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Docs/manual.texi
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......@@ -4113,7 +4113,7 @@ Nothing; We aim towards full ANSI 92 / ANSI 99 compliancy.
@node Comparisons, , TODO, Introduction
@section How MySQL Compares to Other Open Source Databases
@section How MySQL Compares to Other Databases
@cindex databases, MySQL vs. others
@cindex comparisons, MySQL vs. others
......@@ -4124,9 +4124,10 @@ tests against @code{Oracle}, @code{DB/2}, @code{Microsoft SQL Server}
and other commercial products. Due to legal reasons we are restricted
from publishing some of those benchmarks in our reference manual.
This section includes a comparison with @code{PostgreSQL} as it is
also an Open Source database. If you have benchmark results that we
can publish, please contact us at @email{benchmarks@@mysql.com}.
This section includes a comparison with @code{mSQL} for historical
reasons and with @code{PostgreSQL} as it is also an Open Source
database. If you have benchmark results that we can publish, please
contact us at @email{benchmarks@@mysql.com}.
For comparative lists of all supported functions and types as well
as measured operational limits of many different database systems,
......@@ -4134,12 +4135,513 @@ see the @code{crash-me} web page at
@uref{http://www.mysql.com/information/crash-me.php}.
@menu
* Compare PostgreSQL:: How MySQL Compares to PostgreSQL
* Compare mSQL:: How MySQL compares to @code{mSQL}
* Compare PostgreSQL:: How MySQL Compares to @code{PostgreSQL}
@end menu
@node Compare PostgreSQL, , Comparisons, Comparisons
@subsection How MySQL Compares to PostgreSQL
@node Compare mSQL, Compare PostgreSQL, Comparisons, Comparisons
@subsection How MySQL Compares to @code{mSQL}
@cindex mSQL, MySQL vs mSQL, overview
@table @strong
@item Performance
For a true comparison of speed, consult the growing MySQL benchmark
suite. @xref{MySQL Benchmarks}.
Because there is no thread creation overhead, a small parser, few
features, and simple security, @code{mSQL} should be quicker at:
@itemize @bullet
@item
Tests that perform repeated connects and disconnects, running a very
simple query during each connection.
@item
@code{INSERT} operations into very simple tables with few columns and keys.
@item
@code{CREATE TABLE} and @code{DROP TABLE}.
@item
@code{SELECT} on something that isn't an index. (A table scan is very
easy.)
@end itemize
Because these operations are so simple, it is hard to be better at
them when you have a higher startup overhead. After the connection
is established, MySQL should perform much better.
On the other hand, MySQL is much faster than @code{mSQL} (and
most other SQL implementations) on the following:
@itemize @bullet
@item
Complex @code{SELECT} operations.
@item
Retrieving large results (MySQL has a better, faster, and safer
protocol).
@item
Tables with variable-length strings, because MySQL has more efficient
handling and can have indexes on @code{VARCHAR} columns.
@item
Handling tables with many columns.
@item
Handling tables with large record lengths.
@item
@code{SELECT} with many expressions.
@item
@code{SELECT} on large tables.
@item
Handling many connections at the same time. MySQL is fully
multi-threaded. Each connection has its own thread, which means that
no thread has to wait for another (unless a thread is modifying
a table another thread wants to access). In @code{mSQL}, once one
connection is established, all others must wait until the first has
finished, regardless of whether the connection is running a query
that is short or long. When the first connection terminates, the
next can be served, while all the others wait again, etc.
@item
Joins.
@code{mSQL} can become pathologically slow if you change the order of
tables in a @code{SELECT}. In the benchmark suite, a time more than
15000 times slower than MySQL was seen. This is due to @code{mSQL}'s
lack of a join optimiser to order tables in the optimal order.
However, if you put the tables in exactly the right order in
@code{mSQL}2 and the @code{WHERE} is simple and uses index columns,
the join will be relatively fast!
@xref{MySQL Benchmarks}.
@item
@code{ORDER BY} and @code{GROUP BY}.
@item
@code{DISTINCT}.
@item
Using @code{TEXT} or @code{BLOB} columns.
@end itemize
@item SQL Features
@itemize @bullet
@item @code{GROUP BY} and @code{HAVING}.
@code{mSQL} does not support @code{GROUP BY} at all.
MySQL supports a full @code{GROUP BY} with both @code{HAVING} and
the following functions: @code{COUNT()}, @code{AVG()}, @code{MIN()},
@code{MAX()}, @code{SUM()}, and @code{STD()}. @code{COUNT(*)} is
optimised to return very quickly if the @code{SELECT} retrieves from
one table, no other columns are retrieved, and there is no
@code{WHERE} clause. @code{MIN()} and @code{MAX()} may take string
arguments.
@item @code{INSERT} and @code{UPDATE} with calculations.
MySQL can do calculations in an @code{INSERT} or @code{UPDATE}.
For example:
@example
mysql> UPDATE SET x=x*10+y WHERE x<20;
@end example
@item Aliasing.
MySQL has column aliasing.
@item Qualifying column names.
In MySQL, if a column name is unique among the tables used in a
query, you do not have to use the full qualifier.
@item @code{SELECT} with functions.
MySQL has many functions (too many to list here; see @ref{Functions}).
@end itemize
@item Disk Space Efficiency
That is, how small can you make your tables?
MySQL has very precise types, so you can create tables that take
very little space. An example of a useful MySQL datatype is the
@code{MEDIUMINT} that is 3 bytes long. If you have 100,000,000
records, saving even one byte per record is very important.
@code{mSQL2} has a more limited set of column types, so it is
more difficult to get small tables.
@item Stability
This is harder to judge objectively. For a discussion of MySQL
stability, see @ref{Stability}.
We have no experience with @code{mSQL} stability, so we cannot say
anything about that.
@item Price
Another important issue is the license. MySQL has a
more flexible license than @code{mSQL}, and is also less expensive
than @code{mSQL}. Whichever product you choose to use, remember to
at least consider paying for a license or e-mail support.
@item Perl Interfaces
MySQL has basically the same interfaces to Perl as @code{mSQL} with
some added features.
@item JDBC (Java)
MySQL currently has a lot of different JDBC drivers:
@itemize @bullet
@item
The mm driver: A type 4 JDBC driver by Mark Matthews
@email{mmatthew@@ecn.purdue.edu}. This is released under the LGPL.
@item
The Resin driver. This is a commercial JDBC driver released under open
source. @uref{http://www.caucho.com/projects/jdbc-mysql/index.xtp}
@item
The gwe driver: A Java interface by GWE technologies (not supported anymore).
@item
The jms driver: An improved gwe driver by Xiaokun Kelvin ZHU
@email{X.Zhu@@brad.ac.uk} (not supported anymore).
@item
The twz driver: A type 4 JDBC driver by Terrence W. Zellers
@email{zellert@@voicenet.com}. This is commercial but is free for private
and educational use (not supported anymore).
@end itemize
The recommended driver is the mm driver. The Resin driver may also be
good (at least the benchmarks looks good), but we haven't received that
much information about this yet.
We know that @code{mSQL} has a JDBC driver, but we have too little
experience with it to compare.
@item Rate of Development
MySQL has a small core team of developers, but we are quite
used to coding C and C++ very rapidly. Because threads, functions,
@code{GROUP BY}, and so on are still not implemented in @code{mSQL}, it
has a lot of catching up to do. To get some perspective on this, you
can view the @code{mSQL} @file{HISTORY} file for the last year and
compare it with the News section of the MySQL Reference Manual
(@pxref{News}). It should be pretty obvious which one has developed
most rapidly.
@item Utility Programs
Both @code{mSQL} and MySQL have many interesting third-party
tools. Because it is very easy to port upward (from @code{mSQL} to
MySQL), almost all the interesting applications that are available for
@code{mSQL} are also available for MySQL.
MySQL comes with a simple @code{msql2mysql} program that fixes
differences in spelling between @code{mSQL} and MySQL for the
most-used C API functions.
For example, it changes instances of @code{msqlConnect()} to
@code{mysql_connect()}. Converting a client program from @code{mSQL} to
MySQL usually requires only minor effort.
@end table
@menu
* Using mSQL tools:: How to convert @code{mSQL} tools for MySQL
* Protocol differences:: How @code{mSQL} and MySQL Client/Server Communications Protocols Differ
* Syntax differences:: How @code{mSQL} 2.0 SQL Syntax Differs from MySQL
@end menu
@node Using mSQL tools, Protocol differences, Compare mSQL, Compare mSQL
@subsubsection How to Convert @code{mSQL} Tools for MySQL
@cindex MySQL tools, conversion
@cindex converting, tools
@cindex tools, converting
According to our experience, it doesn't take long to convert tools
such as @code{msql-tcl} and @code{msqljava} that use the
@code{mSQL} C API so that they work with the MySQL C API.
The conversion procedure is:
@enumerate
@item
Run the shell script @code{msql2mysql} on the source. This requires
the @code{replace} program, which is distributed with MySQL.
@item
Compile.
@item
Fix all compiler errors.
@end enumerate
Differences between the @code{mSQL} C API and the MySQL C API are:
@itemize @bullet
@item
MySQL uses a @code{MYSQL} structure as a connection type (@code{mSQL}
uses an @code{int}).
@item
@code{mysql_connect()} takes a pointer to a @code{MYSQL} structure as a
parameter. It is easy to define one globally or to use @code{malloc()}
to get one. @code{mysql_connect()} also takes two parameters for
specifying the user and password. You may set these to
@code{NULL, NULL} for default use.
@item
@code{mysql_error()} takes the @code{MYSQL} structure as a parameter.
Just add the parameter to your old @code{msql_error()} code if you are
porting old code.
@item
MySQL returns an error number and a text error message for all
errors. @code{mSQL} returns only a text error message.
@item
Some incompatibilities exist as a result of MySQL supporting
multiple connections to the server from the same process.
@end itemize
@node Protocol differences, Syntax differences, Using mSQL tools, Compare mSQL
@subsubsection How @code{mSQL} and MySQL Client/Server Communications Protocols Differ
@cindex communications protocols
@cindex mSQL vs. MySQL, protocol
There are enough differences that it is impossible
(or at least not easy) to support both.
The most significant ways in which the MySQL protocol differs
from the @code{mSQL} protocol are listed below:
@itemize @bullet
@item
A message buffer may contain many result rows.
@item
The message buffers are dynamically enlarged if the query or the
result is bigger than the current buffer, up to a configurable server
and client limit.
@item
All packets are numbered to catch duplicated or missing packets.
@item
All column values are sent in ASCII. The lengths of columns and rows
are sent in packed binary coding (1, 2, or 3 bytes).
@item
MySQL can read in the result unbuffered (without having to store the
full set in the client).
@item
If a single read/write takes more than 30 seconds, the server closes
the connection.
@item
If a connection is idle for 8 hours, the server closes the connection.
@end itemize
@node Syntax differences, , Protocol differences, Compare mSQL
@subsubsection How @code{mSQL} 2.0 SQL Syntax Differs from MySQL
@noindent
@strong{Column types}
@table @code
@item MySQL
Has the following additional types (among others;
@pxref{CREATE TABLE, , @code{CREATE TABLE}}):
@itemize @bullet
@item
@c FIX bad lingo, needs rephrasing
@code{ENUM} type for one of a set of strings.
@item
@c FIX bad lingo, needs rephrasing
@code{SET} type for many of a set of strings.
@item
@code{BIGINT} type for 64-bit integers.
@end itemize
@item
MySQL also supports
the following additional type attributes:
@itemize @bullet
@item
@code{UNSIGNED} option for integer columns.
@item
@code{ZEROFILL} option for integer columns.
@item
@code{AUTO_INCREMENT} option for integer columns that are a
@code{PRIMARY KEY}.
@xref{mysql_insert_id, , @code{mysql_insert_id()}}.
@item
@code{DEFAULT} value for all columns.
@end itemize
@item mSQL2
@code{mSQL} column types correspond to the MySQL types shown below:
@multitable @columnfractions .15 .85
@item @code{mSQL} @strong{type} @tab @strong{Corresponding MySQL type}
@item @code{CHAR(len)} @tab @code{CHAR(len)}
@item @code{TEXT(len)} @tab @code{TEXT(len)}. @code{len} is the maximal length.
And @code{LIKE} works.
@item @code{INT} @tab @code{INT}. With many more options!
@item @code{REAL} @tab @code{REAL}. Or @code{FLOAT}. Both 4- and 8-byte versions are available.
@item @code{UINT} @tab @code{INT UNSIGNED}
@item @code{DATE} @tab @code{DATE}. Uses ANSI SQL format rather than @code{mSQL}'s own format.
@item @code{TIME} @tab @code{TIME}
@item @code{MONEY} @tab @code{DECIMAL(12,2)}. A fixed-point value with two decimals.
@end multitable
@end table
@noindent
@strong{Index Creation}
@table @code
@item MySQL
Indexes may be specified at table creation time with the @code{CREATE TABLE}
statement.
@item mSQL
Indexes must be created after the table has been created, with separate
@code{CREATE INDEX} statements.
@end table
@noindent
@strong{To Insert a Unique Identifier into a Table}
@table @code
@item MySQL
Use @code{AUTO_INCREMENT} as a column type
specifier.
@xref{mysql_insert_id, , @code{mysql_insert_id()}}.
@item mSQL
Create a @code{SEQUENCE} on a table and select the @code{_seq} column.
@end table
@noindent
@strong{To Obtain a Unique Identifier for a Row}
@table @code
@item MySQL
Add a @code{PRIMARY KEY} or @code{UNIQUE} key to the table and use this.
New in Version 3.23.11: If the @code{PRIMARY} or @code{UNIQUE} key consists of only one
column and this is of type integer, one can also refer to it as
@code{_rowid}.
@item mSQL
Use the @code{_rowid} column. Observe that @code{_rowid} may change over time
depending on many factors.
@end table
@noindent
@strong{To Get the Time a Column Was Last Modified}
@table @code
@item MySQL
Add a @code{TIMESTAMP} column to the table. This column is automatically set
to the current date and time for @code{INSERT} or @code{UPDATE} statements if
you don't give the column a value or if you give it a @code{NULL} value.
@item mSQL
Use the @code{_timestamp} column.
@end table
@noindent
@strong{@code{NULL} Value Comparisons}
@table @code
@item MySQL
MySQL follows
ANSI SQL, and a comparison with @code{NULL} is always @code{NULL}.
@item mSQL
In @code{mSQL}, @code{NULL = NULL} is TRUE. You
must change @code{=NULL} to @code{IS NULL} and @code{<>NULL} to
@code{IS NOT NULL} when porting old code from @code{mSQL} to MySQL.
@end table
@noindent
@strong{String Comparisons}
@table @code
@item MySQL
Normally, string comparisons are performed in case-independent fashion with
the sort order determined by the current character set (ISO-8859-1 Latin1 by
default). If you don't like this, declare your columns with the
@code{BINARY} attribute, which causes comparisons to be done according to the
ASCII order used on the MySQL server host.
@item mSQL
All string comparisons are performed in case-sensitive fashion with
sorting in ASCII order.
@end table
@noindent
@strong{Case-insensitive Searching}
@table @code
@item MySQL
@code{LIKE} is a case-insensitive or case-sensitive operator, depending on
the columns involved. If possible, MySQL uses indexes if the
@code{LIKE} argument doesn't start with a wild-card character.
@item mSQL
Use @code{CLIKE}.
@end table
@noindent
@strong{Handling of Trailing Spaces}
@table @code
@item MySQL
Strips all spaces at the end of @code{CHAR} and @code{VARCHAR}
columns. Use a @code{TEXT} column if this behavior is not desired.
@item mSQL
Retains trailing space.
@end table
@noindent
@strong{@code{WHERE} Clauses}
@table @code
@item MySQL
MySQL correctly prioritises everything (@code{AND} is evaluated
before @code{OR}). To get @code{mSQL} behavior in MySQL, use
parentheses (as shown in an example below).
@item mSQL
Evaluates everything from left to right. This means that some logical
calculations with more than three arguments cannot be expressed in any
way. It also means you must change some queries when you upgrade to
MySQL. You do this easily by adding parentheses. Suppose you
have the following @code{mSQL} query:
@example
mysql> SELECT * FROM table WHERE a=1 AND b=2 OR a=3 AND b=4;
@end example
To make MySQL evaluate this the way that @code{mSQL} would,
you must add parentheses:
@example
mysql> SELECT * FROM table WHERE (a=1 AND (b=2 OR (a=3 AND (b=4))));
@end example
@end table
@noindent
@strong{Access Control}
@table @code
@item MySQL
Has tables to store grant (permission) options per user, host, and
database. @xref{Privileges}.
@item mSQL
Has a file @file{mSQL.acl} in which you can grant read/write privileges for
users.
@end table
@node Compare PostgreSQL, , Compare mSQL, Comparisons
@subsection How MySQL Compares to @code{PostgreSQL}
@cindex PostgreSQL vs. MySQL, overview
......@@ -4166,6 +4668,7 @@ can offer, you should use @code{PostgreSQL}.
@menu
* MySQL-PostgreSQL goals:: MySQL and PostgreSQL development strategies
* MySQL-PostgreSQL features:: Featurewise Comparison of MySQL and PostgreSQL
* MySQL-PostgreSQL benchmarks:: Benchmarking MySQL and PostgreSQL
@end menu
......@@ -4211,7 +4714,7 @@ in our opinion, fewer bugs. Because we are the authors of the MySQL server
code, we are better able to coordinate new features and releases.
@node MySQL-PostgreSQL features, , MySQL-PostgreSQL goals, Compare PostgreSQL
@node MySQL-PostgreSQL features, MySQL-PostgreSQL benchmarks, MySQL-PostgreSQL goals, Compare PostgreSQL
@subsubsection Featurewise Comparison of MySQL and PostgreSQL
@cindex PostgreSQL vs. MySQL, features
......@@ -4470,6 +4973,252 @@ For a complete list of drawbacks, you should also examine the first table
in this section.
@node MySQL-PostgreSQL benchmarks, , MySQL-PostgreSQL features, Compare PostgreSQL
@subsubsection Benchmarking MySQL and PostgreSQL
@cindex PostgreSQL vs. MySQL, benchmarks
The only open source benchmark that we know of that can be used to
benchmark MySQL and PostgreSQL (and other databases) is our own. It can
be found at @uref{http://www.mysql.com/information/benchmarks.html}.
We have many times asked the PostgreSQL developers and some PostgreSQL
users to help us extend this benchmark to make it the definitive benchmark
for databases, but unfortunately we haven't gotten any feedback for this.
We the MySQL developers have, because of this, spent a lot of hours to get
maximum performance from PostgreSQL for the benchmarks, but because we
don't know PostgreSQL intimately, we are sure that there are things that
we have missed. We have on the benchmark page documented exactly how we
did run the benchmark so that it should be easy for anyone to repeat and
verify our results.
The benchmarks are usually run with and without the @code{--fast} option.
When run with @code{--fast} we are trying to use every trick the server can
do to get the code to execute as fast as possible. The idea is that the
normal run should show how the server would work in a default setup and
the @code{--fast} run shows how the server would do if the application
developer would use extensions in the server to make his application run
faster.
When running with PostgreSQL and @code{--fast} we do a @code{VACUUM()}
after every major table @code{UPDATE} and @code{DROP TABLE} to make the
database in perfect shape for the following @code{SELECT}s. The time for
@code{VACUUM()} is measured separately.
When running with PostgreSQL 7.1.1 we could, however, not run with
@code{--fast} because during the @code{INSERT} test, the postmaster (the
PostgreSQL deamon) died and the database was so corrupted that it was
impossible to restart postmaster. After this happened twice, we decided
to postpone the @code{--fast} test until next PostgreSQL release. The
details about the machine we run the benchmark can be found on the
benchmark page.
Before going to the other benchmarks we know of, we would like to give
some background on benchmarks:
It's very easy to write a test that shows @strong{any} database to be the best
database in the world, by just restricting the test to something the
database is very good at and not testing anything that the database is
not good at. If one, after doing this, summarises the result with as
a single figure, things are even easier.
This would be like us measuring the speed of MySQL compared to PostgreSQL
by looking at the summary time of the MySQL benchmarks on our web page.
Based on this MySQL would be more than 40 times faster than PostgreSQL,
something that is of course not true. We could make things even worse
by just taking the test where PostgreSQL performs worst and claim that
MySQL is more than 2000 times faster than PostgreSQL.
The case is that MySQL does a lot of optimisations that PostgreSQL
doesn't do. This is of course also true the other way around. An SQL
optimiser is a very complex thing, and a company could spend years on
just making the optimiser faster and faster.
When looking at the benchmark results you should look for things that
you do in your application and just use these results to decide which
database would be best suited for your application. The benchmark
results also shows things a particular database is not good at and should
give you a notion about things to avoid and what you may have to do in
other ways.
We know of two benchmark tests that claims that PostgreSQL performs better
than MySQL. These both where multi-user tests, a test that we here at
MySQL AB haven't had time to write and include in the benchmark suite,
mainly because it's a big task to do this in a manner that is fair against
all databases.
One is the benchmark paid for by Great Bridge, the company that for 16 months
attempted to build a business based on PostgreSQL but now has ceased
operations. This is the probably worst benchmark we have ever seen anyone
conduct. This was not only tuned to only test what PostgreSQL is absolutely
best at, it was also totally unfair against every other database involved in
the test.
@strong{Note}: We know that even some of the main PostgreSQL
developers did not like the way Great Bridge conducted the benchmark, so we
don't blame the PostgreSQL team for the way the benchmark was done.
This benchmark has been condemned in a lot of postings and newsgroups so
we will here just shortly repeat some things that were wrong with it.
@itemize @bullet
@item
The tests were run with an expensive commercial tool, that makes it
impossible for an open source company like us to verify the benchmarks,
or even check how the benchmarks were really done. The tool is not even
a true benchmark tool, but an application/setup testing tool. To refer
this as a ``standard'' benchmark tool is to stretch the truth a long way.
@item
Great Bridge admitted that they had optimised the PostgreSQL database
(with @code{VACUUM()} before the test) and tuned the startup for the tests,
something they hadn't done for any of the other databases involved. To
say ``This process optimises indexes and frees up disk space a bit. The
optimised indexes boost performance by some margin.'' Our benchmarks
clearly indicate that the difference in running a lot of selects on a
database with and without @code{VACUUM()} can easily differ by a factor
of ten.
@item
The test results were also strange. The AS3AP test documentation
mentions that the test does ``selections, simple joins, projections,
aggregates, one-tuple updates, and bulk updates''.
PostgreSQL is good at doing @code{SELECT}s and @code{JOIN}s (especially
after a @code{VACUUM()}), but doesn't perform as well on @code{INSERT}s or
@code{UPDATE}s. The benchmarks seem to indicate that only @code{SELECT}s
were done (or very few updates). This could easily explain they good results
for PostgreSQL in this test. The bad results for MySQL will be obvious a
bit down in this document.
@item
They did run the so-called benchmark from a Windows machine against a
Linux machine over ODBC, a setup that no normal database user would ever
do when running a heavy multi-user application. This tested more the
ODBC driver and the Windows protocol used between the clients than the
database itself.
@item
When running the database against Oracle and MS-SQL (Great Bridge has
indirectly indicated that the databases they used in the test), they
didn't use the native protocol but instead ODBC. Anyone that has ever
used Oracle knows that all real application uses the native interface
instead of ODBC. Doing a test through ODBC and claiming that the results
had anything to do with using the database in a real-world situation can't
be regarded as fair. They should have done two tests with and without ODBC
to provide the right facts (after having got experts to tune all involved
databases of course).
@item
They refer to the TPC-C tests, but they don't mention anywhere that the
test they did was not a true TPC-C test and they were not even allowed to
call it a TPC-C test. A TPC-C test can only be conducted by the rules
approved by the TPC Council (@uref{http://www.tpc.org/}). Great Bridge
didn't do that. By doing this they have both violated the TPC trademark
and miscredited their own benchmarks. The rules set by the TPC Council
are very strict to ensure that no one can produce false results or make
unprovable statements. Apparently Great Bridge wasn't interested in
doing this.
@item
After the first test, we contacted Great Bridge and mentioned to them
some of the obvious mistakes they had done with MySQL:
@itemize @minus
@item
Running with a debug version of our ODBC driver
@item
Running on a Linux system that wasn't optimised for threads
@item
Using an old MySQL version when there was a recommended newer one available
@item
Not starting MySQL with the right options for heavy multi-user use (the
default installation of MySQL is tuned for minimal resource use).
@end itemize
Great Bridge did run a new test, with our optimised ODBC driver and with
better startup options for MySQL, but refused to either use our updated
glibc library or our standard binary (used by 80% of our users), which was
statically linked with a fixed glibc library.
According to what we know, Great Bridge did nothing to ensure that the
other databases were set up correctly to run well in their test
environment. We are sure however that they didn't contact Oracle or
Microsoft to ask for their advice in this matter ;)
@item
The benchmark was paid for by Great Bridge, and they decided to publish
only partial, chosen results (instead of publishing it all).
@end itemize
Tim Perdue, a long time PostgreSQL fan and a reluctant MySQL user
published a comparison on PHPbuilder
(@uref{http://www.phpbuilder.com/columns/tim20001112.php3}).
When we became aware of the comparison, we phoned Tim Perdue about this
because there were a lot of strange things in his results. For example,
he claimed that MySQL had a problem with five users in his tests, when we
know that there are users with similar machines as his that are using
MySQL with 2000 simultaneous connections doing 400 queries per second.
(In this case the limit was the web bandwidth, not the database.)
It sounded like he was using a Linux kernel that either had some
problems with many threads, such as kernels before 2.4, which had a problem
with many threads on multi-CPU machines. We have documented in this manual
how to fix this and Tim should be aware of this problem.
The other possible problem could have been an old glibc library and
that Tim didn't use a MySQL binary from our site, which is linked with
a corrected glibc library, but had compiled a version of his own with.
In any of the above cases, the symptom would have been exactly what Tim
had measured.
We asked Tim if we could get access to his data so that we could repeat
the benchmark and if he could check the MySQL version on the machine to
find out what was wrong and he promised to come back to us about this.
He has not done that yet.
Because of this we can't put any trust in this benchmark either :(
Over time things also changes and the above benchmarks are not that
relevant anymore. MySQL now have a couple of different table handlers
with different speed/concurrency tradeoffs. @xref{Table types}. It
would be interesting to see how the above tests would run with the
different transactional table types in MySQL. PostgreSQL has of course
also got new features since the test was made. As the above test are
not publicly available there is no way for us to know how the
database would preform in the same tests today.
Conclusion:
The only benchmarks that exist today that anyone can download and run
against MySQL and PostgreSQL is the MySQL benchmarks. We here at MySQL
believe that open source databases should be tested with open source tools!
This is the only way to ensure that no one does tests that nobody can
reproduce and use this to claim that a database is better than another.
Without knowing all the facts it's impossible to answer the claims of the
tester.
The thing we find strange is that every test we have seen about
PostgreSQL, that is impossible to reproduce, claims that PostgreSQL is
better in most cases while our tests, which anyone can reproduce,
clearly shows otherwise. With this we don't want to say that PostgreSQL
isn't good at many things (it is!) or that it isn't faster than MySQL
under certain conditions. We would just like to see a fair test where
they are very good so that we could get some friendly competition going!
For more information about our benchmarks suite @xref{MySQL Benchmarks}.
We are working on an even better benchmark suite, including multi user
tests, and a better documentation of what the individual tests really
do and how to add more tests to the suite.
@node Installing, Tutorial, Introduction, Top
@chapter MySQL Installation
Docs/section.Comparisons.texi deleted 100644 → 0
View file @ 0c0a170f
@c FIX AGL 20011108 Extracted from manual.texi.
@c Contains comparison section, mSQL and PostgreSQL.
@c Also some mSQL to MySQL migration info but that is probably outdated.
@node Comparisons, TODO, Compatibility, Introduction
@section How MySQL Compares to Other Databases
@cindex databases, MySQL vs. others
@cindex comparisons, MySQL vs. others
@menu
* Compare mSQL:: How MySQL compares to @code{mSQL}
* Compare PostgreSQL:: How MySQL compares with PostgreSQL
@end menu
Our users have successfully run their own benchmarks against a number
of @code{Open Source} and traditional database servers. We are aware of
tests against @code{Oracle}, @code{DB/2}, @code{Microsoft SQL Server}
and other commercial products. Due to legal reasons we are restricted
from publishing some of those benchmarks in our reference manual.
This section includes a comparison with @code{mSQL} for historical
reasons and with @code{PostgreSQL} as it is also an Open Source
database. If you have benchmark results that we can publish, please
contact us at @email{benchmarks@@mysql.com}.
For comparative lists of all supported functions and types as well
as measured operational limits of many different database systems,
see the @code{crash-me} web page at
@uref{http://www.mysql.com/information/crash-me.php}.
@node Compare mSQL, Compare PostgreSQL, Comparisons, Comparisons
@subsection How MySQL Compares to @code{mSQL}
@cindex mSQL, MySQL vs mSQL, overview
@table @strong
@item Performance
For a true comparison of speed, consult the growing MySQL benchmark
suite. @xref{MySQL Benchmarks}.
Because there is no thread creation overhead, a small parser, few features, and
simple security, @code{mSQL} should be quicker at:
@itemize @bullet
@item
Tests that perform repeated connects and disconnects, running a very simple
query during each connection.
@item
@code{INSERT} operations into very simple tables with few columns and keys.
@item
@code{CREATE TABLE} and @code{DROP TABLE}.
@item
@code{SELECT} on something that isn't an index. (A table scan is very
easy.)
@end itemize
Because these operations are so simple, it is hard to be better at them when
you have a higher startup overhead. After the connection is established,
MySQL should perform much better.
On the other hand, MySQL is much faster than @code{mSQL} (and
most other SQL implementations) on the following:
@itemize @bullet
@item
Complex @code{SELECT} operations.
@item
Retrieving large results (MySQL has a better, faster, and safer
protocol).
@item
Tables with variable-length strings, because MySQL has more efficient
handling and can have indexes on @code{VARCHAR} columns.
@item
Handling tables with many columns.
@item
Handling tables with large record lengths.
@item
@code{SELECT} with many expressions.
@item
@code{SELECT} on large tables.
@item
Handling many connections at the same time. MySQL is fully
multi-threaded. Each connection has its own thread, which means that
no thread has to wait for another (unless a thread is modifying
a table another thread wants to access). In @code{mSQL}, once one connection
is established, all others must wait until the first has finished, regardless
of whether the connection is running a query that is short or long. When the
first connection terminates, the next can be served, while all the others wait
again, etc.
@item
Joins.
@code{mSQL} can become pathologically slow if you change the order of tables
in a @code{SELECT}. In the benchmark suite, a time more than 15000 times
slower than MySQL was seen. This is due to @code{mSQL}'s lack of a
join optimiser to order tables in the optimal order. However, if you put the
tables in exactly the right order in @code{mSQL}2 and the @code{WHERE} is
simple and uses index columns, the join will be relatively fast!
@xref{MySQL Benchmarks}.
@item
@code{ORDER BY} and @code{GROUP BY}.
@item
@code{DISTINCT}.
@item
Using @code{TEXT} or @code{BLOB} columns.
@end itemize
@item SQL Features
@itemize @bullet
@item @code{GROUP BY} and @code{HAVING}.
@code{mSQL} does not support @code{GROUP BY} at all.
MySQL supports a full @code{GROUP BY} with both @code{HAVING} and
the following functions: @code{COUNT()}, @code{AVG()}, @code{MIN()},
@code{MAX()}, @code{SUM()}, and @code{STD()}. @code{COUNT(*)} is optimised to
return very quickly if the @code{SELECT} retrieves from one table, no other
columns are retrieved, and there is no @code{WHERE} clause. @code{MIN()} and
@code{MAX()} may take string arguments.
@item @code{INSERT} and @code{UPDATE} with calculations.
MySQL can do calculations in an @code{INSERT} or @code{UPDATE}.
For example:
@example
mysql> UPDATE SET x=x*10+y WHERE x<20;
@end example
@item Aliasing.
MySQL has column aliasing.
@item Qualifying column names.
In MySQL, if a column name is unique among the tables used in a
query, you do not have to use the full qualifier.
@item @code{SELECT} with functions.
MySQL has many functions (too many to list here; see @ref{Functions}).
@end itemize
@item Disk Space Efficiency
That is, how small can you make your tables?
MySQL has very precise types, so you can create tables that take
very little space. An example of a useful MySQL datatype is the
@code{MEDIUMINT} that is 3 bytes long. If you have 100,000,000 records,
saving even one byte per record is very important.
@code{mSQL2} has a more limited set of column types, so it is
more difficult to get small tables.
@item Stability
This is harder to judge objectively. For a discussion of MySQL
stability, see @ref{Stability}.
We have no experience with @code{mSQL} stability, so we cannot say
anything about that.
@item Price
Another important issue is the license. MySQL has a
more flexible license than @code{mSQL}, and is also less expensive than
@code{mSQL}. Whichever product you choose to use, remember to at least
consider paying for a license or e-mail support. (You are required to get
a license if you include MySQL with a product that you sell,
of course.)
@item Perl Interfaces
MySQL has basically the same interfaces to Perl as @code{mSQL} with
some added features.
@item JDBC (Java)
MySQL currently has a lot of different JDBC drivers:
@itemize @bullet
@item
The mm driver: A type 4 JDBC driver by Mark Matthews
@email{mmatthew@@ecn.purdue.edu}. This is released under the LGPL.
@item
The Resin driver. This is a commercial JDBC driver released under open
source. @uref{http://www.caucho.com/projects/jdbc-mysql/index.xtp}
@item
The gwe driver: A Java interface by GWE technologies (not supported anymore).
@item
The jms driver: An improved gwe driver by Xiaokun Kelvin ZHU
@email{X.Zhu@@brad.ac.uk} (not supported anymore).
@item
The twz driver: A type 4 JDBC driver by Terrence W. Zellers
@email{zellert@@voicenet.com}. This is commercial but is free for private
and educational use (not supported anymore).
@end itemize
The recommended driver is the mm driver. The Resin driver may also be
good (at least the benchmarks looks good), but we haven't received that much
information about this yet.
We know that @code{mSQL} has a JDBC driver, but we have too little experience
with it to compare.
@item Rate of Development
MySQL has a small core team of developers, but we are quite
used to coding C and C++ very rapidly. Because threads, functions,
@code{GROUP BY}, and so on are still not implemented in @code{mSQL}, it
has a lot of catching up to do. To get some perspective on this, you
can view the @code{mSQL} @file{HISTORY} file for the last year and
compare it with the News section of the MySQL Reference Manual
(@pxref{News}). It should be pretty obvious which one has developed
most rapidly.
@item Utility Programs
Both @code{mSQL} and MySQL have many interesting third-party
tools. Because it is very easy to port upward (from @code{mSQL} to
MySQL), almost all the interesting applications that are available for
@code{mSQL} are also available for MySQL.
MySQL comes with a simple @code{msql2mysql} program that fixes
differences in spelling between @code{mSQL} and MySQL for the
most-used C API functions.
For example, it changes instances of @code{msqlConnect()} to
@code{mysql_connect()}. Converting a client program from @code{mSQL} to
MySQL usually requires only minor effort.
@end table
@menu
* Using mSQL tools:: How to convert @code{mSQL} tools for MySQL
* Protocol differences:: How @code{mSQL} and MySQL Client/Server Communications Protocols Differ
* Syntax differences:: How @code{mSQL} 2.0 SQL Syntax Differs from MySQL
@end menu
@node Using mSQL tools, Protocol differences, Compare mSQL, Compare mSQL
@subsubsection How to Convert @code{mSQL} Tools for MySQL
@cindex MySQL tools, conversion
@cindex converting, tools
@cindex tools, converting
According to our experience, it doesn't take long to convert tools
such as @code{msql-tcl} and @code{msqljava} that use the
@code{mSQL} C API so that they work with the MySQL C API.
The conversion procedure is:
@enumerate
@item
Run the shell script @code{msql2mysql} on the source. This requires the
@code{replace} program, which is distributed with MySQL.
@item
Compile.
@item
Fix all compiler errors.
@end enumerate
Differences between the @code{mSQL} C API and the MySQL C API are:
@itemize @bullet
@item
MySQL uses a @code{MYSQL} structure as a connection type (@code{mSQL}
uses an @code{int}).
@item
@code{mysql_connect()} takes a pointer to a @code{MYSQL} structure as a
parameter. It is easy to define one globally or to use @code{malloc()} to get
one. @code{mysql_connect()} also takes two parameters for specifying the
user and password. You may set these to @code{NULL, NULL} for default use.
@item
@code{mysql_error()} takes the @code{MYSQL} structure as a parameter. Just add
the parameter to your old @code{msql_error()} code if you are porting old code.
@item
MySQL returns an error number and a text error message for all
errors. @code{mSQL} returns only a text error message.
@item
Some incompatibilities exist as a result of MySQL supporting
multiple connections to the server from the same process.
@end itemize
@node Protocol differences, Syntax differences, Using mSQL tools, Compare mSQL
@subsubsection How @code{mSQL} and MySQL Client/Server Communications Protocols Differ
@cindex communications protocols
@cindex mSQL vs. MySQL, protocol
There are enough differences that it is impossible (or at least not easy)
to support both.
The most significant ways in which the MySQL protocol differs
from the @code{mSQL} protocol are listed below:
@itemize @bullet
@item
A message buffer may contain many result rows.
@item
The message buffers are dynamically enlarged if the query or the
result is bigger than the current buffer, up to a configurable server and
client limit.
@item
All packets are numbered to catch duplicated or missing packets.
@item
All column values are sent in ASCII. The lengths of columns and rows are sent
in packed binary coding (1, 2, or 3 bytes).
@item
MySQL can read in the result unbuffered (without having to store the
full set in the client).
@item
If a single read/write takes more than 30 seconds, the server closes
the connection.
@item
If a connection is idle for 8 hours, the server closes the connection.
@end itemize
@menu
* Syntax differences:: How @code{mSQL} 2.0 SQL Syntax Differs from MySQL
@end menu
@node Syntax differences, , Protocol differences, Compare mSQL
@subsubsection How @code{mSQL} 2.0 SQL Syntax Differs from MySQL
@noindent
@strong{Column types}
@table @code
@item MySQL
Has the following additional types (among others;
@pxref{CREATE TABLE, , @code{CREATE TABLE}}):
@itemize @bullet
@item
@c FIX bad lingo, needs rephrasing
@code{ENUM} type for one of a set of strings.
@item
@c FIX bad lingo, needs rephrasing
@code{SET} type for many of a set of strings.
@item
@code{BIGINT} type for 64-bit integers.
@end itemize
@item
MySQL also supports
the following additional type attributes:
@itemize @bullet
@item
@code{UNSIGNED} option for integer columns.
@item
@code{ZEROFILL} option for integer columns.
@item
@code{AUTO_INCREMENT} option for integer columns that are a
@code{PRIMARY KEY}.
@xref{mysql_insert_id, , @code{mysql_insert_id()}}.
@item
@code{DEFAULT} value for all columns.
@end itemize
@item mSQL2
@code{mSQL} column types correspond to the MySQL types shown below:
@multitable @columnfractions .15 .85
@item @code{mSQL} @strong{type} @tab @strong{Corresponding MySQL type}
@item @code{CHAR(len)} @tab @code{CHAR(len)}
@item @code{TEXT(len)} @tab @code{TEXT(len)}. @code{len} is the maximal length.
And @code{LIKE} works.
@item @code{INT} @tab @code{INT}. With many more options!
@item @code{REAL} @tab @code{REAL}. Or @code{FLOAT}. Both 4- and 8-byte versions are available.
@item @code{UINT} @tab @code{INT UNSIGNED}
@item @code{DATE} @tab @code{DATE}. Uses ANSI SQL format rather than @code{mSQL}'s own format.
@item @code{TIME} @tab @code{TIME}
@item @code{MONEY} @tab @code{DECIMAL(12,2)}. A fixed-point value with two decimals.
@end multitable
@end table
@noindent
@strong{Index Creation}
@table @code
@item MySQL
Indexes may be specified at table creation time with the @code{CREATE TABLE}
statement.
@item mSQL
Indexes must be created after the table has been created, with separate
@code{CREATE INDEX} statements.
@end table
@noindent
@strong{To Insert a Unique Identifier into a Table}
@table @code
@item MySQL
Use @code{AUTO_INCREMENT} as a column type
specifier.
@xref{mysql_insert_id, , @code{mysql_insert_id()}}.
@item mSQL
Create a @code{SEQUENCE} on a table and select the @code{_seq} column.
@end table
@noindent
@strong{To Obtain a Unique Identifier for a Row}
@table @code
@item MySQL
Add a @code{PRIMARY KEY} or @code{UNIQUE} key to the table and use this.
New in Version 3.23.11: If the @code{PRIMARY} or @code{UNIQUE} key consists of only one
column and this is of type integer, one can also refer to it as
@code{_rowid}.
@item mSQL
Use the @code{_rowid} column. Observe that @code{_rowid} may change over time
depending on many factors.
@end table
@noindent
@strong{To Get the Time a Column Was Last Modified}
@table @code
@item MySQL
Add a @code{TIMESTAMP} column to the table. This column is automatically set
to the current date and time for @code{INSERT} or @code{UPDATE} statements if
you don't give the column a value or if you give it a @code{NULL} value.
@item mSQL
Use the @code{_timestamp} column.
@end table
@noindent
@strong{@code{NULL} Value Comparisons}
@table @code
@item MySQL
MySQL follows
ANSI SQL, and a comparison with @code{NULL} is always @code{NULL}.
@item mSQL
In @code{mSQL}, @code{NULL = NULL} is TRUE. You
must change @code{=NULL} to @code{IS NULL} and @code{<>NULL} to
@code{IS NOT NULL} when porting old code from @code{mSQL} to MySQL.
@end table
@noindent
@strong{String Comparisons}
@table @code
@item MySQL
Normally, string comparisons are performed in case-independent fashion with
the sort order determined by the current character set (ISO-8859-1 Latin1 by
default). If you don't like this, declare your columns with the
@code{BINARY} attribute, which causes comparisons to be done according to the
ASCII order used on the MySQL server host.
@item mSQL
All string comparisons are performed in case-sensitive fashion with
sorting in ASCII order.
@end table
@noindent
@strong{Case-insensitive Searching}
@table @code
@item MySQL
@code{LIKE} is a case-insensitive or case-sensitive operator, depending on
the columns involved. If possible, MySQL uses indexes if the
@code{LIKE} argument doesn't start with a wild-card character.
@item mSQL
Use @code{CLIKE}.
@end table
@noindent
@strong{Handling of Trailing Spaces}
@table @code
@item MySQL
Strips all spaces at the end of @code{CHAR} and @code{VARCHAR}
columns. Use a @code{TEXT} column if this behavior is not desired.
@item mSQL
Retains trailing space.
@end table
@noindent
@strong{@code{WHERE} Clauses}
@table @code
@item MySQL
MySQL correctly prioritises everything (@code{AND} is evaluated
before @code{OR}). To get @code{mSQL} behavior in MySQL, use
parentheses (as shown in an example below).
@item mSQL
Evaluates everything from left to right. This means that some logical
calculations with more than three arguments cannot be expressed in any
way. It also means you must change some queries when you upgrade to
MySQL. You do this easily by adding parentheses. Suppose you
have the following @code{mSQL} query:
@example
mysql> SELECT * FROM table WHERE a=1 AND b=2 OR a=3 AND b=4;
@end example
To make MySQL evaluate this the way that @code{mSQL} would,
you must add parentheses:
@example
mysql> SELECT * FROM table WHERE (a=1 AND (b=2 OR (a=3 AND (b=4))));
@end example
@end table
@noindent
@strong{Access Control}
@table @code
@item MySQL
Has tables to store grant (permission) options per user, host, and
database. @xref{Privileges}.
@item mSQL
Has a file @file{mSQL.acl} in which you can grant read/write privileges for
users.
@end table
@node Compare PostgreSQL, , Compare mSQL, Comparisons
@subsection How MySQL Compares to PostgreSQL
@cindex PostgreSQL vs. MySQL, overview
When reading the following, please note that both products are continually
evolving. We at MySQL AB and the PostgreSQL developers are both working
on making our respective database as good as possible, so we are both a
serious choice to any commercial database.
The following comparison is made by us at MySQL AB. We have tried to be
as accurate and fair as possible, but because we don't have a full
knowledge of all PostgreSQL features while we know MySQL througly, we
may have got some things wrong. We will however correct these when they
come to our attention.
We would first like to note that PostgreSQL and MySQL are both widely used
products, but with different design goals, even if we are both striving to
be ANSI SQL compatible. This means that for some applications MySQL is
more suited, while for others PostgreSQL is more suited. When choosing
which database to use, you should first check if the database's feature set
satisfies your application. If you need raw speed, MySQL is probably your
best choice. If you need some of the extra features that only PostgreSQL
can offer, you should use @code{PostgreSQL}.
@menu
* MySQL-PostgreSQL goals:: MySQL and PostgreSQL development strategies
* MySQL-PostgreSQL features:: Featurewise Comparison of MySQL and PostgreSQL
* MySQL-PostgreSQL benchmarks:: Benchmarking MySQL and PostgreSQL
@end menu
@node MySQL-PostgreSQL goals, MySQL-PostgreSQL features, Compare PostgreSQL, Compare PostgreSQL
@subsubsection MySQL and PostgreSQL development strategies
@cindex PostgreSQL vs. MySQL, strategies
When adding things to MySQL we take pride to do an optimal, definite
solution. The code should be so good that we shouldn't have any need to
change it in the foreseeable future. We also do not like to sacrifice
speed for features but instead will do our utmost to find a solution
that will give maximal throughput. This means that development will take
a little longer, but the end result will be well worth this. This kind
of development is only possible because all server code are checked by
one of a few (currently two) persons before it's included in the
MySQL server.
We at MySQL AB believe in frequent releases to be able to push out new
features quickly to our users. Because of this we do a new small release
about every three weeks, and a major branch every year. All releases are
throughly tested with our testing tools on a lot of different platforms.
PostgreSQL is based on a kernel with lots of contributors. In this setup
it makes sense to prioritise adding a lot of new features, instead of
implementing them optimally, because one can always optimise things
later if there arises a need for this.
Another big difference between MySQL and PostgreSQL is that
nearly all of the code in the MySQL server are coded by developers that
are employed by MySQL AB and are still working on the server code. The
exceptions are the transaction engines, and the regexp library.
This is in sharp contrast to the PostgreSQL code where the majority of
the code is coded by a big group of people with different backgrounds.
It was only recently that the PostgreSQL developers announced that their
current developer group had finally had time to take a look at all
the code in the current PostgreSQL release.
Both of the above development methods has it's own merits and drawbacks.
We here at MySQL AB think of course that our model is better because our
model gives better code consistency, more optimal and reusable code, and
in our opinion, fewer bugs. Because we are the authors of the MySQL server
code, we are better able to coordinate new features and releases.
@node MySQL-PostgreSQL features, MySQL-PostgreSQL benchmarks, MySQL-PostgreSQL goals, Compare PostgreSQL
@subsubsection Featurewise Comparison of MySQL and PostgreSQL
@cindex PostgreSQL vs. MySQL, features
On the crash-me page
(@uref{http://www.mysql.com/information/crash-me.php})
you can find a list of those database constructs and limits that
one can detect automatically with a program. Note however that a lot of
the numerical limits may be changed with startup options for respective
database. The above web page is however extremely useful when you want to
ensure that your applications works with many different databases or
when you want to convert your application from one datbase to another.
MySQL offers the following advantages over PostgreSQL:
@itemize @bullet
@item
@code{MySQL} is generally much faster than PostgreSQL.
@xref{MySQL-PostgreSQL benchmarks}.
@item
MySQL has a much larger user base than PostgreSQL, therefor the
code is more tested and has historically been more stable than
PostgreSQL. MySQL is the much more used in production
environments than PostgreSQL, mostly thanks to that MySQL AB,
formerly TCX DataKonsult AB, has provided top quality commercial support
for MySQL from the day it was released, whereas until recently
PostgreSQL was unsupported.
@item
MySQL works better on Windows than PostgreSQL does. MySQL runs as a
native Windows application (a service on NT/Win2000/WinXP), while
PostgreSQL is run under the cygwin emulation. We have heard that
PostgreSQL is not yet that stable on Windows but we haven't been able to
verify this ourselves.
@item
MySQL has more APIs to other languages and is supported by more
existing programs than PostgreSQL. @xref{Contrib}.
@item
MySQL works on 24/7 heavy duty systems. In most circumstances
you never have to run any cleanups on MySQL. PostgreSQL doesn't
yet support 24/7 systems because you have to run @code{VACUUM()}
once in a while to reclaim space from @code{UPDATE} and @code{DELETE}
commands and to perform statistics analyses that are critical to get
good performance with PostgreSQL. @code{VACUUM()} is also needed after
adding a lot of new rows to a table. On a busy system with lots of changes,
@code{VACUUM()} must be run very frequently, in the worst cases even
many times a day. During the @code{VACUUM()} run, which may take hours
if the database is big, the database is from a production standpoint,
practically dead. The PostgreSQL team has fixing this on their TODO,
but we assume that this is not an easy thing to fix permanently.
@item
A working, tested replication feature used by sites like:
@itemize @minus
@item Yahoo Finance (@uref{http://finance.yahoo.com/})
@item Mobile.de (@uref{http://www.mobile.de/})
@item Slashdot (@uref{http://www.slashdot.org/})
@end itemize
@item
Included in the MySQL distribution are two different testing suites,
@file{mysql-test-run} and crash-me
(@uref{http://www.mysql.com/information/crash-me.php}), as well
as a benchmark suite. The test system is actively updated with code to
test each new feature and almost all reproduceable bugs that have come to
our attention. We test MySQL with these on a lot of platforms before
every release. These tests are more sophisticated than anything we have
seen from PostgreSQL, and they ensures that the MySQL is kept to a high
standard.
@item
There are far more books in print about MySQL than about PostgreSQL.
O'Reilly, Sams, Que, and New Riders are all major publishers with books
about MySQL. All MySQL features are also documented in the MySQL on-line
manual, because when a new feature is implemented, the MySQL developers
are required to document it before it's included in the source.
@item
MySQL supports more of the standard ODBC functions than @code{PostgreSQL}.
@item
MySQL has a much more sophisticated @code{ALTER TABLE}.
@item
MySQL has support for tables without transactions for applications that
need all speed they can get. The tables may be memory based, @code{HEAP}
tables or disk based @code{MyISAM}. @xref{Table types}.
@item
MySQL has support for two different table handlers that support
transactions, @code{InnoDB} and @code{BerkeleyDB}. Because every
transaction engine performs differently under different conditions, this
gives the application writer more options to find an optimal solution for
his or her setup. @xref{Table types}.
@item
@code{MERGE} tables gives you a unique way to instantly make a view over
a set of identical tables and use these as one. This is perfect for
systems where you have log files that you order for example by month.
@xref{MERGE}.
@item
The option to compress read-only tables, but still have direct access to
the rows in the table, gives you better performance by minimising disk
reads. This is very useful when you are archiving things.
@xref{myisampack}.
@item
MySQL has internal support for fulltext search. @xref{Fulltext Search}.
@item
You can access many databases from the same connection (depending of course
on your privileges).
@item
MySQL is coded from the start to be multi-threaded while PostgreSQL uses
processes. Context switching and access to common storage areas is much
faster between threads than between separate processes, this gives MySQL
a big speed advantage in multi-user applications and also makes it easier
for MySQL to take full advantage of symmetric multiprocessor (SMP) systems.
@item
MySQL has a much more sophisticated privilege system than PostgreSQL.
While PostgreSQL only supports @code{INSERT}, @code{SELECT}, and
@code{UPDATE/DELETE} grants per user on a database or a table, MySQL allows
you to define a full set of different privileges on database, table and
column level. MySQL also allows you to specify the privilege on host and
user combinations. @xref{GRANT}.
@item
MySQL supports a compressed client/server protocol which improves
performance over slow links.
@item
MySQL employs a ``table handler'' concept, and is the only relational
database we know of built around this concept. This allows different
low-level table types to be swapped into the SQL engine, and each table
type can be optimised for different performance characteristics.
@item
All MySQL table types (except @strong{InnoDB}) are implemented as files
(one table per file), which makes it really easy to backup, move, delete
and even symlink databases and tables, even when the server is down.
@item
Tools to repair and optimise @strong{MyISAM} tables (the most common
MySQL table type). A repair tool is only needed when a physical corruption
of a data file happens, usually from a hardware failure. It allows a
majority of the data to be recovered.
@item
Upgrading MySQL is painless. When you are upgrading MySQL, you don't need
to dump/restore your data, as you have to do with most PostgreSQL upgrades.
@end itemize
Drawbacks with MySQL compared to PostgreSQL:
@itemize @bullet
@item
The transaction support in MySQL is not yet as well tested as PostgreSQL's
system.
@item
Because MySQL uses threads, which are not yet flawless on many OSes, one
must either use binaries from @uref{http://www.mysql.com/downloads/}, or
carefully follow our instructions on
@uref{http://www.mysql.com/doc/I/n/Installing_source.html} to get an
optimal binary that works in all cases.
@item
Table locking, as used by the non-transactional @code{MyISAM} tables, is
in many cases faster than page locks, row locks or versioning. The
drawback however is that if one doesn't take into account how table
locks work, a single long-running query can block a table for updates
for a long time. This can usually be avoided when designing the
application. If not, one can always switch the trouble table to use one
of the transactional table types. @xref{Table locking}.
@item
With UDF (user defined functions) one can extend MySQL with both normal
SQL functions and aggregates, but this is not yet as easy or as flexible
as in PostgreSQL. @xref{Adding functions}.
@item
Updates that run over multiple tables is harder to do in MySQL.
This will, however, be fixed in MySQL 4.0 with multi-table @code{UPDATE}
and in MySQL 4.1 with subselects.
In MySQL 4.0 one can use multi-table deletes to delete from many tables
at the same time. @xref{DELETE}.
@end itemize
PostgreSQL currently offers the following advantages over MySQL:
Note that because we know the MySQL road map, we have included in the
following table the version when MySQL should support this feature.
Unfortunately we couldn't do this for previous comparison, because we
don't know the PostgreSQL roadmap.
@multitable @columnfractions .70 .30
@item @strong{Feature} @tab @strong{MySQL version}
@item Subselects @tab 4.1
@item Foreign keys @tab 4.0 and 4.1
@item Views @tab 4.2
@item Stored procedures @tab 4.1
@item Extensible type system @tab Not planned
@item Unions @tab 4.0
@item Full join @tab 4.0 or 4.1
@item Triggers @tab 4.1
@item Constraints @tab 4.1
@item Cursors @tab 4.1 or 4.2
@item Extensible index types like R-trees @tab R-trees are planned for 4.2
@item Inherited tables @tab Not planned
@end multitable
Other reasons to use PostgreSQL:
@itemize @bullet
@item
Standard usage in PostgreSQL is closer to ANSI SQL in some cases.
@item
One can speed up PostgreSQL by coding things as stored procedures.
@item
For geographical data, R-TREES makes PostgreSQL better than MySQL.
@item
The PostgreSQL optimiser can do some optimisation that the current MySQL
optimiser can't do. Most notable is doing joins when you don't have the
proper keys in place and doing a join where you are using different keys
combined with OR. The MySQL benchmark suite at
@uref{http://www.mysql.com/information/benchmarks.html} shows you what
kind of constructs you should watch out for when using different
databases.
@item
PostgreSQL has a bigger team of developers that contribute to the server.
@end itemize
Drawbacks with PostgreSQL compared to MySQL:
@itemize @bullet
@item
@code{VACUUM()} makes PostgreSQL hard to use in a 24/7 environment.
@item
Only transactional tables.
@item
Much slower @code{INSERT}, @code{DELETE}, and @code{UPDATE}.
@end itemize
For a complete list of drawbacks, you should also examine the first table
in this section.
@menu
* MySQL-PostgreSQL benchmarks:: Benchmarking MySQL and PostgreSQL
@end menu
@node MySQL-PostgreSQL benchmarks, , MySQL-PostgreSQL features, Compare PostgreSQL
@subsubsection Benchmarking MySQL and PostgreSQL
@cindex PostgreSQL vs. MySQL, benchmarks
The only open source benchmark that we know of that can be used to
benchmark MySQL and PostgreSQL (and other databases) is our own. It can
be found at @uref{http://www.mysql.com/information/benchmarks.html}.
We have many times asked the PostgreSQL developers and some PostgreSQL
users to help us extend this benchmark to make it the definitive benchmark
for databases, but unfortunately we haven't gotten any feedback for this.
We the MySQL developers have, because of this, spent a lot of hours to get
maximum performance from PostgreSQL for the benchmarks, but because we
don't know PostgreSQL intimately, we are sure that there are things that
we have missed. We have on the benchmark page documented exactly how we
did run the benchmark so that it should be easy for anyone to repeat and
verify our results.
The benchmarks are usually run with and without the @code{--fast} option.
When run with @code{--fast} we are trying to use every trick the server can
do to get the code to execute as fast as possible. The idea is that the
normal run should show how the server would work in a default setup and
the @code{--fast} run shows how the server would do if the application
developer would use extensions in the server to make his application run
faster.
When running with PostgreSQL and @code{--fast} we do a @code{VACUUM()}
after every major table @code{UPDATE} and @code{DROP TABLE} to make the
database in perfect shape for the following @code{SELECT}s. The time for
@code{VACUUM()} is measured separately.
When running with PostgreSQL 7.1.1 we could, however, not run with
@code{--fast} because during the @code{INSERT} test, the postmaster (the
PostgreSQL deamon) died and the database was so corrupted that it was
impossible to restart postmaster. After this happened twice, we decided
to postpone the @code{--fast} test until next PostgreSQL release. The
details about the machine we run the benchmark can be found on the
benchmark page.
Before going to the other benchmarks we know of, we would like to give
some background on benchmarks:
It's very easy to write a test that shows @strong{any} database to be the best
database in the world, by just restricting the test to something the
database is very good at and not testing anything that the database is
not good at. If one, after doing this, summarises the result with as
a single figure, things are even easier.
This would be like us measuring the speed of MySQL compared to PostgreSQL
by looking at the summary time of the MySQL benchmarks on our web page.
Based on this MySQL would be more than 40 times faster than PostgreSQL,
something that is of course not true. We could make things even worse
by just taking the test where PostgreSQL performs worst and claim that
MySQL is more than 2000 times faster than PostgreSQL.
The case is that MySQL does a lot of optimisations that PostgreSQL
doesn't do. This is of course also true the other way around. An SQL
optimiser is a very complex thing, and a company could spend years on
just making the optimiser faster and faster.
When looking at the benchmark results you should look for things that
you do in your application and just use these results to decide which
database would be best suited for your application. The benchmark
results also shows things a particular database is not good at and should
give you a notion about things to avoid and what you may have to do in
other ways.
We know of two benchmark tests that claims that PostgreSQL performs better
than MySQL. These both where multi-user tests, a test that we here at
MySQL AB haven't had time to write and include in the benchmark suite,
mainly because it's a big task to do this in a manner that is fair against
all databases.
One is the benchmark paid for by Great Bridge, the company that for 16 months
attempted to build a business based on PostgreSQL but now has ceased
operations. This is the probably worst benchmark we have ever seen anyone
conduct. This was not only tuned to only test what PostgreSQL is absolutely
best at, it was also totally unfair against every other database involved in
the test.
@strong{Note}: We know that even some of the main PostgreSQL
developers did not like the way Great Bridge conducted the benchmark, so we
don't blame the PostgreSQL team for the way the benchmark was done.
This benchmark has been condemned in a lot of postings and newsgroups so
we will here just shortly repeat some things that were wrong with it.
@itemize @bullet
@item
The tests were run with an expensive commercial tool, that makes it
impossible for an open source company like us to verify the benchmarks,
or even check how the benchmarks were really done. The tool is not even
a true benchmark tool, but an application/setup testing tool. To refer
this as a ``standard'' benchmark tool is to stretch the truth a long way.
@item
Great Bridge admitted that they had optimised the PostgreSQL database
(with @code{VACUUM()} before the test) and tuned the startup for the tests,
something they hadn't done for any of the other databases involved. To
say ``This process optimises indexes and frees up disk space a bit. The
optimised indexes boost performance by some margin.'' Our benchmarks
clearly indicate that the difference in running a lot of selects on a
database with and without @code{VACUUM()} can easily differ by a factor
of ten.
@item
The test results were also strange. The AS3AP test documentation
mentions that the test does ``selections, simple joins, projections,
aggregates, one-tuple updates, and bulk updates''.
PostgreSQL is good at doing @code{SELECT}s and @code{JOIN}s (especially
after a @code{VACUUM()}), but doesn't perform as well on @code{INSERT}s or
@code{UPDATE}s. The benchmarks seem to indicate that only @code{SELECT}s
were done (or very few updates). This could easily explain they good results
for PostgreSQL in this test. The bad results for MySQL will be obvious a
bit down in this document.
@item
They did run the so-called benchmark from a Windows machine against a
Linux machine over ODBC, a setup that no normal database user would ever
do when running a heavy multi-user application. This tested more the
ODBC driver and the Windows protocol used between the clients than the
database itself.
@item
When running the database against Oracle and MS-SQL (Great Bridge has
indirectly indicated that the databases they used in the test), they
didn't use the native protocol but instead ODBC. Anyone that has ever
used Oracle knows that all real application uses the native interface
instead of ODBC. Doing a test through ODBC and claiming that the results
had anything to do with using the database in a real-world situation can't
be regarded as fair. They should have done two tests with and without ODBC
to provide the right facts (after having got experts to tune all involved
databases of course).
@item
They refer to the TPC-C tests, but they don't mention anywhere that the
test they did was not a true TPC-C test and they were not even allowed to
call it a TPC-C test. A TPC-C test can only be conducted by the rules
approved by the TPC Council (@uref{http://www.tpc.org/}). Great Bridge
didn't do that. By doing this they have both violated the TPC trademark
and miscredited their own benchmarks. The rules set by the TPC Council
are very strict to ensure that no one can produce false results or make
unprovable statements. Apparently Great Bridge wasn't interested in
doing this.
@item
After the first test, we contacted Great Bridge and mentioned to them
some of the obvious mistakes they had done with MySQL:
@itemize @minus
@item
Running with a debug version of our ODBC driver
@item
Running on a Linux system that wasn't optimised for threads
@item
Using an old MySQL version when there was a recommended newer one available
@item
Not starting MySQL with the right options for heavy multi-user use (the
default installation of MySQL is tuned for minimal resource use).
@end itemize
Great Bridge did run a new test, with our optimised ODBC driver and with
better startup options for MySQL, but refused to either use our updated
glibc library or our standard binary (used by 80% of our users), which was
statically linked with a fixed glibc library.
According to what we know, Great Bridge did nothing to ensure that the
other databases were set up correctly to run well in their test
environment. We are sure however that they didn't contact Oracle or
Microsoft to ask for their advice in this matter ;)
@item
The benchmark was paid for by Great Bridge, and they decided to publish
only partial, chosen results (instead of publishing it all).
@end itemize
Tim Perdue, a long time PostgreSQL fan and a reluctant MySQL user
published a comparison on PHPbuilder
(@uref{http://www.phpbuilder.com/columns/tim20001112.php3}).
When we became aware of the comparison, we phoned Tim Perdue about this
because there were a lot of strange things in his results. For example,
he claimed that MySQL had a problem with five users in his tests, when we
know that there are users with similar machines as his that are using
MySQL with 2000 simultaneous connections doing 400 queries per second.
(In this case the limit was the web bandwidth, not the database.)
It sounded like he was using a Linux kernel that either had some
problems with many threads, such as kernels before 2.4, which had a problem
with many threads on multi-CPU machines. We have documented in this manual
how to fix this and Tim should be aware of this problem.
The other possible problem could have been an old glibc library and
that Tim didn't use a MySQL binary from our site, which is linked with
a corrected glibc library, but had compiled a version of his own with.
In any of the above cases, the symptom would have been exactly what Tim
had measured.
We asked Tim if we could get access to his data so that we could repeat
the benchmark and if he could check the MySQL version on the machine to
find out what was wrong and he promised to come back to us about this.
He has not done that yet.
Because of this we can't put any trust in this benchmark either :(
Over time things also changes and the above benchmarks are not that
relevant anymore. MySQL now have a couple of different table handlers
with different speed/concurrency tradeoffs. @xref{Table types}. It
would be interesting to see how the above tests would run with the
different transactional table types in MySQL. PostgreSQL has of course
also got new features since the test was made. As the above test are
not publicly available there is no way for us to know how the
database would preform in the same tests today.
Conclusion:
The only benchmarks that exist today that anyone can download and run
against MySQL and PostgreSQL is the MySQL benchmarks. We here at MySQL
believe that open source databases should be tested with open source tools!
This is the only way to ensure that no one does tests that nobody can
reproduce and use this to claim that a database is better than another.
Without knowing all the facts it's impossible to answer the claims of the
tester.
The thing we find strange is that every test we have seen about
PostgreSQL, that is impossible to reproduce, claims that PostgreSQL is
better in most cases while our tests, which anyone can reproduce,
clearly shows otherwise. With this we don't want to say that PostgreSQL
isn't good at many things (it is!) or that it isn't faster than MySQL
under certain conditions. We would just like to see a fair test where
they are very good so that we could get some friendly competition going!
For more information about our benchmarks suite @xref{MySQL Benchmarks}.
We are working on an even better benchmark suite, including multi user
tests, and a better documentation of what the individual tests really
do and how to add more tests to the suite.
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