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 extensively later in this book.
There are many surprises and exceptions you should be aware of before building an application with InnoDB. The table structure of a Memory table persists across a server restart, but no data survives. Memory tables support HASH indexes, which are very fast for lookup queries. MySQL uses the Memory engine internally while processing queries that require a temporary table to hold intermediate results.
We say more about this in later chapters. 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. Archive tables are thus ideal for logging and data acquisition, where analysis tends to scan an entire table, or where you want fast INSERT queries on a replication master.
Replication slaves can use a different storage engine for the same table, which means the table on the slave can have indexes for faster performance on analysis. See Chapter 8 for more about replication. 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.
These features emulate some aspects of transactional and MVCC behaviors, but Archive is not a transactional storage engine. This engine lets you copy files in and out of the database while the server is running. Similarly, if you write data to a CSV table, an external program can read it right away.
CSV tables are especially useful as a data interchange format and for certain kinds of logging. The Federated engine does not store data locally. Each Federated table refers to a table on a remote MySQL server, so it actually connects to a remote server for all operations.
There are many oddities and limitations in the current implementation of this engine. Because of the way the Federated engine works, we think it is most useful for single-row lookups by primary key, or for INSERT queries you want to affect a remote server. It does not perform well for aggregate queries, joins, or other basic operations.
The Blackhole engine has no storage mechanism at all. However, the server writes queries against Blackhole tables to its logs as usual, so they can be replicated to slaves or simply kept in the log.
That makes the Blackhole engine useful for fancy replication setups and audit logging. It was originally designed for high speed real-time performance requirements , with redundancy and load-balancing capabilities. Although it logged to disk, it kept all its data in memory and was optimized for primary key lookups.
There is no storage area network or other big centralized storage solution, which some other types of clusters rely on. The fragments are duplicated, so the system has multiple copies of the same data on different nodes. One physical server is usually dedicated to each node for redundancy and high availability.
The management nodes are used to retrieve the centralized configuration, and for monitoring and control of the cluster nodes. All data nodes communicate with each other, and all MySQL servers connect to all data nodes. Low network latency is critically important for NDB Cluster. This commonly results in much wasted time, because it is simply not designed as a general-purpose storage engine.
Because all data for NDB must be retrieved over the network, complex joins are extremely slow. On the other hand, single-table lookups can be very fast, because multiple data nodes each provide part of the result. You should seek out a book dedicated to the topic if you are interested in it. Falcon uses MVCC and tries to keep running transactions entirely in memory.
This makes rollbacks and recovery operations extremely fast. It supports both pessimistic and optimistic concurrency control, which no other engine currently does. It is similar to InnoDB in many ways, such as its use of clustered indexes. Solid certifies and supports the entire product. One of its distinguishing characteristics is how it uses its transaction logs and data files to avoid write-ahead logging, which reduces much of the overhead of transaction commits.
This architecture gives PBXT the potential to deal with very high write concurrency, and tests have already shown that it can be faster than InnoDB for certain operations. PBXT is a fairly new engine, and it will need to prove itself further in production environments. For example, its implementation of truly durable transactions was completed only recently, while we were writing this book.
It is designed to store and retrieve large chunks of binary data efficiently. The initial 1. Here are some highlights from the roadmap:. Various third parties offer other sometimes proprietary engines, and there are a myriad of special-purpose and experimental engines out there for example, an engine for querying web services. Some of these engines are developed informally, perhaps by just one or two engineers.
When designing MySQL-based applications, you should decide which storage engine to use for storing your data. It also helps to have a good understanding of the application as a whole and its potential for growth.
Armed with this information, you can begin to make good choices about which storage engines can do the job. If you can get away with it, it will usually make your life a lot easier if you choose one storage engine for all your tables. Although many factors can affect your decision about which storage engine s to use, it usually boils down to a few primary considerations.
Here are the main elements you should take into account:. If your application requires transactions, InnoDB is the most stable, well-integrated, proven choice at the time of this writing. However, we expect to see the up-and-coming transactional engines become strong contenders as time passes.
Sometimes specific components of an application such as logging fall into this category. How best to satisfy your concurrency requirements depends on your workload. If you need to allow a mixture of operations to run concurrently without interfering with each other, one of the engines with row-level locking should work well. The need to perform regular backups may also influence your table choices. 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 in one form or another, the choices become less clear. Chapter 11 deals with this topic in more detail. Also bear in mind that using multiple storage engines increases the complexity of backups and server tuning.
If you have a lot of data, you should seriously consider how long it will take to recover from a crash. For example, a lot of applications rely on clustered index optimizations. If a storage engine meets one or more critical requirements, but not others, you need to either compromise or find a clever design solution. In general, there are probably more options than you realize yet, and it might help to come back to this question after reading more.
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. The MyISAM and Archive storage engines would work very well because they have very low overhead and can insert thousands of records per second. The PBXT storage engine is also likely to be particularly suitable for logging purposes. What can you do?
This leaves the master free to insert records and lets you run any query you want on the slave without worrying about how it might affect the real-time logging. Another option is to use a Merge table. Tables that contain data used to construct a catalog or listing of some sort jobs, auctions, real estate, etc. The firsthand experience of recovering from a crash is priceless.
It saves nasty surprises later. 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. When you deal with any sort of order processing, transactions are all but required.
Another important consideration is whether the engine needs to support foreign key constraints. At the time of this writing, InnoDB is likely to be your best bet for order-processing applications, though any of the transactional storage engines is a candidate.
Many clients could be trying to read and write to the table simultaneously, so row-level locking or a design that minimizes updates is the way to go. Threaded discussions are an interesting problem for MySQL users. There are hundreds of freely available PHP and Perl-based systems that provide threaded discussions.
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 the systems work well for small and medium loads. By default, the variable is set to AUTO or 1 and concurrent inserts are handled as just described. If the variable is set to ALWAYS or 2 , concurrent inserts at the end of the table are permitted even for tables that have deleted rows. See Section This is done to ensure that you can re-create an exact copy of your tables by applying the log during a backup operation. See Section 5. In addition, for those statements a read lock is placed on the selected-from table such that inserts into that table are blocked.
The effect is that concurrent inserts for that table must wait as well. It also causes concurrent inserts not to be used. However, this cannot be used if you are going to manipulate the database using processes external to the server while you hold the lock.
Optimizing SQL Statements. Index Merge Optimization. Engine Condition Pushdown Optimization. Index Condition Pushdown Optimization. Nested Join Optimization. Outer Join Optimization. Everyone who logs into my application can view records and I am worried that at some point two users might update or insert a record at the same time. Does MySQL handle this type of concurrency issue gracefully, or is this something that I am going to have to program into my code?
If I do have to program it into my code how do you go about handling a concurrency case like this? Nobody can change the Sold variable during this statement. It is always incremented by 1, even if somebody else is executing the same statement concurrently.
Between these queries, another user can change the table Cars and update Sold. To prevent this, wrap it in a transaction:. Skip to main content. There are currently no items in your cart. Create Account No credit card required. Request new password An e-mail will be sent to your registered e-mail address. What code is in the image?
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