High Availability and Scalability - MariaDB - Databases - Software - Computers


High Availability and Scalability
Prev Next

High Availability and Scalability

Table of Contents

Oracle VM Template for MariaDB Enterprise Edition
Using MariaDB with DRBD
Configuring the DRBD Environment
Configuring MariaDB for DRBD
Optimizing Performance and Reliability
Using Linux HA Heartbeat
Heartbeat Configuration
Using Heartbeat with MariaDB and DRBD
Using Heartbeat with DRBD and dopd
Dealing with System Level Errors in a Heartbeat Cluster
Using MariaDB within an Amazon EC2 Instance
Setting Up MariaDB on an EC2 AMI
EC2 Instance Limitations
Deploying a MariaDB Database Using EC2
Using ZFS Replication
Using ZFS for File System Replication
Configuring MariaDB for ZFS Replication
Handling MariaDB Recovery with ZFS
Using MariaDB with memcached
Installing memcached
Using memcached
Developing a memcached Application
Getting memcached Statistics
memcached FAQ
MySQL Proxy
MySQL Proxy Supported Platforms
Installing MariaDB Proxy
MySQL Proxy Command Options
MySQL Proxy Scripting
Using MariaDB Proxy
MySQL Proxy FAQ

MySQL is deployed into many applications demanding availability and scalability.

Availability refers to the ability to cope with, and if necessary recover from, failures on the host, including failures of MySQL, the operating system, or the hardware and maintenance activity that may otherwise cause downtime. Scalability refers to the ability to spread both the database and the load of your application queries across multiple MariaDB servers.

Because each application has different operational and availability requirements, MariaDB offers a range of certified and supported solutions, delivering the appropriate levels of High Availability (HA) and scalability to meet service level requirements. Such solutions extend from replication, through virtualization and geographically redundant, multi-data center solutions delivering 99.999% uptime.

Selecting the right high availability solution for an application largely depends on:

The primary solutions supported by MariaDB include:

Further options are available using third-party solutions such as DRBD (Distributed Replicated Block Device) and Heartbeat, and more complex scenarios can be solved through a combination of these technologies.

Each architecture used to achieve highly available database services is differentiated by the levels of uptime it offers. These architectures can be grouped into three main categories:

As illustrated in the following figure, each of these architectures offers progressively higher levels of uptime, which must be balanced against potentially greater levels of cost and complexity that each can incur. Simply deploying a high availability architecture is not a guarantee of actually delivering HA. In fact, a poorly implemented and maintained shared-nothing cluster could easily deliver lower levels of availability than a simple data replication solution.

Figure 14.1. Tradeoffs: Cost and Complexity versus Availability

As the number of “nines” in the uptime percentage increases, so does the cost and complexity, progressing from basic replication, to a clustered and virtualized configuration, to shared-nothing clusters replicated across geographic regions. Different kinds of organizations require different “nines” of availability, from Internet service providers and mainstream businesses at 3 nines, online services at 4 nines, and eCommerce, telecom, and military applications at 5 nines.

The following figure maps common application types to architectures, based on best practices observed from the MariaDB user base. It serves as a reference point to investigate which HA architectures can best serve your requirements.

Figure 14.2. High Availability Architectures for Common Application Types

Data Replication is suitable for most types of mid-level applications. Clustered, Virtualized configurations are suitable for all but the highest-end telecom applications. Shared-Nothing, Geo-Replicated Clusters are suitable for the busiest applications such as telecom and OLTP, and the most high-value ones such as e-commerce and finance.

The following table compares the HA and Scalability capabilities of the various MariaDB solutions:

Requirement MySQL Replication MySQL Replication + Linux Heartbeat Heartbeat + DRBD Oracle VM Template MySQL Cluster
Availability
Platform Support All Supported by MariaDB Server Linux Linux Oracle Linux All Supported by MariaDB Cluster
Automated IP Failover No Yes Yes Yes Depends on Connector and Configuration
Automated Database Failover No No Yes Yes Yes
Automatic Data Resynchronization No No Yes N/A - Shared Storage Yes
Typical Failover Time User / Script Dependent Configuration Dependent, 60 seconds and Above Configuration Dependent, 60 seconds and Above Configuration Dependent, 60 seconds and Above 1 Second and Less
Synchronous Replication No, Asynchronous and Semisynchronous No, Asynchronous and Semisynchronous Yes N/A - Shared Storage Yes
Shared Storage No, Distributed No, Distributed No, Distributed Yes No, Distributed
Geographic redundancy support Yes Yes Yes, via MariaDB Replication Yes, via MariaDB Replication Yes, via MariaDB Replication
Update Schema On-Line No No No No Yes
Scalability
Number of Nodes One Master, Multiple Slaves One Master, Multiple Slaves One Active (primary), one Passive (secondary) Node One Active (primary), one Passive (secondary) Node 255
Built-in Load Balancing Reads, via MariaDB Replication Reads, via MariaDB Replication Reads, via MariaDB Replication Reads, via MariaDB Replication & During Failover Yes, Reads and Writes
Supports Read-Intensive Workloads Yes Yes Yes Yes Yes
Supports Write-Intensive Workloads Yes, via Application-Level Sharding Yes, via Application-Level Sharding Yes, via Application-Level Sharding to Multiple Active/Passive Pairs Yes, via Application-Level Sharding to Multiple Active/Passive Pairs Yes, via Auto-Sharding
Scale On-Line (add nodes, repartition, etc.) No No No No Yes

Oracle VM Template for MariaDB Enterprise Edition

Virtualization is a key technology to enable data center efficiency and high availability while providing the foundation for cloud computing. Integrating MariaDB Enterprise Edition with Oracle Linux, the Oracle VM Template is the fastest, easiest, and most reliable way to provision virtualized MariaDB instances, enabling users to meet the explosive demand for highly available services.

The Oracle VM Template enables rapid deployment and eliminates manual configuration efforts. It provides a pre-installed and pre-configured virtualized MariaDB 5.5 Enterprise Edition software image running on Oracle Linux and Oracle VM, certified for production use. The MariaDB software image has undergone extensive integration and quality assurance testing as part of the development process.

In addition to rapid provisioning, MariaDB users also benefit from the integrated high availability features of Oracle VM which are designed to enable organizations to meet stringent SLA (Service Level Agreement) demands through a combination of:

Instructions for the creation, deployment and use of the Oracle VM Template for MariaDB Enterprise Edition are available from:

To download the Oracle VM Template for MariaDB Enterprise, go to http://edelivery.oracle.com/oraclevm and follow these instructions:

Using MariaDB with DRBD

Configuring the DRBD Environment
Configuring MariaDB for DRBD
Optimizing Performance and Reliability

The Distributed Replicated Block Device (DRBD) is a Linux Kernel module that constitutes a distributed storage system. You can use DRBD to share block devices between Linux servers and, in turn, share file systems and data.

DRBD implements a block device which can be used for storage and which is replicated from a primary server to one or more secondary servers. The distributed block device is handled by the DRBD service. Writes to the DRBD block device are distributed among the servers. Each DRBD service writes the information from the DRBD block device to a local physical block device (hard disk).

On the primary data writes are written both to the underlying physical block device and distributed to the secondary DRBD services. On the secondary, the writes received through DRBD and written to the local physical block device. On both the primary and the secondary, reads from the DRBD block device are handled by the underlying physical block device. The information is shared between the primary DRBD server and the secondary DRBD server synchronously and at a block level, and this means that DRBD can be used in high-availability solutions where you need failover support.

Figure 14.3. DRBD Architecture Overview

DRBD Architecture Overview

When used with MySQL, DRBD can be used to ensure availability in the event of a failure. MariaDB is configured to store information on the DRBD block device, with one server acting as the primary and a second machine available to operate as an immediate replacement in the event of a failure.

For automatic failover support, you can combine DRBD with the Linux Heartbeat project, which manages the interfaces on the two servers and automatically configures the secondary (passive) server to replace the primary (active) server in the event of a failure. You can also combine DRBD with MariaDB Replication to provide both failover and scalability within your MariaDB environment.

For information on how to configure DRBD and MySQL, including Heartbeat support, see , "Configuring the DRBD Environment".

For FAQs about using DRBD and MySQL, see "MySQL 5.6 FAQ: MySQL, DRBD, and Heartbeat".Note

Because DRBD is a Linux Kernel module, it is currently not supported on platforms other than Linux.

Configuring the DRBD Environment

Setting Up Your Operating System for DRBD
Installing and Configuring DRBD
Setting Up a DRBD Primary Node
Setting Up a DRBD Secondary Node
Monitoring a DRBD Device
Managing a DRBD Installation
Additional DRBD Configuration Options

To set up DRBD, MySQL, and Heartbeat, you follow a number of steps that affect the operating system, DRBD and your MariaDB installation.

Before starting the installation process, be aware of the following information, terms and requirements on using DRBD:

The overview for the installation and configuration sequence is as follows:

  1. First, set up your operating system and environment. This includes setting the correct host name, updating the system and preparing the available packages and software required by DRBD, and configuring a physical block device to be used with the DRBD block device. See , "Setting Up Your Operating System for DRBD".
  2. Installing DRBD requires installing or compiling the DRBD source code and then configuring the DRBD service to set up the block devices to be shared. See , "Installing and Configuring DRBD".
  3. After configuring DRBD, alter the configuration and storage location of the MariaDB data, as explained in , "Configuring MariaDB for DRBD".

Optionally, configure high availability using the Linux Heartbeat service. See , "Using Linux HA Heartbeat", for more information.

Setting Up Your Operating System for DRBD

To set your Linux environment for using DRBD, follow these system configuration steps:

Before you compile or install DRBD, make sure the following tools and files are in place:

Here are some operating system specific tips for setting up your installation:

Installing and Configuring DRBD

To install DRBD, you can choose either the pre-built binary installation packages, or you can use the source packages and build from source. To build from source, you must have installed the source and development packages.

To install using a binary distribution, ensure that the kernel version number of the binary package matches your currently active kernel. You can use uname to find out this information: 

shell> uname -r
2.6.20-gentoo-r6

Once DRBD has been built and installed, edit the /etc/drbd.conf file and then run a number of commands to build the block device and set up the replication.

Although the steps below are split into those for the primary node and the secondary node, the configuration files for all nodes should be identical, and many of the same steps have to be repeated on each node to enable the DRBD block device.

Building from source:

To download and install from the source code:

  1. Download the source code.
  2. Unpack the package: 

    shell> tar zxf drbd-8.3.0.tar.gz
  3. Change to the extracted directory, and then run make to build the DRBD driver: 

    shell> cd drbd-8.3.0
shell> make
  4. Install the kernel driver and commands: 

    shell> make install

Binary Installation:

Setting Up a DRBD Primary Node

To set up a DRBD primary node, configure the DRBD service, create the first DRBD block device, and then create a file system on the device so that you can store files and data.

The DRBD configuration file /etc/drbd.conf defines a number of parameters for your DRBD configuration, including the frequency of updates and block sizes, security information and the definition of the DRBD devices that you want to create.

The key elements to configure are the on sections which specify the configuration of each node.

To follow the configuration, the sequence below shows only the changes from the default drbd.conf file. Configurations within the file can be both global or tied to specific resource.

  1. Set the synchronization rate between the two nodes. This is the rate at which devices are synchronized in the background after a disk failure, device replacement or during the initial setup. Keep this in check compared to the speed of your network connection. Gigabit Ethernet can support up to 125 MB/second, 100Mbps Ethernet slightly less than a tenth of that (12MBps). If you are using a shared network connection, rather than a dedicated, then gauge accordingly.

    To set the synchronization rate, edit the rate setting within the syncer block: 

    syncer {
 rate 10M;
}

    You may additionally want to set the al-extents parameter. The default for this parameter is 257.

    For more detailed information on synchronization, the effects of the synchronization rate and the effects on network performance, see , "Optimizing the Synchronization Rate".

  2. Set up some basic authentication. DRBD supports a simple password hash exchange mechanism. This helps to ensure that only those hosts with the same shared secret are able to join the DRBD node group. 

    cram-hmac-alg "sha1";
shared-secret 'shared-string';
  3. Now you must configure the host information. You must have the node information for the primary and secondary nodes in the drbd.conf file on each host. Configure the following information for each node:

    • device: The path of the logical block device that is created by DRBD.
    • disk: The block device that stores the data.
    • address: The IP address and port number of the host that holds this DRBD device.
    • meta-disk: The location where the metadata about the DRBD device is stored. If you set this to internal, DRBD uses the physical block device to store the information, by recording the metadata within the last sections of the disk. The exact size depends on the size of the logical block device you have created, but it may involve up to 128MB.

    A sample configuration for our primary server might look like this: 

    on drbd-one {
device /dev/drbd0;
disk /dev/hdd1;
address 192.168.0.240:8888;
meta-disk internal;
}

    The on configuration block should be repeated for the secondary node (and any further) nodes: 

    on drbd-two {
device /dev/drbd0;
disk /dev/hdd1;
address 192.168.0.241:8888;
meta-disk internal;
}

    The IP address of each on block must match the IP address of the corresponding host. Do not set this value to the IP address of the corresponding primary or secondary in each case.

  4. Before starting the primary node, create the metadata for the devices: 

    root-shell> drbdadm create-md all
  5. You are now ready to start DRBD: 

    root-shell> /etc/init.d/drbd start

    DRBD should now start and initialize, creating the DRBD devices that you have configured.

  6. DRBD creates a standard block device - to make it usable, you must create a file system on the block device just as you would with any standard disk partition. Before you can create the file system, you must mark the new device as the primary device (that is, where the data is written and stored), and initialize the device. Because this is a destructive operation, you must specify the command line option to overwrite the raw data: 

    root-shell> drbdadm -- --overwrite-data-of-peer primary all

    If you are using a version of DRBD 0.7.x or earlier, then use a different command-line option: 

    root-shell> drbdadm -- --do-what-I-say primary all

    Now create a file system using your chosen file system type: 

    root-shell> mkfs.ext3 /dev/drbd0
  7. You can now mount the file system and if necessary copy files to the mount point: 

    root-shell> mkdir /mnt/drbd root-shell> mount /dev/drbd0 /mnt/drbd root-shell> echo 'DRBD Device' >/mnt/drbd/samplefile

Your primary node is now ready to use. Next, configure your secondary node or nodes.

Setting Up a DRBD Secondary Node

The configuration process for setting up a secondary node is the same as for the primary node, except that you do not have to create the file system on the secondary node device, as this information is automatically transferred from the primary node.

To set up a secondary node:

  1. Copy the /etc/drbd.conf file from your primary node to your secondary node. It should already contain all the information and configuration that you need, since you had to specify the secondary node IP address and other information for the primary node configuration.
  2. Create the DRBD metadata on the underlying disk device: 

    root-shell> drbdadm create-md all
  3. Start DRBD: 

    root-shell> /etc/init.d/drbd start

Once DRBD has started, it starts to copy the data from the primary node to the secondary node. Even with an empty file system this takes some time, since DRBD is copying the block information from a block device, not simply copying the file system data.

You can monitor the progress of the copy between the primary and secondary nodes by viewing the output of /proc/drbd: 

root-shell> cat /proc/drbd version: 8.0.4 (api:86/proto:86)
SVN Revision: 2947 build by root@drbd-one, 2007-07-30 16:43:05
 0: cs:SyncSource st:Primary/Secondary ds:UpToDate/Inconsistent C r---
 ns:252284 nr:0 dw:0 dr:257280 al:0 bm:15 lo:0 pe:7 ua:157 ap:0
 [==>.................] sync'ed: 12.3% (1845088/2097152)K
 finish: 0:06:06 speed: 4,972 (4,580) K/sec
 resync: used:1/31 hits:15901 misses:16 starving:0 dirty:0 changed:16
 act_log: used:0/257 hits:0 misses:0 starving:0 dirty:0 changed:0

You can monitor the synchronization process by using the watch command to run the command at specific intervals: 

root-shell> watch -n 10 'cat /proc/drbd'

Monitoring a DRBD Device

Once the primary and secondary machines are configured and synchronized, you can get the status information about your DRBD device by viewing the output from /proc/drbd: 

root-shell> cat /proc/drbd version: 8.0.4 (api:86/proto:86)
SVN Revision: 2947 build by root@drbd-one, 2007-07-30 16:43:05
 0: cs:Connected st:Primary/Secondary ds:UpToDate/UpToDate C r---
 ns:2175704 nr:0 dw:99192 dr:2076641 al:33 bm:128 lo:0 pe:0 ua:0 ap:0
 resync: used:0/31 hits:134841 misses:135 starving:0 dirty:0 changed:135
 act_log: used:0/257 hits:24765 misses:33 starving:0 dirty:0 changed:33

The first line provides the version/revision and build information.

The second line starts the detailed status information for an individual resource. The individual field headings are as follows:

In the previous example, the information shown indicates that the nodes are connected, the local node is the primary (because it is listed first), and the local and remote data is up to date with each other. The remainder of the information is statistical data about the device, and the data exchanged that kept the information up to date.

You can also get the status information for DRBD by using the startup script with the status option: 

root-shell> /etc/init.d/drbd status
 * status: started
 * drbd driver loaded OK; device status: ... [ ok ]
version: 8.3.0 (api:88/proto:86-89)
GIT-hash: 9ba8b93e24d842f0dd3fb1f9b90e8348ddb95829 build by root@gentoo1.vmbear, 2009-03-14 23:00:06
 0: cs:Connected ro:Secondary/Secondary ds:UpToDate/UpToDate C r---
 ns:0 nr:0 dw:0 dr:8385604 al:0 bm:0 lo:0 pe:0 ua:0 ap:0 ep:1 wo:b oos:0

The information and statistics are the same.

Managing a DRBD Installation

For administration, the main command is drbdadm. There are a number of commands supported by this tool that control the connectivity and status of the DRBD devices.Note

For convenience, a bash completion script provides tab completion for options to drbdadm. The file drbdadm.bash_completion can be found within the standard DRBD source package within the scripts directory. To enable, copy the file to /etc/bash_completion.d/drbdadm. You can load it manually by using: 

shell> source /etc/bash_completion.d/drbdadm

The most common commands are those to set the primary/secondary status of the local device. You can manually set this information for a number of reasons, including to check the physical status of the secondary device (since you cannot mount a DRBD device in primary mode), or when you are temporarily moving the responsibility of keeping the data in check to a different machine (for example, during an upgrade or physical move of the normal primary node). You can set state of all local device to be the primary using this command: 

root-shell> drbdadm primary all

Or switch the local device to be the secondary using: 

root-shell> drbdadm secondary all

To change only a single DRBD resource, specify the resource name instead of all.

You can temporarily disconnect the DRBD nodes: 

root-shell> drbdadm disconnect all

Reconnect them using connect: 

root-shell> drbdadm connect all

For other commands and help with drbdadm see the DRBD documentation.

Additional DRBD Configuration Options

Additional options you can configure:

With the configuration file suitably configured and ready to use, now populate the lower-level device with the metadata information, and then start the DRBD service.

Configuring MariaDB for DRBD

Once you have configured DRBD and have an active DRBD device and file system, you can configure MariaDB to use the chosen device to store the MariaDB data.

When performing a new installation of MySQL, you can either select to install MariaDB entirely onto the DRBD device, or just configure the data directory to be located on the new file system.

In either case, the files and installation must take place on the primary node, because that is the only DRBD node on which you can mount the DRBD device file system as read/write.

Store the following files and information on your DRBD device:

To set up MariaDB to use your new DRBD device and file system:

  1. If you are migrating an existing MariaDB installation, stop MySQL: 

    shell> mysqladmin shutdown
  2. Copy the my.cnf onto the DRBD device. If you are not already using a configuration file, copy one of the sample configuration files from the MariaDB distribution. 

    root-shell> mkdir /mnt/drbd/mysql root-shell> cp /etc/my.cnf /mnt/drbd/mysql
  3. Copy your MariaDB data directory to the DRBD device and mounted file system. 

    root-shell> cp -R /var/lib/mysql /drbd/mysql/data
  4. Edit the configuration file to reflect the change of directory by setting the value of the datadir option. If you have not already enabled the binary log, also set the value of the log-bin option. 

    datadir = /drbd/mysql/data
 log-bin = mysql-bin
  5. Create a symbolic link from /etc/my.cnf to the new configuration file on the DRBD device file system. 

    root-shell> ln -s /drbd/mysql/my.cnf /etc/my.cnf
  6. Now start MariaDB and check that the data that you copied to the DRBD device file system is present. 

    root-shell> /etc/init.d/mysql start

Your MariaDB data should now be located on the file system running on your DRBD device. The data is physically stored on the underlying device that you configured for the DRBD device. Meanwhile, the content of your MariaDB databases is copied to the secondary DRBD node.

Note that you cannot access the information on your secondary node, as a DRBD device working in secondary mode is not available for use.

Optimizing Performance and Reliability

Using Bonded Ethernet Network Interfaces
Optimizing the Synchronization Rate

Because of the nature of the DRBD system, the critical requirements are for a very fast exchange of the information between the two hosts. To ensure that your DRBD setup is available to switch over in the event of a failure as quickly as possible, you must transfer the information between the two hosts using the fastest method available.

Typically, a dedicated network circuit should be used for exchanging DRBD data between the two hosts. Use a separate, additional, network interface for your standard network connection. For an example of this layout, see Figure 14.4, "DRBD Architecture Using Separate Network Interfaces".

Figure 14.4. DRBD Architecture Using Separate Network Interfaces

DRBD Architecture Using Separate Network Interfaces

The dedicated DRBD network interfaces should be configured to use a nonrouted TCP/IP network configuration. For example, you might set the primary to use 192.168.0.1 and the secondary 192.168.0.2. These networks and IP addresses should not be part of the normal network subnet.Note

The preferred setup, whenever possible, is to use a direct cable connection (using a crossover cable with Ethernet, for example) between the two machines. This eliminates the risk of loss of connectivity due to switch failures.

Using Bonded Ethernet Network Interfaces

For a set-up where there is a high-throughput of information being written, consider using bonded network interfaces. This is where you combine the connectivity of more than one network port, increasing the throughput linearly according to the number of bonded connections.

Bonding also provides an additional benefit in that with multiple network interfaces effectively supporting the same communications channel, a fault within a single network interface in a bonded group does not stop communication. For example, imagine you have a bonded setup with four network interfaces providing a single interface channel between two DRBD servers. If one network interface fails, communication can continue on the other three without interruption, although at a lower speed.

To enable bonded connections you must enable bonding within the kernel. Then configure the module to specify the bonded devices and then configure each new bonded device just as you would a standard network device:

Once the bonded devices are configured, reboot your systems.

You can monitor the status of a bonded connection using the /proc file system: 

root-shell> cat /proc/net/bonding/bond0
Bonding Mode: fault-tolerance (active-backup)
Primary Slave: None Currently Active Slave: eth1
MII Status: up MII Polling Interval (ms): 100
Up Delay (ms): 200
Down Delay (ms): 200
Slave Interface: eth1
MII Status: up Link Failure Count: 0
Permanent HW addr: 00:11:22:33:44:55
Slave Interface: eth2
MII Status: up Link Failure Count: 0
Permanent HW addr: 00:11:22:33:44:56

Optimizing the Synchronization Rate

The syncer rate configuration parameter should be configured with care as the synchronization rate can have a significant effect on the performance of the DRBD setup in the event of a node or disk failure where the information is being synchronized from the Primary to the Secondary node.

In DRBD, there are two distinct ways of data being transferred between peer nodes:

Both replication and synchronization can take place at the same time. For example, the block devices can be synchronized while they are actively being used by the primary node. Any I/O that updates on the primary node automatically triggers replication of the modified block. In the event of a failure within an HA environment, it is highly likely that synchronization and replication will take place at the same time.

Unfortunately, if the synchronization rate is set too high, then the synchronization process uses up all the available network bandwidth between the primary and secondary nodes. In turn, the bandwidth available for replication of changed blocks is zero, which stalls replication and blocks I/O, and ultimately the application fails or degrades.

To avoid enabling the syncer rate to consume the available network bandwidth and prevent the replication of changed blocks, set the syncer rate to less than the maximum network bandwidth.

Avoid setting the sync rate to more than 30% of the maximum bandwidth available to your device and network bandwidth. For example, if your network bandwidth is based on Gigabit ethernet, you should achieve 110MB/s. Assuming your disk interface is capable of handling data at 110MB/s or more, then the sync rate should be configured as 33M (33MB/s). If your disk system works at a rate lower than your network interface, use 30% of your disk interface speed.

Depending on the application, you might limit the synchronization rate. For example, on a busy server you could configure a significantly slower synchronization rate to ensure the replication rate is not affected.

The al-extents parameter controls the number of 4MB blocks of the underlying disk that can be written to at the same time. Increasing this parameter lowers the frequency of the metadata transactions required to log the changes to the DRBD device, which in turn lowers the number of interruptions in your I/O stream when synchronizing changes. This can lower the latency of changes to the DRBD device. However, if a crash occurs on your primary, then all of the blocks in the activity log (that is, the number of al-extents blocks) must be completely resynchronized before replication can continue.

Using Linux HA Heartbeat

Heartbeat Configuration
Using Heartbeat with MariaDB and DRBD
Using Heartbeat with DRBD and dopd
Dealing with System Level Errors in a Heartbeat Cluster

The Heartbeat program provides a basis for verifying the availability of resources on one or more systems within a cluster. In this context a resource includes MySQL, the file systems on which the MariaDB data is being stored and, if you are using DRBD, the DRBD device being used for the file system. Heartbeat also manages a virtual IP address; use this virtual IP address for all communication to the MariaDB instance.

A cluster within the context of Heartbeat is defined as two computers notionally providing the same service. By definition, each computer in the cluster is physically capable of providing the same services as all the others in the cluster. However, because the cluster is designed for high-availability, only one of the servers is actively providing the service at any one time. Each additional server within the cluster is a "hot-spare" that can be brought into service in the event of a failure of the master, its next connectivity or the connectivity of the network in general.

The basics of Heartbeat are very simple. Within the Heartbeat cluster (see Figure 14.5, "Heartbeat Architecture", each machine sends a 'heartbeat' signal to the other hosts in the cluster. The other cluster nodes monitor this heartbeat. The heartbeat can be transmitted over many different systems, including shared network devices, dedicated network interfaces and serial connections. Failure to get a heartbeat from a node is treated as failure of the node. Although we do not know the reason for the failure (it could be an OS failure, a hardware failure in the server, or a failure in the network switch), it is safe to assume that if no heartbeat is produced there is a fault.

Figure 14.5. Heartbeat Architecture

Heartbeat Architecture

In addition to checking the heartbeat from the server, the system can also check the connectivity (using ping) to another host on the network, such as the network router. This enables Heartbeat to detect a failure of communication between a server and the router (and therefore failure of the server, since it is no longer capable of providing the necessary service), even if the heartbeat between the servers in the clusters is working fine.

In the event of a failure, the resources on the failed host are disabled, and the resources on one of the replacement hosts is enabled instead. In addition, the Virtual IP address for the cluster is redirected to the new host in place of the failed device.

When used with MariaDB and DRBD, the MariaDB data is replicated from the master to the slave using the DRBD device, but MariaDB is only running on the master. When the master fails, the slave switches the DRBD devices to be primary, the file systems on those devices are mounted, and MariaDB is started. The original master (if still available) has its resources disabled, which means shutting down MariaDB and unmounting the file systems and switching the DRBD device to secondary.

Heartbeat Configuration

Heartbeat configuration requires three files located in /etc/ha.d. The ha.cf contains the main heartbeat configuration, including the list of the nodes and times for identifying failures. haresources contains the list of resources to be managed within the cluster. The authkeys file contains the security information for the cluster.

The contents of these files should be identical on each host within the Heartbeat cluster. It is important that you keep these files in sync across all the hosts. Any changes in the information on one host should be copied to the all the others.

For these examples n example of the ha.cf file is shown below: 

logfacility local0
keepalive 500ms deadtime 10
warntime 5
initdead 30
mcast bond0 225.0.0.1 694 2 0
mcast bond1 225.0.0.2 694 1 0
auto_failback off node drbd1
node drbd2

The individual lines in the file can be identified as follows:

An optional additional set of information provides the configuration for a ping test that checks the connectivity to another host. Use this to ensure that you have connectivity on the public interface for your servers, so the ping test should be to a reliable host such as a router or switch. The additional lines specify the destination machine for the ping, which should be specified as an IP address, rather than a host name; the command to run when a failure occurs, the authority for the failure and the timeout before an nonresponse triggers a failure. A sample configure is shown below: 

ping 10.0.0.1
respawn hacluster /usr/lib64/heartbeat/ipfail apiauth ipfail gid=haclient uid=hacluster deadping 5

In the above example, the ipfail command, which is part of the Heartbeat solution, is called on a failure and 'fakes' a fault on the currently active server. Configure the user and group ID under which the command is executed (using the apiauth). The failure is triggered after 5 seconds.Note

The deadping value must be less than the deadtime value.

The authkeys file holds the authorization information for the Heartbeat cluster. The authorization relies on a single unique 'key' that is used to verify the two machines in the Heartbeat cluster. The file is used only to confirm that the two machines are in the same cluster and is used to ensure that the multiple clusters can co-exist within the same network.

Using Heartbeat with MariaDB and DRBD

To use Heartbeat in combination with MySQL, use DRBD (see , "Using MariaDB with DRBD") or another solution that enables sharing the MariaDB database files in event of a system failure. In these examples, DRBD is used as the data sharing solution.

Heartbeat manages the configuration of different resources to manage the switching between two servers in the event of a failure. The resource configuration defines the individual services that should be brought up (or taken down) in the event of a failure.

The haresources file within /etc/ha.d defines the resources that should be managed, and the individual resource mentioned in this file in turn relates to scripts located within /etc/ha.d/resource.d. The resource definition is defined all on one line: 

drbd1 drbddisk Filesystem::/dev/drbd0::/drbd::ext3 mysql 10.0.0.100

The line is notionally split by whitespace. The first entry (drbd1) is the name of the preferred host; that is the server that is normally responsible for handling the service. The last field is virtual IP address or name that should be used to share the service. This is the IP address that should be used to connect to the MariaDB server. It is automatically allocated to the server that is active when Heartbeat starts.

The remaining fields between these two fields define the resources that should be managed. Each Field should contain the name of the resource (and each name should refer to a script within /etc/ha.d/resource.d). In the event of a failure, these resources are started on the backup server by calling the corresponding script (with a single argument, start), in order from left to right. If there are additional arguments to the script, you can use a double colon to separate each additional argument.

In the above example, we manage the following resources:

To be notified of the failure by email, add another line to the haresources file with the address for warnings and the warning text: 

MailTo::youremail@address.com::DRBDFailure

With the Heartbeat configuration in place, copy the haresources, authkeys and ha.cf files from your primary and secondary servers to make sure that the configuration is identical. Then start the Heartbeat service, either by calling /etc/init.d/heartbeat start or by rebooting both primary and secondary servers.

You can test the configuration by running a manual failover, connect to the primary node and run: 

root-shell> /usr/lib64/heartbeat/hb_standby

This causes the current node to relinquish its resources cleanly to the other node.

Using Heartbeat with DRBD and dopd

As a further extension to using DRBD and Heartbeat together, you can enable dopd. The dopd daemon handles the situation where a DRBD node is out of date compared to the master and prevents the slave from being promoted to master in the event of a failure. This stops a situation where you have two machines that have been masters ending up different data on the underlying device.

For example, imagine that you have a two server DRBD setup, master and slave. If the DRBD connectivity between master and slave fails, then the slave is out of the sync with the master. If Heartbeat identifies a connectivity issue for master and then switches over to the slave, the slave DRBD device is promoted to the primary device, even though the data on the slave and the master is not in synchronization.

In this situation, with dopd enabled, the connectivity failure between the master and slave would be identified and the metadata on the slave would be set to Outdated. Heartbeat refuses to switch over to the slave even if the master failed. In a dual-host solution this would effectively render the cluster out of action, as there is no additional fail over server. In an HA cluster with three or more servers, control would be passed to the slave that has an up to date version of the DRBD device data.

To enable dopd, modify the Heartbeat configuration and specify dopd as part of the commands executed during the monitoring process. Add the following lines to your ha.cf file: 

respawn hacluster /usr/lib/heartbeat/dopd apiauth dopd gid=haclient uid=hacluster

Make sure you make the same modification on both your primary and secondary nodes.

Reload the Heartbeat configuration: 

root-shell> /etc/init.d/heartbeat reload

Modify your DRBD configuration by configuration the outdate-peer option. Add the configuration line into the common section of /etc/drbd.conf on both hosts. An example of the full block is shown below: 

common {
 handlers {
 outdate-peer '/usr/lib/heartbeat/drbd-peer-outdater';
 }
}

Finally, set the fencing option on your DRBD configured resources: 

resource my-resource {
 disk {
 fencing resource-only;
 }
}

Now reload your DRBD configuration: 

root-shell> drbdadmin adjust all

You can test the system by unplugging your DRBD link and monitoring the output from /proc/drbd.

Dealing with System Level Errors in a Heartbeat Cluster

Because a kernel panic or oops may indicate potential problem with your server, configure your server to remove itself from the cluster in the event of a problem. Typically on a kernel panic, your system automatically triggers a hard reboot. For a kernel oops, a reboot may not happen automatically, but the issue that caused that oops may still lead to potential problems.

You can force a reboot by setting the kernel.panic and kernel.panic_on_oops parameters of the kernel control file /etc/sysctl.conf. For example: 

 kernel.panic_on_oops = 1
 kernel.panic = 1

You can also set these parameters during runtime by using the sysctl command. You can either specify the parameters on the command line: 

shell> sysctl -w kernel.panic=1

Or you can edit your sysctl.conf file and then reload the configuration information: 

shell> sysctl -p

Setting both these parameters to a positive value (representing the number of seconds to wait before rebooting), causes the system to reboot. Your second heartbeat node should then detect that the server is down and then switch over to the failover host.

Using MariaDB within an Amazon EC2 Instance

Setting Up MariaDB on an EC2 AMI
EC2 Instance Limitations
Deploying a MariaDB Database Using EC2

The Amazon Elastic Compute Cloud (EC2) service provides virtual servers that you can build and deploy to run a variety of different applications and services, including MySQL. The EC2 service is based around the Xen framework, supporting x86, Linux based, platforms with individual instances of a virtual machine referred to as an Amazon Machine Image (AMI). You have complete (root) access to the AMI instance that you create, enabling you to configure and install your AMI in any way you choose.

To use EC2, you create an AMI based on the configuration and applications that you intend to use, and upload the AMI to the Amazon Simple Storage Service (S3). From the S3 resource, you can deploy one or more copies of the AMI to run as an instance within the EC2 environment. The EC2 environment provides management and control of the instance and contextual information about the instance while it is running.

Because you can create and control the AMI, the configuration, and the applications, you can deploy and create any environment you choose. This includes a basic MariaDB server in addition to more extensive replication, HA and scalability scenarios that enable you to take advantage of the EC2 environment, and the ability to deploy additional instances as the demand for your MariaDB services and applications grow.

To aid the deployment and distribution of work, three different Amazon EC2 instances are available, small (identified as m1.small), large (m1.large) and extra large (m1.xlarge). The different types provide different levels of computing power measured in EC2 computer units (ECU). A summary of the different instance configurations is shown here.

Small Large Extra Large
Platform 32-bit 64-bit 64-bit
CPU cores 1 2 4
ECUs 1 4 8
RAM 1.7GB 7.5GB 15GB
Storage 150GB 840GB 1680GB
I/O Performance Medium High High

The typical model for deploying and using MariaDB within the EC2 environment is to create a basic AMI that you can use to hold your database data and application. Once the basic environment for your database and application has been created you can then choose to deploy the AMI to a suitable instance. Here the flexibility of having an AMI that can be re-deployed from the small to the large or extra large EC2 instance makes it easy to upgrade the hardware environment without rebuilding your application or database stack.

To get started with MariaDB on EC2, including information on how to set up and install MariaDB within an EC2 installation and how to port and migrate your data to the running instance, see , "Setting Up MariaDB on an EC2 AMI".

For tips and advice on how to create a scalable EC2 environment using MySQL, including guides on setting up replication, see , "Deploying a MariaDB Database Using EC2".

Setting Up MariaDB on an EC2 AMI

There are many different ways of setting up an EC2 AMI with MySQL, including using any of the pre-configured AMIs supplied by Amazon.

The default Getting Started AMI provided by Amazon uses Fedora Core 4, and you can install MariaDB by using yum: 

shell> yum install mysql

This installs both the MariaDB server and the Perl DBD::mysql driver for the Perl DBI API.

Alternatively, you can use one of the AMIs that include MariaDB within the standard installation.

Finally, you can also install a standard version of MariaDB downloaded from the MariaDB Web site. The installation process and instructions are identical to any other installation of MariaDB on Linux. See , Installing and Upgrading MySQL.

The standard configuration for MariaDB places the data files in the default location, /var/lib/mysql. The default data directory on an EC2 instance is /mnt (although on the large and extra large instance you can alter this configuration). You must edit /etc/my.cnf to set the datadir option to point to the larger storage area.Important

The first time you use the main storage location within an EC2 instance it needs to be initialized. The initialization process starts automatically the first time you write to the device. You can start using the device right away, but the write performance of the new device is significantly lower on the initial writes until the initialization process has finished.

To avoid this problem when setting up a new instance, you should start the initialization process before populating your MariaDB database. One way to do this is to use dd to write to the file system: 

root-shell> dd if=/dev/zero of=initialize bs=1024M count=50

The preceding creates a 50GB on the file system and starts the initialization process. Delete the file once the process has finished.

The initialization process can be time-consuming. On the small instance, initialization takes between two and three hours. For the large and extra large drives, the initialization can be 10 or 20 hours, respectively.

In addition to configuring the correct storage location for your MariaDB data files, also consider setting the following other settings in your instance before you save the instance configuration for deployment:

Once you have configured your AMI with MariaDB and the rest of your application stack, save the AMI so that you can deploy and reuse the instance.

Once you have your application stack configured in an AMI, populating your MariaDB database with data should be performed by creating a dump of your database using mysqldump, transferring the dump to the EC2 instance, and then reloading the information into the EC2 instance database.

Before using your instance with your application in a production situation, be aware of the limitations of the EC2 instance environment. See , "EC2 Instance Limitations". To begin using your MariaDB AMI, consult the notes on deployment. See , "Deploying a MariaDB Database Using EC2".

EC2 Instance Limitations

Be aware of the following limitations of the EC2 instances before deploying your applications. Although these shouldn't affect your ability to deploy within the Amazon EC2 environment, they may alter the way you setup and configure your environment to support your application.

Deploying a MariaDB Database Using EC2

Because you cannot guarantee the uptime and availability of your EC2 instances, when deploying MariaDB within the EC2 environment, use an approach that enables you to easily distribute work among your EC2 instances. There are a number of ways of doing this. Using sharding techniques, where you split the application across multiple servers dedicating specific blocks of your dataset and users to different servers is an effective way of doing this. As a general rule, it is easier to create more EC2 instances to support more users than to upgrade the instance to a larger machine.

The EC2 architecture works best when you treat the EC2 instances as temporary, cache-based solutions, rather than as a long-term, high availability solution. In addition to using multiple machines, take advantage of other services, such as memcached to provide additional caching for your application to help reduce the load on the MariaDB server so that it can concentrate on writes. On the large and extra large instances within EC2, the RAM available can provide a large memory cache for data.

Most types of scale-out topology that you would use with your own hardware can be used and applied within the EC2 environment. However, use the limitations and advice already given to ensure that any potential failures do not lose you any data. Also, because the relative power of each EC2 instance is so low, be prepared to alter your application to use sharding and add further EC2 instances to improve the performance of your application.

For example, take the typical scale-out environment shown following, where a single master replicates to one or more slaves (three in this example), with a web server running on each replication slave.Typical standard scale-out structure

You can reproduce this structure completely within the EC2 environment, using an EC2 instance for the master, and one instance for each of the web and MariaDB slave servers.Note

Within the EC2 environment, internal (private) IP addresses used by the EC2 instances are constant. Always use these internal addresses and names when communicating between instances. Only use public IP addresses when communicating with the outside world - for example, when publicizing your application.

To ensure reliability of your database, add at least one replication slave dedicated to providing an active backup and storage to the Amazon S3 facility. You can see an example of this in the following topology.Typical standard scale-out structure with backup using EC2

Using memcached within your EC2 instances should provide better performance. The large and extra large instances have a significant amount of RAM. To use memcached in your application, when loading information from the database, first check whether the item exists in the cache. If the data you are looking for exists in the cache, use it. If not, reload the data from the database and populate the cache.

Sharding divides up data in your entire database by allocating individual machines or machine groups to provide a unique set of data according to an appropriate group. For example, you might put all users with a surname ending in the letters A-D onto a single server. When a user connects to the application and their surname is known, queries can be redirected to the appropriate MariaDB server.

When using sharding with EC2, separate the web server and MariaDB server into separate EC2 instances, and then apply the sharding decision logic into your application. Once you know which MariaDB server you should be using for accessing the data you then distribute queries to the appropriate server. You can see a sample of this in the following illustration.Using sharding in EC2 to spread the load Warning

With sharding and EC2, be careful that the potential for failure of an instance does not affect your application. If the EC2 instance that provides the MariaDB server for a particular shard fails, then all of the data on that shard becomes unavailable.

Using ZFS Replication

Using ZFS for File System Replication
Configuring MariaDB for ZFS Replication
Handling MariaDB Recovery with ZFS

To support high availability environments, providing an instant copy of the information on both the currently active machine and the hot backup is a critical part of the HA solution. There are many solutions to this problem, including , Replication and , "Using MariaDB with DRBD".

The ZFS file system provides functionality to create a snapshot of the file system contents, transfer the snapshot to another machine, and extract the snapshot to recreate the file system. You can create a snapshot at any time, and you can create as many snapshots as you like. By continually creating, transferring, and restoring snapshots, you can provide synchronization between one or more machines in a fashion similar to DRBD.

The following example shows a simple Solaris system running with a single ZFS pool, mounted at /scratchpool: 

Filesystem size used avail capacity Mounted on
/dev/dsk/c0d0s0 4.6G 3.7G 886M 82% /
/devices 0K 0K 0K 0% /devices ctfs 0K 0K 0K 0% /system/contract proc 0K 0K 0K 0% /proc mnttab 0K 0K 0K 0% /etc/mnttab swap 1.4G 892K 1.4G 1% /etc/svc/volatile objfs 0K 0K 0K 0% /system/object
/usr/lib/libc/libc_hwcap1.so.1
 4.6G 3.7G 886M 82% /lib/libc.so.1
fd 0K 0K 0K 0% /dev/fd swap 1.4G 40K 1.4G 1% /tmp swap 1.4G 28K 1.4G 1% /var/run
/dev/dsk/c0d0s7 26G 913M 25G 4% /export/home scratchpool 16G 24K 16G 1% /scratchpool

The MariaDB data is stored in a directory on /scratchpool. To help demonstrate some of the basic replication functionality, there are also other items stored in /scratchpool as well: 

total 17
drwxr-xr-x 31 root bin 50 Jul 21 07:32 DTT/
drwxr-xr-x 4 root bin 5 Jul 21 07:32 SUNWmlib/
drwxr-xr-x 14 root sys 16 Nov 5 09:56 SUNWspro/
drwxrwxrwx 19 1000 1000 40 Nov 6 19:16 emacs-22.1/

To create a snapshot of the file system, you use zfs snapshot, specifying the pool and the snapshot name: 

root-shell> zfs snapshot scratchpool@snap1

To list the snapshots already taken: 

root-shell> zfs list -t snapshot NAME USED AVAIL REFER MOUNTPOINT scratchpool@snap1 0 - 24.5K -
scratchpool@snap2 0 - 24.5K -

The snapshots themselves are stored within the file system metadata, and the space required to keep them varies as time goes on because of the way the snapshots are created. The initial creation of a snapshot is very quick, because instead of taking an entire copy of the data and metadata required to hold the entire snapshot, ZFS records only the point in time and metadata of when the snapshot was created.

As more changes to the original file system are made, the size of the snapshot increases because more space is required to keep the record of the old blocks. If you create lots of snapshots, say one per day, and then delete the snapshots from earlier in the week, the size of the newer snapshots might also increase, as the changes that make up the newer state have to be included in the more recent snapshots, rather than being spread over the seven snapshots that make up the week.

You cannot directly back up the snapshots because they exist within the file system metadata rather than as regular files. To get the snapshot into a format that you can copy to another file system, tape, and so on, you use the zfs send command to create a stream version of the snapshot.

For example, to write the snapshot out to a file: 

root-shell> zfs send scratchpool@snap1 >/backup/scratchpool-snap1

Or tape: 

root-shell> zfs send scratchpool@snap1 >/dev/rmt/0

You can also write out the incremental changes between two snapshots using zfs send: 

root-shell> zfs send scratchpool@snap1 scratchpool@snap2 >/backup/scratchpool-changes

To recover a snapshot, you use zfs recv, which applies the snapshot information either to a new file system, or to an existing one.

Using ZFS for File System Replication

Because zfs send and zfs recv use streams to exchange data, you can use them to replicate information from one system to another by combining zfs send, ssh, and zfs recv.

For example, to copy a snapshot of the scratchpool file system to a new file system called slavepool on a new server, you would use the following command. This sequence combines the snapshot of scratchpool, the transmission to the slave machine (using ssh with login credentials), and the recovery of the snapshot on the slave using zfs recv: 

root-shell> zfs send scratchpool@snap1 |ssh id@host pfexec zfs recv -F slavepool

The first part of the pipeline, zfs send scratchpool@snap1, streams the snapshot. The ssh command, and the command that it executes on the other server, pfexec zfs recv -F slavepool, receives the streamed snapshot data and writes it to slavepool. In this instance, I've specified the -F option which forces the snapshot data to be applied, and is therefore destructive. This is fine, as I'm creating the first version of my replicated file system.

On the slave machine, the replicated file system contains the exact same content: 

root-shell> ls -al /slavepool/
total 23
drwxr-xr-x 6 root root 7 Nov 8 09:13 ./
drwxr-xr-x 29 root root 34 Nov 9 07:06 ../
drwxr-xr-x 31 root bin 50 Jul 21 07:32 DTT/
drwxr-xr-x 4 root bin 5 Jul 21 07:32 SUNWmlib/
drwxr-xr-x 14 root sys 16 Nov 5 09:56 SUNWspro/
drwxrwxrwx 19 1000 1000 40 Nov 6 19:16 emacs-22.1/

Once a snapshot has been created, to synchronize the file system again, you create a new snapshot and then use the incremental snapshot feature of zfs send to send the changes between the two snapshots to the slave machine again: 

root-shell> zfs send -i scratchpool@snapshot1 scratchpool@snapshot2 |ssh id@host pfexec zfs recv slavepool

This operation only succeeds if the file system on the slave machine has not been modified at all. You cannot apply the incremental changes to a destination file system that has changed. In the example above, the ls command would cause problems by changing the metadata, such as the last access time for files or directories.

To prevent changes on the slave file system, set the file system on the slave to be read-only: 

root-shell> zfs set readonly=on slavepool

Setting readonly means that you cannot change the file system on the slave by normal means, including the file system metadata. Operations that would normally update metadata (like our ls) silently perform their function without attempting to update the file system state.

In essence, the slave file system is nothing but a static copy of the original file system. However, even when configured to to be read-only, a file system can have snapshots applied to it. With the file system set to read only, re-run the initial copy: 

root-shell> zfs send scratchpool@snap1 |ssh id@host pfexec zfs recv -F slavepool

Now you can make changes to the original file system and replicate them to the slave.

Configuring MariaDB for ZFS Replication

Configuring MariaDB on the source file system is a case of creating the data on the file system that you intend to replicate. The configuration file in the example below has been updated to use /scratchpool/mysql-data as the data directory, and now you can initialize the tables: 

root-shell> mysql_install_db --defaults-file=/etc/mysql/5.5/my.cnf --user=mysql

To synchronize the initial information, perform a new snapshot and then send an incremental snapshot to the slave using zfs send: 

root-shell> zfs snapshot scratchpool@snap2
root-shell> zfs send -i scratchpool@snap1 scratchpool@snap2|ssh id@host pfexec zfs recv slavepool

Doublecheck that the slave has the data by looking at the MariaDB data directory on the slavepool: 

root-shell> ls -al /slavepool/mysql-data/

Now you can start up MySQL, create some data, and then replicate the changes using zfs send/ zfs recv to the slave to synchronize the changes.

The rate at which you perform the synchronization depends on your application and environment. The limitation is the speed required to perform the snapshot and then to send the changes over the network.

To automate the process, create a script that performs the snapshot, send, and receive operation, and use cron to synchronize the changes at set times or intervals. For automated operations, see Tim Foster's zfs replication tool.

Handling MariaDB Recovery with ZFS

When using ZFS replication to provide a constant copy of your data, ensure that you can recover your tables, either manually or automatically, in the event of a failure of the original system.

In the event of a failure, follow this sequence:

  1. Stop the script on the master, if it is still up and running.
  2. Set the slave file system to be read/write: 

    root-shell> zfs set readonly=off slavepool
  3. Start up mysqld on the slave. If you are using InnoDB, you get auto-recovery, if it is needed, to make sure the table data is correct, as shown here when I started up from our mid-INSERT snapshot: 

    InnoDB: The log sequence number in ibdata files does not match InnoDB: the log sequence number in the ib_logfiles!
081109 15:59:59 InnoDB: Database was not shut down normally!
InnoDB: Starting crash recovery.
InnoDB: Reading tablespace information from the .ibd files...
InnoDB: Restoring possible half-written data pages from the doublewrite InnoDB: buffer...
081109 16:00:03 InnoDB: Started; log sequence number 0 1142807951
081109 16:00:03 [Note] /slavepool/mysql-5.0.67-solaris10-i386/bin/mysqld: ready for connections.
Version: '5.0.67' socket: '/tmp/mysql.sock' port: 3306 MariaDB Community Server (GPL)

Use InnoDB tables and a regular synchronization schedule to reduce the risk for significant data loss. On MyISAM tables, you might need to run REPAIR TABLE, and you might even have lost some information.

Using MariaDB with memcached

Installing memcached
Using memcached
Developing a memcached Application
Getting memcached Statistics
memcached FAQ

memcached is a simple, highly scalable key-based cache that stores data and objects wherever dedicated or spare RAM is available for quick access by applications, without going through layers of parsing or disk I/O. To use, you run the memcached command on one or more hosts and then use the shared cache to store objects. For more usage instructions, see , "Using memcached"

Benefits of using memcached include:

The typical usage environment is to modify your application so that information is read from the cache provided by memcached. If the information is not in memcached, then the data is loaded from the MariaDB database and written into the cache so that future requests for the same object benefit from the cached data.

For a typical deployment layout, see Figure 14.6, "memcached Architecture Overview".

Figure 14.6. memcached Architecture Overview

memcached Architecture Overview

In the example structure, any of the clients can contact one of the memcached servers to request a given key. Each client is configured to talk to all of the servers shown in the illustration. Within the client, when the request is made to store the information, the key used to reference the data is hashed and this hash is then used to select one of the memcached servers. The selection of the memcached server takes place on the client before the server is contacted, keeping the process lightweight.

The same algorithm is used again when a client requests the same key. The same key generates the same hash, and the same memcached server is selected as the source for the data. Using this method, the cached data is spread among all of the memcached servers, and the cached information is accessible from any client. The result is a distributed, memory-based, cache that can return information, particularly complex data and structures, much faster than natively reading the information from the database.

The data held within a traditional memcached server is never stored on disk (only in RAM, which means there is no persistence of data), and the RAM cache is always populated from the backing store (a MariaDB database). If a memcached server fails, the data can always be recovered from the MariaDB database.

memcached Integration with MariaDB Storage Engines

In April 2011, MariaDB announced the preview of a new memcached interface for the InnoDB and MariaDB Cluster storage engines.

Using the memcached API, web services can directly access the InnoDB and MariaDB Cluster storage engines without transformations to SQL, ensuring low latency and high throughput for read/write queries. Operations such as SQL parsing are eliminated and more of the server's hardware resources (CPU, memory and I/O) are dedicated to servicing the query within the storage engine itself. The memcached data can be persisted to disk while still cached in memory for fast retrieval.

These are targeted to be incorporated into future MariaDB 5.6 Milestone and MariaDB Cluster Development Releases.

You can learn more about these interfaces from this Dev Zone article: http://dev.mysql.com/tech-resources/articles/nosql-to-mysql-with-memcached.html.

Installing memcached

You can build and install memcached from the source code directly, or you can use an existing operating system package or installation.

Installing memcached from a Binary Distribution

To install memcached on a Red Hat, or Fedora host, use yum: 

root-shell> yum install memcached
Note

On CentOS, you may be able to obtain a suitable RPM from another source, or use the source tarball.

To install memcached on a Debian or Ubuntu host, use apt-get: 

root-shell> apt-get install memcached

To install memcached on a Gentoo host, use emerge: 

root-shell> emerge install memcached

Building memcached from Source

On other Unix-based platforms, including Solaris, AIX, HP-UX and Mac OS X, and Linux distributions not mentioned already, you must install from source. For Linux, make sure you have a 2.6-based kernel, which includes the improved epoll interface. For all platforms, ensure that you have libevent 1.1 or higher installed. You can obtain libevent from libevent web page.

You can obtain the source for memcached from memcached Web site.

To build memcached, follow these steps:

  1. Extract the memcached source package: 

    shell> gunzip -c memcached-1.2.5.tar.gz | tar xf -
  2. Change to the memcached-1.2.5 directory: 

    shell> cd memcached-1.2.5
  3. Run configure 

    shell> ./configure

    Some additional options you might specify to the configure:

    • --prefix

      To specify a different installation directory, use the --prefix option: 

      shell> ./configure --prefix=/opt

      The default is to use the /usr/local directory.

    • --with-libevent

      If you have installed libevent and configure cannot find the library, use the --with-libevent option to specify the location of the installed library.

    • --enable-64bit

      To build a 64-bit version of memcached (which enables you to use a single instance with a large RAM allocation), use --enable-64bit.

    • --enable-threads

      To enable multi-threading support in memcached, which improves the response times on servers with a heavy load, use --enable-threads. You must have support for the POSIX threads within your operating system to enable thread support. For more information on the threading support, see , "memcached Thread Support".

    • --enable-dtrace

      memcached includes a range of DTrace threads that can be used to monitor and benchmark a memcached instance. For more information, see , "Using memcached and DTrace".

  4. Run make to build memcached: 

    shell> make
  5. Run make install to install memcached: 

    shell> make install

Using memcached

memcached Deployment
Using Namespaces
Data Expiry
memcached Hashing/Distribution Types
Using memcached and DTrace
Memory Allocation within memcached
memcached Thread Support
memcached Logs

To start using memcached, start the memcached service on one or more servers. Running memcached sets up the server, allocates the memory and starts listening for connections from clients.Note

You do not need to be a privileged user (root) to run memcached except to listen on one of the privileged TCP/IP ports (below 1024). You must, however, use a user that has not had their memory limits restricted using setrlimit or similar.

To start the server, run memcached as a nonprivileged (that is, non-root) user: 

shell> memcached

By default, memcached uses the following settings:

Typically, you would specify the full combination of options that you want when starting memcached, and normally provide a startup script to handle the initialization of memcached. For example, the following line starts memcached with a maximum of 1024MB RAM for the cache, listening on port 11211 on the IP address 192.168.0.110, running has a background daemon: 

shell> memcached -d -m 1024 -p 11211 -l 192.168.0.110

To ensure that memcached is started up on boot, check the init script and configuration parameters.

memcached supports the following options:

memcached Deployment

When using memcached you can use a number of different potential deployment strategies and topologies. The exact strategy to use depends on your application and environment. When developing a system for deploying memcached within your system, keep in mind the following points:

Using Namespaces

The memcached cache is a very simple massive key/value storage system, and as such there is no way of compartmentalizing data automatically into different sections. For example, if you are storing information by the unique ID returned from a MariaDB database, then storing the data from two different tables could run into issues because the same ID might be valid in both tables.

Some interfaces provide an automated mechanism for creating namespaces when storing information into the cache. In practice, these namespaces are merely a prefix before a given ID that is applied every time a value is stored or retrieve from the cache.

You can implement the same basic principle by using keys that describe the object and the unique identifier within the key that you supply when the object is stored. For example, when storing user data, prefix the ID of the user with user: or user-.Note

Using namespaces or prefixes only controls the keys stored/retrieved. There is no security within memcached, and therefore no way to enforce that a particular client only accesses keys with a particular namespace. Namespaces are only useful as a method of identifying data and preventing corruption of key/value pairs.

Data Expiry

There are two types of data expiry within a memcached instance. The first type is applied at the point when you store a new key/value pair into the memcached instance. If there is not enough space within a suitable slab to store the value, then an existing least recently used (LRU) object is removed (evicted) from the cache to make room for the new item.

The LRU algorithm ensures that the object that is removed is one that is either no longer in active use or that was used so long ago that its data is potentially out of date or of little value. However, in a system where the memory allocated to memcached is smaller than the number of regularly used objects required in the cache, a lot of expired items could be removed from the cache even though they are in active use. You use the statistics mechanism to get a better idea of the level of evictions (expired objects). For more information, see , "Getting memcached Statistics".

You can change this eviction behavior by setting the -M command-line option when starting memcached. This option forces an error to be returned when the memory has been exhausted, instead of automatically evicting older data.

The second type of expiry system is an explicit mechanism that you can set when a key/value pair is inserted into the cache, or when deleting an item from the cache. Using an expiration time can be a useful way of ensuring that the data in the cache is up to date and in line with your application needs and requirements.

A typical scenario for explicitly setting the expiry time might include caching session data for a user when accessing a Web site. memcached uses a lazy expiry mechanism where the explicit expiry time that has been set is compared with the current time when the object is requested. Only objects that have not expired are returned.

You can also set the expiry time when explicitly deleting an object from the cache. In this case, the expiry time is really a timeout and indicates the period when any attempts to set the value for a given key are rejected.

memcached Hashing/Distribution Types

The memcached client interface supports a number of different distribution algorithms that are used in multi-server configurations to determine which host should be used when setting or getting data from a given memcached instance. When you get or set a value, a hash is constructed from the supplied key and then used to select a host from the list of configured servers. Because the hashing mechanism uses the supplied key as the basis for the hash, the same server is selected during both set and get operations.

You can think of this process as follows. Given an array of servers (a, b, and c), the client uses a hashing algorithm that returns an integer based on the key being stored or retrieved. The resulting value is then used to select a server from the list of servers configured in the client. Most standard client hashing within memcache clients uses a simple modulus calculation on the value against the number of configured memcached servers. You can summarize the process in pseudocode as: 

@memcservers = ['a.memc','b.memc','c.memc'];
$value = hash($key);
$chosen = $value % length(@memcservers);

Replacing the above with values: 

@memcservers = ['a.memc','b.memc','c.memc'];
$value = hash('myid');
$chosen = 7009 % 3;

In the above example, the client hashing algorithm chooses the server at index 1 (% 3 = 1), and store or retrieve the key and value with that server.Note

This selection and hashing process is handled automatically by the memcached client you are using; you need only provide the list of memcached servers to use.

You can see a graphical representation of this below in Figure 14.7, "memcached Hash Selection".

Figure 14.7. memcached Hash Selection

memcached Hash Selection

The same hashing and selection process takes place during any operation on the specified key within the memcached client.

Using this method provides a number of advantages:

Providing that the list of servers configured within the client remains the same, the same stored key returns the same value, and therefore selects the same server.

However, if you do not use the same hashing mechanism then the same data may be recorded on different servers by different interfaces, both wasting space on your memcached and leading to potential differences in the information.Note

One way to use a multi-interface compatible hashing mechanism is to use the libmemcached library and the associated interfaces. Because the interfaces for the different languages (including C, Ruby, Perl and Python) use the same client library interface, they always generate the same hash code from the ID.

The problem with client-side selection of the server is that the list of the servers (including their sequential order) must remain consistent on each client using the memcached servers, and the servers must be available. If you try to perform an operation on a key when:

When the hashing algorithm is used on the given key, but with a different list of servers, the hash calculation may choose a different server from the list.

If a new memcached instance is added into the list of servers, as new.memc is in the example below, then a GET operation using the same key, myid, can result in a cache-miss. This is because the same value is computed from the key, which selects the same index from the array of servers, but index 2 now points to the new server, not the server c.memc where the data was originally stored. This would result in a cache miss, even though the key exists within the cache on another memcached instance.

Figure 14.8. memcached Hash Selection with New memcached instance

memcached Hash Selection with New memcached instance

This means that servers c.memc and new.memc both contain the information for key myid, but the information stored against the key in eachs server may be different in each instance. A more significant problem is a much higher number of cache-misses when retrieving data, as the addition of a new server changes the distribution of keys, and this in turn requires rebuilding the cached data on the memcached instances, causing an increase in database reads.

The same effect can occur if you actively manage the list of servers configured in your clients, adding and removing the configured memcached instances as each instance is identified as being available. For example, removing a memcached instance when the client notices that the instance can no longer be contacted can cause the server selection to fail as described here.

To prevent this causing significant problems and invalidating your cache, you can select the hashing algorithm used to select the server. There are two common types of hashing algorithm, consistent and modula.

With consistent hashing algorithms, the same key when applied to a list of servers always uses the same server to store or retrieve the keys, even if the list of configured servers changes. This means that you can add and remove servers from the configure list and always use the same server for a given key. There are two types of consistent hashing algorithms available, Ketama and Wheel. Both types are supported by libmemcached, and implementations are available for PHP and Java.

Any consistent hashing algorithm has some limitations. When you add servers to an existing list of configured servers, keys are distributed to the new servers as part of the normal distribution. When you remove servers from the list, the keys are re-allocated to another server within the list, meaning that the cache needs to be re-populated with the information. Also, a consistent hashing algorithm does not resolve the issue where you want consistent selection of a server across multiple clients, but where each client contains a different list of servers. The consistency is enforced only within a single client.

With a modula hashing algorithm, the client selects a server by first computing the hash and then choosing a server from the list of configured servers. As the list of servers changes, so the server selected when using a modula hashing algorithm also changes. The result is the behavior described above; changes to the list of servers mean that different servers are selected when retrieving data, leading to cache misses and increase in database load as the cache is re-seeded with information.

If you use only a single memcached instance for each client, or your list of memcached servers configured for a client never changes, then the selection of a hashing algorithm is irrelevant, as it has no noticeable effect.

If you change your servers regularly, or you use a common set of servers that are shared among a large number of clients, then using a consistent hashing algorithm should help to ensure that your cache data is not duplicated and the data is evenly distributed.

Using memcached and DTrace

memcached includes a number of different DTrace probes that can be used to monitor the operation of the server. The probes included can monitor individual connections, slab allocations, and modifications to the hash table when a key/value pair is added, updated, or removed.

For more information on DTrace and writing DTrace scripts, read the DTrace User Guide.

Support for DTrace probes was added to memcached 1.2.6 includes a number of DTrace probes that can be used to help monitor your application. DTrace is supported on Solaris 10, OpenSolaris, Mac OS X 10.5 and FreeBSD. To enable the DTrace probes in memcached, build from source and use the --enable-dtrace option. For more information, see , "Installing memcached".

The probes supported by memcached are:

Memory Allocation within memcached

When you first start memcached, the memory that you have configured is not automatically allocated. Instead, memcached only starts allocating and reserving physical memory once you start saving information into the cache.

When you start to store data into the cache, memcached does not allocate the memory for the data on an item by item basis. Instead, a slab allocation is used to optimize memory usage and prevent memory fragmentation when information expires from the cache.

With slab allocation, memory is reserved in blocks of 1MB. The slab is divided up into a number of blocks of equal size. When you try to store a value into the cache, memcached checks the size of the value that you are adding to the cache and determines which slab contains the right size allocation for the item. If a slab with the item size already exists, the item is written to the block within the slab.

If the new item is bigger than the size of any existing blocks, then a new slab is created, divided up into blocks of a suitable size. If an existing slab with the right block size already exists, but there are no free blocks, a new slab is created. If you update an existing item with data that is larger than the existing block allocation for that key, then the key is re-allocated into a suitable slab.

For example, the default size for the smallest block is 88 bytes (40 bytes of value, and the default 48 bytes for the key and flag data). If the size of the first item you store into the cache is less than 40 bytes, then a slab with a block size of 88 bytes is created and the value stored.

If the size of the data that you intend to store is larger than this value, then the block size is increased by the chunk size factor until a block size large enough to hold the value is determined. The block size is always a function of the scale factor, rounded up to a block size which is exactly divisible into the chunk size.

For a sample of the structure, see Figure 14.9, "Memory Allocation in memcached".

Figure 14.9. Memory Allocation in memcached

Memory Allocation in memcached

The result is that you have multiple pages allocated within the range of memory allocated to memcached. Each page is 1MB in size (by default), and is split into a different number of chunks, according to the chunk size required to store the key/value pairs. Each instance has multiple pages allocated, and a page is always created when a new item needs to be created requiring a chunk of a particular size. A slab may consist of multiple pages, and each page within a slab contains an equal number of chunks.

The chunk size of a new slab is determined by the base chunk size combined with the chunk size growth factor. For example, if the initial chunks are 104 bytes in size, and the default chunk size growth factor is used (1.25), then the next chunk size allocated would be the best power of 2 fit for 104*1.25, or 136 bytes.

Allocating the pages in this way ensures that memory does not get fragmented. However, depending on the distribution of the objects that you store, it may lead to an inefficient distribution of the slabs and chunks if you have significantly different sized items. For example, having a relatively small number of items within each chunk size may waste a lot of memory with just few chunks in each allocated page.

You can tune the growth factor to reduce this effect by using the -f command line option, which adapts the growth factor applied to make more effective use of the chunks and slabs allocated. For information on how to determine the current slab allocation statistics, see , "memcached Slabs Statistics".

If your operating system supports it, you can also start memcached with the -L command line option. This option preallocates all the memory during startup using large memory pages. This can improve performance by reducing the number of misses in the CPU memory cache.

memcached Thread Support

If you enable the thread implementation within when building memcached from source, then memcached uses multiple threads in addition to the libevent system to handle requests.

When enabled, the threading implementation operates as follows:

Using threads can increase the performance on servers that have multiple CPU cores available, as the requests to update the hash table can be spread between the individual threads. To minimize overhead from the locking mechanism employed, experiment with different thread values to achieve the best performance based on the number and type of requests within your given workload.

memcached Logs

If you enable verbose mode, using the -v, -vv, or -vvv options, then the information output by memcached includes details of the operations being performed.

Without the verbose options, memcached normally produces no output during normal operating.

All of the verbosity levels in memcached are designed to be used during debugging or examination of issues. The quantity of information generated, particularly when using -vvv, is significant, particularly on a busy server. Also be aware that writing the error information out, especially to disk, may negate some of the performance gains you achieve by using memcached. Therefore, use in production or deployment environments is not recommended.

Developing a memcached Application

Basic memcached Operations
Using memcached as a MariaDB Caching Layer
Using libmemcached with C and C++
Using MariaDB and memcached with Perl
Using MariaDB and memcached with Python
Using MariaDB and memcached with PHP
Using MariaDB and memcached with Ruby
Using MariaDB and memcached with Java
Using the MariaDB memcached User-Defined Functions
memcached Protocol

A number of language interfaces let applications store and retrieve information with memcached servers. You can write memcached applications in popular languages such as Perl, PHP, Python, Ruby, C, and Java.

Data stored into a memcached server is referred to by a single string (the key), with storage into the cache and retrieval from the cache using the key as the reference. The cache therefore operates like a large associative array or hash table. It is not possible to structure or otherwise organize the information stored in the cache. To emulate database notions such as multiple tables or composite key values, you must use encode the extra information into the strings used as keys. For example, to store or look up the address corresponding to a specific latitude and longitude, you might turn those two numeric values into a single comma-separated string to use as a key.

Basic memcached Operations

The interface to memcached supports the following methods for storing and retrieving information in the cache, and these are consistent across all the different APIs, although the language specific mechanics might be different:

In all implementations, most or all of these functions are duplicated through the corresponding native language interface.

When practical, use memcached to store full items, rather than caching a single column value from the database. For example, when displaying a record about an object (invoice, user history, or blog post), load all the data for the associated entry from the database, and compile it into the internal structure that would normally be required by the application. Save the complete object in the cache.

Complex data structures cannot be stored directly. Most interfaces serialize the data for you, that is, put it in a textual form that can reconstruct the original pointers and nesting. Perl uses Storable, PHP uses serialize, Python uses cPickle (or Pickle) and Java uses the Serializable interface. In most cases, the serialization interface used is customizable. To share data stored in memcached instances between different language interfaces, consider using a common serialization solution such as JSON (Javascript Object Notation).

Using memcached as a MariaDB Caching Layer

When using memcached to cache MariaDB data, your application must retrieve data from the database and load the appropriate key-value pairs into the cache. Then, subsequent lookups can be done directly from the cache.

Because MariaDB has its own in-memory caching mechanisms for queried data, such as the InnoDB buffer pool and the MariaDB query cache, look for opportunities beyond loading individual column values or rows into the cache. Prefer to cache composite values, such as those retrieved from multiple tables through a join query, or result sets assembled from multiple rows.Caution

Limit the information in the cache to non-sensitive data, because there is no security required to access or update the information within a memcached instance. Anybody with access to the machine has the ability to read, view and potentially update the information. To keep the data secure, encrypt the information before caching it. To restrict the users capable of connecting to the server, either disable network access, or use IPTables or similar techniques to restrict access to the memcached ports to a select set of hosts.

You can introduce memcached to an existing application, even if caching was not part of the original design. In many languages and environments the changes to the application will be just a few lines, first to attempt to read from the cache when loading data, fall back to the old method if the information is not cached, and to update the cache with information once the data has been read.

The general sequence for using memcached in any language as a caching solution for MariaDB is as follows:

  1. Request the item from the cache.
  2. If the item exists, use the item data.
  3. If the item does not exist, load the data from MySQL, and store the value into the cache. This means the value is available to the next client that requests it from the cache.

For a flow diagram of this sequence, see Figure 14.10, "Typical memcached Application Flowchart".

Figure 14.10. Typical memcached Application Flowchart

Typical memcached Application Flowchart
Adapting Database Best Practices to memcached Applications

The most direct way to cache MariaDB data is to use a 2-column table, where the first column is a primary key. Because of the uniqueness requirements for memcached keys, make sure your database schema makes appropriate use of primary keys and unique constraints.

If you combine multiple column values into a single memcached item value, choose data types to make it easy to parse the value back into its components, for example by using a separator character between numeric values.

The queries that map most easily to memcached lookups are those with a single WHERE clause, using an = or IN operator. For complicated WHERE clauses, or those using operators such as <, >, BETWEEN, or LIKE, memcached does not provide a simple or efficient way to scan through or filter the keys or associated values, so typically you perform those operations as SQL queries on the underlying database.

Using libmemcached with C and C++

libmemcached Base Functions
libmemcached Server Functions
libmemcached Set Functions
libmemcached Get Functions
Controlling libmemcached Behaviors
libmemcached Command-Line Utilities

The libmemcached library provides both C and C++ interfaces to memcached and is also the basis for a number of different additional API implementations, including Perl, Python and Ruby. Understanding the core libmemcached functions can help when using these other interfaces.

The C library is the most comprehensive interface library for memcached and provides functions and operational systems not always exposed in interfaces not based on the libmemcached library.

The different functions can be divided up according to their basic operation. In addition to functions that interface to the core API, a number of utility functions provide extended functionality, such as appending and prepending data.

To build and install libmemcached, download the libmemcached package, run configure, and then build and install: 

shell> tar xjf libmemcached-0.21.tar.gz shell> cd libmemcached-0.21
shell> ./configure shell> make shell> make install

On many Linux operating systems, you can install the corresponding libmemcached package through the usual yum, apt-get, or similar commands.

To build an application that uses the library, first set the list of servers. Either directly manipulate the servers configured within the main memcached_st structure, or separately populate a list of servers, and then add this list to the memcached_st structure. The latter method is used in the following example. Once the server list has been set, you can call the functions to store or retrieve data. A simple application for setting a preset value to localhost is provided here: 

#include <stdio.h>
#include <string.h>
#include <unistd.h>
#include <libmemcached/memcached.h>
int main(int argc, char *argv[])
{
 memcached_server_st *servers = NULL;
 memcached_st *memc;
 memcached_return rc;
 char *key= 'keystring';
 char *value= 'keyvalue';
 memcached_server_st *memcached_servers_parse (char *server_strings);
 memc= memcached_create(NULL);
 servers= memcached_server_list_append(servers, 'localhost', 11211, &rc);
 rc= memcached_server_push(memc, servers);
 if (rc == MEMCACHED_SUCCESS)
 fprintf(stderr,'Added server successfully\n');
 else
 fprintf(stderr,'Couldn't add server: %s\n',memcached_strerror(memc, rc));
 rc= memcached_set(memc, key, strlen(key), value, strlen(value), (time_t)0, (uint32_t)0);
 if (rc == MEMCACHED_SUCCESS)
 fprintf(stderr,'Key stored successfully\n');
 else
 fprintf(stderr,'Couldn't store key: %s\n',memcached_strerror(memc, rc));
 return 0;
}

To test the success of an operation, use the return value, or populated result code, for a given function. The value is always set to MEMCACHED_SUCCESS if the operation succeeded. In the event of a failure, use the memcached_strerror() function to translate the result code into a printable string.

To build the application, specify the memcached library: 

shell> gcc -o memc_basic memc_basic.c -lmemcached

Running the above sample application, after starting a memcached server, should return a success message: 

shell> memc_basic Added server successfully Key stored successfully
libmemcached Base Functions

The base libmemcached functions let you create, destroy and clone the main memcached_st structure that is used to interface with the memcached servers. The main functions are defined below: 

memcached_st *memcached_create (memcached_st *ptr);

Creates a new memcached_st structure for use with the other libmemcached API functions. You can supply an existing, static, memcached_st structure, or NULL to have a new structured allocated. Returns a pointer to the created structure, or NULL on failure. 

void memcached_free (memcached_st *ptr);

Frees the structure and memory allocated to a previously created memcached_st structure. 

memcached_st *memcached_clone(memcached_st *clone, memcached_st *source);

Clones an existing memcached structure from the specified source, copying the defaults and list of servers defined in the structure.

libmemcached Server Functions

The libmemcached API uses a list of servers, stored within the memcached_server_st structure, to act as the list of servers used by the rest of the functions. To use memcached, you first create the server list, and then apply the list of servers to a valid libmemcached object.

Because the list of servers, and the list of servers within an active libmemcached object can be manipulated separately, you can update and manage server lists while an active libmemcached interface is running.

The functions for manipulating the list of servers within a memcached_st structure are: 

memcached_return
 memcached_server_add (memcached_st *ptr,
 char *hostname,
 unsigned int port);

Adds a server, using the given hostname and port into the memcached_st structure given in ptr. 

memcached_return
 memcached_server_add_unix_socket (memcached_st *ptr,
 char *socket);

Adds a Unix socket to the list of servers configured in the memcached_st structure. 

unsigned int memcached_server_count (memcached_st *ptr);

Returns a count of the number of configured servers within the memcached_st structure. 

memcached_server_st *
 memcached_server_list (memcached_st *ptr);

Returns an array of all the defined hosts within a memcached_st structure. 

memcached_return
 memcached_server_push (memcached_st *ptr,
 memcached_server_st *list);

Pushes an existing list of servers onto list of servers configured for a current memcached_st structure. This adds servers to the end of the existing list, and duplicates are not checked.

The memcached_server_st structure can be used to create a list of memcached servers which can then be applied individually to memcached_st structures. 

memcached_server_st *
 memcached_server_list_append (memcached_server_st *ptr,
 char *hostname,
 unsigned int port,
 memcached_return *error);

Adds a server, with hostname and port, to the server list in ptr. The result code is handled by the error argument, which should point to an existing memcached_return variable. The function returns a pointer to the returned list. 

unsigned int memcached_server_list_count (memcached_server_st *ptr);

Returns the number of the servers in the server list. 

void memcached_server_list_free (memcached_server_st *ptr);

Frees the memory associated with a server list. 

memcached_server_st *memcached_servers_parse (char *server_strings);

Parses a string containing a list of servers, where individual servers are separated by a comma, space, or both, and where individual servers are of the form server[:port]. The return value is a server list structure.

libmemcached Set Functions

The set-related functions within libmemcached provide the same functionality as the core functions supported by the memcached protocol. The full definition for the different functions is the same for all the base functions (add, replace, prepend, append). For example, the function definition for memcached_set() is: 

memcached_return
 memcached_set (memcached_st *ptr,
 const char *key,
 size_t key_length,
 const char *value,
 size_t value_length,
 time_t expiration,
 uint32_t flags);

The ptr is the memcached_st structure. The key and key_length define the key name and length, and value and value_length the corresponding value and length. You can also set the expiration and optional flags. For more information, see , "Controlling libmemcached Behaviors".

The following table outlines the remainder of the set-related functions.

libmemcached Function Equivalent to
memcached_set(memc, key, key_length, value, value_length, expiration, flags) Generic set() operation.
memcached_add(memc, key, key_length, value, value_length, expiration, flags) Generic add() function.
memcached_replace(memc, key, key_length, value, value_length, expiration, flags) Generic replace().
memcached_prepend(memc, key, key_length, value, value_length, expiration, flags) Prepends the specified value before the current value of the specified key.
memcached_append(memc, key, key_length, value, value_length, expiration, flags) Appends the specified value after the current value of the specified key.
memcached_cas(memc, key, key_length, value, value_length, expiration, flags, cas) Overwrites the data for a given key as long as the corresponding cas value is still the same within the server.
memcached_set_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the generic set(), but has the option of an additional master key that can be used to identify an individual server.
memcached_add_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the generic add(), but has the option of an additional master key that can be used to identify an individual server.
memcached_replace_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the generic replace(), but has the option of an additional master key that can be used to identify an individual server.
memcached_prepend_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the memcached_prepend(), but has the option of an additional master key that can be used to identify an individual server.
memcached_append_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the memcached_append(), but has the option of an additional master key that can be used to identify an individual server.
memcached_cas_by_key(memc, master_key, master_key_length, key, key_length, value, value_length, expiration, flags) Similar to the memcached_cas(), but has the option of an additional master key that can be used to identify an individual server.

The by_key methods add two further arguments that define the master key, to be used and applied during the hashing stage for selecting the servers. You can see this in the following definition: 

memcached_return
 memcached_set_by_key(memcached_st *ptr,
 const char *master_key,
 size_t master_key_length,
 const char *key,
 size_t key_length,
 const char *value,
 size_t value_length,
 time_t expiration,
 uint32_t flags);

All the functions return a value of type memcached_return, which you can compare against the MEMCACHED_SUCCESS constant.

libmemcached Get Functions

The libmemcached functions provide both direct access to a single item, and a multiple-key request mechanism that provides much faster responses when fetching a large number of keys simultaneously.

The main get-style function, which is equivalent to the generic get() is memcached_get(). This function returns a string pointer, pointing to the value associated with the specified key. 

char *memcached_get (memcached_st *ptr,
 const char *key, size_t key_length,
 size_t *value_length,
 uint32_t *flags,
 memcached_return *error);

A multi-key get, memcached_mget(), is also available. Using a multiple key get operation is much quicker to do in one block than retrieving the key values with individual calls to memcached_get(). To start the multi-key get, call memcached_mget(): 

memcached_return
 memcached_mget (memcached_st *ptr,
 char **keys, size_t *key_length,
 unsigned int number_of_keys);

The return value is the success of the operation. The keys parameter should be an array of strings containing the keys, and key_length an array containing the length of each corresponding key. number_of_keys is the number of keys supplied in the array.

To fetch the individual values, use memcached_fetch() to get each corresponding value. 

char *memcached_fetch (memcached_st *ptr,
 const char *key, size_t *key_length,
 size_t *value_length,
 uint32_t *flags,
 memcached_return *error);

The function returns the key value, with the key, key_length and value_length parameters being populated with the corresponding key and length information. The function returns NULL when there are no more values to be returned. A full example, including the populating of the key data and the return of the information is provided here. 

#include <stdio.h>
#include <sstring.h>
#include <unistd.h>
#include <libmemcached/memcached.h>
int main(int argc, char *argv[])
{
 memcached_server_st *servers = NULL;
 memcached_st *memc;
 memcached_return rc;
 char *keys[]= {'huey', 'dewey', 'louie'};
 size_t key_length[3];
 char *values[]= {'red', 'blue', 'green'};
 size_t value_length[3];
 unsigned int x;
 uint32_t flags;
 char return_key[MEMCACHED_MAX_KEY];
 size_t return_key_length;
 char *return_value;
 size_t return_value_length;
 memc= memcached_create(NULL);
 servers= memcached_server_list_append(servers, 'localhost', 11211, &rc);
 rc= memcached_server_push(memc, servers);
 if (rc == MEMCACHED_SUCCESS)
 fprintf(stderr,'Added server successfully\n');
 else
 fprintf(stderr,'Couldn't add server: %s\n',memcached_strerror(memc, rc));
 for(x= 0; x < 3; x++)
 {
 key_length[x] = strlen(keys[x]);
 value_length[x] = strlen(values[x]);
 rc= memcached_set(memc, keys[x], key_length[x], values[x],
 value_length[x], (time_t)0, (uint32_t)0);
 if (rc == MEMCACHED_SUCCESS)
 fprintf(stderr,'Key %s stored successfully\n',keys[x]);
 else
 fprintf(stderr,'Couldn't store key: %s\n',memcached_strerror(memc, rc));
 }
 rc= memcached_mget(memc, keys, key_length, 3);
 if (rc == MEMCACHED_SUCCESS)
 {
 while ((return_value= memcached_fetch(memc, return_key, &return_key_length,
 &return_value_length, &flags, &rc)) != NULL)
 {
 if (rc == MEMCACHED_SUCCESS)
 {
 fprintf(stderr,'Key %s returned %s\n',return_key, return_value);
 }
 }
 }
 return 0;
}

Running the above application produces the following output: 

shell> memc_multi_fetch Added server successfully Key huey stored successfully Key dewey stored successfully Key louie stored successfully Key huey returned red Key dewey returned blue Key louie returned green
Controlling libmemcached Behaviors

The behavior of libmemcached can be modified by setting one or more behavior flags. These can either be set globally, or they can be applied during the call to individual functions. Some behaviors also accept an additional setting, such as the hashing mechanism used when selecting servers.

To set global behaviors: 

memcached_return
 memcached_behavior_set (memcached_st *ptr,
 memcached_behavior flag,
 uint64_t data);

To get the current behavior setting: 

uint64_t
 memcached_behavior_get (memcached_st *ptr,
 memcached_behavior flag);
Behavior Description
MEMCACHED_BEHAVIOR_NO_BLOCK Caused libmemcached to use asynchronous I/O.
MEMCACHED_BEHAVIOR_TCP_NODELAY Turns on no-delay for network sockets.
MEMCACHED_BEHAVIOR_HASH Without a value, sets the default hashing algorithm for keys to use MD5. Other valid values include MEMCACHED_HASH_DEFAULT, MEMCACHED_HASH_MD5, MEMCACHED_HASH_CRC, MEMCACHED_HASH_FNV1_64, MEMCACHED_HASH_FNV1A_64, MEMCACHED_HASH_FNV1_32, and MEMCACHED_HASH_FNV1A_32.
MEMCACHED_BEHAVIOR_DISTRIBUTION Changes the method of selecting the server used to store a given value. The default method is MEMCACHED_DISTRIBUTION_MODULA. You can enable consistent hashing by setting MEMCACHED_DISTRIBUTION_CONSISTENT. MEMCACHED_DISTRIBUTION_CONSISTENT is an alias for the value MEMCACHED_DISTRIBUTION_CONSISTENT_KETAMA.
MEMCACHED_BEHAVIOR_CACHE_LOOKUPS Cache the lookups made to the DNS service. This can improve the performance if you are using names instead of IP addresses for individual hosts.
MEMCACHED_BEHAVIOR_SUPPORT_CAS Support CAS operations. By default, this is disabled because it imposes a performance penalty.
MEMCACHED_BEHAVIOR_KETAMA Sets the default distribution to MEMCACHED_DISTRIBUTION_CONSISTENT_KETAMA and the hash to MEMCACHED_HASH_MD5.
MEMCACHED_BEHAVIOR_POLL_TIMEOUT Modify the timeout value used by poll(). Supply a signed int pointer for the timeout value.
MEMCACHED_BEHAVIOR_BUFFER_REQUESTS Buffers IO requests instead of them being sent. A get operation, or closing the connection causes the data to be flushed.
MEMCACHED_BEHAVIOR_VERIFY_KEY Forces libmemcached to verify that a specified key is valid.
MEMCACHED_BEHAVIOR_SORT_HOSTS If set, hosts added to the list of configured hosts for a memcached_st structure are placed into the host list in sorted order. This breaks consistent hashing if that behavior has been enabled.
MEMCACHED_BEHAVIOR_CONNECT_TIMEOUT In nonblocking mode this changes the value of the timeout during socket connection.
libmemcached Command-Line Utilities

In addition to the main C library interface, libmemcached also includes a number of command-line utilities that can be useful when working with and debugging memcached applications.

All of the command-line tools accept a number of arguments, the most critical of which is servers, which specifies the list of servers to connect to when returning information.

The main tools are:

Using MariaDB and memcached with Perl

The Cache::Memcached module provides a native interface to the Memcache protocol, and provides support for the core functions offered by memcached. Install the module using your operating system's package management system, or using CPAN: 

root-shell> perl -MCPAN -e 'install Cache::Memcached'

To use memcached from Perl through the Cache::Memcached module, first create a new Cache::Memcached object that defines the list of servers and other parameters for the connection. The only argument is a hash containing the options for the cache interface. For example, to create a new instance that uses three memcached servers: 

use Cache::Memcached;
my $cache = new Cache::Memcached {
 'servers' => [
 '192.168.0.100:11211',
 '192.168.0.101:11211',
 '192.168.0.102:11211',
 ],
};
Note

When using the Cache::Memcached interface with multiple servers, the API automatically performs certain operations across all the servers in the group. For example, getting statistical information through Cache::Memcached returns a hash that contains data on a host-by-host basis, as well as generalized statistics for all the servers in the group.

You can set additional properties on the cache object instance when it is created by specifying the option as part of the option hash. Alternatively, you can use a corresponding method on the instance:

Once the Cache::Memcached object instance has been configured, you can use the set() and get() methods to store and retrieve information from the memcached servers. Objects stored in the cache are automatically serialized and deserialized using the Storable module.

The Cache::Memcached interface supports the following methods for storing/retrieving data, and relate to the generic methods as shown in the table.

Cache::Memcached Function Equivalent to
get() Generic get().
get_multi(keys) Gets multiple keys from memcache using just one query. Returns a hash reference of key/value pairs.
set() Generic set().
add() Generic add().
replace() Generic replace().
delete() Generic delete().
incr() Generic incr().
decr() Generic decr().

Below is a complete example for using memcached with Perl and the Cache::Memcached module: 

#!/usr/bin/perl use Cache::Memcached;
use DBI;
use Data::Dumper;
# Configure the memcached server my $cache = new Cache::Memcached {
 'servers' => [
 'localhost:11211',
 ],
 };
# Get the film name from the command line
# memcached keys must not contain spaces, so create
# a key name by replacing spaces with underscores my $filmname = shift or die 'Must specify the film name\n';
my $filmkey = $filmname;
$filmkey =~ s/ /_/;
# Load the data from the cache my $filmdata = $cache->get($filmkey);
# If the data wasn't in the cache, then we load it from the database if (!defined($filmdata))
{
 $filmdata = load_filmdata($filmname);
 if (defined($filmdata))
 {
# Set the data into the cache, using the key
 if ($cache->set($filmkey,$filmdata))
 {
 print STDERR 'Film data loaded from database and cached\n';
 }
 else
 {
 print STDERR 'Couldn't store to cache\n';
 }
 }
 else
 {
 die 'Couldn't find $filmname\n';
 }
}
else
{
 print STDERR 'Film data loaded from Memcached\n';
}
sub load_filmdata
{
 my ($filmname) = @_;
 my $dsn = 'DBI:mysql:database=sakila;host=localhost;port=3306';
 $dbh = DBI->connect($dsn, 'sakila','password');
 my ($filmbase) = $dbh->selectrow_hashref(sprintf('select * from film where title = %s',
 $dbh->quote($filmname)));
 if (!defined($filmname))
 {
 return (undef);
 }
 $filmbase->{stars} =
 $dbh->selectall_arrayref(sprintf('select concat(first_name,' ',last_name) ' .
 'from film_actor left join (actor) ' .
 'on (film_actor.actor_id = actor.actor_id) ' .
 ' where film_id=%s',
 $dbh->quote($filmbase->{film_id})));
 return($filmbase);
}

The example uses the Sakila database, obtaining film data from the database and writing a composite record of the film and actors to memcached. When calling it for a film does not exist, you get this result: 

shell> memcached-sakila.pl 'ROCK INSTINCT'
Film data loaded from database and cached

When accessing a film that has already been added to the cache: 

shell> memcached-sakila.pl 'ROCK INSTINCT'
Film data loaded from Memcached

Using MariaDB and memcached with Python

The Python memcache module interfaces to memcached servers, and is written in pure Python (that is, without using one of the C APIs). You can download and install a copy from Python Memcached.

To install, download the package and then run the Python installer: 

python setup.py install running install running bdist_egg running egg_info creating python_memcached.egg-info
...
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing python_memcached-1.43-py2.4.egg creating /usr/lib64/python2.4/site-packages/python_memcached-1.43-py2.4.egg Extracting python_memcached-1.43-py2.4.egg to /usr/lib64/python2.4/site-packages Adding python-memcached 1.43 to easy-install.pth file Installed /usr/lib64/python2.4/site-packages/python_memcached-1.43-py2.4.egg Processing dependencies for python-memcached==1.43
Finished processing dependencies for python-memcached==1.43

Once installed, the memcache module provides a class-based interface to your memcached servers. Serialization of Python structures is handled by using the Python cPickle or pickle modules.

To create a new memcache interface, import the memcache module and create a new instance of the memcache.Client class: 

import memcache memc = memcache.Client(['127.0.0.1:11211'])

The first argument should be an array of strings containing the server and port number for each memcached instance to use. You can enable debugging by setting the optional debug parameter to 1.

By default, the hashing mechanism used is crc32. This provides a basic module hashing algorithm for selecting among multiple servers. You can change the function used by setting the value of memcache.serverHashFunction to the alternate function to use. For example: 

from zlib import adler32
memcache.serverHashFunction = adler32

Once you have defined the servers to use within the memcache instance, the core functions provide the same functionality as in the generic interface specification. A summary of the supported functions is provided in the following table.

Python memcache Function Equivalent to
get() Generic get().
get_multi(keys) Gets multiple values from the supplied array of keys. Returns a hash reference of key/value pairs.
set() Generic set().
set_multi(dict [, expiry [, key_prefix]]) Sets multiple key/value pairs from the supplied dict.
add() Generic add().
replace() Generic replace().
prepend(key, value [, expiry]) Prepends the supplied value to the value of the existing key.
append(key, value [, expiry[) Appends the supplied value to the value of the existing key.
delete() Generic delete().
delete_multi(keys [, expiry [, key_prefix]] ) Deletes all the keys from the hash matching each string in the array keys.
incr() Generic incr().
decr() Generic decr().
Note

Within the Python memcache module, all the *_multi()functions support an optional key_prefix parameter. If supplied, then the string is used as a prefix to all key lookups. For example, if you call: 

memc.get_multi(['a','b'], key_prefix='users:')

The function retrieves the keys users:a and users:b from the servers.

Here is an example showing the storage and retrieval of information to a memcache instance, loading the raw data from MySQL: 

import sys import MySQLdb import memcache memc = memcache.Client(['127.0.0.1:11211'], debug=1);
try:
 conn = MySQLdb.connect (host = 'localhost',
 user = 'sakila',
 passwd = 'password',
 db = 'sakila')
except MySQLdb.Error, e:
 print 'Error %d: %s' % (e.args[0], e.args[1])
 sys.exit (1)
popularfilms = memc.get('top5films')
if not popularfilms:
 cursor = conn.cursor()
 cursor.execute('select film_id,title from film order by rental_rate desc limit 5')
 rows = cursor.fetchall()
 memc.set('top5films',rows,60)
 print 'Updated memcached with MariaDB data'
else:
 print 'Loaded data from memcached'
 for row in popularfilms:
 print '%s, %s' % (row[0], row[1])

When executed for the first time, the data is loaded from the MariaDB database and stored to the memcached server. 

shell> python memc_python.py Updated memcached with MariaDB data

Because the data is automatically serialized using cPickle/pickle, when you load the data back from memcached, you can use the object directly. In the example above, the information stored to memcached is in the form of rows from a Python DB cursor. When accessing the information (within the 60 second expiry time), the data is loaded from memcached and dumped: 

shell> python memc_python.py Loaded data from memcached
2, ACE GOLDFINGER
7, AIRPLANE SIERRA
8, AIRPORT POLLOCK
10, ALADDIN CALENDAR
13, ALI FOREVER

The serialization and deserialization happens automatically, but be aware that serialization of Python data may be incompatible with other interfaces and languages. You can change the serialization module used during initialization, for example to use JSON, which is more easily exchanged.

Using MariaDB and memcached with PHP

PHP provides support for the Memcache functions through a PECL extension. To enable the PHP memcache extensions, build PHP using the --enable-memcache option to configure when building from source.

If you are installing on a Red Hat-based server, you can install the php-pecl-memcache RPM: 

root-shell> yum --install php-pecl-memcache

On Debian-based distributions, use the php-memcache package.

To set global runtime configuration options, specify the values in the following table within your php.ini file:

Configuration option Default Description
memcache.allow_failover 1 Specifies whether another server in the list should be queried if the first server selected fails.
memcache.max_failover_attempts 20 Specifies the number of servers to try before returning a failure.
memcache.chunk_size 8192 Defines the size of network chunks used to exchange data with the memcached server.
memcache.default_port 11211 Defines the default port to use when communicating with the memcached servers.
memcache.hash_strategy standard Specifies which hash strategy to use. Set to consistent to enable servers to be added or removed from the pool without causing the keys to be remapped to other servers. When set to standard, an older (modula) strategy is used that potentially uses different servers for storage.
memcache.hash_function crc32 Specifies which function to use when mapping keys to servers. crc32 uses the standard CRC32 hash. fnv uses the FNV-1a hashing algorithm.

To create a connection to a memcached server, create a new Memcache object and then specify the connection options. For example: 

<?php
$cache = new Memcache;
$cache->connect('localhost',11211);
?>

This opens an immediate connection to the specified server.

To use multiple memcached servers, you need to add servers to the memcache object using addServer(): 

bool Memcache::addServer ( string $host [, int $port [, bool $persistent
 [, int $weight [, int $timeout [, int $retry_interval
 [, bool $status [, callback $failure_callback
 ]]]]]]] )

The server management mechanism within the php-memcache module is a critical part of the interface as it controls the main interface to the memcached instances and how the different instances are selected through the hashing mechanism.

To create a simple connection to two memcached instances: 

<?php
$cache = new Memcache;
$cache->addServer('192.168.0.100',11211);
$cache->addServer('192.168.0.101',11211);
?>

In this scenario, the instance connection is not explicitly opened, but only opened when you try to store or retrieve a value. To enable persistent connections to memcached instances, set the $persistent argument to true. This is the default setting, and causes the connections to remain open.

To help control the distribution of keys to different instances, use the global memcache.hash_strategy setting. This sets the hashing mechanism used to select. You can also add another weight to each server, which effectively increases the number of times the instance entry appears in the instance list, therefore increasing the likelihood of the instance being chosen over other instances. To set the weight, set the value of the $weight argument to more than one.

The functions for setting and retrieving information are identical to the generic functional interface offered by memcached, as shown in this table:

PECL memcache Function Equivalent to
get() Generic get().
set() Generic set().
add() Generic add().
replace() Generic replace().
delete() Generic delete().
increment() Generic incr().
decrement() Generic decr().

A full example of the PECL memcache interface is provided below. The code loads film data from the Sakila database when the user provides a film name. The data stored into the memcached instance is recorded as a mysqli result row, and the API automatically serializes the information for you. 

<?php
$memc = new Memcache;
$memc->addServer('localhost','11211');
?>
<html xmlns='http://www.w3.org/1999/xhtml' xml:lang='en' lang='en'>
<head>
 <meta http-equiv='Content-Type' content='text/html; charset=utf-8' />
 <title>Simple Memcache Lookup</title>
</head>
<body>
<form method='post'>
 <p><b>Film</b>: <input type='text' size='20' name='film'></p>
<input type='submit'>
</form>
<hr/>
<?php
 echo 'Loading data...\n';
$value = $memc->get($_REQUEST['film']);
if ($value)
 {
 printf('<p>Film data for %s loaded from memcache</p>',$value['title']);
 foreach (array_keys($value) as $key)
 {
 printf('<p><b>%s</b>: %s</p>',$key, $value[$key]);
 }
 }
 else
 {
 $con = new mysqli('localhost','sakila','password','sakila') or
 die ('<h1>Database problem</h1>' . mysqli_connect_error());
 $result = $con->query(sprintf('select * from film where title ='%s'',$_REQUEST['film']));
 $row = $result->fetch_array(MYSQLI_ASSOC);
 $memc->set($row['title'],$row);
 printf('<p>Loaded %s from MySQL</p>',$row['title']);
 }
?>

With PHP, the connections to the memcached instances are kept open as long as the PHP and associated Apache instance remain running. When adding or removing servers from the list in a running instance (for example, when starting another script that mentions additional servers), the connections are shared, but the script only selects among the instances explicitly configured within the script.

To ensure that changes to the server list within a script do not cause problems, make sure to use the consistent hashing mechanism.

Using MariaDB and memcached with Ruby

There are a number of different modules for interfacing to memcached within Ruby. The Ruby-MemCache client library provides a native interface to memcached that does not require any external libraries, such as libmemcached. You can obtain the installer package from http://www.deveiate.org/projects/RMemCache.

To install, extract the package and then run install.rb: 

shell> install.rb

If you have RubyGems, you can install the Ruby-MemCache gem: 

shell> gem install Ruby-MemCache Bulk updating Gem source index for: http://gems.rubyforge.org Install required dependency io-reactor? [Yn] y Successfully installed Ruby-MemCache-0.0.1
Successfully installed io-reactor-0.05
Installing ri documentation for io-reactor-0.05...
Installing RDoc documentation for io-reactor-0.05...

To use a memcached instance from within Ruby, create a new instance of the MemCache object. 

require 'memcache'
memc = MemCache::new '192.168.0.100:11211'

You can add a weight to each server to increase the likelihood of the server being selected during hashing by appending the weight count to the server host name/port string: 

require 'memcache'
memc = MemCache::new '192.168.0.100:11211:3'

To add servers to an existing list, you can append them directly to the MemCache object: 

memc += ['192.168.0.101:11211']

To set data into the cache, you can just assign a value to a key within the new cache object, which works just like a standard Ruby hash object: 

memc['key'] = 'value'

Or to retrieve the value: 

print memc['key']

For more explicit actions, you can use the method interface, which mimics the main memcached API functions, as summarized in the following table:

Ruby MemCache Method Equivalent to
get() Generic get().
get_hash(keys) Get the values of multiple keys, returning the information as a hash of the keys and their values.
set() Generic set().
set_many(pairs) Set the values of the keys and values in the hash pairs.
add() Generic add().
replace() Generic replace().
delete() Generic delete().
incr() Generic incr().
decr() Generic decr().

Using MariaDB and memcached with Java

The com.danga.MemCached class within Java provides a native interface to memcached instances. You can obtain the client from http://whalin.com/memcached/. The Java class uses hashes that are compatible with libmemcached, so you can mix and match Java and libmemcached applications accessing the same memcached instances. The serialization between Java and other interfaces are not compatible. If this is a problem, use JSON or a similar nonbinary serialization format.

On most systems, you can download the package and use the jar directly.

To use the com.danga.MemCached interface, you create a MemCachedClient instance and then configure the list of servers by configuring the SockIOPool. Through the pool specification you set up the server list, weighting, and the connection parameters to optimized the connections between your client and the memcached instances that you configure.

Generally, you can configure the memcached interface once within a single class, then use this interface throughout the rest of your application.

For example, to create a basic interface, first configure the MemCachedClient and base SockIOPool settings: 

public class MyClass {
 protected static MemCachedClient mcc = new MemCachedClient();
 static {
 String[] servers =
 {
 'localhost:11211',
 };
 Integer[] weights = { 1 };
 SockIOPool pool = SockIOPool.getInstance();
 pool.setServers( servers );
 pool.setWeights( weights );

In the above sample, the list of servers is configured by creating an array of the memcached instances to use. You can then configure individual weights for each server.

The remainder of the properties for the connection are optional, but you can set the connection numbers (initial connections, minimum connections, maximum connections, and the idle timeout) by setting the pool parameters: 

pool.setInitConn( 5 );
pool.setMinConn( 5 );
pool.setMaxConn( 250 );
pool.setMaxIdle( 1000 * 60 * 60 * 6

Once the parameters have been configured, initialize the connection pool: 

pool.initialize();

The pool, and the connection to your memcached instances should now be ready to use.

To set the hashing algorithm used to select the server used when storing a given key, use pool.setHashingAlg(): 

pool.setHashingAlg( SockIOPool.NEW_COMPAT_HASH );

Valid values are NEW_COMPAT_HASH, OLD_COMPAT_HASH and NATIVE_HASH are also basic modula hashing algorithms. For a consistent hashing algorithm, use CONSISTENT_HASH. These constants are equivalent to the corresponding hash settings within libmemcached.

Java com.danga.MemCached Method Equivalent to
get() Generic get().
getMulti(keys) Get the values of multiple keys, returning the information as Hash map using java.lang.String for the keys and java.lang.Object for the corresponding values.
set() Generic set().
add() Generic add().
replace() Generic replace().
delete() Generic delete().
incr() Generic incr().
decr() Generic decr().

Using the MariaDB memcached User-Defined Functions

The memcached MariaDB User-Defined Functions (UDFs) enable you to set and retrieve objects through a SQL interface, in MariaDB 5.0 or greater. This technique has the following benefits:

To install the MariaDB memcached UDFs, download the UDF package from https://launchpad.net/memcached-udfs. Unpack the package and run configure to configure the build process. When running configure, use the --with-mysql option and specify the location of the mysql_config command. 

shell> tar zxf memcached_functions_mysql-1.1.tar.gz
shell> cd memcached_functions_mysql-1.1
shell> # If memcached library is not found, set LDFLAGS=-Llibrary_directory before next command.
shell> ./configure --with-mysql=/usr/local/mysql/bin/mysql_config

Now build and install the functions: 

shell> make
shell> make install

Copy the MariaDB memcached UDFs into your MariaDB plugins directory: 

shell> cp /usr/local/lib/libmemcached_functions_mysql* /usr/local/mysql/lib/mysql/plugins/

The plugin directory is given by the value of the plugin-dir system variable. For more information, see , "Compiling and Installing User-Defined Functions".

Once installed, you must initialize the function within MariaDB using CREATE and specifying the return value and library. For example, to add the memc_get() function: 

mysql> CREATE FUNCTION memc_get RETURNS STRING SONAME 'libmemcached_functions_mysql.so';

Repeat this process for each function to provide access to within MySQL. Once you have created the association, the information is retained, even over restarts of the MariaDB server. To simplify the process, use the SQL script provided in the memcached UDFs package: 

shell> mysql <sql/install_functions.sql

Alternatively, if you have Perl installed, then you can use the supplied Perl script, which checks for the existence of each function and creates the function/library association if it is not already defined: 

shell> utils/install.pl --silent

The --silent option installs everything automatically. Without this option, the script asks whether to install each of the available functions.

The interface remains consistent with the other APIs and interfaces. To set up a list of servers, use the memc_servers_set() function, which accepts a single string containing and comma-separated list of servers: 

mysql> SELECT memc_servers_set('192.168.0.1:11211,192.168.0.2:11211');
Note

The list of servers used by the memcached UDFs is not persistent over restarts of the MariaDB server. If the MariaDB server fails, then you must re-set the list of memcached servers.

To set a value, use memc_set: 

mysql> SELECT memc_set('myid', 'myvalue');

To retrieve a stored value: 

mysql> SELECT memc_get('myid');

The list of functions supported by the UDFs, in relation to the standard protocol functions, is shown in the following table:

MySQL memcached UDF Function Equivalent to
memc_get() Generic get().
memc_get_by_key(master_key, key, value) Like the generic get(), but uses the supplied master key to select the server to use.
memc_set() Generic set().
memc_set_by_key(master_key, key, value) Like the generic set(), but uses the supplied master key to select the server to use.
memc_add() Generic add().
memc_add_by_key(master_key, key, value) Like the generic add(), but uses the supplied master key to select the server to use.
memc_replace() Generic replace().
memc_replace_by_key(master_key, key, value) Like the generic replace(), but uses the supplied master key to select the server to use.
memc_prepend(key, value) Prepend the specified value to the current value of the specified key.
memc_prepend_by_key(master_key, key, value) Prepend the specified value to the current value of the specified key, but uses the supplied master key to select the server to use.
memc_append(key, value) Append the specified value to the current value of the specified key.
memc_append_by_key(master_key, key, value) Append the specified value to the current value of the specified key, but uses the supplied master key to select the server to use.
memc_delete() Generic delete().
memc_delete_by_key(master_key, key, value) Like the generic delete(), but uses the supplied master key to select the server to use.
memc_increment() Generic incr().
memc_decrement() Generic decr().

The respective *_by_key() functions are useful to store a specific value into a specific memcached server, possibly based on a differently calculated or constructed key.

The memcached UDFs include some additional functions:

memcached Protocol

Using the TCP Text Protocol

Communicating with a memcached server can be achieved through either the TCP or UDP protocols. When using the TCP protocol, you can use a simple text based interface for the exchange of information.

Using the TCP Text Protocol

When communicating with memcached, you can connect to the server using the port configured for the server. You can open a connection with the server without requiring authorization or login. As soon as you have connected, you can start to send commands to the server. When you have finished, you can terminate the connection without sending any specific disconnection command. Clients are encouraged to keep their connections open to decrease latency and improve performance.

Data is sent to the memcached server in two forms:

Both text lines (commands and responses) and unstructured data are always terminated with the string \r\n. Because the data being stored may contain this sequence, the length of the data (returned by the client before the unstructured data is transmitted should be used to determine the end of the data.

Commands to the server are structured according to their operation:

For reference, a list of the different commands supported and their formats is provided below.

Table 14.1. memcached Command Reference

Command Command Formats
set set key flags exptime length, set key flags exptime length noreply
add add key flags exptime length, add key flags exptime length noreply
replace replace key flags exptime length, replace key flags exptime length noreply
append append key length, append key length noreply
prepend prepend key length, prepend key length noreply
cas cas key flags exptime length casunique, cas key flags exptime length casunique noreply
get get key1 [key2 ... keyn]
gets
delete delete key, delete key noreply, delete key expiry, delete key expiry noreply
incr incr key, incr key noreply, incr key value, incr key value noreply
decr decr key, decr key noreply, decr key value, decr key value noreply
stat stat, stat name, stat name value

When sending a command to the server, the response from the server is one of the settings in the following table. All response values from the server are terminated by \r\n:

Table 14.2. memcached Protocol Responses

String Description
STORED Value has successfully been stored.
NOT_STORED The value was not stored, but not because of an error. For commands where you are adding a or updating a value if it exists (such as add and replace), or where the item has already been set to be deleted.
EXISTS When using a cas command, the item you are trying to store already exists and has been modified since you last checked it.
NOT_FOUND The item you are trying to store, update or delete does not exist or has already been deleted.
ERROR You submitted a nonexistent command name.
CLIENT_ERROR errorstring There was an error in the input line, the detail is contained in errorstring.
SERVER_ERROR errorstring There was an error in the server that prevents it from returning the information. In extreme conditions, the server may disconnect the client after this error occurs.
VALUE keys flags length The requested key has been found, and the stored key, flags and data block are returned, of the specified length.
DELETED The requested key was deleted from the server.
STAT name value A line of statistics data.
END The end of the statistics data.

Getting memcached Statistics

memcached General Statistics
memcached Slabs Statistics
memcached Item Statistics
memcached Size Statistics
memcached Detail Statistics
Using memcached-tool

The memcached system has a built-in statistics system that collects information about the data being stored into the cache, cache hit ratios, and detailed information on the memory usage and distribution of information through the slab allocation used to store individual items. Statistics are provided at both a basic level that provide the core statistics, and more specific statistics for specific areas of the memcached server.

This information can be useful to ensure that you are getting the correct level of cache and memory usage, and that your slab allocation and configuration properties are set at an optimal level.

The stats interface is available through the standard memcached protocol, so the reports can be accessed by using telnet to connect to the memcached. The supplied memcached-tool includes support for obtaining the , "memcached Slabs Statistics" and , "memcached General Statistics" information. For more information, see , "Using memcached-tool".

Alternatively, most of the language API interfaces provide a function for obtaining the statistics from the server.

For example, to get the basic stats using telnet: 

shell> telnet localhost 11211
Trying ::1...
Connected to localhost.
Escape character is '^]'.
stats STAT pid 23599
STAT uptime 675
STAT time 1211439587
STAT version 1.2.5
STAT pointer_size 32
STAT rusage_user 1.404992
STAT rusage_system 4.694685
STAT curr_items 32
STAT total_items 56361
STAT bytes 2642
STAT curr_connections 53
STAT total_connections 438
STAT connection_structures 55
STAT cmd_get 113482
STAT cmd_set 80519
STAT get_hits 78926
STAT get_misses 34556
STAT evictions 0
STAT bytes_read 6379783
STAT bytes_written 4860179
STAT limit_maxbytes 67108864
STAT threads 1
END

When using Perl and the Cache::Memcached module, the stats() function returns information about all the servers currently configured in the connection object, and total statistics for all the memcached servers as a whole.

For example, the following Perl script obtains the stats and dumps the hash reference that is returned: 

use Cache::Memcached;
use Data::Dumper;
my $memc = new Cache::Memcached;
$memc->set_servers(\@ARGV);
print Dumper($memc->stats());

When executed on the same memcached as used in the Telnet example above we get a hash reference with the host by host and total statistics: 

$VAR1 = {
 'hosts' => {
 'localhost:11211' => {
 'misc' => {
 'bytes' => '2421',
 'curr_connections' => '3',
 'connection_structures' => '56',
 'pointer_size' => '32',
 'time' => '1211440166',
 'total_items' => '410956',
 'cmd_set' => '588167',
 'bytes_written' => '35715151',
 'evictions' => '0',
 'curr_items' => '31',
 'pid' => '23599',
 'limit_maxbytes' => '67108864',
 'uptime' => '1254',
 'rusage_user' => '9.857805',
 'cmd_get' => '838451',
 'rusage_system' => '34.096988',
 'version' => '1.2.5',
 'get_hits' => '581511',
 'bytes_read' => '46665716',
 'threads' => '1',
 'total_connections' => '3104',
 'get_misses' => '256940'
 },
 'sizes' => {
 '128' => '16',
 '64' => '15'
 }
 }
 },
 'self' => {},
 'total' => {
 'cmd_get' => 838451,
 'bytes' => 2421,
 'get_hits' => 581511,
 'connection_structures' => 56,
 'bytes_read' => 46665716,
 'total_items' => 410956,
 'total_connections' => 3104,
 'cmd_set' => 588167,
 'bytes_written' => 35715151,
 'curr_items' => 31,
 'get_misses' => 256940
 }
 };

The statistics are divided up into a number of distinct sections, and then can be requested by adding the type to the stats command. Each statistics output is covered in more detail in the following sections.

memcached General Statistics

The output of the general statistics provides an overview of the performance and use of the memcached instance. The statistics returned by the command and their meaning is shown in the following table.

The following terms are used to define the value type for each statistics value:

Statistic Data type Description Version
pid 32u Process ID of the memcached instance.
uptime 32u Uptime (in seconds) for this memcached instance.
time 32u Current time (as epoch).
version string Version string of this instance.
pointer_size string Size of pointers for this host specified in bits (32 or 64).
rusage_user 32u:32u Total user time for this instance (seconds:microseconds).
rusage_system 32u:32u Total system time for this instance (seconds:microseconds).
curr_items 32u Current number of items stored by this instance.
total_items 32u Total number of items stored during the life of this instance.
bytes 64u Current number of bytes used by this server to store items.
curr_connections 32u Current number of open connections.
total_connections 32u Total number of connections opened since the server started running.
connection_structures 32u Number of connection structures allocated by the server.
cmd_get 64u Total number of retrieval requests (get operations).
cmd_set 64u Total number of storage requests (set operations).
get_hits 64u Number of keys that have been requested and found present.
get_misses 64u Number of items that have been requested and not found.
delete_hits 64u Number of keys that have been deleted and found present. 1.3.x
delete_misses 64u Number of items that have been delete and not found. 1.3.x
incr_hits 64u Number of keys that have been incremented and found present. 1.3.x
incr_misses 64u Number of items that have been incremented and not found. 1.3.x
decr_hits 64u Number of keys that have been decremented and found present. 1.3.x
decr_misses 64u Number of items that have been decremented and not found. 1.3.x
cas_hits 64u Number of keys that have been compared and swapped and found present. 1.3.x
cas_misses 64u Number of items that have been compared and swapped and not found. 1.3.x
cas_badvalue 64u Number of keys that have been compared and swapped, but the comparison (original) value did not match the supplied value. 1.3.x
evictions 64u Number of valid items removed from cache to free memory for new items.
bytes_read 64u Total number of bytes read by this server from network.
bytes_written 64u Total number of bytes sent by this server to network.
limit_maxbytes 32u Number of bytes this server is permitted to use for storage.
threads 32u Number of worker threads requested.
conn_yields 64u Number of yields for connections (related to the -R option). 1.4.0

The most useful statistics from those given here are the number of cache hits, misses, and evictions.

A large number of get_misses may just be an indication that the cache is still being populated with information. The number should, over time, decrease in comparison to the number of cache get_hits. If, however, you have a large number of cache misses compared to cache hits after an extended period of execution, it may be an indication that the size of the cache is too small and you either need to increase the total memory size, or increase the number of the memcached instances to improve the hit ratio.

A large number of evictions from the cache, particularly in comparison to the number of items stored is a sign that your cache is too small to hold the amount of information that you regularly want to keep cached. Instead of items being retained in the cache, items are being evicted to make way for new items keeping the turnover of items in the cache high, reducing the efficiency of the cache.

memcached Slabs Statistics

To get the slabs statistics, use the stats slabs command, or the API equivalent.

The slab statistics provide you with information about the slabs that have created and allocated for storing information within the cache. You get information both on each individual slab-class and total statistics for the whole slab. 

STAT 1:chunk_size 104
STAT 1:chunks_per_page 10082
STAT 1:total_pages 1
STAT 1:total_chunks 10082
STAT 1:used_chunks 10081
STAT 1:free_chunks 1
STAT 1:free_chunks_end 10079
STAT 9:chunk_size 696
STAT 9:chunks_per_page 1506
STAT 9:total_pages 63
STAT 9:total_chunks 94878
STAT 9:used_chunks 94878
STAT 9:free_chunks 0
STAT 9:free_chunks_end 0
STAT active_slabs 2
STAT total_malloced 67083616
END

Individual stats for each slab class are prefixed with the slab ID. A unique ID is given to each allocated slab from the smallest size up to the largest. The prefix number indicates the slab class number in relation to the calculated chunk from the specified growth factor. Hence in the example, 1 is the first chunk size and 9 is the 9th chunk allocated size.

The different parameters returned for each chunk size and the totals are shown in the following table.

Statistic Description Version
chunk_size Space allocated to each chunk within this slab class.
chunks_per_page Number of chunks within a single page for this slab class.
total_pages Number of pages allocated to this slab class.
total_chunks Number of chunks allocated to the slab class.
used_chunks Number of chunks allocated to an item..
free_chunks Number of chunks not yet allocated to items.
free_chunks_end Number of free chunks at the end of the last allocated page.
get_hits Number of get hits to this chunk 1.3.x
cmd_set Number of set commands on this chunk 1.3.x
delete_hits Number of delete hits to this chunk 1.3.x
incr_hits Number of increment hits to this chunk 1.3.x
decr_hits Number of decrement hits to this chunk 1.3.x
cas_hits Number of CAS hits to this chunk 1.3.x
cas_badval Number of CAS hits on this chunk where the existing value did not match 1.3.x
mem_requested The true amount of memory of memory requested within this chunk 1.4.1

The following additional statistics cover the information for the entire server, rather than on a chunk by chunk basis:

Statistic Description Version
active_slabs Total number of slab classes allocated.
total_malloced Total amount of memory allocated to slab pages.

The key values in the slab statistics are the chunk_size, and the corresponding total_chunks and used_chunks parameters. These given an indication of the size usage of the chunks within the system. Remember that one key/value pair is placed into a chunk of a suitable size.

From these stats, you can get an idea of your size and chunk allocation and distribution. If you store many items with a number of largely different sizes, consider adjusting the chunk size growth factor to increase in larger steps to prevent chunk and memory wastage. A good indication of a bad growth factor is a high number of different slab classes, but with relatively few chunks actually in use within each slab. Increasing the growth factor creates fewer slab classes and therefore makes better use of the allocated pages.

memcached Item Statistics

To get the items statistics, use the stats items command, or the API equivalent.

The items statistics give information about the individual items allocated within a given slab class. 

STAT items:2:number 1
STAT items:2:age 452
STAT items:2:evicted 0
STAT items:2:evicted_nonzero 0
STAT items:2:evicted_time 2
STAT items:2:outofmemory 0
STAT items:2:tailrepairs 0
...
STAT items:27:number 1
STAT items:27:age 452
STAT items:27:evicted 0
STAT items:27:evicted_nonzero 0
STAT items:27:evicted_time 2
STAT items:27:outofmemory 0
STAT items:27:tailrepairs 0

The prefix number against each statistics relates to the corresponding chunk size, as returned by the stats slabs statistics. The result is a display of the number of items stored within each chunk within each slab size, and specific statistics about their age, eviction counts, and out of memory counts. A summary of the statistics is given in the following table.

Statistic Description
number The number of items currently stored in this slab class.
age The age of the oldest item within the slab class, in seconds.
evicted The number of items evicted to make way for new entries.
evicted_time The time of the last evicted entry
evicted_nonzero The time of the last evicted non-zero entry 1.4.0
outofmemory The number of items for this slab class that have triggered an out of memory error (only value when the -M command line option is in effect).
tailrepairs Number of times the entries for a particular ID need repairing

Item level statistics can be used to determine how many items are stored within a given slab and their freshness and recycle rate. You can use this to help identify whether there are certain slab classes that are triggering a much larger number of evictions that others.

memcached Size Statistics

To get size statistics, use the stats sizes command, or the API equivalent.

The size statistics provide information about the sizes and number of items of each size within the cache. The information is returned as two columns, the first column is the size of the item (rounded up to the nearest 32 byte boundary), and the second column is the count of the number of items of that size within the cache: 

96 35
128 38
160 807
192 804
224 410
256 222
288 83
320 39
352 53
384 33
416 64
448 51
480 30
512 54
544 39
576 10065
Caution

Running this statistic locks up your cache as each item is read from the cache and its size calculated. On a large cache, this may take some time and prevent any set or get operations until the process completes.

The item size statistics are useful only to determine the sizes of the objects you are storing. Since the actual memory allocation is relevant only in terms of the chunk size and page size, the information is only useful during a careful debugging or diagnostic session.

memcached Detail Statistics

For memcached 1.3.x and higher, you can enable and obtain detailed statistics about the get, set, and del operations on theindividual keys stored in the cache, and determine whether the attempts hit (found) a particular key. These operations are only recorded while the detailed stats analysis is turned on.

To enable detailed statistics, you must send the stats detail on command to the memcached server: 

$ telnet localhost 11211
Trying 127.0.0.1...
Connected to tiger.
Escape character is '^]'.stats detail on
OK

Individual statistics are recorded for every get, set and del operation on a key, including keys that are not currently stored in the server. For example, if an attempt is made to obtain the value of key abckey and it does not exist, the get operating on the specified key are recorded while detailed statistics are in effect, even if the key is not currently stored. The hits, that is, the number of get or del operations for a key that exists in the server are also counted.

To turn detailed statistics off, send the stats detail off command to the memcached server: 

$ telnet localhost 11211
Trying 127.0.0.1...
Connected to tiger.
Escape character is '^]'.stats detail on
OK

To obtain the detailed statistics recorded during the process, send the stats detail dump command to the memcached server: 

stats detail dump PREFIX hykkey get 0 hit 0 set 1 del 0
PREFIX xyzkey get 0 hit 0 set 1 del 0
PREFIX yukkey get 1 hit 0 set 0 del 0
PREFIX abckey get 3 hit 3 set 1 del 0
END

You can use the detailed statistics information to determine whether your memcached clients are using a large number of keys that do not exist in the server by comparing the hit and get or del counts. Because the information is recorded by key, you can also determine whether the failures or operations are clustered around specific keys.

Using memcached-tool

The memcached-tool, located within the scripts directory within the memcached source directory. The tool provides convenient access to some reports and statistics from any memcached instance.

The basic format of the command is: 

shell> ./memcached-tool hostname:port [command]

The default output produces a list of the slab allocations and usage. For example: 

shell> memcached-tool localhost:11211 display
 # Item_Size Max_age Pages Count Full? Evicted Evict_Time OOM
 1 80B 93s 1 20 no 0 0 0
 2 104B 93s 1 16 no 0 0 0
 3 136B 1335s 1 28 no 0 0 0
 4 176B 1335s 1 24 no 0 0 0
 5 224B 1335s 1 32 no 0 0 0
 6 280B 1335s 1 34 no 0 0 0
 7 352B 1335s 1 36 no 0 0 0
 8 440B 1335s 1 46 no 0 0 0
 9 552B 1335s 1 58 no 0 0 0
 10 696B 1335s 1 66 no 0 0 0
 11 872B 1335s 1 89 no 0 0 0
 12 1.1K 1335s 1 112 no 0 0 0
 13 1.3K 1335s 1 145 no 0 0 0
 14 1.7K 1335s 1 123 no 0 0 0
 15 2.1K 1335s 1 198 no 0 0 0
 16 2.6K 1335s 1 199 no 0 0 0
 17 3.3K 1335s 1 229 no 0 0 0
 18 4.1K 1335s 1 248 yes 36 2 0
 19 5.2K 1335s 2 328 no 0 0 0
 20 6.4K 1335s 2 316 yes 387 1 0
 21 8.1K 1335s 3 381 yes 492 1 0
 22 10.1K 1335s 3 303 yes 598 2 0
 23 12.6K 1335s 5 405 yes 605 1 0
 24 15.8K 1335s 6 384 yes 766 2 0
 25 19.7K 1335s 7 357 yes 908 170 0
 26 24.6K 1336s 7 287 yes 1012 1 0
 27 30.8K 1336s 7 231 yes 1193 169 0
 28 38.5K 1336s 4 104 yes 1323 169 0
 29 48.1K 1336s 1 21 yes 1287 1 0
 30 60.2K 1336s 1 17 yes 1093 169 0
 31 75.2K 1337s 1 13 yes 713 168 0
 32 94.0K 1337s 1 10 yes 278 168 0
 33 117.5K 1336s 1 3 no 0 0 0

This output is the same if you specify the command as display: 

shell> memcached-tool localhost:11211 display
 # Item_Size Max_age Pages Count Full? Evicted Evict_Time OOM
 1 80B 93s 1 20 no 0 0 0
 2 104B 93s 1 16 no 0 0 0
...

The output shows a summarized version of the output from the slabs statistics. The columns provided in the output are shown below:

You can also obtain a dump of the general statistics for the server using the stats command: 

shell> memcached-tool localhost:11211 stats 
#localhost:11211 Field Value
 accepting_conns 1
 bytes 162
 bytes_read 485
 bytes_written 6820
 cas_badval 0
 cas_hits 0
 cas_misses 0
 cmd_flush 0
 cmd_get 4
 cmd_set 2
 conn_yields 0
 connection_structures 11
 curr_connections 10
 curr_items 2
 decr_hits 0
 decr_misses 1
 delete_hits 0
 delete_misses 0
 evictions 0
 get_hits 4
 get_misses 0
 incr_hits 0
 incr_misses 2
 limit_maxbytes 67108864
 listen_disabled_num 0
 pid 12981
 pointer_size 32
 rusage_system 0.013911
 rusage_user 0.011876
 threads 4
 time 1255518565
 total_connections 20
 total_items 2
 uptime 880
 version 1.4.2

memcached FAQ

Questions

Questions and Answers

15.6.5.1: Can MariaDB actually trigger/store the changed data to memcached?

Yes. You can use the MariaDB UDFs for memcached and either write statements that directly set the values in the memcached server, or use triggers or stored procedures to do it for you. For more information, see , "Using the MariaDB memcached User-Defined Functions"

15.6.5.2: Can memcached be run on a Windows environment?

No. Currently memcached is available only on the Unix/Linux platform. There is an unofficial port available, see http://www.codeplex.com/memcachedproviders.

15.6.5.3: What is the max size of an object you can store in memcached and is that configurable?

The default maximum object size is 1MB. In memcached 1.4.2 and later you can change the maximum size of an object using the -I command line option.

For versions before this, to increase this size, you have to re-compile memcached. You can modify the value of the POWER_BLOCK within the slabs.c file within the source.

In memcached 1.4.2 and higher you can configure the maximum supported object size by using the -I command-line option. For example, to increase the maximum object size to 5MB: 

$ memcached -I 5m

If an object is larger than the maximum object size, you must manually split it. memcached is very simple, you give it a key and some data, it tries to cache it in RAM. If you try to store more than the default maximum size, the value is just truncated for speed reasons.

15.6.5.4: Is it true memcached will be much more effective with db-read-intensive applications than with db-write-intensive applications?

Yes. memcached plays no role in database writes, it is a method of caching data already read from the database in RAM.

15.6.5.5: Is there any overhead in not using persistent connections? If persistent is always recommended, what are the downsides (for example, locking up)?

If you don't use persistent connections when communicating with memcached, there will be a small increase in the latency of opening the connection each time. The effect is comparable to use nonpersistent connections with MySQL.

In general, the chance of locking or other issues with persistent connections is minimal, because there is very little locking within memcached. If there is a problem, eventually your request will time out and return no result, so your application will need to load from MariaDB again.

15.6.5.6: How does an event such as a crash of one of the memcached servers handled by the memcached client?

There is no automatic handling of this. If your client fails to get a response from a server, it should fall back to loading the data from the MariaDB database.

The client APIs all provide the ability to add and remove memcached instances on the fly. If within your application you notice that memcached server is no longer responding, you can remove the server from the list of servers, and keys will automatically be redistributed to another memcached server in the list. If retaining the cache content on all your servers is important, make sure you use an API that supports a consistent hashing algorithm. For more information, see , "memcached Hashing/Distribution Types".

15.6.5.7: What's a recommended hardware config for a memcached server?

memcached has a very low processing overhead. All that is required is spare physical RAM capacity. The point is not that you should necessarily deploy a dedicated memcached server. If you have web, application, or database servers that have spare RAM capacity, then use them with memcached.

If you want to build and deploy a dedicated memcached servers, then you use a relatively low-power CPU, lots of RAM and one or more Gigabit Ethernet interfaces.

15.6.5.8: Is memcached more effective for video and audio as opposed to textual read/writes

memcached doesn't care what information you are storing. To memcached, any value you store is just a stream of data. Remember, though, that the maximum size of an object you can store in memcached is 1MB, but can be configured to be larger by using the -I option in memcached 1.4.2 and later, or by modifying the source in versions before 1.4.2. If you plan on using memcached with audio and video content, you will probably want to increase the maximum object size. Also remember that memcached is a solution for caching information for reading. It shouldn't be used for writes, except when updating the information in the cache.

15.6.5.9: Can memcached work with ASPX?

There are ports and interfaces for many languages and environments. ASPX relies on an underlying language such as C# or VisualBasic, and if you are using ASP.NET then there is a C# memcached library. For more information, see .

15.6.5.10: How expensive is it to establish a memcache connection? Should those connections be pooled?

Opening the connection is relatively inexpensive, because there is no security, authentication or other handshake taking place before you can start sending requests and getting results. Most APIs support a persistent connection to a memcached instance to reduce the latency. Connection pooling would depend on the API you are using, but if you are communicating directly over TCP/IP, then connection pooling would provide some small performance benefit.

15.6.5.11: How will the data will be handled when the memcached server is down?

The behavior is entirely application dependent. Most applications will fall back to loading the data from the database (just as if they were updating the memcached) information. If you are using multiple memcached servers, you may also want to remove a server from the list to prevent the missing server affecting performance. This is because the client will still attempt to communicate the memcached that corresponds to the key you are trying to load.

15.6.5.12: How are auto-increment columns in the MariaDB database coordinated across multiple instances of memcached?

They aren't. There is no relationship between MariaDB and memcached unless your application (or, if you are using the MariaDB UDFs for memcached, your database definition) creates one.

If you are storing information based on an auto-increment key into multiple instances of memcached then the information will only be stored on one of the memcached instances anyway. The client uses the key value to determine which memcached instance to store the information, it doesn't store the same information across all the instances, as that would be a waste of cache memory.

15.6.5.13: Is compression available?

Yes. Most of the client APIs support some sort of compression, and some even allow you to specify the threshold at which a value is deemed appropriate for compression during storage.

15.6.5.14: Can we implement different types of memcached as different nodes in the same server - so can there be deterministic and non-deterministic in the same server?

Yes. You can run multiple instances of memcached on a single server, and in your client configuration you choose the list of servers you want to use.

15.6.5.15: What are best practices for testing an implementation, to ensure that it improves performance, and to measure the impact of memcached configuration changes? And would you recommend keeping the configuration very simple to start?

The best way to test the performance is to start up a memcached instance. First, modify your application so that it stores the data just before the data is about to be used or displayed into memcached. Since the APIs handle the serialization of the data, it should just be a one-line modification to your code. Then, modify the start of the process that would normally load that information from MariaDB with the code that requests the data from memcached. If the data cannot be loaded from memcached, default to the MariaDB process.

All of the changes required will probably amount to just a few lines of code. To get the best benefit, make sure you cache entire objects (for example, all the components of a web page, blog post, discussion thread, etc.), rather than using memcached as a simple cache of individual rows of MariaDB tables.

Keeping the configuration simple at the start, or even over the long term, is easy with memcached. Once you have the basic structure up and running, the only change you may want to make is to add more servers into the list of servers used by your clients. You don't need to manage the memcached servers, and there is no complex configuration, just add more servers to the list and let the client API and the memcached servers make the decisions.

MySQL Proxy

MySQL Proxy Supported Platforms
Installing MariaDB Proxy
MySQL Proxy Command Options
MySQL Proxy Scripting
Using MariaDB Proxy
MySQL Proxy FAQ

The MariaDB Proxy is an application that communicates over the network using the MariaDB network protocol and provides communication between one or more MariaDB servers and one or more MariaDB clients. Because MariaDB Proxy uses the MariaDB network protocol, it can be used without modification with any MySQL-compatible client that uses the protocol. This includes the mysql command-line client, any clients that uses the MariaDB client libraries, and any connector that supports the MariaDB network protocol.

In the most basic configuration, MariaDB Proxy simply interposes itself between the server and clients, passing queries from the clients to the MariaDB Server and returning the responses from the MariaDB Server to the appropriate client. In more advanced configurations, the MariaDB Proxy can also monitor and alter the communication between the client and the server. Query interception enables you to add profiling, and interception of the exchanges is scriptable using the Lua scripting language.

By intercepting the queries from the client, the proxy can insert additional queries into the list of queries sent to the server, and remove the additional results when they are returned by the server. Using this functionality you can return the results from the original query to the client while adding informational statements to each query, for example, to monitor their execution time or progress, and separately log the results.

The proxy enables you to perform additional monitoring, filtering, or manipulation of queries without requiring you to make any modifications to the client and without the client even being aware that it is communicating with anything but a genuine MariaDB server.

This documentation covers MariaDB Proxy 0.8.2.Warning

MySQL Proxy is currently an Alpha release and should not be used within production environments.Important

MySQL Proxy is compatible with MariaDB 5.0 or later. Testing has not been performed with Version 4.1. Please provide feedback on your experiences using the MySQL Proxy Forum.

MySQL Proxy Supported Platforms

MySQL Proxy is currently available as a precompiled binary for the following platforms:

Other Unix/Linux platforms not listed should be compatible by using the source package and building MariaDB Proxy locally.

System requirements for the MariaDB Proxy application are the same as the main MariaDB server. Currently MariaDB Proxy is compatible only with MariaDB 5.0.1 and later. MariaDB Proxy is provided as a standalone, statically linked binary. You need not have MariaDB or Lua installed.

Installing MariaDB Proxy

Installing MariaDB Proxy from a Binary Distribution
Installing MariaDB Proxy from a Source Distribution
Installing MariaDB Proxy from the Bazaar Repository
Setting Up MariaDB Proxy as a Windows Service

You have three choices for installing MariaDB Proxy:

Installing MariaDB Proxy from a Binary Distribution

If you download a binary package, you must extract and copy the package contents to your desired installation directory. The package contains files required by MariaDB Proxy, including additional Lua scripts and other components required for execution.

To install, unpack the archive into the desired directory, then modify your PATH environment variable so that you can use the mysql-proxy command directly: 

shell> cd /usr/local
shell> tar zxf mysql-proxy-0.8.2-platform.tar.gz
shell> PATH=$PATH:/usr/local/mysql-proxy-0.8.2-platform/sbin

To update the path globally on a system, you might need administrator privileges to modify the appropriate /etc/profile, /etc/bashrc, or other system configuration file.

On Windows, you can update the PATH environment variable using this procedure:

  1. On the Windows desktop, right-click the My Computer icon, and select Properties.
  2. Next select the Advanced tab from the System Properties menu that appears, and click the Environment Variables button.
  3. Under System Variables, select Path, then click the Edit button. The Edit System Variable dialogue should appear.

The Microsoft Visual C++ runtime libraries are a requirement for running MariaDB Proxy as of version 0.8.2. Users that do not have these libraries must download and install the Microsoft Visual C++ 2008 Service Pack 1 Redistributable Package MFC Security Update. Use the following link to obtain the package: 

http://www.microsoft.com/download/en/details.aspx?displaylang=en&id=26368

Installing MariaDB Proxy from a Source Distribution

You can download a source package and compile the MariaDB Proxy yourself. To build from source, you must have the following prerequisite components installed:

Note

On some operating systems, you might need to manually build the required components to get the latest version. If you have trouble compiling MariaDB Proxy, consider using a binary distributions instead.

After verifying that the prerequisite components are installed, configure and build MariaDB Proxy: 

shell> tar zxf mysql-proxy-0.8.2.tar.gz
shell> cd mysql-proxy-0.8.2
shell> ./configure
shell> make

To test the build, use the check target to make: 

shell> make check

The tests try to connect to localhost using the root user. To provide a password, set the MYSQL_PASSWORD environment variable: 

shell> MYSQL_PASSWORD=root_pwd make check

You can install using the install target: 

shell> make install

By default, mysql-proxy is installed into /usr/local/sbin/mysql-proxy. The Lua example scripts are installed into /usr/local/share.

Installing MariaDB Proxy from the Bazaar Repository

The MariaDB Proxy source is available through a public Bazaar repository and is the quickest way to get the latest releases and fixes.

A build from the Bazaar repository requires that the following prerequisite components be installed:

The mysql-proxy source is hosted on Launchpad. To check out a local copy of the Bazaar repository, use bzr: 

shell> bzr branch lp:mysql-proxy

The preceding command downloads a complete version of the Bazaar repository for mysql-proxy. The main source files are located within the trunk subdirectory. The configuration scripts must be generated before you can configure and build mysql-proxy. The autogen.sh script generates the required configuration scripts for you: 

shell> sh ./autogen.sh

The autogen.sh script creates the standard configure script, which you then use to configure and build with make: 

shell> ./configure
shell> make
shell> make install

To create a standalone source distribution, identical to the source distribution available for download, use this command: 

shell> make distcheck

The preceding command creates the file mysql-proxy-0.8.2.tar.gz (with the corresponding current version) within the current directory.

Setting Up MariaDB Proxy as a Windows Service

The MariaDB distribution on Windows includes the mysql-proxy-svc.exe command that enables a MariaDB Proxy instance to be managed by the Windows service control manager. You can control the service, including automatically starting and stopping it during boot, reboot and shutdown, without separately running the MariaDB Proxy application.

To set up a MariaDB Proxy service, use the sc command to create a new service using the MariaDB Proxy service command. Specify the MariaDB Proxy options on the sc command line, and identify the service with a unique name. For example, to configure a new MariaDB Proxy instance that will automatically start when your system boots, redirecting queries to the local MariaDB server: 

C:\> sc create 'Proxy' DisplayName= 'MySQL Proxy' start= 'auto' »
 binPath= 'C:\Program Files\MySQL\mysql-proxy-0.8.2\bin\mysql-proxy-svc.exe »
 --proxy-backend-addresses=127.0.0.1:3306'
Note

The space following the equal sign after each property is required; failure to include it results in an error.

The preceding command creates a new service called Proxy. You can start and stop the service using the net start|stop command with the service name. The service is not automatically started after it is created. To start the service: 

C:\> net start proxy
The MariaDB Proxy service is starting.
The MariaDB Proxy service was started successfully.

You can specify additional command-line options to the sc command. You can also set up multiple MariaDB Proxy services on the same machine (providing they are configured to listen on different ports and/or IP addresses.

You can delete a service that you have created: 

C:\> sc delete proxy

For more information on creating services using sc, see How to create a Windows service by using Sc.exe.

MySQL Proxy Command Options

To start MariaDB Proxy, you can run it directly from the command line: 

shell> mysql-proxy

For most situations, you specify at least the host name or address and the port number of the backend MariaDB server to which the MariaDB Proxy should pass queries.

You can specify options to mysql-proxy either on the command line, or by using a configuration file and the --defaults-file command-line option to specify the file location.

If you use a configuration file, format it as follows:

Failure to adhere to any of these requirements causes mysql-proxy to generate an error during startup.

The following tables list the supported configuration file and command-line options.

Table 14.3. mysql-proxy Help Options

Format Option File Description Introduction Deprecated Removed
--help help Show help options
--help-admin help-admin Show admin module options
--help-all help-all Show all help options
--help-proxy help-proxy Show proxy module options

Table 14.4. mysql-proxy Admin Options

Format Option File Description Introduction Deprecated Removed
--admin-address=host:port admin-address=host:port The admin module listening host and port
--admin-lua-script=file_name admin-lua-script=file_name Script to execute by the admin module
--admin-password=password admin-password=password Authentication password for admin module
--admin-username=user_name admin-username=user_name Authentication user name for admin module
--proxy-address=host:port proxy-address=host:port The listening proxy server host and port

Table 14.5. mysql-proxy Proxy Options

Format Option File Description Introduction Deprecated Removed
--no-proxy no-proxy Do not start the proxy module
--proxy-backend-addresses=host:port proxy-backend-addresses=host:port The MariaDB server host and port
--proxy-fix-bug-25371 proxy-fix-bug-25371 Enable the fix for Bug #25371 for older libmysql versions 0.8.1
--proxy-lua-script=file_name proxy-lua-script=file_name Filename for Lua script for proxy operations
--proxy-pool-no-change-user proxy-pool-no-change-user Do not use the protocol CHANGE_USER command to reset the connection when coming from the connection pool
--proxy-read-only-backend-addresses=host:port proxy-read-only-backend-addresses=host:port The MariaDB server host and port (read only)
--proxy-skip-profiling proxy-skip-profiling Disable query profiling

Table 14.6. mysql-proxy Applications Options

Format Option File Description Introduction Deprecated Removed
--basedir=dir_name basedir=dir_name The base directory prefix for paths in the configuration
--daemon daemon Start in daemon mode
--defaults-file=file_name The configuration file to use
--event-threads=count event-threads=count The number of event-handling threads
--keepalive keepalive Try to restart the proxy if a crash occurs
--log-backtrace-on-crash log-backtrace-on-crash Try to invoke the debugger and generate a backtrace on crash
--log-file=file_name log-file=file_name The file where error messages are logged
--log-level=level log-level=level The logging level
--log-use-syslog log-use-syslog Log errors to syslog
--lua-cpath=dir_name lua-cpath=dir_name Set the LUA_CPATH
--lua-path=dir_name lua-path=dir_name Set the LUA_PATH
--max-open-files=count max-open-files=count The maximum number of open files to support
--pid-file=file_name pid-file=file_name File in which to store the process ID
--plugin-dir=dir_name plugin-dir=dir_name Directory containing plugin files
--plugins=plugin,... plugins=plugin,... List of plugins to load
--user=user_name user=user_name The user to use when running mysql-proxy
--version version Show version information

Except as noted in the following details, all of the options can be used within the configuration file by supplying the option and the corresponding value. For example: 

[mysql-proxy]
log-file = /var/log/mysql-proxy.log log-level = message

The most common usage is as a simple proxy service (that is, without additional scripting). For basic proxy operation, you must specify at least one proxy-backend-addresses option to specify the MariaDB server to connect to by default: 

shell> mysql-proxy --proxy-backend-addresses=MySQL.example.com:3306

The default proxy port is 4040, so you can connect to your MariaDB server through the proxy by specifying the host name and port details: 

shell> mysql --host=localhost --port=4040

If your server requires authentication information, this will be passed through natively without alteration by mysql-proxy, so you must also specify the required authentication information: 

shell> mysql --host=localhost --port=4040 \
 --user=user_name --password=password

You can also connect to a read-only port (which filters out UPDATE and INSERT queries) by connecting to the read-only port. By default the host name is the default, and the port is 4042, but you can alter the host/port information by using the --proxy-read-only-backend-addresses command-line option.

For more detailed information on how to use these command-line options, and mysql-proxy in general in combination with Lua scripts, see , "Using MariaDB Proxy".

MySQL Proxy Scripting

Proxy Scripting Sequence During Query Injection
Internal Structures
Capturing a Connection with connect_server()
Examining the Handshake with read_handshake()
Examining the Authentication Credentials with read_auth()
Accessing Authentication Information with read_auth_result()
Manipulating Queries with read_query()
Manipulating Results with read_query_result()

You can control how MariaDB Proxy manipulates and works with the queries and results that are passed on to the MariaDB server through the use of the embedded Lua scripting language. You can find out more about the Lua programming language from the Lua Web site.

The following diagram shows an overview of the classes exposed by MariaDB Proxy.MySQL Proxy architecture

The primary interaction between MariaDB Proxy and the server is provided by defining one or more functions through an Lua script. A number of functions are supported, according to different events and operations in the communication sequence between a client and one or more backend MariaDB servers:

The following table describes the direction of information flow at the point when the function is triggered.

Function Supplied Information Direction
connect_server() None Client to Server
read_handshake() None Server to Client
read_auth() None Client to Server
read_auth_result() None Server to Client
read_query() Query Client to Server
read_query_result() Query result Server to Client

By default, all functions return a result that indicates whether the data should be passed on to the client or server (depending on the direction of the information being transferred). This return value can be overridden by explicitly returning a constant indicating that a particular response should be sent. For example, it is possible to construct result set information by hand within read_query() and to return the result set directly to the client without ever sending the original query to the server.

In addition to these functions, a number of built-in structures provide control over how MariaDB Proxy forwards queries and returns the results by providing a simplified interface to elements such as the list of queries and the groups of result sets that are returned.

Proxy Scripting Sequence During Query Injection

The following figure gives an example of how the proxy might be used when injecting queries into the query queue. Because the proxy sits between the client and MariaDB server, what the proxy sends to the server, and the information that the proxy ultimately returns to the client, need not match or correlate. Once the client has connected to the proxy, the sequence shown in the following diagram occurs for each individual query sent by the client.MySQL Proxy architecture

  1. When the client submits one query to the proxy, the read_query() function within the proxy is triggered. The function adds the query to the query queue.
  2. Once manipulation by read_query() has completed, the queries are submitted, sequentially, to the MariaDB server.
  3. The MariaDB server returns the results from each query, one result set for each query submitted. The read_query_result() function is triggered for each result set, and each invocation can decide which result set to return to the client

For example, you can queue additional queries into the global query queue to be processed by the server. This can be used to add statistical information by adding queries before and after the original query, changing the original query: 

SELECT * FROM City;

Into a sequence of queries: 

SELECT NOW();
SELECT * FROM City;
SELECT NOW();

You can also modify the original statement; for example, to add EXPLAIN to each statement executed to get information on how the statement was processed, again altering our original SQL statement into a number of statements: 

SELECT * FROM City;
EXPLAIN SELECT * FROM City;

In both of these examples, the client would have received more result sets than expected. Regardless of how you manipulate the incoming query and the returned result, the number of queries returned by the proxy must match the number of original queries sent by the client.

You could adjust the client to handle the multiple result sets sent by the proxy, but in most cases you will want the existence of the proxy to remain transparent. To ensure that the number of queries and result sets match, you can use the MariaDB Proxy read_query_result() to extract the additional result set information and return only the result set the client originally requested back to the client. You can achieve this by giving each query that you add to the query queue a unique ID, then filter out queries that do not match the original query ID when processing them with read_query_result().

Internal Structures

There are a number of internal structures within the scripting element of MariaDB Proxy. The primary structure is proxy and this provides an interface to the many common structures used throughout the script, such as connection lists and configured backend servers. Other structures, such as the incoming packet from the client and result sets are only available within the context of one of the scriptable functions.

Attribute Description
connection A structure containing the active client connections. For a list of attributes, see proxy.connection.
servers A structure containing the list of configured backend servers. For a list of attributes, see proxy.global.backends.
queries A structure containing the queue of queries that will be sent to the server during a single client query. For a list of attributes, see proxy.queries.
PROXY_VERSION The version number of MariaDB Proxy, encoded in hex. You can use this to check that the version number supports a particular option from within the Lua script. Note that the value is encoded as a hex value, so to check the version is at least 0.5.1 you compare against 0x00501.

proxy.connection

The proxy.connection object is read only, and provides information about the current connection, and is split into a client and server tables. This enables you to examine information about both the incoming client connections to the proxy (client), and to the backend servers (server).

Attribute Description
client.default_db Default database requested by the client
client.username User name used to authenticate
client.scrambled_password The scrambled version of the password used to authenticate
client.dst.name The combined address:port of the Proxy port used by this client (should match the --proxy-address configuration parameter)
client.dst.address The IP address of the of the Proxy port used by this client
client.dst.port The port number of the of the Proxy port used by this client
client.src.name The combined address:port of the client (originating) TCP/IP endpoint
client.src.address The IP address of the client (originating) TCP/IP port
client.src.port The port of the client (originating) TCP/IP endpoint
server.scramble_buffer The scramble buffer used to scramble the password
server.mysqld_version The MariaDB version number of the server
server.thread_id The ID of the thread handling the connection to the current server
server.dst.name The combined address:port for the backend server for the current connection (i.e. the connection to the MariaDB server)
server.dst.address The address for the backend server
server.dst.port The port for the backend server
server.src.name The combined address:port for the TCP/IP endpoint used by the Proxy to connect to the backend server
server.src.address The address of the endpoint for the proxy-side connection to the MariaDB server
server.src.port The port of the endpoint for the proxy-side connection to the MariaDB server

proxy.global.backends

The proxy.global.backends table is partially writable and contains an array of all the configured backend servers and the server metadata (IP address, status, etc.). You can determine the array index of the current connection using proxy.connection['backend_ndx'] which is the index into this table of the backend server being used by the active connection.

The attributes for each entry within the proxy.global.backends table are shown in this table.

Attribute Description
dst.name The combined address:port of the backend server.
dst.address The IP address of the backend server.
dst.port The port of the backend server.
connected_clients The number of clients currently connected.
state The status of the backend server. See Backend State/Type Constants.
type The type of the backend server. You can use this to identify whether the backed was configured as a standard read/write backend, or a read-only backend. You can compare this value to the proxy.BACKEND_TYPE_RW and proxy.BACKEND_TYPE_RO.

proxy.queries

The proxy.queries object is a queue representing the list of queries to be sent to the server. The queue is not populated automatically, but if you do not explicitly populate the queue, queries are passed on to the backend server verbatim. Also, if you do not populate the query queue by hand, the read_query_result() function is not triggered.

The following methods are supported for populating the proxy.queries object.

Function Description
append(id,packet,[options]) Appends a query to the end of the query queue. The id is an integer identifier that you can use to recognize the query results when they are returned by the server. The packet should be a properly formatted query packet. The optional options should be a table containing the options specific to this packet.
prepend(id,packet) Prepends a query to the query queue. The id is an identifier that you can use to recognize the query results when they are returned by the server. The packet should be a properly formatted query packet.
reset() Empties the query queue.
len() Returns the number of query packets in the queue.

For example, you could append a query packet to the proxy.queries queue by using the append(): 

proxy.queries:append(1,packet)

The optional third argument to append() should contain the options for the packet. To have access to the result set through the read_query_result() function, set the resultset_is_needed flag to true: 

proxy.queries:append( 1, packet, { resultset_is_needed = true } )

If that flag is false (the default), proxy will:

The default mode is therefore quicker and useful if you only want to monitor the queries sent, and the basic statistics.

To perform any kind of manipulation on the returned data, you must set the flag to true, which will:

proxy.response

The proxy.response structure is used when you want to return your own MariaDB response, instead of forwarding a packet that you have received a backend server. The structure holds the response type information, an optional error message, and the result set (rows/columns) to return.

Attribute Description
type The type of the response. The type must be either MYSQLD_PACKET_OK or MYSQLD_PACKET_ERR. If the MYSQLD_PACKET_ERR, you should set the value of the mysql.response.errmsg with a suitable error message.
errmsg A string containing the error message that will be returned to the client.
resultset A structure containing the result set information (columns and rows), identical to what would be returned when returning a results from a SELECT query.

When using proxy.response you either set proxy.response.type to proxy.MYSQLD_PACKET_OK and then build resultset to contain the results to return, or set proxy.response.type to proxy.MYSQLD_PACKET_ERR and set the proxy.response.errmsg to a string with the error message. To send the completed result set or error message, you should return the proxy.PROXY_SEND_RESULT to trigger the return of the packet information.

An example of this can be seen in the tutorial-resultset.lua script within the MariaDB Proxy package: 

if string.lower(command) == 'show' and string.lower(option) == 'querycounter' then
 ---
 -- proxy.PROXY_SEND_RESULT requires
 --
 -- proxy.response.type to be either
 -- * proxy.MYSQLD_PACKET_OK or
 -- * proxy.MYSQLD_PACKET_ERR
 --
 -- for proxy.MYSQLD_PACKET_OK you need a resultset
 -- * fields
 -- * rows
 --
 -- for proxy.MYSQLD_PACKET_ERR
 -- * errmsg
 proxy.response.type = proxy.MYSQLD_PACKET_OK
 proxy.response.resultset = {
 fields = {
 { type = proxy.MYSQL_TYPE_LONG, name = 'global_query_counter', },
 { type = proxy.MYSQL_TYPE_LONG, name = 'query_counter', },
 },
 rows = {
 { proxy.global.query_counter, query_counter }
 }
 }
 -- we have our result, send it back
 return proxy.PROXY_SEND_RESULT elseif string.lower(command) == 'show' and string.lower(option) == 'myerror' then
 proxy.response.type = proxy.MYSQLD_PACKET_ERR
 proxy.response.errmsg = 'my first error'
 return proxy.PROXY_SEND_RESULT

proxy.response.resultset

The proxy.response.resultset structure should be populated with the rows and columns of data to return. The structure contains the information about the entire result set, with the individual elements of the data shown in the following table.

Attribute Description
fields The definition of the columns being returned. This should be a dictionary structure with the type specifying the MariaDB data type, and the name specifying the column name. Columns should be listed in the order of the column data that will be returned.
flags A number of flags related to the result set. Valid flags include auto_commit (whether an automatic commit was triggered), no_good_index_used (the query executed without using an appropriate index), and no_index_used (the query executed without using any index).
rows The actual row data. The information should be returned as an array of arrays. Each inner array should contain the column data, with the outer array making up the entire result set.
warning_count The number of warnings for this result set.
affected_rows The number of rows affected by the original statement.
insert_id The last insert ID for an auto-incremented column in a table.
query_status The status of the query operation. You can use the MYSQLD_PACKET_OK or MYSQLD_PACKET_ERR constants to populate this parameter.

For an example showing how to use this structure, see proxy.response.

Proxy Return State Constants

The following constants are used internally by the proxy to specify the response to send to the client or server. All constants are exposed as values within the main proxy table.

Constant Description
PROXY_SEND_QUERY Causes the proxy to send the current contents of the queries queue to the server.
PROXY_SEND_RESULT Causes the proxy to send a result set back to the client.
PROXY_IGNORE_RESULT Causes the proxy to drop the result set (nothing is returned to the client).

As constants, these entities are available without qualification in the Lua scripts. For example, at the end of the read_query_result() you might return PROXY_IGNORE_RESULT: 

return proxy.PROXY_IGNORE_RESULT

Packet State Constants

The following states describe the status of a network packet. These items are entries within the main proxy table.

Constant Description
MYSQLD_PACKET_OK The packet is OK
MYSQLD_PACKET_ERR The packet contains error information
MYSQLD_PACKET_RAW The packet contains raw data

Backend State/Type Constants

The following constants are used either to define the status or type of the backend MariaDB server to which the proxy is connected. These items are entries within the main proxy table.

Constant Description
BACKEND_STATE_UNKNOWN The current status is unknown
BACKEND_STATE_UP The backend is known to be up (available)
BACKEND_STATE_DOWN The backend is known to be down (unavailable)
BACKEND_TYPE_UNKNOWN Backend type is unknown
BACKEND_TYPE_RW Backend is available for read/write
BACKEND_TYPE_RO Backend is available only for read-only use

Server Command Constants

The following values are used in the packets exchanged between the client and server to identify the information in the rest of the packet. These items are entries within the main proxy table. The packet type is defined as the first character in the sent packet. For example, when intercepting packets from the client to edit or monitor a query, you would check that the first byte of the packet was of type proxy.COM_QUERY.

Constant Description
COM_SLEEP Sleep
COM_QUIT Quit
COM_INIT_DB Initialize database
COM_QUERY Query
COM_FIELD_LIST Field List
COM_CREATE_DB Create database
COM_DROP_DB Drop database
COM_REFRESH Refresh
COM_SHUTDOWN Shutdown
COM_STATISTICS Statistics
COM_PROCESS_INFO Process List
COM_CONNECT Connect
COM_PROCESS_KILL Kill
COM_DEBUG Debug
COM_PING Ping
COM_TIME Time
COM_DELAYED_INSERT Delayed insert
COM_CHANGE_USER Change user
COM_BINLOG_DUMP Binlog dump
COM_TABLE_DUMP Table dump
COM_CONNECT_OUT Connect out
COM_REGISTER_SLAVE Register slave
COM_STMT_PREPARE Prepare server-side statement
COM_STMT_EXECUTE Execute server-side statement
COM_STMT_SEND_LONG_DATA Long data
COM_STMT_CLOSE Close server-side statement
COM_STMT_RESET Reset statement
COM_SET_OPTION Set option
COM_STMT_FETCH Fetch statement
COM_DAEMON Daemon (MySQL 5.1 only)
COM_ERROR Error

MySQL Type Constants

These constants are used to identify the field types in the query result data returned to clients from the result of a query. These items are entries within the main proxy table.

Constant Field Type
MYSQL_TYPE_DECIMAL Decimal
MYSQL_TYPE_NEWDECIMAL Decimal (MySQL 5.0 or later)
MYSQL_TYPE_TINY Tiny
MYSQL_TYPE_SHORT Short
MYSQL_TYPE_LONG Long
MYSQL_TYPE_FLOAT Float
MYSQL_TYPE_DOUBLE Double
MYSQL_TYPE_NULL Null
MYSQL_TYPE_TIMESTAMP Timestamp
MYSQL_TYPE_LONGLONG Long long
MYSQL_TYPE_INT24 Integer
MYSQL_TYPE_DATE Date
MYSQL_TYPE_TIME Time
MYSQL_TYPE_DATETIME Datetime
MYSQL_TYPE_YEAR Year
MYSQL_TYPE_NEWDATE Date (MySQL 5.0 or later)
MYSQL_TYPE_ENUM Enumeration
MYSQL_TYPE_SET Set
MYSQL_TYPE_TINY_BLOB Tiny Blob
MYSQL_TYPE_MEDIUM_BLOB Medium Blob
MYSQL_TYPE_LONG_BLOB Long Blob
MYSQL_TYPE_BLOB Blob
MYSQL_TYPE_VAR_STRING Varstring
MYSQL_TYPE_STRING String
MYSQL_TYPE_TINY Tiny (compatible with MYSQL_TYPE_CHAR)
MYSQL_TYPE_ENUM Enumeration (compatible with MYSQL_TYPE_INTERVAL)
MYSQL_TYPE_GEOMETRY Geometry
MYSQL_TYPE_BIT Bit

Capturing a Connection with connect_server()

When the proxy accepts a connection from a MariaDB client, the connect_server() function is called.

There are no arguments to the function, but you can use and if necessary manipulate the information in the proxy.connection table, which is unique to each client session.

For example, if you have multiple backend servers, you can specify which server that connection should use by setting the value of proxy.connection.backend_ndx to a valid server number. The following code chooses between two servers based on whether the current time in minutes is odd or even: 

function connect_server()
 print('--> a client really wants to talk to a server')
 if (tonumber(os.date('%M')) % 2 == 0) then
 proxy.connection.backend_ndx = 2
 print('Choosing backend 2')
 else
 proxy.connection.backend_ndx = 1
 print('Choosing backend 1')
 end
 print('Using ' .. proxy.global.backends[proxy.connection.backend_ndx].dst.name)
end

This example also displays the IP address/port combination by accessing the information from the internal proxy.global.backends table.

Examining the Handshake with read_handshake()

Handshake information is sent by the server to the client after the initial connection (through connect_server()) has been made. The handshake information contains details about the MariaDB version, the ID of the thread that will handle the connection information, and the IP address of the client and server. This information is exposed through the proxy.connection structure.

For example, you can print out the handshake data and refuse clients by IP address with the following function: 

function read_handshake()
 print('<-- let's send him some information about us')
 print(' mysqld-version: ' .. proxy.connection.server.mysqld_version)
 print(' thread-id : ' .. proxy.connection.server.thread_id)
 print(' scramble-buf : ' .. string.format('%q',proxy.connection.server.scramble_buffer))
 print(' server-addr : ' .. proxy.connection.server.dst.name)
 print(' client-addr : ' .. proxy.connection.client.dst.name)
 if not proxy.connection.client.src.name:match('^127.0.0.1:') then
 proxy.response.type = proxy.MYSQLD_PACKET_ERR
 proxy.response.errmsg = 'only local connects are allowed'
 print('we don't like this client');
 return proxy.PROXY_SEND_RESULT
 end end

Note that you must return an error packet to the client by using proxy.PROXY_SEND_RESULT.

Examining the Authentication Credentials with read_auth()

The read_auth() function is triggered when an authentication handshake is initiated by the client. In the execution sequence, read_auth() occurs immediately after read_handshake(), so the server selection has already been made, but the connection and authorization information has not yet been provided to the backend server.

You can obtain the authentication information by examining the proxy.connection.client structure. For more information, see proxy.connection.

For example, you can print the user name and password supplied during authorization using: 

function read_auth()
 print(' username : ' .. proxy.connection.client.username)
 print(' password : ' .. string.format('%q', proxy.connection.client.scrambled_password))
end

You can interrupt the authentication process within this function and return an error packet back to the client by constructing a new packet and returning proxy.PROXY_SEND_RESULT: 

proxy.response.type = proxy.MYSQLD_PACKET_ERR proxy.response.errmsg = 'Logins are not allowed'
return proxy.PROXY_SEND_RESULT

Accessing Authentication Information with read_auth_result()

The return packet from the server during authentication is captured by read_auth_result(). The only argument to this function is the authentication packet returned by the server. As the packet is a raw MariaDB network protocol packet, you must access the first byte to identify the packet type and contents. The MYSQLD_PACKET_ERR and MYSQLD_PACKET_OK constants can be used to identify whether the authentication was successful: 

function read_auth_result(auth)
 local state = auth.packet:byte()
 if state == proxy.MYSQLD_PACKET_OK then
 print('<-- auth ok');
 elseif state == proxy.MYSQLD_PACKET_ERR then
 print('<-- auth failed');
 else
 print('<-- auth ... don't know: ' .. string.format('%q', auth.packet));
 end end

If a long-password capable client tries to authenticate to a server that supports long passwords, but the user password provided is actually short, read_auth_result() will be called twice. The first time, auth.packet:byte() will equal 254, indicating that the client should try again using the old password protocol. The second time time read_auth_result()/ is called, auth.packet:byte() will indicate whether the authentication actually succeeded.

Manipulating Queries with read_query()

The read_query() function is called once for each query submitted by the client and accepts a single argument, the query packet that was provided. To access the content of the packet, you must parse the packet contents manually.

For example, you can intercept a query packet and print out the contents using the following function definition: 

function read_query( packet )
 if packet:byte() == proxy.COM_QUERY then
 print('we got a normal query: ' .. packet:sub(2))
 end end

This example checks the first byte of the packet to determine the type. If the type is COM_QUERY (see Server Command Constants), we extract the query from the packet and print it. The structure of the packet type supplied is important. In the case of a COM_QUERY packet, the remaining contents of the packet are the text of the query string. In this example, no changes have been made to the query or the list of queries that will ultimately be sent to the MariaDB server.

To modify a query, or add new queries, you must populate the query queue (proxy.queries), then execute the queries that you have placed into the queue. If you do not modify the original query or the queue, the query received from the client is sent to the MariaDB server verbatim.

When adding queries to the queue, you should follow these guidelines:

Normally, the read_query() and read_query_result() function are used in conjunction with each other to inject additional queries and remove the additional result sets. However, read_query_result() is only called if you populate the query queue within read_query().

Manipulating Results with read_query_result()

The read_query_result() is called for each result set returned by the server only if you have manually injected queries into the query queue. If you have not manipulated the query queue, this function is not called. The function supports a single argument, the result packet, which provides a number of properties:

By accessing the result information from the MariaDB server, you can extract the results that match the queries that you injected, return different result sets (for example, from a modified query), and even create your own result sets.

The following Lua script, for example, will output the query, followed by the query time and response time (that is, the time to execute the query and the time to return the data for the query) for each query sent to the server: 

function read_query( packet )
 if packet:byte() == proxy.COM_QUERY then
 print('we got a normal query: ' .. packet:sub(2))
 proxy.queries:append(1, packet )
 return proxy.PROXY_SEND_QUERY
 end end function read_query_result(inj)
 print('query-time: ' .. (inj.query_time / 1000) .. 'ms')
 print('response-time: ' .. (inj.response_time / 1000) .. 'ms')
end

You can access the rows of returned results from the result set by accessing the rows property of the resultset property of the result that is exposed through read_query_result(). For example, you can iterate over the results showing the first column from each row using this Lua fragment: 

for row in inj.resultset.rows do
 print('injected query returned: ' .. row[1])
end

Just like read_query(), read_query_result() can return different values for each result according to the result returned. If you have injected additional queries into the query queue, for example, remove the results returned from those additional queries and return only the results from the query originally submitted by the client.

The following example injects additional SELECT NOW() statements into the query queue, giving them a different ID to the ID of the original query. Within read_query_result(), if the ID for the injected queries is identified, we display the result row, and return the proxy.PROXY_IGNORE_RESULT from the function so that the result is not returned to the client. If the result is from any other query, we print out the query time information for the query and return the default, which passes on the result set unchanged. We could also have explicitly returned proxy.PROXY_IGNORE_RESULT to the MariaDB client. 

function read_query( packet )
 if packet:byte() == proxy.COM_QUERY then
 proxy.queries:append(2, string.char(proxy.COM_QUERY) .. 'SELECT NOW()', {resultset_is_needed = true} )
 proxy.queries:append(1, packet, {resultset_is_needed = true})
 proxy.queries:append(2, string.char(proxy.COM_QUERY) .. 'SELECT NOW()', {resultset_is_needed = true} )
 return proxy.PROXY_SEND_QUERY
 end end function read_query_result(inj)
 if inj.id == 2 then
 for row in inj.resultset.rows do
 print('injected query returned: ' .. row[1])
 end
 return proxy.PROXY_IGNORE_RESULT
 else
 print('query-time: ' .. (inj.query_time / 1000) .. 'ms')
 print('response-time: ' .. (inj.response_time / 1000) .. 'ms')
 end end

For further examples, see , "Using MariaDB Proxy".

Using MariaDB Proxy

Using the Administration Interface

There are a number of different ways to use MariaDB Proxy. At the most basic level, you can allow MariaDB Proxy to pass queries from clients to a single server. To use MariaDB Proxy in this mode, you just have to specify on the command line the backend server to which the proxy should connect: 

shell> mysql-proxy --proxy-backend-addresses=sakila:3306

If you specify multiple backend MariaDB servers, the proxy connects each client to each server in a round-robin fashion. Suppose that you have two MariaDB servers, A and B. The first client to connect is connected to server A, the second to server B, the third to server A. For example: 

shell> mysql-proxy \
 --proxy-backend-addresses=narcissus:3306 \
 --proxy-backend-addresses=nostromo:3306

When you specify multiple servers in this way, the proxy automatically identifies when a MariaDB server has become unavailable and marks it accordingly. New connections are automatically attached to a server that is available, and a warning is reported to the standard output from mysql-proxy: 

network-mysqld.c.367: connect(nostromo:3306) failed: Connection refused network-mysqld-proxy.c.2405: connecting to backend (nostromo:3306) failed, marking it as down for ...

Lua scripts enable a finer level of control, both over the connections and their distribution and how queries and result sets are processed. When using an Lua script, you must specify the name of the script on the command line using the --proxy-lua-script option: 

shell> mysql-proxy --proxy-lua-script=mc.lua --proxy-backend-addresses=sakila:3306

When you specify a script, the script is not executed until a connection is made. This means that faults with the script are not raised until the script is executed. Script faults will not affect the distribution of queries to backend MariaDB servers.Note

Because a script is not read until the connection is made, you can modify the contents of the Lua script file while the proxy is still running and the modified script is automatically used for the next connection. This ensures that MariaDB Proxy remains available because it need not be restarted for the changes to take effect.

Using the Administration Interface

The mysql-proxy administration interface can be accessed using any MariaDB client using the standard protocols. You can use the administration interface to gain information about the proxy server as a whole - standard connections to the proxy are isolated to operate as if you were connected directly to the backend MariaDB server.

In mysql-proxy 0.8.0 and earlier, a rudimentary interface was built into the proxy. In later versions this was replaced so that you must specify an administration script to be used when users connect to the administration interface.

To use the administration interface, specify the user name and password required to connect to the admin service, using the --admin-username and --admin-password options. You must also specify the Lua script to be used as the interface to the administration service by using the admin-lua-script script option to point to a Lua script.

For example, you can create a basic interface to the internal components of the mysql-proxy system using the following script, written by Diego Medina: 

--[[
 Copyright 2008, 2010, Oracle and/or its affiliates. All rights reserved.
 This program is free software; you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by
 the Free Software Foundation; version 2 of the License.
 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 GNU General Public License for more details.
 You should have received a copy of the GNU General Public License
 along with this program; if not, write to the Free Software
 Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
--]]
-- admin.lua
--[[
 See http://forge.mysql.com/tools/tool.php?id=78
 (Thanks to Jan Kneschke)
 See http://www.chriscalender.com/?p=41
 (Thanks to Chris Calender)
 See http://datacharmer.blogspot.com/2009/01/mysql-proxy-is-back.html
 (Thanks Giuseppe Maxia)
--]]
function set_error(errmsg) 
 proxy.response = {
 type = proxy.MYSQLD_PACKET_ERR,
 errmsg = errmsg or 'error'
 }
end function read_query(packet)
 if packet:byte() ~= proxy.COM_QUERY then
 set_error('[admin] we only handle text-based queries (COM_QUERY)')
 return proxy.PROXY_SEND_RESULT
 end
 local query = packet:sub(2)
 local rows = { }
 local fields = { }
 -- try to match the string up to the first non-alphanum
 local f_s, f_e, command = string.find(packet, '^%s*(%w+)', 2)
 local option
 if f_e then
 -- if that match, take the next sub-string as option
 f_s, f_e, option = string.find(packet, '^%s+(%w+)', f_e + 1)
 end
 -- we got our commands, execute it
 if command == 'show' and option == 'querycounter' then
 ---
 -- proxy.PROXY_SEND_RESULT requires
 --
 -- proxy.response.type to be either
 -- * proxy.MYSQLD_PACKET_OK or
 -- * proxy.MYSQLD_PACKET_ERR
 --
 -- for proxy.MYSQLD_PACKET_OK you need a resultset
 -- * fields
 -- * rows
 --
 -- for proxy.MYSQLD_PACKET_ERR
 -- * errmsg
 proxy.response.type = proxy.MYSQLD_PACKET_OK
 proxy.response.resultset = {
 fields = {
 { type = proxy.MYSQL_TYPE_LONG, name = 'query_counter', },
 },
 rows = {
 { proxy.global.query_counter }
 }
 }
 -- we have our result, send it back
 return proxy.PROXY_SEND_RESULT
 elseif command == 'show' and option == 'myerror' then
 proxy.response.type = proxy.MYSQLD_PACKET_ERR
 proxy.response.errmsg = 'my first error'
 return proxy.PROXY_SEND_RESULT
 elseif string.sub(packet, 2):lower() == 'select help' then
 return show_process_help()
 elseif string.sub(packet, 2):lower() == 'show proxy processlist' then
 return show_process_table()
 elseif query == 'SELECT * FROM backends' then
 fields = { 
 { name = 'backend_ndx', 
 type = proxy.MYSQL_TYPE_LONG },
 { name = 'address',
 type = proxy.MYSQL_TYPE_STRING },
 { name = 'state',
 type = proxy.MYSQL_TYPE_STRING },
 { name = 'type',
 type = proxy.MYSQL_TYPE_STRING },
 }
 for i = 1, #proxy.global.backends do
 local b = proxy.global.backends[i]
 rows[#rows + 1] = {
 i, b.dst.name, b.state, b.type 
 }
 end
 else
 set_error()
 return proxy.PROXY_SEND_RESULT
 end
 proxy.response = {
 type = proxy.MYSQLD_PACKET_OK,
 resultset = {
 fields = fields,
 rows = rows
 }
 }
 return proxy.PROXY_SEND_RESULT end function make_dataset (header, dataset)
 proxy.response.type = proxy.MYSQLD_PACKET_OK
 proxy.response.resultset = {
 fields = {},
 rows = {}
 }
 for i,v in pairs (header) do
 table.insert(proxy.response.resultset.fields, {type = proxy.MYSQL_TYPE_STRING, name = v})
 end
 for i,v in pairs (dataset) do
 table.insert(proxy.response.resultset.rows, v )
 end
 return proxy.PROXY_SEND_RESULT end function show_process_table()
 local dataset = {}
 local header = { 'Id', 'IP Address', 'Time' }
 local rows = {}
 for t_i, t_v in pairs (proxy.global.process) do
 for s_i, s_v in pairs ( t_v ) do
 table.insert(rows, { t_i, s_v.ip, os.date('%c',s_v.ts) })
 end
 end
 return make_dataset(header,rows)
end function show_process_help()
 local dataset = {}
 local header = { 'command', 'description' }
 local rows = {
 {'SELECT HELP', 'This command.'},
 {'SHOW PROXY PROCESSLIST', 'Show all connections and their true IP Address.'},
 }
 return make_dataset(header,rows)
end function dump_process_table()
 proxy.global.initialize_process_table()
 print('current contents of process table')
 for t_i, t_v in pairs (proxy.global.process) do
 print ('session id: ', t_i)
 for s_i, s_v in pairs ( t_v ) do
 print ( '\t', s_i, s_v.ip, s_v.ts )
 end
 end
 print ('---END PROCESS TABLE---')
end
--[[ Help we use a simple string-match to split commands are word-boundaries mysql> show querycounter is split into command = 'show'
option = 'querycounter'
spaces are ignored, the case has to be as is.
mysql> show myerror returns a error-packet
--]]

The script works in combination with a main proxy script, reporter.lua: 

--[[
 Copyright 2008, 2010, Oracle and/or its affiliates. All rights reserved.
 This program is free software; you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by
 the Free Software Foundation; version 2 of the License.
 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 GNU General Public License for more details.
 You should have received a copy of the GNU General Public License
 along with this program; if not, write to the Free Software
 Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
--]]
-- reporter.lua
--[[
 See http://forge.mysql.com/tools/tool.php?id=78
 (Thanks to Jan Kneschke)
 See http://www.chriscalender.com/?p=41
 (Thanks to Chris Calender)
 See http://datacharmer.blogspot.com/2009/01/mysql-proxy-is-back.html
 (Thanks Giuseppe Maxia)
--]]
proxy.global.query_counter = proxy.global.query_counter or 0
function proxy.global.initialize_process_table()
 if proxy.global.process == nil then
 proxy.global.process = {}
 end
 if proxy.global.process[proxy.connection.server.thread_id] == nil then
 proxy.global.process[proxy.connection.server.thread_id] = {}
 end end function read_auth_result( auth )
 local state = auth.packet:byte()
 if state == proxy.MYSQLD_PACKET_OK then
 proxy.global.initialize_process_table()
 table.insert( proxy.global.process[proxy.connection.server.thread_id],
 { ip = proxy.connection.client.src.name, ts = os.time() } )
 end end function disconnect_client()
 local connection_id = proxy.connection.server.thread_id
 if connection_id then
 -- client has disconnected, set this to nil
 proxy.global.process[connection_id] = nil
 end end
---
-- read_query() can return a resultset
--
-- You can use read_query() to return a result-set.
--
-- @param packet the mysql-packet sent by the client
--
-- @return
-- * nothing to pass on the packet as is,
-- * proxy.PROXY_SEND_QUERY to send the queries from the proxy.queries queue
-- * proxy.PROXY_SEND_RESULT to send your own result-set
--
function read_query( packet )
 -- a new query came in in this connection
 -- using proxy.global.* to make it available to the admin plugin
 proxy.global.query_counter = proxy.global.query_counter + 1
end

To use the script, save the first script to a file (admin.lua in the following example) and the other to reporter.lua, then run mysql-proxy specifying the admin script and a backend MariaDB server: 

shell> mysql-proxy --admin-lua-script=admin.lua --admin-password=password \ »
 --admin-username=root --proxy-backend-addresses=127.0.0.1:3306 -proxy-lua-script=reporter.lua

In a different window, connect to the MariaDB server through the proxy: 

shell> mysql --user=root --password=password --port=4040
Welcome to the MariaDB monitor. Commands end with ; or \g.
Your MariaDB connection id is 1798669
Server version: 5.0.70-log Gentoo Linux mysql-5.0.70-r1
Type 'help;' or '\h' for help. Type '\c' to clear the buffer.
mysql>

In another different window, connect to the mysql-proxy admin service using the specified user name and password: 

shell> mysql --user=root --password=password --port=4041 --host=localhost
Welcome to the MariaDB monitor. Commands end with ; or \g.
Your MariaDB connection id is 1
Server version: 5.0.99-agent-admin Type 'help;' or '\h' for help. Type '\c' to clear the buffer.
mysql>

To monitor the status of the proxy, ask for a list of the current active processes: 

mysql> show proxy processlist;
+---------+---------------------+--------------------------+
| Id | IP Address | Time |
+---------+---------------------+--------------------------+
| 1798669 | 192.168.0.112:52592 | Wed Jan 20 16:58:00 2010 | 
+---------+---------------------+--------------------------+
1 row in set (0.00 sec)
mysql>

For more information on the example, see MySQL Proxy Admin Example.

MySQL Proxy FAQ

Questions

Questions and Answers

15.7.6.1: In load balancing, how can I separate reads from writes?

There is no automatic separation of queries that perform reads or writes to the different backend servers. However, you can specify to mysql-proxy that one or more of the "backend" MariaDB servers are read only. 

shell> mysql-proxy \
--proxy-backend-addresses=10.0.1.2:3306 \
--proxy-read-only-backend-addresses=10.0.1.3:3306 &

15.7.6.2: How do I use a socket with MariaDB Proxy? Proxy change logs mention that support for UNIX sockets has been added.

Specify the path to the socket: 

--proxy-backend-addresses=/path/to/socket

15.7.6.3: Can I use MariaDB Proxy with all versions of MySQL?

MySQL Proxy is designed to work with MariaDB 5.0 or higher, and supports the MariaDB network protocol for 5.0 and higher.

15.7.6.4: Can I run MariaDB Proxy as a daemon?

Use the --daemon option. To keep track of the process ID, the daemon can be started with the --pid-file=file option to save the PID to a known file name. On version 0.5.x, the Proxy cannot be started natively as a daemon.

15.7.6.5: Do proxy applications run on a separate server? If not, what is the overhead incurred by Proxy on the DB server side?

You can run the proxy on the application server, on its own box, or on the DB-server depending on the use case.

15.7.6.6: With load balancing, what happens to transactions? Are all queries sent to the same server?

Without any special customization the whole connection is sent to the same server. That keeps the whole connection state intact.

15.7.6.7: Is it possible to use MariaDB Proxy with updating a Lucene index (or Solr) by making TCP calls to that server to update?

Yes, but it is not advised for now.

15.7.6.8: Is the system context switch expensive, how much overhead does the Lua script add?

Lua is fast and the overhead should be small enough for most applications. The raw packet overhead is around 400 microseconds.

15.7.6.9: How much latency does a proxy add to a connection?

In the range of 400 microseconds per request.

15.7.6.10: Do you have to make one large script and call it at proxy startup, can I change scripts without stopping and restarting (interrupting) the proxy?

You can just change the script and the proxy will reload it when a client connects.

15.7.6.11: If MariaDB Proxy has to live on same machine as MySQL, are there any tuning considerations to ensure both perform optimally?

MySQL Proxy can live on any box: application, database, or its own box. MariaDB Proxy uses comparatively little CPU or RAM, with negligible additional requirements or overhead.

15.7.6.12: I currently use SQL Relay for efficient connection pooling with a number of Apache processes connecting to a MariaDB server. Can MariaDB Proxy currently accomplish this? My goal is to minimize connection latency while keeping temporary tables available.

Yes.

15.7.6.13: Are these reserved function names (for example, error_result()) that get automatically called?

Only functions and values starting with proxy.* are provided by the proxy. All others are user provided.

15.7.6.14: As the script is re-read by MariaDB Proxy, does it cache this or is it looking at the file system with each request?

It looks for the script at client-connect and reads it if it has changed, otherwise it uses the cached version.

15.7.6.15: Given that there is a connect_server() function, can a Lua script link up with multiple servers?

MySQL Proxy provides some tutorials in the source package; one is examples/tutorial-keepalive.lua.

15.7.6.16: Is the MariaDB Proxy an API?

No, MariaDB Proxy is an application that forwards packets from a client to a server using the MariaDB network protocol. The MariaDB Proxy provides a API allowing you to change its behavior.

15.7.6.17: The global namespace variable example with quotas does not persist after a reboot, is that correct?

Yes. If you restart the proxy, you lose the results, unless you save them in a file.

15.7.6.18: Can MariaDB Proxy handle SSL connections?

No, being the man-in-the-middle, Proxy cannot handle encrypted sessions because it cannot share the SSL information.

15.7.6.19: Could MariaDB Proxy be used to capture passwords?

The MariaDB network protocol does not allow passwords to be sent in cleartext, all you could capture is the encrypted version.

15.7.6.20: Are there tools for isolating problems? How can someone figure out whether a problem is in the client, the database, or the proxy?

You can set a debug script in the proxy, which is an exceptionally good tool for this purpose. You can see very clearly which component is causing the problem, if you set the right breakpoints.

15.7.6.21: Is MariaDB Proxy similar to what is provided by Java connection pools?

Yes and no. Java connection pools are specific to Java applications, MariaDB Proxy works with any client API that talks the MariaDB network protocol. Also, connection pools do not provide any functionality for intelligently examining the network packets and modifying the contents.

15.7.6.22: So authentication with connection pooling has to be done at every connection? What is the authentication latency?

You can skip the round-trip and use the connection as it was added to the pool. As long as the application cleans up the temporary tables it used. The overhead is (as always) around 400 microseconds.

15.7.6.23: If you have multiple databases on the same box, can you use proxy to connect to databases on default port 3306?

Yes, MariaDB Proxy can listen on any port, provided that none of the MariaDB servers are listening on the same port.

15.7.6.24: What about caching the authorization information so clients connecting are given back-end connections that were established with identical authorization information, thus saving a few more round trips?

There is an --proxy-pool-no-change-user option that provides this functionality.

15.7.6.25: Is there any big web site using MariaDB Proxy? For what purpose and what transaction rate have they achieved?

Yes, gaiaonline. They have tested MariaDB Proxy and seen it handle 2400 queries per second through the proxy.

15.7.6.26: How does MariaDB Proxy compare to DBSlayer?

DBSlayer is a REST->MySQL tool, MariaDB Proxy is transparent to your application. No change to the application is needed.

15.7.6.27: I tried using MariaDB Proxy without any Lua script to try a round-robin type load balancing. In this case, if the first database in the list is down, MariaDB Proxy would not connect the client to the second database in the list.

This issue is fixed in version 0.7.0.

15.7.6.28: Is it "safe" to use LuaSocket with proxy scripts?

You can, but it is not advised because it may block.

15.7.6.29: How different is MariaDB Proxy from DBCP (Database connection pooling) for Apache in terms of connection pooling?

Connection Pooling is just one use case of the MariaDB Proxy. You can use it for a lot more and it works in cases where you cannot use DBCP (for example, if you do not have Java).

15.7.6.30: MySQL Proxy can handle about 5000 connections, what is the limit on a MariaDB server?

The server limit is given by the value of the max_connections system variable. The default value is version dependent.

15.7.6.31: Would the Java-only connection pooling solution work for multiple web servers? With this, I would assume that you can pool across many web servers at once?

Yes. But you can also start one proxy on each application server to get a similar behavior as you have it already.Copyright 1997, 2012, Oracle and/or its affiliates. All rights reserved. Legal Notices

Prev Next
Storage Engines Home Chapter 15. Replication