For whatever reason, this was created as a varchar rather than a char. Is it worth the trouble of migrating the database to convert it to a char? This is in MySQL 5. With CHAR fields, what you allocate is exactly what you get. For example, CHAR 15 allocates and stores 15 bytes, no matter how characters you place in the field.
String manipulation is simple and straightforward since the size of the data field is totally predictable. For example VARCHAR 15 actually allocates dynamically up to 16 bytes, up to 15 for data and, at least, 1 additional byte to store the the length of the data.
If you have the string 'hello' to store that will take 6 bytes, not 5. String manipulation must always perform some form of length checking in all cases. The tradeoff is more evident when you do two things: 1. Storing millions or billions of rows 2. This is not any conjecture on my part. The example in the book did something like the following:.
It worked as published. However, it did produce a table almost double in size, but that simply goes back to tradeoff 1. You could analyze the data being stored to see what MySQL recommends for column definition. Just run the following against any table:. This will traverse the entire table and recommend column definitions for every column based on the data it contains, the minimum field values, maximum field values, and so forth.
Here is a good example:. If you are storing IP addresses, the mask for such a column is at most 15 characters xxx. I would jump right at CHAR 15 in a heartbeat because the lengths of IP addresses will not vary all that much and the added complexity of string manipulation controlled by an additional byte. With great power comes great responsibility cliche but true. When it comes to MD5, the computation of strlen internally should be eliminated when switching the entire row format.
There would be no need to change the field definition. In exchange, the table would expand to about twice its size. Thanks to Ovais see comments for pointing out that char 32 can use a lot more than 32 bytes if you are using a multibyte character set.
Thanks to Rick James for pointing out that you should use the unhex function to convert the a hex string to binary:. It is not worth changing in my opinion. Here is a demonstration though. In addition the database engine can calculate the jump forward page better when searching the pages.
Note: all columns must be fixed-width for the advantage. Even 1 variable width column ruins the calculate-ahead performance edge. Be careful with generalizations. That tests proves absolutely nothing. The avg length for the productnumber column is 8.
That is pretty high. And the bigger difference between max7specified length compared to the avg length, the more advantage variable length types will have. For instance, I changed the length in the script from 10 to 15 and cut down what is actually stored to 6, just as a different example.
This time variable length table costs pages and fixed length costs pages. So, the answer is, as always "It depends". There are so many factors coming into play, so without knowing pretty much details about this particular case, we can only say so much. The "it depends" answer is of course much more meaningful when the one reading that reply understand the architecture of SQL Server, like difference in row layout, how SQL Server can traverse an index and things like that.
With that knowledge and a specific case, an informed decision can be made. The only generalization I like to do here is "If the data is ov variable length nature, then go with varchar. And vice versa. Use varchar above Here is an amazing proof though. ProdFix is only smaller because the rows lack the null bitmap and the whole variable-width column section.
If you have additional variable-width columns on the table, and use string values that aren't the maximum length then VARCHAR will save space, and usually therefore improve performance.
So this is really the only place I would use CHAR: where the strings are always the same length, within a couple of characters. Note also that CHAR is only stored in the fixed-width portion of the row for uncompressed tables. For compressed tables all columns are stored as variable-width. Binary strings are very similar to conventional strings, but they store bytes instead of characters.
These types are useful when you need to store binary data and want MySQL to compare the values as bytes instead of characters. The advantage of byte-wise comparisons is more than just a matter of case insensitivity.
As a result, binary comparisons can be much simpler than character comparisons, so they are faster. Is there any advantage to using the shorter column?
As it turns out, there is a big advantage. The larger column can use much more memory, because MySQL often allocates fixed-size chunks of memory to hold values internally. This is especially bad for sorting or operations that use in-memory temporary tables. The same thing happens with filesorts that use on-disk temporary tables. Each value requires from one to four bytes of storage space in the row and enough space in external storage to actually hold the value.
This can result in a serious performance overhead. The worst-case length allocation also applies to sorting of values, so this trick can help with both kinds of problems: creating large temporary tables and sort files, and creating them on disk. Suppose you have a table with 10 million rows, which uses a couple of gigabytes on disk. This can use up to 3 bytes per character, for a worst-case size of 3, bytes.
Sometimes you can use an ENUM column instead of conventional string types. An ENUM column can store a predefined set of distinct string values. MySQL stores them very compactly, packed into one or two bytes depending on the number of values in the list. The three rows actually store integers, not strings. You can see the dual nature of the values by retrieving them in a numeric context:. Another surprise is that an ENUM field sorts by the internal integer values, not by the strings themselves:.
You can work around this by specifying ENUM members in the order in which you want them to sort. This is usually offset by their smaller size, but not always. To illustrate, we benchmarked how quickly MySQL performs such a join on a table in one of our applications. The table has a fairly wide primary key:. The table contains about , rows and is only about 10 MB, so it fits entirely in memory.
The service column contains 5 distinct values with an average length of 4 characters, and the method column contains 71 values with an average length of 20 characters. We made a copy of this table and converted the service and method columns to ENUM , as follows:. We then measured the performance of joining the tables by the primary key columns. Here is the query we used:. Table shows the results. Also, the primary key itself is only about half the size after the conversion.
Because this is an InnoDB table, if there are any other indexes on this table, reducing the primary key size will make them much smaller, too. We explain this in the next chapter. The finest granularity of time MySQL can store is one second. MariaDB has microsecond-granularity temporal types. Most of the temporal types have no alternatives, so there is no question of which one is the best choice. The only question is what to do when you need to store both the date and the time.
For many applications, either will work, but in some cases, one works better than the other. This type can hold a large range of values, from the year to the year , with a precision of one second. This uses eight bytes of storage space. This is the ANSI standard way to represent dates and times. The MySQL server, operating system, and client connections all have time zone settings. The former preserves values relative to the time zone in use, while the latter preserves the textual representation of the date and time.
The integer format is often less convenient to deal with, so we do not recommend doing this. What if you need to store a date and time value with subsecond resolution? MySQL currently does not have an appropriate data type for this, but you can use your own storage format: you can use the BIGINT data type and store the value as a timestamp in microseconds, or you can use a DOUBLE and store the fractional part of the second after the decimal point.
Both approaches will work well. MySQL has a few storage types that use individual bits within a value to store data compactly.
All of these types are technically string types, regardless of the underlying storage format and manipulations:. Before MySQL 5. But in MySQL 5. We discuss the new behavior here.
BIT behavior varies between storage engines. MyISAM rounds that to three bytes for storage. However, if you retrieve the value in a numeric context, the result is the number to which the bit string converts. Keep this in mind if you need to compare the result to another value.
For example, if you store the value b'' which is the binary equivalent of 57 into a BIT 8 column and retrieve it, you will get the string containing the character code This can be very confusing, so we recommend that you use BIT with caution. For most applications, we think it is a better idea to avoid this type.
This column is capable of storing either the absence of a value NULL or a zero-length value the empty string. An alternative to SET is to use an integer as a packed set of bits.
You can make this easier by defining named constants for each bit in your application code. The drawback is that your queries are harder to write and understand what does it mean when bit 5 is set? An example application for packed bits is an access control list ACL that stores permissions. Choosing a good data type for an identifier column is very important. When choosing a type for an identifier column, you need to consider not only the storage type, but also how MySQL performs computations and comparisons on that type.
Once you choose a type, make sure you use the same type in all related tables. Choose the smallest size that can hold your required range of values, and leave room for future growth if necessary. If you use this value as a foreign key in other tables, three bytes can make a big difference.
Here are a few tips:. You could add columns to the lookup table for descriptive text, to generate a glossary, or to provide meaningful labels in a pull-down menu on a website.
Avoid string types for identifiers if possible, because they take up a lot of space and are generally slower than integer types.
MyISAM uses packed indexes for strings by default, which can make lookups much slower. This causes page splits, random disk accesses, and clustered index fragmentation for clustered storage engines.
More about this in the next chapter. Random values cause caches to perform poorly for all types of queries because they defeat locality of reference, which is how caching works. You can retrieve the values in hexadecimal format with the HEX function.
Badly written schema migration programs and programs that autogenerate schemas can cause severe performance problems. Be sure to double-check a schema if it was created for you automatically. Sometimes they store each property of each object in a separate row, even using timestamp-based versioning, so there are multiple versions of each property! This design may appeal to developers, because it lets them work in an object-oriented fashion without needing to think about how the data is stored.
A timestamp with subsecond resolution is one example; we showed you some options for storing such data earlier in the chapter. Another example is an IPv4 address. However, they are really unsigned bit integers, not strings. The dotted-quad notation is just a way of writing it out so that humans can read it more easily. You should store IP addresses as unsigned integers. Although there are universally bad and good design principles, there are also issues that arise from how MySQL is implemented, and that means you can make MySQL-specific mistakes, too.
But it can be costly to turn the row buffer into the row data structure with the decoded columns. The cost of this conversion depends on the number of columns. We discovered that this can become expensive when we investigated an issue with high CPU consumption for a customer with extremely wide tables hundreds of columns , even though only a few columns were actually used.
Even at many fewer joins than 61, however, the cost of planning and optimizing the query can become problematic for MySQL. The schema was sprinkled liberally with this pattern. An ENUM permits the column to hold one value from a set of defined values. A SET permits the column to hold one or more values from a set of defined values. Sometimes these can be easy to confuse. We wrote earlier about the benefits of avoiding NULL , and indeed we suggest considering alternatives when possible. Perhaps you can use zero, a special value, or an empty string instead.
However, you can take this to extremes. That bogus all-zeros value can cause lots of problems. There are usually many ways to represent any given data, ranging from fully normalized to fully denormalized and anything in between. In a normalized database, each fact is represented once and only once. Conversely, in a denormalized database, information is duplicated, or stored in multiple places. There are many good books on the topic and resources online; here, we just give a brief introduction to the aspects you need to know for this chapter.
The problem with this schema is that inconsistencies can occur while the data is being modified. Say Brown takes over as the head of the Accounting department. To avoid these problems, we need to normalize the table by separating the employee and department entities.
0コメント