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High Performance MySQL, 3rd Edition

Cover of High Performance MySQL, 3rd Edition by Baron Schwartz... Published by O'Reilly Media, Inc.
  1. High Performance MySQL
  2. Foreword
  3. Preface
    1. How This Book Is Organized
      1. A Broad Overview
      2. Building a Solid Foundation
      3. Configuring Your Application
      4. MySQL as an Infrastructure Component
      5. Miscellaneous Useful Topics
    2. Software Versions and Availability
    3. Conventions Used in This Book
    4. Using Code Examples
    5. Safari® Books Online
    6. How to Contact Us
    7. Acknowledgments for the Third Edition
    8. Acknowledgments for the Second Edition
      1. From Baron
      2. From Peter
      3. From Vadim
      4. From Arjen
    9. Acknowledgments for the First Edition
      1. From Jeremy
      2. From Derek
  4. 1. MySQL Architecture and History
    1. MySQL’s Logical Architecture
      1. Connection Management and Security
      2. Optimization and Execution
    2. Concurrency Control
      1. Read/Write Locks
      2. Lock Granularity
    3. Transactions
      1. Isolation Levels
      2. Deadlocks
      3. Transaction Logging
      4. Transactions in MySQL
    4. Multiversion Concurrency Control
    5. MySQL’s Storage Engines
      1. The InnoDB Engine
      2. The MyISAM Engine
      3. Other Built-in MySQL Engines
      4. Third-Party Storage Engines
      5. Selecting the Right Engine
      6. Table Conversions
    6. A MySQL Timeline
    7. MySQL’s Development Model
    8. Summary
  5. 2. Benchmarking MySQL
    1. Why Benchmark?
    2. Benchmarking Strategies
      1. What to Measure
    3. Benchmarking Tactics
      1. Designing and Planning a Benchmark
      2. How Long Should the Benchmark Last?
      3. Capturing System Performance and Status
      4. Getting Accurate Results
      5. Running the Benchmark and Analyzing Results
      6. The Importance of Plotting
    4. Benchmarking Tools
      1. Full-Stack Tools
      2. Single-Component Tools
    5. Benchmarking Examples
      1. http_load
      2. MySQL Benchmark Suite
      3. sysbench
      4. dbt2 TPC-C on the Database Test Suite
      5. Percona’s TPCC-MySQL Tool
    6. Summary
  6. 3. Profiling Server Performance
    1. Introduction to Performance Optimization
      1. Optimization Through Profiling
      2. Interpreting the Profile
    2. Profiling Your Application
      1. Instrumenting PHP Applications
    3. Profiling MySQL Queries
      1. Profiling a Server’s Workload
      2. Profiling a Single Query
      3. Using the Profile for Optimization
    4. Diagnosing Intermittent Problems
      1. Single-Query Versus Server-Wide Problems
      2. Capturing Diagnostic Data
      3. A Case Study in Diagnostics
    5. Other Profiling Tools
      1. Using the USER_STATISTICS Tables
      2. Using strace
    6. Summary
  7. 4. Optimizing Schema and Data Types
    1. Choosing Optimal Data Types
      1. Whole Numbers
      2. Real Numbers
      3. String Types
      4. Date and Time Types
      5. Bit-Packed Data Types
      6. Choosing Identifiers
      7. Special Types of Data
    2. Schema Design Gotchas in MySQL
    3. Normalization and Denormalization
      1. Pros and Cons of a Normalized Schema
      2. Pros and Cons of a Denormalized Schema
      3. A Mixture of Normalized and Denormalized
    4. Cache and Summary Tables
      1. Materialized Views
      2. Counter Tables
    5. Speeding Up ALTER TABLE
      1. Modifying Only the .frm File
      2. Building MyISAM Indexes Quickly
    6. Summary
  8. 5. Indexing for High Performance
    1. Indexing Basics
      1. Types of Indexes
    2. Benefits of Indexes
    3. Indexing Strategies for High Performance
      1. Isolating the Column
      2. Prefix Indexes and Index Selectivity
      3. Multicolumn Indexes
      4. Choosing a Good Column Order
      5. Clustered Indexes
      6. Covering Indexes
      7. Using Index Scans for Sorts
      8. Packed (Prefix-Compressed) Indexes
      9. Redundant and Duplicate Indexes
      10. Unused Indexes
      11. Indexes and Locking
    4. An Indexing Case Study
      1. Supporting Many Kinds of Filtering
      2. Avoiding Multiple Range Conditions
      3. Optimizing Sorts
    5. Index and Table Maintenance
      1. Finding and Repairing Table Corruption
      2. Updating Index Statistics
      3. Reducing Index and Data Fragmentation
    6. Summary
  9. 6. Query Performance Optimization
    1. Why Are Queries Slow?
    2. Slow Query Basics: Optimize Data Access
      1. Are You Asking the Database for Data You Don’t Need?
      2. Is MySQL Examining Too Much Data?
    3. Ways to Restructure Queries
      1. Complex Queries Versus Many Queries
      2. Chopping Up a Query
      3. Join Decomposition
    4. Query Execution Basics
      1. The MySQL Client/Server Protocol
      2. The Query Cache
      3. The Query Optimization Process
      4. The Query Execution Engine
      5. Returning Results to the Client
    5. Limitations of the MySQL Query Optimizer
      1. Correlated Subqueries
      2. UNION Limitations
      3. Index Merge Optimizations
      4. Equality Propagation
      5. Parallel Execution
      6. Hash Joins
      7. Loose Index Scans
      8. MIN() and MAX()
      9. SELECT and UPDATE on the Same Table
    6. Query Optimizer Hints
    7. Optimizing Specific Types of Queries
      1. Optimizing COUNT() Queries
      2. Optimizing JOIN Queries
      3. Optimizing Subqueries
      4. Optimizing GROUP BY and DISTINCT
      5. Optimizing LIMIT and OFFSET
      6. Optimizing SQL_CALC_FOUND_ROWS
      7. Optimizing UNION
      8. Static Query Analysis
      9. Using User-Defined Variables
    8. Case Studies
      1. Building a Queue Table in MySQL
      2. Computing the Distance Between Points
      3. Using User-Defined Functions
    9. Summary
  10. 7. Advanced MySQL Features
    1. Partitioned Tables
      1. How Partitioning Works
      2. Types of Partitioning
      3. How to Use Partitioning
      4. What Can Go Wrong
      5. Optimizing Queries
      6. Merge Tables
      1. Updatable Views
      2. Performance Implications of Views
      3. Limitations of Views
    3. Foreign Key Constraints
    4. Storing Code Inside MySQL
      1. Stored Procedures and Functions
      2. Triggers
      3. Events
      4. Preserving Comments in Stored Code
    5. Cursors
    6. Prepared Statements
      1. Prepared Statement Optimization
      2. The SQL Interface to Prepared Statements
      3. Limitations of Prepared Statements
    7. User-Defined Functions
    8. Plugins
    9. Character Sets and Collations
      1. How MySQL Uses Character Sets
      2. Choosing a Character Set and Collation
      3. How Character Sets and Collations Affect Queries
    10. Full-Text Searching
      1. Natural-Language Full-Text Searches
      2. Boolean Full-Text Searches
      3. Full-Text Changes in MySQL 5.1
      4. Full-Text Tradeoffs and Workarounds
      5. Full-Text Configuration and Optimization
    11. Distributed (XA) Transactions
      1. Internal XA Transactions
      2. External XA Transactions
    12. The MySQL Query Cache
      1. How MySQL Checks for a Cache Hit
      2. How the Cache Uses Memory
      3. When the Query Cache Is Helpful
      4. How to Configure and Maintain the Query Cache
      5. InnoDB and the Query Cache
      6. General Query Cache Optimizations
      7. Alternatives to the Query Cache
    13. Summary
  11. 8. Optimizing Server Settings
    1. How MySQL’s Configuration Works
      1. Syntax, Scope, and Dynamism
      2. Side Effects of Setting Variables
      3. Getting Started
      4. Iterative Optimization by Benchmarking
    2. What Not to Do
    3. Creating a MySQL Configuration File
      1. Inspecting MySQL Server Status Variables
    4. Configuring Memory Usage
      1. How Much Memory Can MySQL Use?
      2. Per-Connection Memory Needs
      3. Reserving Memory for the Operating System
      4. Allocating Memory for Caches
      5. The InnoDB Buffer Pool
      6. The MyISAM Key Caches
      7. The Thread Cache
      8. The Table Cache
      9. The InnoDB Data Dictionary
    5. Configuring MySQL’s I/O Behavior
      1. InnoDB I/O Configuration
      2. MyISAM I/O Configuration
    6. Configuring MySQL Concurrency
      1. InnoDB Concurrency Configuration
      2. MyISAM Concurrency Configuration
    7. Workload-Based Configuration
      1. Optimizing for BLOB and TEXT Workloads
      2. Optimizing for Filesorts
    8. Completing the Basic Configuration
    9. Safety and Sanity Settings
    10. Advanced InnoDB Settings
    11. Summary
  12. 9. Operating System and Hardware Optimization
    1. What Limits MySQL’s Performance?
    2. How to Select CPUs for MySQL
      1. Which Is Better: Fast CPUs or Many CPUs?
      2. CPU Architecture
      3. Scaling to Many CPUs and Cores
    3. Balancing Memory and Disk Resources
      1. Random Versus Sequential I/O
      2. Caching, Reads, and Writes
      3. What’s Your Working Set?
      4. Finding an Effective Memory-to-Disk Ratio
      5. Choosing Hard Disks
    4. Solid-State Storage
      1. An Overview of Flash Memory
      2. Flash Technologies
      3. Benchmarking Flash Storage
      4. Solid-State Drives (SSDs)
      5. PCIe Storage Devices
      6. Other Types of Solid-State Storage
      7. When Should You Use Flash?
      8. Using Flashcache
      9. Optimizing MySQL for Solid-State Storage
    5. Choosing Hardware for a Replica
    6. RAID Performance Optimization
      1. RAID Failure, Recovery, and Monitoring
      2. Balancing Hardware RAID and Software RAID
      3. RAID Configuration and Caching
    7. Storage Area Networks and Network-Attached Storage
      1. SAN Benchmarks
      2. Using a SAN over NFS or SMB
      3. MySQL Performance on a SAN
      4. Should You Use a SAN?
    8. Using Multiple Disk Volumes
    9. Network Configuration
    10. Choosing an Operating System
    11. Choosing a Filesystem
    12. Choosing a Disk Queue Scheduler
    13. Threading
    14. Swapping
    15. Operating System Status
      1. How to Read vmstat Output
      2. How to Read iostat Output
      3. Other Helpful Tools
      4. A CPU-Bound Machine
      5. An I/O-Bound Machine
      6. A Swapping Machine
      7. An Idle Machine
    16. Summary
  13. 10. Replication
    1. Replication Overview
      1. Problems Solved by Replication
      2. How Replication Works
    2. Setting Up Replication
      1. Creating Replication Accounts
      2. Configuring the Master and Replica
      3. Starting the Replica
      4. Initializing a Replica from Another Server
      5. Recommended Replication Configuration
    3. Replication Under the Hood
      1. Statement-Based Replication
      2. Row-Based Replication
      3. Statement-Based or Row-Based: Which Is Better?
      4. Replication Files
      5. Sending Replication Events to Other Replicas
      6. Replication Filters
    4. Replication Topologies
      1. Master and Multiple Replicas
      2. Master-Master in Active-Active Mode
      3. Master-Master in Active-Passive Mode
      4. Master-Master with Replicas
      5. Ring Replication
      6. Master, Distribution Master, and Replicas
      7. Tree or Pyramid
      8. Custom Replication Solutions
    5. Replication and Capacity Planning
      1. Why Replication Doesn’t Help Scale Writes
      2. When Will Replicas Begin to Lag?
      3. Plan to Underutilize
    6. Replication Administration and Maintenance
      1. Monitoring Replication
      2. Measuring Replication Lag
      3. Determining Whether Replicas Are Consistent with the Master
      4. Resyncing a Replica from the Master
      5. Changing Masters
      6. Switching Roles in a Master-Master Configuration
    7. Replication Problems and Solutions
      1. Errors Caused by Data Corruption or Loss
      2. Using Nontransactional Tables
      3. Mixing Transactional and Nontransactional Tables
      4. Nondeterministic Statements
      5. Different Storage Engines on the Master and Replica
      6. Data Changes on the Replica
      7. Nonunique Server IDs
      8. Undefined Server IDs
      9. Dependencies on Nonreplicated Data
      10. Missing Temporary Tables
      11. Not Replicating All Updates
      12. Lock Contention Caused by InnoDB Locking Selects
      13. Writing to Both Masters in Master-Master Replication
      14. Excessive Replication Lag
      15. Oversized Packets from the Master
      16. Limited Replication Bandwidth
      17. No Disk Space
      18. Replication Limitations
    8. How Fast Is Replication?
    9. Advanced Features in MySQL Replication
    10. Other Replication Technologies
    11. Summary
  14. 11. Scaling MySQL
    1. What Is Scalability?
      1. A Formal Definition
    2. Scaling MySQL
      1. Planning for Scalability
      2. Buying Time Before Scaling
      3. Scaling Up
      4. Scaling Out
      5. Scaling by Consolidation
      6. Scaling by Clustering
      7. Scaling Back
    3. Load Balancing
      1. Connecting Directly
      2. Introducing a Middleman
      3. Load Balancing with a Master and Multiple Replicas
    4. Summary
  15. 12. High Availability
    1. What Is High Availability?
    2. What Causes Downtime?
    3. Achieving High Availability
      1. Improving Mean Time Between Failures
      2. Improving Mean Time to Recovery
    4. Avoiding Single Points of Failure
      1. Shared Storage or Replicated Disk
      2. Synchronous MySQL Replication
      3. Replication-Based Redundancy
    5. Failover and Failback
      1. Promoting a Replica or Switching Roles
      2. Virtual IP Addresses or IP Takeover
      3. Middleman Solutions
      4. Handling Failover in the Application
    6. Summary
  16. 13. MySQL in the Cloud
    1. Benefits, Drawbacks, and Myths of the Cloud
    2. The Economics of MySQL in the Cloud
    3. MySQL Scaling and HA in the Cloud
    4. The Four Fundamental Resources
    5. MySQL Performance in Cloud Hosting
      1. Benchmarks for MySQL in the Cloud
    6. MySQL Database as a Service (DBaaS)
      1. Amazon RDS
      2. Other DBaaS Solutions
    7. Summary
  17. 14. Application-Level Optimization
    1. Common Problems
    2. Web Server Issues
      1. Finding the Optimal Concurrency
    3. Caching
      1. Caching Below the Application
      2. Application-Level Caching
      3. Cache Control Policies
      4. Cache Object Hierarchies
      5. Pregenerating Content
      6. The Cache as an Infrastructure Component
      7. Using HandlerSocket and memcached Access
    4. Extending MySQL
    5. Alternatives to MySQL
    6. Summary
  18. 15. Backup and Recovery
    1. Why Backups?
    2. Defining Recovery Requirements
    3. Designing a MySQL Backup Solution
      1. Online or Offline Backups?
      2. Logical or Raw Backups?
      3. What to Back Up
      4. Storage Engines and Consistency
      5. Replication
    4. Managing and Backing Up Binary Logs
      1. The Binary Log Format
      2. Purging Old Binary Logs Safely
    5. Backing Up Data
      1. Making a Logical Backup
      2. Filesystem Snapshots
    6. Recovering from a Backup
      1. Restoring Raw Files
      2. Restoring Logical Backups
      3. Point-in-Time Recovery
      4. More Advanced Recovery Techniques
      5. InnoDB Crash Recovery
    7. Backup and Recovery Tools
      1. MySQL Enterprise Backup
      2. Percona XtraBackup
      3. mylvmbackup
      4. Zmanda Recovery Manager
      5. mydumper
      6. mysqldump
    8. Scripting Backups
    9. Summary
  19. 16. Tools for MySQL Users
    1. Interface Tools
    2. Command-Line Utilities
    3. SQL Utilities
    4. Monitoring Tools
      1. Open Source Monitoring Tools
      2. Commercial Monitoring Systems
      3. Command-Line Monitoring with Innotop
    5. Summary
  20. A. Forks and Variants of MySQL
    1. Percona Server
    2. MariaDB
    3. Drizzle
    4. Other MySQL Variants
    5. Summary
  21. B. MySQL Server Status
    1. System Variables
      1. Thread and Connection Statistics
      2. Binary Logging Status
      3. Command Counters
      4. Temporary Files and Tables
      5. Handler Operations
      6. MyISAM Key Buffer
      7. File Descriptors
      8. Query Cache
      9. SELECT Types
      10. Sorts
      11. Table Locking
      12. InnoDB-Specific
      13. Plugin-Specific
      1. Header
      6. FILE I/O
      8. LOG
    6. Replication Status
      1. InnoDB Tables
      2. Tables in Percona Server
    8. The Performance Schema
    9. Summary
  22. C. Transferring Large Files
    1. Copying Files
      1. A Naïve Example
      2. A One-Step Method
      3. Avoiding Encryption Overhead
      4. Other Options
    2. File Copy Benchmarks
  23. D. Using EXPLAIN
    1. Invoking EXPLAIN
      1. Rewriting Non-SELECT Queries
    2. The Columns in EXPLAIN
      1. The id Column
      2. The select_type Column
      3. The table Column
      4. The type Column
      5. The possible_keys Column
      6. The key Column
      7. The key_len Column
      8. The ref Column
      9. The rows Column
      10. The filtered Column
      11. The Extra Column
    3. Tree-Formatted Output
    4. Improvements in MySQL 5.6
  24. E. Debugging Locks
    1. Lock Waits at the Server Level
      1. Table Locks
      2. The Global Read Lock
      3. Name Locks
      4. User Locks
    2. Lock Waits in InnoDB
      1. Using the INFORMATION_SCHEMA Tables
  25. F. Using Sphinx with MySQL
    1. A Typical Sphinx Search
    2. Why Use Sphinx?
      1. Efficient and Scalable Full-Text Searching
      2. Applying WHERE Clauses Efficiently
      3. Finding the Top Results in Order
      4. Optimizing GROUP BY Queries
      5. Generating Parallel Result Sets
      6. Scaling
      7. Aggregating Sharded Data
    3. Architectural Overview
      1. Installation Overview
      2. Typical Partition Use
    4. Special Features
      1. Phrase Proximity Ranking
      2. Support for Attributes
      3. Filtering
      4. The SphinxSE Pluggable Storage Engine
      5. Advanced Performance Control
    5. Practical Implementation Examples
      1. Full-Text Searching on
      2. Full-Text Searching on
      3. Optimizing Selects on
      4. Optimizing GROUP BY on
      5. Optimizing Sharded JOIN Queries on
    6. Summary
  26. Index
  27. About the Authors
  28. Colophon
  29. Copyright

Chapter 1. MySQL Architecture and History

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.

MySQL’s Logical Architecture

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.

A logical view of the MySQL server architecture

Figure 1-1. A logical view of the MySQL server architecture

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[4] or communicate with each other; they simply respond to requests from the server.

Connection Management and Security

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.[5]

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 accesses the Country table in the world database).

Optimization and Execution

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.

Concurrency Control

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.

Read/Write Locks

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.

Lock Granularity

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.

Table locks

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, READ LOCAL 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 ALTER TABLE, regardless of the storage engine.

Row locks

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: checking and savings. To move $200 from Jane’s checking account to her savings account, you need to perform at least three steps:

  1. Make sure her checking account balance is greater than $200.

  2. Subtract $200 from her checking account balance.

  3. 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 START TRANSACTION statement and then either make its changes permanent with COMMIT or discard the changes with ROLLBACK. So, the SQL for our sample transaction might look like this:

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;

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 you.

Isolation Levels

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:


In the READ 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 READ 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 data.


REPEATABLE READ solves the problems that READ 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 chapter.

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.

Table 1-1. ANSI SQL isolation levels

Isolation level

Dirty reads possible

Nonrepeatable reads possible

Phantom reads possible

Locking reads






















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 StockPrice table:

Transaction #1
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';
Transaction #2
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';

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

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.

Transactions in MySQL

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 AUTOCOMMIT for the current connection by setting a variable:

| Variable_name | Value |
| autocommit    | ON    |
1 row in set (0.00 sec)
mysql> SET AUTOCOMMIT = 1;

The values 1 and ON are equivalent, as are 0 and OFF. When you run with AUTOCOMMIT=0, you are always in a transaction, until you issue a COMMIT or 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 a transaction.

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:


MySQL recognizes all four ANSI standard isolation levels, and InnoDB supports all of them.

Mixing storage engines in transactions

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.

Implicit and explicit locking

InnoDB uses a two-phase locking protocol. It can acquire locks at any time during a transaction, but it does not release them until a COMMIT or 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:[6]



MySQL also supports the LOCK TABLES and UNLOCK TABLES 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 engine.

We often see applications that have been converted from MyISAM to InnoDB but are still using LOCK TABLES. This is no longer necessary because of row-level locking, and it can cause severe performance problems.


The interaction between LOCK TABLES and transactions is complex, and there are unexpected behaviors in some server versions. Therefore, we recommend that you never use LOCK TABLES unless you are in a transaction and AUTOCOMMIT is disabled, no matter what storage engine you are using.

Multiversion Concurrency Control

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 REPEATABLE READ:


InnoDB must examine each row to ensure that it meets two criteria:

  1. 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.

  2. 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 REPEATABLE READ and READ COMMITTED isolation levels. READ UNCOMMITTED isn’t MVCC-compatible[7] because queries don’t read the row version that’s appropriate for their transaction version; they read the newest version, no matter what. SERIALIZABLE isn’t MVCC-compatible because reads lock every row they return.

MySQL’s Storage Engines

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 definition.

You can use the SHOW TABLE STATUS command (or in MySQL 5.0 and newer versions, query the INFORMATION_SCHEMA tables) to display information about tables. For example, to examine the user table in the mysql database, execute the following:

*************************** 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
        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 Type, not Engine.


The row format. For a MyISAM table, this can be Dynamic, Fixed, or Compressed. Dynamic rows vary in length because they contain variable-length fields such as VARCHAR or BLOB. Fixed rows, which are always the same size, are made up of fields that don’t vary in length, such as CHAR and 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 INSERT statements.


The next AUTO_INCREMENT value.


When the table was first created.


When data in the table last changed.


When the table was last checked using CHECK TABLE or myisamchk.


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.”

The InnoDB Engine

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’s history

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 BLOB 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 overview

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 a next-key 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.

The MyISAM Engine

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 for the MAX_ROWS and 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.

MyISAM features

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:

Locking and concurrency

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 CHECK TABLE mytable and REPAIR TABLE 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 offline.

Index features

You can create indexes on the first 500 characters of BLOB and TEXT columns in MyISAM tables. MyISAM supports full-text indexes, which index individual words for complex search operations. For more information on indexing, see Chapter 5.

Delayed key writes

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.

Compressed MyISAM tables

Some tables never change once they’re created and filled with data. These might be well suited to compressed MyISAM 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.

MyISAM performance

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.

Other Built-in MySQL Engines

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

The Archive engine supports only INSERT and 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 INSERT queries.

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 storage.

The Blackhole engine

The Blackhole engine has no storage mechanism at all. It discards every 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

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.

The Federated engine

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.

The Memory engine

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:

  • For “lookup” or “mapping” tables, such as a table that maps postal codes to state names

  • For caching the results of periodically aggregated data

  • For intermediate results when analyzing data

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 not support TEXT or BLOB column types, and they support only fixed-size rows, so they really store VARCHARs as 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 TEXT or 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 with 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 storage engine

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).

The NDB Cluster engine

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.

Third-Party Storage Engines

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.

OLTP storage engines

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).

Column-oriented storage engines

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.

Community storage engines

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 is a full-text search storage engine that claims to offer accuracy and high speed.


This engine from Open Query supports graph operations (think “find the shortest path between nodes”) that are impractical or impossible to perform in SQL.


This engine implements a queue inside MySQL, with support for operations that SQL itself makes quite difficult or impossible to do in a single statement.


This engine provides a SQL interface to the Sphinx full-text search server, which we discuss more in Appendix F.


This engine partitions data into several partitions, effectively implementing transparent sharding, and executes your queries in parallel across shards, which can be located on different servers.


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.

Selecting the Right 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 SELECT or INSERT queries. Sometimes specific components of an application (such as logging) fall into this category.


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.

Crash recovery

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.

Special features

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_01 or 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.

Read-only or read-mostly tables

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.

Order processing

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.

Bulletin boards and threaded discussion forums

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:

mysql> 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.

CD-ROM applications

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.

Large data volumes

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.

Table Conversions

There are several ways to convert a table from one storage engine to another, each with advantages and disadvantages. In the following sections, we cover three of the most common ways.


The easiest way to move a table from one engine to another is with an ALTER TABLE statement. The following command converts mytable to InnoDB:

mysql> 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.

Dump and import

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 a DROP TABLE command before the 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:

mysql> 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. Assuming that id is the primary key, run this query repeatedly (using larger values of x and y each time) until you’ve copied all the data to the new table:

mysql> INSERT INTO innodb_table SELECT * FROM myisam_table
    -> WHERE id BETWEEN x AND y;
mysql> COMMIT;

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!

Tools such as Percona Toolkit’s pt-online-schema-change (based on Facebook’s online schema change technique) can remove the error-prone and tedious manual work from schema changes.

A MySQL Timeline

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!

Version 3.23 (2001)

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.

Version 4.0 (2003)

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.

Version 4.1 (2005)

More query syntax features were introduced, including subqueries and INSERT ON DUPLICATE KEY UPDATE. The UTF-8 character set was supported. There was a new binary protocol and prepared statement support.

Version 5.0 (2006)

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.

Version 5.1 (2008)

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,[8] released the InnoDB plugin storage engine.

Version 5.5 (2010)

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 PERFORMANCE_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.

Version 5.6 (Unreleased)

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 (Canceled)

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[9] 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 4.1

MySQL 5.0

MySQL 5.1

MySQL 5.1 with InnoDB plugin

MySQL 5.5

MySQL 5.6[a]


















































[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.

Readonly benchmarks of several MySQL versions

Figure 1-2. Readonly benchmarks of several MySQL versions

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 Model

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[10] 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 ALTER to be nontransactional, by the way; absolutely everything InnoDB does is transactional.)

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.

[4] One exception is InnoDB, which does parse foreign key definitions, because the MySQL server doesn’t yet implement them itself.

[5] 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.

[6] These locking hints are frequently abused and should usually be avoided; see Chapter 6 for more details.

[7] 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.

[8] Oracle also now owns BerkeleyDB.

[9] The term “disruptive innovation” originated in Clayton M. Christensen’s book The Innovator’s Dilemma (Harper).

[10] GA stands for generally available, which means “production quality” to pointy-haired bosses.

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