The size of a SET object is determined by the number of different set members. A SET can have a maximum of 64 members. For descriptions of WKB and internal storage formats for spatial values, see Section However, there is an overhead imposed by the binary encoding, including metadata and dictionaries needed for lookup, of the individual values stored in the JSON document.
For example, a string stored in a JSON document requires 4 to 10 bytes additional storage, depending on the length of the string and the size of the object or array in which it is stored.
Numeric Type Attributes. Out-of-Range and Overflow Handling. Date and Time Data Type Syntax. Fractional Seconds in Time Values. Conversion Between Date and Time Types. The Geometry Class Hierarchy. GeometryCollection Class. MultiLineString Class. Supported Spatial Data Formats. Geometry Well-Formedness and Validity. Spatial Reference System Support. Creating Spatial Columns. Populating Spatial Columns. The very first databases exist because it is not possible to randomly access a sequential file containing variable length data.
How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Asked 9 years, 6 months ago. Active 7 months ago. Viewed 40k times. Tallboy Tallboy You may want to look into the different database engines it uses. Some do store data in a "big file" while others are in memory. For the speed of access, it uses fixed size columns and rows, so getting to a particular record is easier than scanning all text.
It can also keep indices of data to make retrieval even faster. The default and most typically used engine is my isam. Greg but even a Memory table is stored somewhere to ensure persistence.
Bottom line is that it is stored somewhere on disk. Namphibian data in memory tables is not persistent, only the structure is — Cez. Cez so the data structure is not persistent then? Like decades old, which is really eons in our industry's terms. One consequence of that is that people had plenty of time to optimize the heck out of the DBMS code base.
You could, in theory, achieve all these things through files, but I suspect you'd end-up with something that looks awfully close to a DBMS even if you had the time and resources to actually do it.
So, why reinvent the wheel unless you didn't want the wheel in the first place ;? Furthermore, to minimize the probability of "logical" corruption due to application bugs and promote code reuse, most DBMSes support declarative constraints domain, key and referential , triggers and stored procedures.
Technically everything is a "file" including folders.. Having said that, yes relational databases, MySQL included store data in a Data file on the hard drive. Unless you wrote your own db of course.. 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. 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. This keeps all other read and write operations at bay. Table locks have variations for good performance in specific situations. 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. 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.
A transaction is a group of SQL queries that are treated atomically , as a single unit of work. Little of this section is specific to MySQL. A banking application is the classic example of why transactions are necessary.
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. So, the SQL for our sample transaction might look like this:.
What happens if the database server crashes while performing line 4? Who knows? And what if another process comes along between lines 3 and 4 and removes the entire checking account balance? 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. The database should always move from one consistent state to the next. The results of a transaction are usually invisible to other transactions until the transaction is complete. Durability is a slightly fuzzy concept, however, because there are actually many levels.
We discuss what durability really means in MySQL in later chapters. It is generally extremely difficult or impossible to do this with application logic. Just as with increased lock granularity, the downside of this extra security is that the database server has to do more work. You can decide whether your application needs transactions. Isolation is more complex than it looks. Lower isolation levels typically allow higher concurrency and have lower overhead.
You should read the manuals for whichever storage engines you decide to use. At this level, many problems can occur unless you really, really know what you are doing and have a good reason for doing it.
Reading uncommitted data is also known as a dirty read. The default isolation level for most database systems but not MySQL! This means you can run the same statement twice and see different data. InnoDB and XtraDB solve the phantom read problem with multiversion concurrency control, which we explain later in this chapter. At this level, a lot of timeouts and lock contention can occur. Table summarizes the various isolation levels and the drawbacks associated with each one.
A deadlock is when two or more transactions are mutually holding and requesting locks on the same resources, creating a cycle of dependencies. Deadlocks occur when transactions try to lock resources in a different order. They can happen whenever multiple transactions lock the same resources. For example, consider these two transactions running against the StockPrice table:. 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.
Deadlocks have a dual nature: some are unavoidable because of true data conflicts, and some are caused by how a storage engine works. Deadlocks cannot be broken without rolling back one of the transactions, either partially or wholly. They are a fact of life in transactional systems, and your applications should be designed to handle them. Many applications can simply retry their transactions from the beginning. Transaction logging helps make transactions more efficient.
Instead of updating the tables on disk each time a change occurs, the storage engine can change its in-memory copy of the data. This is very fast. The storage engine can then write a record of the change to the transaction log, which is on disk and therefore durable. 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.
The recovery method varies between storage engines. We discuss some specific properties of each engine in the next section. MySQL then starts a new transaction immediately. Certain commands, when issued during an open transaction, cause MySQL to commit the transaction before they execute. You can set the isolation level for the whole server in the configuration file, or just for your session:.
Instead, the underlying storage engines implement transactions themselves. 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.
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.
InnoDB uses a two-phase locking protocol. 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.
These have their uses, but they are not a substitute for transactions. If you need transactions, use a transactional storage engine. This is no longer necessary because of row-level locking, and it can cause severe performance problems.
Instead, they use row-level locking in conjunction with a technique for increasing concurrency known as multiversion concurrency control MVCC. 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! Each storage engine implements MVCC differently. Some of the variations include optimistic and pessimistic concurrency control. 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. InnoDB must examine each row to ensure that it meets two criteria:. InnoDB must find a version of the row that is at least as old as the transaction i. This ensures that either the row existed before the transaction began, or the transaction created or altered the row.
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. 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. 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. For example, to examine the user table in the mysql database, execute the following:. You might also notice a lot of other information and statistics in the output.
The row format. The number of rows in the table. For InnoDB, it is an estimate. This field contains a variety of extra information. 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.
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