but must be enabled specially.
The Archive engine supports only
SELECT queries, and it does not support
indexes until MySQL 5.1. It causes much less disk I/O than MyISAM,
because it buffers data writes and compresses each row with zlib as it’s inserted. Also, each
SELECT query requires a full table
scan. Archive tables are thus best for logging and data acquisition,
where analysis tends to scan an entire table, or where you want fast
Archive supports row-level locking and a special buffer system
for high-concurrency inserts. It gives consistent reads by stopping a
SELECT after it has retrieved the
number of rows that existed in the table when the query began. It also
makes bulk inserts invisible until they’re complete. These features
emulate some aspects of transactional and MVCC behaviors, but Archive
is not a transactional storage engine. It is simply a storage engine
that’s optimized for high-speed inserting and compressed
The Blackhole engine has no storage mechanism at all. It
INSERT instead of
storing it. However, the server writes queries against Blackhole
tables to its logs, so they can be replicated or simply kept in the
log. That makes the Blackhole engine popular for fancy replication
setups and audit logging, although we’ve seen enough problems caused
by such setups that we don’t recommend them.
The CSV engine can treat comma-separated values (CSV) files as tables, but it does not support indexes on them. This engine lets you copy files into and out of the database while the server is running. If you export a CSV file from a spreadsheet and save it in the MySQL server’s data directory, the server can read it immediately. Similarly, if you write data to a CSV table, an external program can read it right away. CSV tables are thus useful as a data interchange format.
This storage engine is sort of a proxy to other servers. It opens a client connection to another server and executes queries against a table there, retrieving and sending rows as needed. It was originally marketed as a competitor to features supported in many enterprise-grade proprietary database servers, such as Microsoft SQL Server and Oracle, but that was always a stretch, to say the least. Although it seemed to enable a lot of flexibility and neat tricks, it has proven to be a source of many problems and is disabled by default. A successor to it, FederatedX, is available in MariaDB.
Memory tables (formerly called
HEAP tables) are
useful when you need fast access to data that either never changes or
doesn’t need to persist after a restart. Memory tables can be up to an
order of magnitude faster than MyISAM tables. All of their data is
stored in memory, so queries don’t have to wait for disk I/O. The
table structure of a Memory table persists across a server restart,
but no data survives.
Here are some good uses for Memory tables:
Memory tables support
HASH indexes, which
are very fast for lookup queries. Although Memory tables are very
fast, they often don’t work well as a general-purpose replacement for disk-based tables. They
use table-level locking, which gives low write concurrency. They do
BLOB column types,
and they support only fixed-size rows, so they really store
CHARs, which can waste memory. (Some of
these limitations are lifted in Percona Server.)
MySQL uses the Memory engine internally while processing queries
that require a temporary table to hold intermediate results. If the
intermediate result becomes too large for a Memory table, or has
BLOB columns, MySQL will convert it to a
MyISAM table on disk. We say more about this in later chapters.
People often confuse Memory tables with temporary tables, which are ephemeral tables created
CREATE TEMPORARY TABLE.
Temporary tables can use any storage engine; they are not the same
thing as tables that use the Memory storage engine. Temporary tables
are visible only to a single connection and disappear entirely when
the connection closes.
The Merge engine is a variation of MyISAM. A Merge table is the combination of several identical MyISAM tables into one virtual table. This can be useful when you use MySQL in logging and data warehousing applications, but it has been deprecated in favor of partitioning (see Chapter 7).
MySQL AB acquired the NDB database from Sony Ericsson in 2003 and built the NDB Cluster storage engine as an interface between the SQL used in MySQL and the native NDB protocol. The combination of the MySQL server, the NDB Cluster storage engine, and the distributed, shared-nothing, fault-tolerant, highly available NDB database is known as MySQL Cluster. We discuss MySQL Cluster later in this book.
Because MySQL offers a pluggable storage engine API, beginning around 2007 a bewildering array of storage engines started springing up to serve special purposes. Some of these were included with the server, but most were third-party products or open source projects. We’ll discuss a few of the storage engines that we’ve observed to be useful enough that they remain relevant even as the diversity has thinned out a bit.
Percona’s XtraDB storage engine, which is included with Percona Server and MariaDB, is a modified version of InnoDB. Its improvements are targeted at performance, measurability, and operational flexibility. It is a drop-in replacement for InnoDB with the ability to read and write InnoDB’s data files compatibly, and to execute all queries that InnoDB can execute.
There are several other OLTP storage engines that are roughly
similar to InnoDB in some important ways, such as offering ACID
compliance and MVCC. One is PBXT, the creation of Paul McCullagh and Primebase GMBH. It sports
engine-level replication, foreign key constraints, and an intricate
architecture that positions it for solid-state storage and efficient
handling of large values such as
BLOBs. PBXT is widely regarded as a community storage engine
and is included with MariaDB.
TokuDB uses a new index data structure called Fractal Trees, which are cache-oblivious, so they don’t slow down as they get larger than memory, nor do they age or fragment. TokuDB is marketed as a Big Data storage engine, because it has high compression ratios and can support lots of indexes on large data volumes. At the time of writing it is in early production release status, and has some important limitations around concurrency. This makes it best suited for use cases such as analytical datasets with high insertion rates, but that could change in future versions.
RethinkDB was originally positioned as a storage engine designed for solid-state storage, although it seems to have become less niched as time has passed. Its most distinctive technical characteristic could be said to be its use of an append-only copy-on-write B-Tree index data structure. It is still in early development, and we’ve neither evaluated it nor seen it in use.
Falcon was promoted as the next-generation transactional storage engine for MySQL around the time of Sun’s acquisition of MySQL AB, but it has long since been canceled. Jim Starkey, the primary architect of Falcon, has founded a new company to build a cloud-enabled NewSQL database called NuoDB (formerly NimbusDB).
MySQL is row-oriented by default, meaning that each row’s data is stored together, and the server works in units of rows as it executes queries. But for very large volumes of data, a column-oriented approach can be more efficient; it allows the engine to retrieve less data when full rows aren’t needed, and when each column is stored separately, it can often be compressed more effectively.
The leading column-oriented storage engine is Infobright, which works well at very large sizes (tens of terabytes). It is designed for analytical and data warehousing use cases. It works by storing data in blocks, which are highly compressed. It maintains a set of metadata for each block, which allows it to skip blocks or even to complete queries simply by looking at the metadata. It has no indexes—that’s the point; at such huge sizes, indexes are useless, and the block structure is a kind of quasi-index. Infobright requires a customized version of the server, because portions of the server have to be rewritten to work with column-oriented data. Some queries can’t be executed by the storage engine in column-oriented mode, and cause the server to fall back to row-by-row mode, which is slow. Infobright is available in both open source–community and proprietary commercial versions.
Another column-oriented storage engine is Calpont’s InfiniDB, which is also available in commercial and community versions. InfiniDB offers the ability to distribute queries across a cluster of machines. We haven’t seen anyone use it in production, though.
By the way, if you’re in the market for a column-oriented database that isn’t MySQL, we’ve also evaluated LucidDB and MonetDB. You can find benchmarks and opinions on the MySQL Performance Blog, although they will probably become somewhat outdated as time passes.
A full list of community storage engines would run into the scores, and perhaps even to triple digits if we researched them exhaustively. However, it’s safe to say that most of them serve very limited niches, and many aren’t known or used by more than a few people. We’ll just mention a few of them. We haven’t seen most of these in production use. Caveat emptor!
Aria, formerly named Maria, is the original MySQL creator’s planned successor to MyISAM. It’s available in MariaDB. Many of the features that were planned for it seem to have been deferred in favor of improvements elsewhere in the MariaDB server. At the time of writing it is probably best to describe it as a crash-safe version of MyISAM, with several other improvements such as the ability to cache data (not just indexes) in its own memory.
This engine provides a SQL interface to the Sphinx full-text search server, which we discuss more in Appendix F.
This engine supports vertical partitioning of tables through a sort of proxy storage engine. That is, you can chop a table into several sets of columns and store those independently, but query them as a single table. It’s by the same author as the Spider engine.
Which engine should you use? InnoDB is usually the right choice, which is why we’re glad that Oracle made it the default engine in MySQL 5.5. The decision of which engine to use can be summed up by saying, “Use InnoDB unless you need a feature it doesn’t provide, and for which there is no good alternative approach.” For example, when we need full-text search, we usually prefer to use InnoDB in combination with Sphinx, rather than choosing MyISAM for its full-text indexing capabilities. Sometimes we choose something other than InnoDB when we don’t need InnoDB’s features and another engine provides a compelling benefit without downsides. For instance, we might use MyISAM when its limited scalability, poor support for concurrency, and lack of crash resilience aren’t an issue, but InnoDB’s increased space consumption is a problem.
We prefer not to mix and match different storage engines unless absolutely needed. It makes things much more complicated and exposes you to a whole new set of potential bugs and edge-case behaviors. The interactions between the storage engines and the server are complex enough without adding multiple storage engines into the mix. For example, multiple storage engines make it difficult to perform consistent backups or to configure the server properly.
If you believe that you do need a different engine, here are some factors you should consider:
If your application requires transactions, InnoDB
(or XtraDB) is the most stable, well-integrated, proven choice.
MyISAM is a good choice if a task doesn’t require transactions and
issues primarily either
INSERT queries. Sometimes
specific components of an application (such as logging) fall into
The need to perform regular backups might also influence your choice. If your server can be shut down at regular intervals for backups, the storage engines are equally easy to deal with. However, if you need to perform online backups, you basically need InnoDB.
If you have a lot of data, you should seriously consider how long it will take to recover from a crash. MyISAM tables become corrupt more easily and take much longer to recover than InnoDB tables. In fact, this is one of the most important reasons why a lot of people use InnoDB when they don’t need transactions.
Finally, you sometimes find that an application relies on particular features or optimizations that only some of MySQL’s storage engines provide. For example, a lot of applications rely on clustered index optimizations. On the other hand, only MyISAM supports geospatial search inside MySQL. If a storage engine meets one or more critical requirements, but not others, you need to either compromise or find a clever design solution. You can often get what you need from a storage engine that seemingly doesn’t support your requirements.
You don’t need to decide right now. There’s a lot of material on each storage engine’s strengths and weaknesses in the rest of the book, and lots of architecture and design tips as well. In general, there are probably more options than you realize yet, and it might help to come back to this question after reading more. If you’re not sure, just stick with InnoDB. It’s a safe default and there’s no reason to choose anything else if you don’t know yet what you need.
These topics might seem rather abstract without some sort of real-world context, so let’s consider some common database applications. We’ll look at a variety of tables and determine which engine best matches with each table’s needs. We give a summary of the options in the next section.
Suppose you want to use MySQL to log a record of every telephone call from a central telephone switch in real time. Or maybe you’ve installed mod_log_sql for Apache, so you can log all visits to your website directly in a table. In such an application, speed is probably the most important goal; you don’t want the database to be the bottleneck. The MyISAM and Archive storage engines would work very well because they have very low overhead and can insert thousands of records per second.
Things will get interesting, however, if you decide it’s time to start running reports to summarize the data you’ve logged. Depending on the queries you use, there’s a good chance that gathering data for the report will significantly slow the process of inserting records. What can you do?
One solution is to use MySQL’s built-in replication feature to clone the data onto a second server, and then run your time- and CPU-intensive queries against the data on the replica. This leaves the master free to insert records and lets you run any query you want on the replica without worrying about how it might affect the real-time logging.
You can also run queries at times of low load, but don’t rely on this strategy continuing to work as your application grows.
Another option is to log to a table that contains the year and
name or number of the month in its name, such as
web_logs_2012_jan. While you’re busy running
queries against tables that are no longer being written to, your
application can log records to its current table uninterrupted.
Tables that contain data used to construct a catalog or listing of some sort (jobs, auctions, real estate, etc.) are usually read from far more often than they are written to. This seemingly makes them good candidates for MyISAM—if you don’t mind what happens when MyISAM crashes. Don’t underestimate how important this is; a lot of users don’t really understand how risky it is to use a storage engine that doesn’t even try to get their data written to disk. (MyISAM just writes the data to memory and assumes the operating system will flush it to disk sometime later.)
It’s an excellent idea to run a realistic load simulation on a test server and then literally pull the power plug. The firsthand experience of recovering from a crash is priceless. It saves nasty surprises later.
Don’t just believe the common “MyISAM is faster than InnoDB” folk wisdom. It is not categorically true. We can name dozens of situations where InnoDB leaves MyISAM in the dust, especially for applications where clustered indexes are useful or where the data fits in memory. As you read the rest of this book, you’ll get a sense of which factors influence a storage engine’s performance (data size, number of I/O operations required, primary keys versus secondary indexes, etc.), and which of them matter to your application.
When we design systems such as these, we use InnoDB. MyISAM might seem to work okay in the beginning, but it will absolutely fall on its face when the application gets busy. Everything will lock up, and you’ll lose data when you have a server crash.
When you deal with any sort of order processing, transactions are all but required. Half-completed orders aren’t going to endear customers to your service. Another important consideration is whether the engine needs to support foreign key constraints. InnoDB is your best bet for order-processing applications.
Threaded discussions are an interesting problem for MySQL users. There are hundreds of freely available PHP and Perl-based systems that provide threaded discussions. Many of them aren’t written with database efficiency in mind, so they tend to run a lot of queries for each request they serve. Some were written to be database-independent, so their queries do not take advantage of the features of any one database system. They also tend to update counters and compile usage statistics about the various discussions. Many of the systems also use a few monolithic tables to store all their data. As a result, a few central tables become the focus of heavy read and write activity, and the locks required to enforce consistency become a substantial source of contention.
Despite their design shortcomings, most of these systems work well for small and medium loads. However, if a website grows large enough and generates significant traffic, it will become very slow. The obvious solution is to switch to a different storage engine that can handle the heavy read/write volume, but users who attempt this are sometimes surprised to find that the system runs even more slowly than it did before!
What these users don’t realize is that the system is using a particular query, normally something like this:
SELECT COUNT(*) FROM table;
The problem is that not all engines can run that query quickly: MyISAM can, but other engines might not. There are similar examples for every engine. Later chapters will help you keep such a situation from catching you by surprise and show you how to find and fix the problems if it does.
If you ever need to distribute a CD-ROM- or DVD-ROM-based application that uses MySQL data files, consider using MyISAM or compressed MyISAM tables, which can easily be isolated and copied to other media. Compressed MyISAM tables use far less space than uncompressed ones, but they are read-only. This can be problematic in certain applications, but because the data is going to be on read-only media anyway, there’s little reason not to use compressed tables for this particular task.
How big is too big? We’ve built and managed—or helped build and manage—many InnoDB databases in the 3 TB to 5 TB range, or even larger. That’s on a single server, not sharded. It’s perfectly feasible, although you have to choose your hardware wisely, practice smart physical design, and plan for your server to be I/O-bound. At these sizes, MyISAM is just a nightmare when it crashes.
If you’re going really big, such as tens of terabytes, you’re probably building a data warehouse. In this case, Infobright is where we’ve seen the most success. Some very large databases that aren’t suitable for Infobright might be candidates for TokuDB instead.
ALTER TABLE mytable ENGINE = InnoDB;
This syntax works for all storage engines, but there’s a catch: it can take a lot of time. MySQL will perform a row-by-row copy of your old table into a new table. During that time, you’ll probably be using all of the server’s disk I/O capacity, and the original table will be read-locked while the conversion runs. So, take care before trying this technique on a busy table. Instead, you can use one of the methods discussed next, which involve making a copy of the table first.
When you convert from one storage engine to another, any storage engine–specific features are lost. For example, if you convert an InnoDB table to MyISAM and back again, you will lose any foreign keys originally defined on the InnoDB table.
To gain more control over the conversion process, you
might choose to first dump the table to a text file using the
mysqldump utility. Once you’ve dumped the table,
you can simply edit the dump file to adjust the
CREATE TABLE statement it contains. Be sure
to change the table name as well as its type, because you can’t have
two tables with the same name in the same database even if they are of
different types—and mysqldump defaults to writing
DROP TABLE command
CREATE TABLE, so you
might lose your data if you are not careful!
The third conversion technique is a compromise between
the first mechanism’s speed and the safety of the second. Rather than
dumping the entire table or converting it all at once, create the new
table and use MySQL’s
INSERT ... SELECT
syntax to populate it, as follows:
CREATE TABLE innodb_table LIKE myisam_table;mysql>
ALTER TABLE innodb_table ENGINE=InnoDB;mysql>
INSERT INTO innodb_table SELECT * FROM myisam_table;
That works well if you don’t have much data, but if you do, it’s
often more efficient to populate the table incrementally, committing
the transaction between each chunk so the undo logs don’t grow huge.
id is the primary
key, run this query repeatedly (using larger values of
each time) until you’ve copied all the data to the new table:
INSERT INTO innodb_table SELECT * FROM myisam_table->
WHERE id BETWEEN x AND y;mysql>
After doing so, you’ll be left with the original table, which you can drop when you’re done with it, and the new table, which is now fully populated. Be careful to lock the original table if needed to prevent getting an inconsistent copy of the data!
It is helpful to understand MySQL’s version history as a frame of reference when choosing which version of the server you want to run. Plus, it’s kind of fun for old-timers to remember what it used to be like in the good old days!
This release of MySQL is generally regarded as the moment MySQL “arrived” and became a viable option for widespread use. MySQL was still not much more than a query language over flat files, but MyISAM was introduced to replace ISAM, an older and much more limited storage engine. InnoDB was available, but was not shipped in the standard binary distribution because it was so new. If you wanted to use InnoDB, you had to compile the server yourself with support for it. Version 3.23 introduced full-text indexing and replication. Replication was to become the killer feature that propelled MySQL to fame as the database that powered much of the Internet.
New syntax features appeared, such as support for
UNION and multi-table
DELETE statements. Replication was
rewritten to use two threads on the replica, instead of one thread
that did all the work and suffered from task switching. InnoDB was
shipped as a standard part of the server, with its full feature set:
row-level locking, foreign keys, and so on. The query cache was
introduced in version 4.0 (and hasn’t changed much since then).
Support for SSL connections was also introduced.
More query syntax features were introduced, including
INSERT ON DUPLICATE KEY
UPDATE. The UTF-8 character set was supported. There was a
new binary protocol and prepared statement support.
A number of “enterprise” features appeared in this release: views, triggers, stored procedures, and stored functions. The ISAM engine was removed completely, but new storage engines such as Federated were introduced.
This release was the first under Sun Microsystems’s ownership after its acquisition of MySQL AB, and was over five years in the making. Version 5.1 introduced partitioning, row-based replication, and a variety of plugin APIs, including the pluggable storage engine API. The BerkeleyDB storage engine—MySQL’s first transactional storage engine—was removed and some others, such as Federated, were deprecated. Also, Oracle, now the owner of Innobase Oy, released the InnoDB plugin storage engine.
MySQL 5.5 was the first release following Oracle’s acquisition
of Sun (and therefore MySQL). It focused on improvements to
performance, scalability, replication, partitioning, and support for
Microsoft Windows, but included many other improvements as well.
InnoDB became the default storage engine, and many legacy features and deprecated options and behaviors
were scrubbed. The
_SCHEMA database was added,
along with a first batch of enhanced instrumentation. New
plugin APIs for replication, authentication, and auditing were
added. A plugin for semisynchronous replication was available, and
Oracle released commercial plugins for authentication and thread
pooling in 2011. There were also major architectural changes to
InnoDB, such as a partitioned buffer pool.
MySQL 5.6 will have a raft of new features, including the
first major improvements to the query optimizer in many years, more
plugin APIs (e.g., one for full-text search), replication
improvements, and greatly expanded instrumentation in the
PERFORMANCE_SCHEMA database. The InnoDB
team is also hard at work, with a huge variety of changes and
improvements having been released in development milestones and lab
previews. Whereas MySQL 5.5 seemed to be about firming up and fixing
the fundamentals, with a limited number of new introductions, MySQL
5.6 appears to be focused on advancing server development and
performance, using 5.5’s success as a springboard.
Version 6.0 is confusing because of the overlapping chronology. It was announced during the 5.1 development years. There were rumors or promises of many new features, such as online backups and server-level foreign keys for all storage engines, subquery improvements, and thread pooling. This release was canceled, and Sun resumed development with version 5.4, which was eventually released as version 5.5. Many of the features of the 6.0 codebase have been (or will be) released in versions 5.5 and 5.6.
We’d summarize MySQL’s history this way: it was clearly a disruptive innovation early in its lifecycle, with limited and sometimes second-class functionality, but its features and low price made it a killer application to power the explosion of the Internet. In the early 5.x releases, it tried to move into enterprise territory with features such as views and stored procedures, but these were buggy and brittle, so it wasn’t always smooth sailing. In hindsight, MySQL 5.0’s flood of bug fixes didn’t settle down until around the 5.0.50 releases, and MySQL 5.1 didn’t fare much better. The 5.0 and 5.1 releases were delayed, and the Sun and Oracle acquisitions made many observers fearful. But in our opinion, things are on track: MySQL 5.5 was the highest-quality release in MySQL’s history, Oracle’s ownership is making MySQL much more palatable to enterprise customers, and version 5.6 promises great improvements in functionality and performance.
Speaking of performance, we thought it would be interesting to show a basic benchmark of the server’s performance over time. We decided not to benchmark versions older than 4.1, because it’s very rare to see 4.0 and older in production these days. In addition, an apples-to-apples benchmark is very hard to produce across so many different versions, for reasons you’ll read more about in the next chapter. We had lots of fun crafting a benchmark method that would work uniformly across the server versions that we did use, and it took many tries to get it right. Table 1-2 shows the results in transactions per second for several levels of concurrency.
Table 1-2. Readonly benchmarks of several MySQL versions
MySQL 5.1 with InnoDB plugin
[a] At the time of our benchmark, MySQL 5.6 was not yet released as GA.
This is a little easier to see in graphical form, which we’ve shown in Figure 1-2.
Before we interpret the results, we need to tell you a little bit about the benchmark itself. We ran it on our Cisco UCS C250 machine, which has two six-core CPUs, each with two hardware threads. The server contains 384 GB of RAM, but we ran the benchmark with a 2.5 GB dataset, so we configured MySQL with a 4 GB buffer pool. The benchmark was the standard SysBench read-only workload, with all data in InnoDB, fully in-memory and CPU-bound. We ran the benchmark for 60 minutes for each measurement point, measuring throughput every 10 seconds and using 900 seconds of measurements after the server warmed up and stabilized to generate the final results.
Now, looking at the results, two broad trends are clear. First, MySQL versions that include the InnoDB plugin perform much better at higher concurrency, which is to say that they are more scalable. This is to be expected, because we know older versions are seriously limited at high concurrency. Second, newer MySQL versions are slower than older versions in single-threaded workloads, which you might not have expected but is easily explained by noting that this is a very simple read-only workload. Newer server versions have a more complex SQL grammar, and lots of other features and improvements that enable more complex queries but are simply additional overhead for the simple queries we’re benchmarking here. Older versions of the server are simpler and thus have an advantage for simple queries.
We wanted to show you a more complex read/write benchmark (such as TPC-C) over a broader range of concurrencies, but we found it ultimately impossible to do across such a diversity of server versions. We can say that in general, newer versions of the server have better and more consistent performance on more complex workloads, especially at higher concurrency, and with a larger dataset.
Which version should you use? This depends on your business more than on your technical needs. You should ideally build on the newest version that’s available, but of course you might choose to wait until the first bugs have been worked out of a brand-new release. If you’re building an application that’s not in production yet, you might even consider building it on the upcoming release so that you delay your upgrade lifecycle as much as possible.
MySQL’s development process and release model have changed greatly over the years, but now appear to have settled down into a steady rhythm. Oracle releases new development milestones periodically, with previews of features that will eventually be included in the next GA release. These are for testing and feedback, not for production use, but Oracle’s statement is that they’re high quality and essentially ready to release at any time—and we see no reason to disagree with that. Oracle also periodically releases lab previews, which are special builds that include only a selected feature for interested parties to evaluate. These features are not guaranteed to be included in the next release of the server. And finally, once in a while Oracle will bundle up the features it deems to be ready and ship a new GA release of the server.
MySQL remains GPL-licensed and open source, with the full source code (except for commercially licensed plugins, of course) available to the community. Oracle seems to understand that it would be unwise to ship different versions of the server to the community and its paying customers. MySQL AB tried that, which resulted in its paying customers becoming the bleeding-edge guinea pigs, robbing them of the benefit of community testing and feedback. That policy was the reverse of what enterprise customers need, and was discontinued in the Sun days.
Now that Oracle is releasing some server plugins for paying customers only, MySQL is for all intents and purposes following the so-called open-core model. Although there’s been some murmuring over the release of proprietary plugins for the server, it comes from a minority and has sometimes been exaggerated. Most MySQL users we know (and we know a lot of them) don’t seem to mind. The commercially licensed, pay-only plugins are acceptable to those users who actually need them.
In any case, the proprietary extensions are just that: extensions. They do not represent a crippleware development model, and the server is more than adequate without them. Frankly, we appreciate the way that Oracle is building more features as plugins. If the features were built right into the server with no API, there would be no choice: you’d get exactly one implementation, with limited opportunity to build something that suited you better. For example, if Oracle eventually releases InnoDB’s full-text search functionality as a plugin, it will be an opportunity to use the same API to develop a similar plugin for Sphinx or Lucene, which many people might find more useful. We also appreciate clean APIs inside the server. They help to promote higher-quality code, and who doesn’t want that?
MySQL has a layered architecture, with server-wide services and query execution on top and storage engines underneath. Although there are many different plugin APIs, the storage engine API is the most important. If you understand that MySQL executes queries by handing rows back and forth across the storage engine API, you’ve grasped one of the core fundamentals of the server’s architecture.
MySQL was built around ISAM (and later MyISAM), and multiple storage
engines and transactions were added later. Many of the server’s quirks
reflect this legacy. For example, the way that MySQL commits transactions
when you execute an
ALTER TABLE is a
direct result of the storage engine architecture, as well as the fact that
the data dictionary is stored in .frm files. (There’s
nothing in InnoDB that forces an
to be nontransactional, by the way; absolutely everything InnoDB does is
The storage engine API has its downsides. Sometimes choice isn’t a good thing, and the explosion of storage engines in the heady days of the 5.0 and 5.1 versions of MySQL might have introduced too much choice. In the end, InnoDB turns out to be a very good storage engine for something like 95% or more of users (that’s just a rough guess). All those other engines usually just make things more complicated and brittle, although there are special cases where an alternative is definitely called for.
Oracle’s acquisition of first InnoDB and then MySQL brought both products under one roof, where they can be codeveloped. This appears to be working out well for everyone: InnoDB and the server itself are getting better by leaps and bounds in many ways, MySQL remains GPL’ed and fully open source, the community and customers alike are getting a solid and stable database, and the server is becoming ever more extensible and useful.
 One exception is InnoDB, which does parse foreign key definitions, because the MySQL server doesn’t yet implement them itself.
 MySQL 5.5 and newer versions support an API that can accept thread-pooling plugins, so a small pool of threads can service many connections.
 There is no formal standard that defines MVCC, so different engines and databases implement it very differently, and no one can say any of them is wrong.
 Oracle also now owns BerkeleyDB.
 The term “disruptive innovation” originated in Clayton M. Christensen’s book The Innovator’s Dilemma (Harper).
 GA stands for generally available, which means “production quality” to pointy-haired bosses.