MySQL Performance: MyISAM

MyISAM is a table-locking based engine. Table-locking prevents all other processes from writing data to the target table. This locking behavior encompasses the entire MyISAM table, no matter how minor the data change. This type of locking preserves the integrity of the data but at the cost of performance. The performance penalty for using table-locking based engines like MyISAM become more laborious as the row count in the table grows. Very large tables will see a significant delay in performance when data needs to be written to a MyISAM table.

Unlike InnoDB, there is little that can be adjusted on the server level to optimize MyISAM tables for performance. Instead, MyISAM based performance relies heavily on query construction using best practices. There are a number of session level variables that can be adjusted on the fly to boost the performance of the invoking query. However, optimization on the query level is beyond the purview of this article.

MyISAM optimization on the server level comes down to two major practices.

  1. Determining when to use MyISAM tables versus InnoDB tables
  2. MyISAM Key Cache optimization & maintenance

When to Use MyISAM vs. InnoDB Tables

Although MyISAM is not intended as a performance solution, it does have its niche roles where it outperforms the more robust InnoDB engine. Identifying these niches in your database structure can help squeeze the best performance out of your database and associated applications. To determine whether a table should be MyISAM or InnoDB, it’s best to keep the following rule of thumb in mind.

Rule of Thumb: When to Use MyISAM
MySQL®️ Recommends: MyISAM is most effective on primarily read operations and degrades in performance as concurrency rises. This is due to the impact of table locking limits on simultaneous write operations. The following situations hold true:

– MyISAM performs best on small tables.

– MyISAM performs well on medium or large tables with mostly read operations.

– MyISAM performs poorly on large and very large tables.

MyISAM Key Cache Optimization & Maintenance

The MyISAM Key Cache (also called the Key Buffer), represents the amount of memory pre-allocated for storing Index data of MyISAM tables. Optimizing the Key Cache is done by setting an adequate key_buffer_size, ensuring it is large enough to include as much of the total length of all MyISAM table Indexes as well as a minor portion for administrative overhead.

Key_buffer_size is equal to 3840M for adequate caching.

Important MySQL®️ Warning
MySQL®️ Warns: Setting too large a value here can cause the system to begin thrashing. This is due to MySQL’s reliance on the operating system’s file system cache, which is needed for data read operations. It is imperative that enough free memory is left for file system cache.

The amount of overhead the key_buffer_size requires for administrative tasks is small but grows along with the size of table indexes. Using a generic value of 5% of total index length of all MyISAM tables ensures enough room is available in the Key Cache for overhead. However, on extremely large database setups, the required overhead can be lowered further by increasing  key_cache_block_size. This is due to more rows being accessed at once, reducing the need for administrative overhead to frame those additional rows.

How to Calculate MyISAM Key Cache Size (key_buffer_size)

There are a couple of different methods used for calculating a proper key_buffer_size. These are listed and detailed below.

  1. Calculate key_buffer_size from ALL MyISAM Index Lengths
  2. Dedicated Server: Calculate key_buffer_size from total physical memory (MySQL Recommended)
  3. Shared Server: Calculate key_buffer_size from available system memory
Calculate key_buffer_size from ALL MyISAM Index Lengths

To keep the memory usage of the key_buffer_size as small as possible while having little risk of setting the value too small. This method relies on calculating the total length of the Indexes for all MyISAM tables currently on the server.

Rule of Thumb: Calculate key_buffer_size from all MyISAM Index lengths.
Set key_buffer_size to the total Index Length of all MyISAM table Indexes plus 5% for overhead. The sample script below helps to make this calculation by reading the necessary values from the INFORMATION_SCHEMA database. The percent (PCT) value used for calculating overhead can be adjusted. The highlighted 5 in the first line can be set to the desired percentage needed.

Script: Calculate key_buffer_size from Index Lengths

mysql -e "set @overhead = 5 / 100;
        select count(INDEX_LENGTH) as Indexes,
        sum(INDEX_LENGTH) as Total_Index_Length,
        floor(@overhead * 100) as PCT,
        floor(sum(INDEX_LENGTH)*@overhead) as Overhead,
        floor(sum(INDEX_LENGTH)*(1+@overhead)) as key_buffer_size
        FROM information_schema.TABLES WHERE ENGINE = 'MyISAM';"

Example Output:

+---------+--------------------+------+----------+-----------------+
| Indexes | Total_Index_Length | PCT | Overhead | key_buffer_size |
+---------+--------------------+------+----------+-----------------+
| 744 | 5775360 | 5 | 288768 | 6064128 |
+---------+--------------------+------+----------+-----------------+

  Dedicated Server Calculate key_buffer_size from total physical memory (MySQL Recommended)

MySQLⓇ also has some recommendations for finding an appropriate key_buffer_size value. Their approach only considers total physical system memory. Though this is an acceptable method, it accounts little for the actual size of the server’s MyISAM indexes and often results in over-tuning the buffer size. Below is the rule and a script to help calculate this value for any given server.

Rule of Thumb: key_buffer_size (Dedicated Servers)
When MySQL is the only major service on a server.
MySQL®️ Recommends: Dedicated MySQL servers whose primary role is handling MySQL services can use a value as large as 25% of physical memory in order to get better performance from indexing reads and multiple writes.
The following awk script calculates this setting based on 25% of the MemTotal value available in /proc/meminfo.

Script: Calculate key_buffer_size for Dedicated Servers

awk '/MemTotal/{$3="GB";$2=sprintf("%.0f",$2/1048576); print;$1="  Mem80%:";$2=sprintf("%.0f",$2*.25);print}' /proc/meminfo

Example Output:

MemTotal: 15 GB
Mem80%: 3.75 GB

 Shared Server:  Calculate key_buffer_size from available system memory

This rule is akin to the previous MySQL recommended rule. However, this one is modified to use available system memory instead of total physical memory. This makes the rule much more suitable for shared servers, yet is still rather generic.

Rule of Thumb: key_buffer_size (Shared Servers)

When MySQL runs alongside other major services like Web, Email, etc... (e.g., cPanel, Plesk)

Due to dynamic resource usage, there is not a one-size-fits-all calculation for shared servers. It becomes prudent to calculate the available system memory used after subtracting memory requirements of all other system services.

One way to accomplish this is to use a generic calculation instead. Reserving something between 15 and 30 percent of available system memory is a reasonable approach. However, finding the exact setting requires some guesswork and testing.

The following awk script reads MemAvail from /proc/meminfo and provides a selection of percentage based calculations to choose for the server.

Script: Calculate key_buffer_size  for Shared Servers

awk '/MemAvail/{$3="G";_=$2;$2=sprintf("% 3.0f",_/1048576);print;
for (i=30;i>=15;i-=5) {$1="MemAvail_"i"%:"; $2=sprintf("%3.0f",_*(i/100)/1048576); $4=sprintf("| %.0f M",_*(i/100)/1024);print}}' /proc/meminfo

Example Output:

MemAvailable:  10 G
MemAvail_30%:   3 G | 3087 M
MemAvail_25%:   3 G | 2573 M
MemAvail_20%:   3 G | 2058 M
MemAvail_15%:   3 G | 1544 M

MyISAM Key Cache Maintenance (Performance Tuning)

As with most MySQL optimization, it is important to re-evaluate performance periodically. MySQL makes it possible to examine the performance of the Key Cache by using the SHOW GLOBAL STATUS and SHOW GLOBAL VARIABLES statements and reviewing some specific variables. This capability is instrumental in making sure the Key Cache is performing well. There are a few different metrics to consider when evaluating the Key Cache Performance. These metrics are outlined in the following sub-sections.

Calculating Key Cache Usage Rate

One useful metric is the Key Cache Usage Rate. This measures the percentage of the Key Cache that is filled, a helpful detail when determining if the key_buffer_size is large enough or if memory is being wasted on a key_buffer_size that is too large for the server’s MyISAM tablespace. Calculating the Key Cache Usage Rate requires variables from both SHOW STATUS and SHOW VARIABLES statements. Below is the list of needed variable names and the equation used to calculate Key Cache Usage Rate.

To calculate key cache usage rate use this formula.

Script: Calculate Key Cache Usage Rate

mysql -e '
  select VARIABLE_VALUE into @Key_blocks_unused FROM information_schema.global_status
    WHERE VARIABLE_NAME = "Key_blocks_unused";
  select @Key_blocks_unused as Key_blocks_unused,
    @@key_cache_block_size as key_cache_block_size,
    @@key_buffer_size as key_buffer_size\G
  select truncate((1-((@Key_blocks_unused*@@key_cache_block_size)/@@key_buffer_size))*100, 2)
   as "(1-((Key_blocks_unused × key_cache_block_size) ÷ key_buffer_size)) × 100"\G
'

Example Output:

*************************** 1. row ***************************
Key_blocks_unused: 26476
key_cache_block_size: 1024
key_buffer_size: 33554432
*************************** 1. row ***************************
(1-((Key_blocks_unused × key_cache_block_size) ÷ key_buffer_size)) × 100: 19.20

Calculating Key Cache Miss Rate

Another useful Key Cache statistics is the Key Cache Miss Rate. This shows how often items that could be in the Key Cache are being read from a physical disk. The larger the rate, the more often items are read from disk instead of the Key Cache which indicates the current key_buffer_size is too small and should be increased. While a lower rate here indicates the Key Cache is being under-utilized. This indicates the key_buffer_size is too large and consuming excess memory needlessly.

Use this formula to calculate key cache miss rate.

Script: Calculate Key Cache Miss Rate

mysql -e '
  select VARIABLE_VALUE into @Key_reads FROM information_schema.global_status
    WHERE VARIABLE_NAME = "Key_reads";
  select VARIABLE_VALUE into @Key_read_requests FROM information_schema.global_status 
    WHERE VARIABLE_NAME = "Key_read_requests";
  select @Key_reads as"Key_reads (From Disk)", 
         @Key_read_requests as "Key_read_requests (Total)", 
         truncate(@Key_reads/@Key_read_requests*100, 2) as "Key_Cache_Miss_Rate%"\G
'

Example Output:

*************************** 1. row ***************************
Key_reads (From Disk): 1275
Key_read_requests (Total): 1209967
Key_Cache_Miss_Rate%: 0.10

Calculating Key Cache Flush Rate

The Key Cache Flush Rate measures how frequently the Key Cache is written to disk. Frequent flushing can slow down MyISAM operations as writing data to a physical disk is much slower than writing to the Key Cache in memory. The closer the Key Cache Flush Rate is to 100% the more frequently MyISAM flushes to disk. The lower the rate, the less often disk writes are conducted.

Use this formula to calculate key cache flush rate.

Script: Calculate Key Cache Miss Rate

mysql -e '
  select VARIABLE_VALUE into @Key_reads FROM information_schema.global_status
    WHERE VARIABLE_NAME = "Key_reads";
  select VARIABLE_VALUE into @Key_read_requests FROM information_schema.global_status 
    WHERE VARIABLE_NAME = "Key_read_requests";
  select @Key_reads as"Key_reads (From Disk)", 
         @Key_read_requests as "Key_read_requests (Total)", 
         truncate(@Key_reads/@Key_read_requests*100, 2) as "Key_Cache_Miss_Rate%"\G
'

Example Output:

*************************** 1. row ***************************
Key_reads (From Disk): 1275
Key_read_requests (Total): 1209967
Key_Cache_Miss_Rate%: 0.10

Improving Key Cache Flush Rate

The Key Cache Flush Rate can be improved (reduced) in a couple of different ways. One method is on the query level. Making sure that UPDATE queries on MyISAM tables are changing multiple rows in one single query whenever possible. This reduces the amount of flushing needed to conclude writing changed data by addressing them in a group together with a single write operation to disk instead of individually.

The other method for reducing the Key Cache Flush Rate is intended for large MyISAM tables. These tables should take advantage of the DELAY_KEY_WRITE create table attribute. This attribute only functions on MyISAM tables and instructs the engine to delay all Key Cache writing operations until after tables are closed, instead of at the moment data changes. This provides the biggest performance increase when enabled on large or medium tables with large amounts of volatile (changing) data.

Enabling DELAY_KEY_WRITE should be done on a per table basis and should be part of the initial CREATE TABLE query for any MyISAM table expected to be large or volatile. It is also simple to modify an existing MyISAM table so that it will start using the delayed write enhancement. This is done with a simple ALTER TABLE query like so:

Important MySQLⓇ Warning
MySQL®️ Warning: When running with –external-locking, any table using DELAY_KEY_WRITE has absolutely no protection from index corruption.

Example: Enable DELAY_KEY_WRITE on an existing MyISAM table

mysql -e 'ALTER TABLE databaseName.tableName DELAY_KEY_WRITE = 1;'

Example Output:

Query OK, 0 rows affected (0.02 sec)
Records: 0  Duplicates: 0  Warnings: 0

One final method still relates to the DELAY_KEY_WRITE table attribute. It is possible, using the DELAY_KEY_WRITE system variable, to force DELAY_KEY_WRITE behavior on for ALL MyISAM tables. This is done by setting the DELAY_KEY_WRITE system variable within the system configuration file to ALL instead of the default ON.

The delay_key_write directive is suggested to be set to ALL instead of ON, ensuring new tables will inherit this option.

Risk versus Reward

MySQL stresses the potential risk when planning on using the  DELAY_KEY_WRITE property with MyISAM.

Important MySQL®️ Warning

MySQL®️ Warning: When using tables with DELAY_KEY_WRITE enabled, it is suggested to backup indexes using the –myisam-recover-options directive.

Also see:

Section 5.1.7, Server Command Options

Section 16.2.1, MyISAM Startup Options

Example Output:

[mysqld] myisam-recover-options=BACKUP,FORCE

As with most performance enhancement, there is usually a sacrifice to be made for speed. It is important to understand that data integrity, particularly the integrity of your table indexes are more corruptible when running with a the DELAY_KEY_WRITE feature enabled. For this reason, it is important to have alternative means of warding off corruption with other solutions like the –myisam-recover-options feature to create a backup of indexes. Another common practice with high-performance database server is using a secondary power source or battery backup, so services have time to shut down properly during a power loss scenario.

 

Other MyISAM Directives

There are several other techniques for tuning your MyISAM Cache Key and its behavior. These techniques fall outside the scope of the current article. However, more details about these techniques are available on the MySQL website here: 16.2.1 MyISAM Startup Options.

 

Install and Connect to PostgreSQL 10 on Ubuntu 16.04

PostgreSQL (pronounced “post-gress-Q-L”) is a household name for open source relational database management systems. Its object-relational meaning that you’ll be able to use objects, classes database schemas and in the query language.  In this tutorial, we will show you how to install and connect to your PostgreSQL database on Ubuntu 16.04.

 

Step 1: Install PostgreSQL

First, we’ll obtain the authentication keys need to validate packages from the PostgreSQL repo.

wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key add -
sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt/ $(lsb_release -sc)-pgdg main" > /etc/apt/sources.list.d/PostgreSQL.list'

As a best practice, we will update our server before installing PostgreSQL.

apt-get -y update

After the update is complete, we’ll run the following command to install PostgreSQL

apt-get install postgresql-10

 

Step 2: Logging into PostgreSQL

Once installed PostgreSQL creates a default user named “postgres”.  This user works in a way different to that of other popular databases like MySQL.  PostgreSQL users can change the method of authentication, but by default, it uses a mode called ident. Ident takes your OS username and compares it with the allowed database usernames.

You must first switch to the default Postgres user

su - postgres

You’ll now see that you are logged in as that user via the prompt change

postgres@host2:~$

Afterward, you can then enter the PostgreSQL terminal by typing:

psql

You’ll know you are connected by the message below:

psql (9.5.14)
Type "help" for help.
postgres=#

 

Step 3: Logging out of PostSQL

To exit out of your postgresql environment use the following command

\q

Now that you’ve created your PostgreSQL world it’s time to stretch your feet!  Let’s start creating and listing databases using our world renown Cloud VPS servers.

 

Listing and Switching Databases in PostgreSQL

PostgreSQL (pronounced “post-gress-Q-L”) is a household name for open source relational database management systems. Its object-relational meaning that you’ll be able to use objects, classes in database schemas and the query language. As part of our PostgreSQL series, we’ll show you how to list and switch between databases quickly.

Pre-flight

Log into your Ubuntu 16.04 server

Step 1: Login to your Database
su - postgres

Step 2: Enter the PostgreSQL environment
psql

With the psql command, you’ll be greeted by its current version and command prompt.

psql (9.5.14)
Type "help" for help.
postgres=#

Step 3: List Your PostgreSQL databases
Often, you’ll need to switch from database to database, but first, we will list the available database in PostgreSQL

postgres=# \list

By default, PosgreSQL has 3 databases: postgres, template0 and template1

Step 4: Switching Between Databases in PostgreSQL
Switching between databases is another way of saying you are closing one connection and opening another. When you need to change between databases, you’ll use the “connect” command, which is conveniently shortened to \c, followed by the database name.

\connect dbname

Or:

\c dbname

Creating and Deleting a PostgreSQL Database

PostgreSQL (pronounced “post-gress-Q-L”) is a household name for open source relational database management systems. Its object-relational meaning that you’ll be able to use objects, classes in database schemas and the query language.  In this tutorial, we will be demonstrating some essentials like creating, listing and deleting a database.

If you have already installed PostgreSQL to your Ubuntu 16.04 server using our last tutorial, your next step is to create a database.  You’ll be accomplishing this task by using the default superuser, postgres, to log in.

Step 1: Login as the Postgres User

su - postgres

Step 2: Enter the PostgreSQL Environment

psql

With the psql command, you’ll be greeted by its current version and command prompt.

psql (9.5.14)
Type "help" for help.
postgres=#

Step 3: Creating the PostgreSQL Database

Let’s create our first database by typing in the command below.  Replace dbname with the database name of your choice.

CREATE DATABASE dbname;

 

Verify Creation of PostgreSQL Database

Using the following command allows us to view the databases in our PostgreSQL instance (you can ignore, delete or utilize the default databases: postgres, template0, template1)

postgres=# \list

 

Deleting a PostgreSQL Database

Once you’ve backed up your removing your PostgreSQL database is a cinch!  Its similar to creating a database but we will be using the drop command. In my command line example, the database name is “dbname”. By using the list command in the previous section, you’ll be able to view your databases’ names. Replace dbname with your database’s name in the command below.

DROP DATABASE dbname;

 

MySQL Performance: InnoDB Buffers & Directives

As discussed earlier in our MySQL Performance series, the InnoDB storage engine is designed to be a high-performance database for very large datasets. The row-locking technique it uses allows for many read and write requests to occur on a single table concurrently. This is a vast improvement in speed over traditional table-locking of the MyISAM engine. This part of our MySQL Performance series will focus on configuring InnoDB tables for maximum concurrency with minimal disk input/output (I/O).

Disk I/O & Temporary Tables

Disk I/O & Temporary Tables.

A simple and effective change that can provide a noticeable performance boost immediately is the practice of forcing all temporary table writes into memory instead of writing to a hard disk. Writing temp tables to RAM reduces the impact MySQL has on overall system disk I/O, which greatly improves performance. This change is done by leveraging the /dev/shm tmpfs partition in Linux. This special device gives server processes the ability to write files directly into RAM as if it were a standard disk drive.

 

InnoDB Log File Size

InnoDB Log File Size

The InnoDB log file is not your traditional log file. It’s a special binary redo log, which InnoDB uses to store redo activity for every write request handled by the server. Every write query executed gets a redo entry in the log file so that change can be recovered in the event of a crash. Because of this reason, the InnoDB log file size plays a critical role in MySQL write query performance, particularly on write-heavy databases.

Rule of Thumb: innodb_log_file_size
MySQL®️ Recommends: “The larger the value, the less checkpoint flush activity is required in the buffer pool, saving disk I/O.” A log file size of 1GB is sufficient for most situation. This may need to be raised when database growth exceeded several dozen GB in size.
Notice
MySQL®️ Warns: “Larger log files also make crash recovery slower, although improvements to recovery performance make log file size less of a consideration than it was in earlier versions of MySQL. “

Due to the special nature of the InnoDB Redo Logs, this setting cannot be changed without first deleting the existing InnoDB log files. The following procedure for adjusting the innodb_log_file_size directive must be followed before MySQL will start again.

Changing innodb_log_file_size Procedure:

  1. Stop MySQL Serviceservice mysql stop
  1. Update innodb_log_file_size within [mysqld] header in /etc/my.cnf file.innodb_log_file_size=1G
  1. Move the existing log files out of the mysql directorymv /var/lib/mysql/ib_logfile* /backup
  1. Start MySQL Serviceservice mysql start

 

InnoDB Buffer Pool (IBP)

The InnoDB Buffer Pool plays a critical role in MySQL Performance. The IBP is a reserved portion of system memory where InnoDB table and index data are cached. This allows frequently accessed data to be returned quickly, without the need of spinning up a physical hard drive. The more InnoDB tablespace that is cached in memory, the less often MySQL will access physical disks, which manifests as faster query response times and improved overall system performance.

There are multiple directives which control IBP behavior. Tuning these to match the servers existing tablespace data, with some room to grow, will optimize the performance of any queries using InnoDB tables. Since InnoDB is the recommended engine for very large tables, optimizing the IBP gives major performance gains.

 

InnoDB Buffer Pool Size

InnoDB Buffer Pool Size

The larger the IBP, the more InnoDB functions as a high-performance in-memory database. However, setting the size too large can be detrimental to overall system performance. A balance must be struck between the IBP size and needed memory for other system services. In an ideal configuration, the IBP should be as large as possible, without causing excessive swapping (i.e., Thrashing) to occur. There are some simple rules to follow when making these determinations.

Rule of Thumb: innodb_buffer_pool_size (Dedicated Servers)

When MySQL is the only major service on a server.

The recommended configuration by MySQL is for innodb_buffer_pool_size to use as much as 80% of the system’s total physical memory.

Calculate innodb_buffer_pool_size Script: (Dedicated Server)

awk '
/MemTotal/{
$3="GB"
$2=sprintf("%.0f",$2/1048576)
print
$1="  Mem80%:"
$2=sprintf("%.0f",$2*.8)
print
}' /proc/meminfo
Example Output:

MemTotal: 15 GB
Mem80%: 12 GB

Rule of Thumb: innodb_buffer_pool_size (Shared Servers)

When MySQL runs alongside other major services like Web, Email, etc... (e.g. cPanel, Plesk)

Due to their varied resource requirements, there is no one-size-fits-all calculation for shared servers. It becomes necessary to calculate the memory requirement needs of all other critical services on the server and subtract those from total system memory to find a proper amount of available memory which can be assigned to innodb_buffer_pool_size.

A simplified method is to use a generic calculation. A conservative starting point is assigning between 30 and 80 percent of available system memory, instead of total physical memory. However, determining the exact setting may require some guesswork and testing.

The following awk script reads MemAvail from /proc/meminfo and provides a selection of percentage based calculations to choose for the server.

Calculate innodb_buffer_pool_size Script: (Shared Server)

awk '
/MemAvail/{
$3="G";_=$2
$2=sprintf("% 3.0f",_/1048576)
print
for (i=80;i>=25;i-=10) {
$1="MemAvail_"i"%:"
$2=sprintf("% 3.0f",_*(i/100)/1048576)
$4=sprintf("| %.0f M",_*(i/100)/1024)
print
}
}' /proc/meminfo

Example Output:

MemAvailable:  10 G
MemAvail_80%:   8 G | 8405 M
MemAvail_70%:   7 G | 7354 M
MemAvail_60%:   6 G | 6304 M
MemAvail_50%:   5 G | 5253 M
MemAvail_40%:   4 G | 4203 M
MemAvail_30%:   3 G | 3152 M

 

InnoDB Buffer Pool Instances

InnoDB Buffer Pool Instances

The innodb_buffer_pool_instances directive controls the number of memory pages Innodb creates. MySQL ignores this directive unless the innodb_buffer_pool_size is greater than 1G/1024M. When larger than 1G, the buffer pool is divided up into a number of equal sized memory pages specified by this directive.

Rule of Thumb: innodb_buffer_pool_instances
MySQL®️ Recommends: “For best efficiency, specify a combination of innodb_buffer_pool_instances and innodb_buffer_pool_size so that each buffer pool instance is at least 1GB.”

  ‣ Rule Exception: innodb_buffer_pool_instances should not exceed Total_IO_Threads

Total_IO_Threads=(innodb_read_io_threads + innodb_write_io_threads)

Preference a 1:1 Ratio for: Total_Instances:Total_IO_Threads

 

InnoDB I/O Threads

InnoDB I/O ThreadsInnoDB I/O Threads

Input/Output threads are sub-processes of MySQL which directly access the IBP memory pages. There are two types of I/O threads: innodb_read_io_threads and innodb_write_io_threads. These threads read and write, respectively, to the individual memory pages created by innodb_buffer_pool_instances.

The default value for both of these thread types is 4, making a total of 8 threads running concurrently. This pairs up with the default number of memory pages created when innodb_buffer_pool_size is larger than 1G (x8). This synergy is a key factor when improving InnoDB performance on very large database servers with large volumes of RAM and CPU cores. When increasing innodb_buffer_pool_instances past the default of 8, it becomes optimal to increase the number of threads to handle the extra memory pages concurrently. The goal being, to keep the synergy of total I/O threads equal to total memory pages:

( innodb_read_io_threads + innodb_write_io_threads ) = innodb_buffer_pool_instances

When to change InnoDB I/O Threads

Two conditions should be satisfied before adjusting I/O threads for a server.

  1. When innodb_buffer_pool_size is larger than 8 Gigabytes, this means there are more memory pages than I/O threads to handle them concurrently.
  2. When the server has more than 8 CPU cores to devote to the MySQL Service.

It is not enough to merely divide the innodb_buffer_pool_instances in half to get equal numbers of read and write threads. The goal is to increase the number of threads primarily used by the specific server’s query workload. For instance, if your server reads more data than it writes, increasing write threads would be counter-productive. The same holds true in reverse.

How to Calculate MySQL Read:Write Ratio

The global statistics kept by a MySQL server can be leveraged to determine a systems Read:Write Ratio. The following MySQL queries can be used to calculate the Total_Reads and Total_Writes of a server.

Total_Reads = Com_select

SHOW GLOBAL STATUS LIKE 'Com_select';

Total_Writes = Com_delete + Com_insert + Com_replace + Com_update

SHOW GLOBAL STATUS WHERE Variable_name IN ('Com_insert', 'Com_update', 'Com_replace', 'Com_delete');

  • If the Total_Reads is greater than Total_Writes, the server is considered Read_Heavy.
  • If the Total_Writes is greater than Total_Reads, the server is considered Write_Heavy.

The following awk script will print the necessary statistics variables from MySQL and automatically calculate a simplified Read:Write Ratio to help determine whether the system in question is Read_Heavy or Write_Heavy.

Calculate Read:Write Ratio Script:

mysql -e 'SHOW GLOBAL STATUS;'|\
awk '
$1~/Com_(delete|insert|update|replace)$/{
w += $2
printf $0 "\t + Total_Writes = " w "\n"
}
$1~/Com_(select)/{
r += $2
printf $0 "\t + Total_Reads = " r "\n"
}
END {
printf "\nRead:Write Ratio:\n\t" r ":" w " "
if (r >= w) {
R=sprintf("%.0f",r/w)
print R ":1"
} else {
W=sprintf("%.0f",w/r)
print "1:" W
}
}'

Example Output:

Com_delete    14916     + Total_Writes = 14916
Com_insert    87413     + Total_Writes = 102329
Com_replace   0         + Total_Writes = 102329
Com_select    675528    + Total_Reads = 675528
Com_update    18976     + Total_Writes = 121305
Read:Write Ratio:
675528:121305 6:1

Note:
Adjust the first line’s mysql statement as needed to connect to the appropriate server. e.g.,

mysql -h localhost -u root -p -e 'SHOW GLOBAL STATUS;' |\

Hardware CPU Core Considerations

When adjusting innodb_read_io_threads or innodb_write_io_threads, keep the total threads between the two equal to the number of CPU cores available to MySQL. This ensures maximum concurrency as each memory page can be accessed simultaneously by each individual CPU core.

Rule of Thumb: InnoDB I/O Threads (High-Performance) ‣innodb_read_io_threads + innodb_write_io_threads
The total number of InnoDB I/O Threads should not surpass the total number of CPU cores available to MySQL.

When increasing Innodb I/O Threads, increase only the read or write threads depending on whether the server is Read_Heavy or Write_Heavy.

Other Directives

There are several more advanced techniques for fine tuning the InnoDB Buffer Pool and its behavior. These are beyond the scope of this article. However, you can find more details about these techniques on the MySQL website here: 15.5.1 Buffer Pool.

 

MySQL Performance: System Configuration File & Routine Maintenance

The majority of work needed when adjusting the MySQL server is editing the applicable directives within a MySQL configuration file. There are multiple, optional configuration files that MySQL looks for when starting up. They are read in the following order:

MySQL Configuration Files:

MySQL Configuration Files:

1./etc/my.cnf

2./etc/mysql/my.cnf

3.SYSCONFDIR/my.cnf

4.$MYSQL_HOME/my.cnf

5.~/.my.cnf

Any of these files can contain MySQL server directives. For the sake of simplicity, this article assumes the default file /etc/my.cnf is being used for all examples. This is the most widely known file path in the majority of cases. However, some Linux distributions favor others than the ones listed from above. Though the file location is changed in these other distributions, the syntax and recommendations in this article series apply equally to all Linux based systems.

 

MySQL Configuration File Syntax

Each entry inside the configuration file applies to the most recent section header. Section headers are made up of a single line with the name of the header encapsulated within square brackets (e.g. [name]). When optimizing the MySQL service daemon,  changes will need to be within in the [mysqld] section header. The example below illustrates how this should look.

Example: Configuration File Syntax

[mysqld] innodb_buffer_pool_size=2G
innodb_buffer_pool_instances=2

Note
The trailing d character after mysql is required to apply settings to the MySQL service daemon. The [mysql] header without a trailing d character is incorrect and applies only to MySQL client.

 

Applying MySQL Config Changes

The vast majority of directive changes merely require a restart of the MySQL service. There are a handful of other directives which may require an additional task to be undertaken while the MySQL service is offline. These tasks will be outlined in their specific section later in the article.

Restart MySQL Service:

service mysql restart

Important:
Refrain from making several optimization changes to your existing MySQL config at one time. This is especially true when adjusting production level servers. Making small incremental changes will make it easier to identify problems with individual changes and isolate settings that may not work well for your unique setup.

Routine Maintenance

In the follow-up articles to this part of the MySQL Performance series, we will be outlining several directives and some suggested techniques to use for configuring those directives. It is imperative to understand that MySQL Optimization is an ongoing, ever-evolving configuration. As sites grow, so do data sets and workload behavior. The settings you configure today, will eventually become obsolete and probably sooner than you would like. Because of this eventuality, it is vitally important that routine maintenance is conducted on your configuration.

What are some tasks to perform routinely:
  1. Reevaluate all buffers and directives that have been modified previously. This includes the changes discussed and recommended in the entirety of this article series.
  2. Reassess MySQL Query Statistics to determine workload behaviors.
  3. Reassess tablespace data, rate of growth or other trends in data consumption.
  4. Archive old data from large or heavy trafficked tables to ease the burden of read/write requests to those tables.
  5. Reevaluate indexes and their performance. Create new, better indexes and remove old, unused indexes.

Performance Expectations

Although, the changes recommended in this article series aim to squeeze the best performance out of MySQL, the performance increase you may see is entirely subjective. Following these recommendations should help smooth the edges on any servers hitting the bottlenecks they are designed to target. However, there is no guarantee that these changes will have a positive or noticeable impact on the application level.

The biggest issues facing application performance are often on the coding level and not the server level. The underlying MySQL server configuration only carries application performance so far. Identifying problematic queries and inefficient coding practices that serialize your workload are problems that even a finely tuned MySQL server configuration just cannot correct. If you are in doubt, or your application performance seems to suffer despite all the server level optimization in the world, then you’re probably hitting code level performance problems, and we recommend contacting a qualified Database Administrator (DBA) to evaluate your application performance properly.

MySQL Performance: Converting MySQL to MariaDB

As we explored in our previous article of our MySQL Perfomance Series: MySQL vs. MariaDB there are very few downsides to using MariaDB over standard MySQL. Our high-availbility MariaDBs have proven itself to be a worthy successor with easily migitated drawbacks.  As the last article in our series we will focus on upgrading to various MySQL and MariaDB version on the following servers:

CentOS 6/7

Ubuntu 14.04/16.04

Continue reading “MySQL Performance: Converting MySQL to MariaDB”

MySQL Performance: MySQL vs. MariaDB

MariaDB and MySQL logos.

We continue our series on MySQL Optimization by focusing on the differences and similarities between MariaDB and MySQL. MariaDB is a popular alternative to using traditional MySQL for database hosting. The free market of hosting allows additional actors to compete with the long-standing database solution, MySQL. MariaDB is the most prominent alternative to MySQL and is a favorite among industry giants such as Google, Craigslist, Wikipedia, Arch Linux, RedHat, CentOS, Fedora, and cPanel. In a direct performance comparison, MariaDB leaps to the head of the pack through performance optimizations, easy of conversion and compatibility.

Why Switch to MariaDB?

MariaDB is mostly a clone of MySQL, but it also provides several improvements over running the standard MySQL instance. MariaDB aims for a drop-in replacement to MySQL, which makes the switch painless and straightforward. Combine the simplicity of switching with a long list of improvements, MariaDB brings both performance and cutting-edge features to the table. Below are some of its selling points over standard MySQL.

More Storage Engines Options: There is 12 new storage engine built into MariaDB. Among these are CONNECT, Spider, and SphinxSE. Visit their Storage Engines page for a complete list of these engines, how they function, and ways to leverage them to optimize your database.

Speed improvements: MariaDB sports many new speed improvements compared directly with standard MySQL. This improved performance makes MariaDB stand apart from the baseline performance of traditional MySQL servers. Like MySQL, MariaDB has dozens of features for speed optimization including disk access, JOIN and EXPLAIN improvements, subquery, derived tables/views, execution control, and optimizer control.

Faster Indexes/Cache: When using the MEMORY storage engine, MariaDB can complete INSERT statements up to 24% faster than traditional MySQL servers, along with CHECKSUM TABLE and MyISAM Segment Key Cache being 4x faster.

Speedier and Larger Connection Pool: MariaDB benefits from an improved pool of threads that run faster and support up to 200,000+ connections where standard MySQL falls short.

Improved Replication: MariaDB sports faster and safer replication with updates being up to 2x faster than with traditional MySQL Replication setups. Now possible, parallel replication allows the existence of Active/Active or Master/Master configurations. MariaDB replication is backward compatible with MySQL servers, so migrating your cluster to MariaDB is possible by utilizing one node at a time.

New Extensions/Features: There are several new extension and features, to name a few, the WITH, JSON and KILL statements. DECIMAL sees an increased from 30 to 38 decimals while KILL ALL queries for a specified user.

List of Features: Available MariaDB’s website is a comprehensive list of improvements and features, located here: MariaDB versus MySQL-Features.

 

What is the Downside to Using MariaDB?

The MariaDB project is entirely open-source and free, unlike MySQL which uses dual licensing to keep its Enterprise edition specific features proprietary. The MariaDB developers actively update and enhance with cutting-edge features that standard MySQL does not carry.

Missing Features: MySQL uses some proprietary code in its Enterprise Edition. MariaDB does not have access to this propriety content and is a closed source, meaning the features are only available to MySQL Enterprise users.

Mitigation: MariaDB has addressed this concern through allowing viable open-source alternative plugins for MariaDB, providing the same functionality that the MySQL Enterprise Edition offers. This allows the vast majority of standard MySQL and MySQL Enterprise Edition users the option to switch to MariaDB, taking advantage of its robust performance enhancing features.

Delayed MySQL Compatibility: MariaDB originally forked from MySQL 5.5, so this is the base starting point for the MariaDB source code. Meaning that newer features and bug fixes developed for standard MySQL after version 5.5 are not part of the existing MariaDB source code.

Mitigation: MariaDB conducts monthly merges of the standard MySQL source code to ensure both compatibility and feature/bug-fix adoption. This kind of regular code merging, allows MariaDB to maintain its allure as a drop-in replacement for practically any existing versions of standard MySQL. However, there will always be a delay causing MariaDB to lag behind MySQL’s newer features and bug patches.

MySQL Performance: How To Leverage MySQL Database Indexing

A Mysql Indexing Logo

Throughout this tutorial, we will cover some of the fundamentals of indexing. As part of the MySQL series, we will introduce capabilities of MySQL indexing and the role it plays in optimizing database performance. Liquid Web recommends consulting with a DBA before making any changes to your production level application.

What is Indexing?

Indexing is a powerful structure in MySQL which can be leveraged to get the fastest response times from common queries. MySQL queries achieve efficiency by generating a smaller table, called an index, from a specified column or set of columns. These columns, called a key, can be used to enforce uniqueness. Below is a simple visualization of an example index using two columns as a key.

+------+----------+----------+
| ROW | COLUMN_1 | COLUMN_2 |
+------+----------+----------+
| 1 | data1 | data2 |
+------+----------+----------+
| 2 | data1 | data1 |
+------+----------+----------+
| 3 | data1 | data1 |
+------+----------+----------+
| 4 | data1 | data1 |
+------+----------+----------+
| 5 | data1 | data1 |
+------+----------+----------+

Queries utilize indexes to identify and retrieve the targeted data, even if they are a combination of keys. Without an index, running that same query results in an inspection of every row for the needed data. Indexing produces a shortcut, with much faster query times on expansive tables. A textbooks analogy may provide another common way to visualize how indexes function.
This analogy compares MySQL indexing to indexing in the back of a book.

When to Enable Indexing?

Indexing is only advantageous for huge tables with regularly accessed information. For instance, to continue with our textbook analogy, it makes little sense to index a children’s storybook with only a dozen pages. It’s more efficient to simply read the book to find each occurrence of the word “turtle” than it would be to set up and maintain indexes, query for those indexes, and then review each page provided. In the computing world, those extra tasks surrounding indexing represent wasted resources which would be better purposed by not indexing.

Without indexes, when tables grow to enormous proportions, response times suffer from queries targeting those obtuse tables. Inefficient queries manifest into latency within application or website performance. We commonly identify this latency by using the MySQL slow query log feature. You can find more details about using the slow query log feature in the first article in this series: MySQL Performance: Identifying Long Queries.
Once a colossal table hits its tipping point, it reaches the potential for downtime for applications and websites. Conducting routine evaluations for growing database establishes optimal database performance and sidesteps long queries’ inherent interruptions.

MySQL Indexing Pros vs. Cons

There are benefits and downsides to using MySQL indexing, and we’ll discuss the significant pros and cons for your consideration. These aspects will guide you to decide whether indexing is an appropriate choice for your situation.

quick data transmissions and ideal for OLAP.

What Information Does One Index?

Selecting what to index is probably the most challenging part to indexing your databases. Determining what is important enough to index and what is benign enough to not index. Generally speaking, indexing works best on those columns that are the subject of the WHERE clauses in your commonly executed queries. Consider the following simplified table:

ID, TITLE, LAST_NAME, FIRST_NAME, MAIDEN_NAME, DOB, GENDER, AGE, DESCRIPTION, HISTORY, ETC...

If your queries rely on testing the WHERE clause using LAST_NAME and FIRST_NAME then indexing by these two columns would significantly increase query response times. Alternately, if your queries rely on a simple ID lookup, indexing by ID would be the better choice.

These examples are merely a rudimentary example, and there are several types of indexing structures built-in to MySQL. The following MySQL page discusses these types of indexes in greater detail, and a recommended read for anyone considering indexing: How MySQL Uses Indexes

What is a Unique Index?

Another point for consideration when evaluating which columns to serve as the key in your index is whether to use the UNIQUE constraint. Setting the UNIQUE constraint will enforce uniqueness based on the configured indexing key. As with any key, this can be a single column or a concatenation of multiple columns. The function of this constraint ensures that there are no duplicate entries in the table based on the configured key.

UNIQUE constraints increase write speeds, a taxation of implementation.

What is a Primary Key Index?

As commonly invoked as the UNIQUE constraint the PRIMARY KEY is used to optimize indexes. This constraint ensures that the designated PRIMARY KEY cannot be of a null value. As a result, a performance boost occurs when running on an InnoDB storage engine for the table in question. This boost is due to how InnoDB physically stores data, placing null valued rows in the key out of contiguous sequence with rows that have values. Enabling this constraint ensures the rows of the table are kept in contiguous order for quicker responses.

Primary Key Index is absolutely necessary for large tables.

Managing Indexes

Now we will cover some of the basics of manipulating indexes using MySQL syntax. In examples, we will include the creation, deletion, and listing of indexes.Keywords for Managing Indexes: dbName, tableName, indexName Keep in mind, these examples have placeholder entries for the specific keywords. These keywords are self-explanatory by nature for easy reading, and below is an outline of them.

Instead of tableName you can use dbName.tableName.

Listing/Showing Indexes

Tables can have multiple indexes. Managing indexes will inevitably require being able to list the existing indexes on a table. The syntax for viewing an index is below.

SHOW INDEX FROM tableName;

SHOW INDEX FROM tableName; shows all indexes.

Indexing are present on 3 different columns.

Creating Indexes

Index creation has a simple syntax. The difficulty is in determining what columns need indexing and whether enforcing uniqueness is necessary. Below we will illustrate how to create indexes with and without a PRIMARY KEY and UNIQUE constraints.

As previously mentioned, tables can have multiple indexes. Multiple indexing is useful for creating indexes attuned to the queries required by your application or website. The default settings allow for up to 16 indexes per table, increase this number but is generally more than is necessary. Indexes can be created during a table’s creation or added on to the table as additional indexes later on. We will go over both methods below.

Creating too many indexes can add latency, but if you must then increase buffers in MySQL config.

Example: Create a Table with a Standard Index

CREATE TABLE tableName (
ID int,
LName varchar(255),
FName varchar(255),
DOB varchar(255),
LOC varchar(255),
INDEX ( ID )
);
You can create an index for several columns, using ID as the index.

Example: Create a Table with Unique Index & Primary Key

CREATE TABLE tableName (
ID int,
LName varchar(255),
FName varchar(255),
DOB varchar(255),
LOC varchar(255),
PRIMARY KEY (ID),
UNIQUE INDEX ( ID )
);
You can create an Primary Key and UNIQUE constraint over several columns.

Example: Add an Index to Existing Table

CREATE INDEX indexName ON tableName (ID, LName, FName, LOC);CREATE INDEX statement creates an index and names it.

Example: Add an Index to Existing Table with Primary Key

CREATE UNIQUE INDEX indexName ON tableName (ID, LName, FName, LOC);the CREATE UNIQUE command can add an index to a table ensuring no duplicate data.

Deleting Indexes

While managing indexes, you may find it necessary to remove some. Deleting indexes is also a very simple process, see the example below:

DROP INDEX indexName ON tableName;The DROP INDEX command lets us drop indexes on particular column.

There are many ways to optimize your database for true efficiency. If you would like to learn more or convert the search engines types available in MySQL read through our MyISAM vs. InnoDB tutorial.  Or if you are need of high functioning databases check out our MySQL product page to view different options.

Apache Performance Tuning: Configuring MPM Directives

 

Our previous article in this series focused on defining and fitting MPM to match your environment.  Building off of our last tutorial we will be discussing specific details on how to adjust the previously mentioned Apache configuration directives on the various types of Liquid Web servers.  

Continue reading “Apache Performance Tuning: Configuring MPM Directives”