Author: Severalnines

How to perform Schema Changes in MySQL & MariaDB in a Safe Way

Before you attempt to perform any schema changes on your production databases, you should make sure that you have a rock solid rollback plan; and that your change procedure has been successfully tested and validated in a separate environment. At the same time, it’s your responsibility to make sure that the change causes none or the least possible impact acceptable to the business. It’s definitely not an easy task.

In this article, we will take a look at how to perform database changes on MySQL and MariaDB in a controlled way. We will talk about some good habits in your day-to-day DBA work. We’ll focus on pre-requirements and tasks during the actual operations and problems that you may face when you deal with database schema changes. We will also talk about open source tools that may help you in the process.

Test and rollback scenarios


There are many ways to lose your data. Schema upgrade failure is one of them. Unlike application code, you can’t drop a bundle of files and declare that a new version has been successfully deployed. You also can’t just put back an older set of files to rollback your changes. Of course, you can run another SQL script to change the database again, but there are cases when the only accurate way to roll back changes is by restoring the entire database from backup.

However, what if you can’t afford to rollback your database to the latest backup, or your maintenance window is not big enough (considering system performance), so you can’t perform a full database backup before the change?

One may have a sophisticated, redundant environment, but as long as data is modified in both primary and standby locations, there is not much to do about it. Many scripts can just be run once, or the changes are impossible to undo. Most of the SQL change code falls into two groups:

Run once – you can’t add the same column to the table twice.
Impossible to undo – once you’ve dropped that column, it’s gone. You could undoubtedly restore your database, but that’s not precisely an undo.

You can tackle this problem in at least two possible ways. One would be to enable the binary log and take a backup, which is compatible with PITR. Such backup has to be full, complete and consistent. For xtrabackup, as long as it contains a full dataset, it will be PITR-compatible. For mysqldump, there is an option to make it PITR-compatible too. For smaller changes, a variation of mysqldump backup would be to take only a subset of data to change. This can be done with –where option. The backup should be part of the planned maintenance.

mysqldump -u -p –lock-all-tables –where=”WHERE employee_id=100″ mydb employees> backup_table_tmp_change_07132018.sql

Another possibility is to use CREATE TABLE AS SELECT.

You can store data or simple structure changes in the form of a fixed temporary table. With this approach you will get a source if you need to rollback your changes. It may be quite handy if you don’t change much data. The rollback can be done by taking data out from it. If any failures occur while copying the data to the table, it is automatically dropped and not created, so make sure that your statement creates a copy you need.

Obviously, there are some limitations too.

Because the ordering of the rows in the underlying SELECT statements cannot always be determined, CREATE TABLE … IGNORE SELECT and CREATE TABLE … REPLACE SELECT are flagged as unsafe for statement-based replication. Such statements produce a warning in the error log when using statement-based mode and are written to the binary log using the row-based format when using MIXED mode.

A very simple example of such method could be:

CREATE TABLE tmp_employees_change_07132018 AS SELECT * FROM employees where employee_id=100;
UPDATE employees SET salary=120000 WHERE employee_id=100;

Another interesting option may be MariaDB flashback database. When a wrong update or delete happens, and you would like to revert to a state of the database (or just a table) at a certain point in time, you may use the flashback feature.

Point-in-time rollback enables DBAs to recover data faster by rolling back transactions to a previous point in time rather than performing a restore from a backup. Based on ROW-based DML events, flashback can transform the binary log and reverse purposes. That means it can help undo given row changes fast. For instance, it can change DELETE events to INSERTs and vice versa, and it will swap WHERE and SET parts of the UPDATE events. This simple idea can dramatically speed up recovery from certain types of mistakes or disasters. For those who are familiar with the Oracle database, it’s a well known feature. The limitation of MariaDB flashback is the lack of DDL support.

Create a delayed replication slave

Since version 5.6, MySQL supports delayed replication. A slave server can lag behind the master by at least a specified amount of time. The default delay is 0 seconds. Use the MASTER_DELAY option for CHANGE MASTER TO to set the delay to N seconds:


It would be a good option if you didn’t have time to prepare a proper recovery scenario. You need to have enough delay to notice the problematic change. The advantage of this approach is that you don’t need to restore your database to take out data needed to fix your change. Standby DB is up and running, ready to pick up data which minimizes the time needed.

Create an asynchronous slave which is not part of the cluster

When it comes to Galera cluster, testing changes is not easy. All nodes run the same data, and heavy load can harm flow control. So you not only need to check if changes applied successfully, but also what the impact to the cluster state was. To make your test procedure as close as possible to the production workload, you may want to add an asynchronous slave to your cluster and run your test there. The test will not impact synchronization between cluster nodes, because technically it’s not part of the cluster, but you will have an option to check it with real data. Such slave can be easily added from ClusterControl.

ClusterControl add asynchronous slave

As shown in the above screenshot, ClusterControl can automate the process of adding an asynchronous slave in a few ways. You can add the node to the cluster, delay the slave. To reduce the impact on the master, you can use an existing backup instead of the master as the data source when building the slave.

Clone database and measure time

A good test should be as close as possible to the production change. The best way to do this is to clone your existing environment.

ClusterControl Clone Cluster for test

Perform changes via replication

To have better control over your changes, you can apply them on a slave server ahead of time and then do the switchover. For statement-based replication, this works fine, but for row-based replication, this can work up to a certain degree. Row-based replication enables extra columns to exist at the end of the table, so as long as it can write the first columns, it will be fine. First apply these setting to all slaves, then failover to one of the slaves and then implement the change to the master and attach that as a slave. If your modification involves inserting or removing a column in the middle of the table, it will work with row-based replication.


During the maintenance window, we do not want to have application traffic on the database. Sometimes it is hard to shut down all applications spread over the whole company. Alternatively, we want to allow only some specific hosts to access MySQL from remote (for example the monitoring system or the backup server). For this purpose, we can use the Linux packet filtering. To see what packet filtering rules are available, we can run the following command:

iptables -L INPUT -v

To close the MySQL port on all interfaces we use:

iptables -A INPUT -p tcp –dport mysql -j DROP

and to open the MySQL port again after the maintenance window:

iptables -D INPUT -p tcp –dport mysql -j DROP

For those without root access, you can change max_connection to 1 or ‘skip networking’.


To get the logging process started, use the tee command at the MySQL client prompt, like this:

mysql> tee /tmp/my.out;

That command tells MySQL to log both the input and output of your current MySQL login session to a file named /tmp/my.out .Then execute your script file with source command.

To get a better idea of your execution times, you can combine it with the profiler feature. Start the profiler with

SET profiling = 1;

Then execute your Query with


you see a list of queries the profiler has statistics for. So finally, you choose which query to examine with


Schema migration tools

Many times, a straight ALTER on the master is not possible – most of the cases it causes lag on the slave, and this may not be acceptable to the applications. What can be done, though, is to execute the change in a rolling mode. You can start with slaves and, once the change is applied to the slave, migrate one of the slaves as a new master, demote the old master to a slave and execute the change on it.

A tool that may help with such a task is Percona’s pt-online-schema-change. Pt-online-schema-change is straightforward – it creates a temporary table with the desired new schema (for instance, if we added an index, or removed a column from a table). Then, it creates triggers on the old table. Those triggers are there to mirror changes that happen on the original table to the new table. Changes are mirrored during the schema change process. If a row is added to the original table, it is also added to the new one. It emulates the way that MySQL alters tables internally, but it works on a copy of the table you wish to alter. It means that the original table is not locked, and clients may continue to read and change data in it.

Related resources

 Schema Management Tips for MySQL & MariaDB

 Online schema change for MySQL & MariaDB – comparing GitHub’s gh-ost vs pt-online-schema-change

 Online schema change with gh-ost – throttling and changing configuration at runtime

 How to Overcome Accidental Data Deletion in MySQL & MariaDB

 How to Recover MySQL Galera Cluster from an Asynchronous Slave?

Likewise, if a row is modified or deleted on the old table, it is also applied in the new table. Then, a background process of copying data (using LOW_PRIORITY INSERT) between old and new table begins. Once data has been copied, RENAME TABLE is executed.

Another intresting tool is gh-ost. Gh-ost creates a temporary table with the altered schema, just like pt-online-schema-change does. It executes INSERT queries, which use the following pattern to copy data from old to new table. Nevertheless it does not use triggers. Unfortunately triggers may be the source of many limitations. gh-ost uses the binary log stream to capture table changes and asynchronously applies them onto the ghost table. Once we verified that gh-ost can execute our schema change correctly, it’s time to actually execute it. Keep in mind that you may need to manually drop old tables that were created by gh-ost during the process of testing the migration. You can also use –initially-drop-ghost-table and –initially-drop-old-table flags to ask gh-ost to do it for you. The final command to execute is exactly the same as we used to test our change, we just added –execute to it.

pt-online-schema-change and gh-ost are very popular among Galera users. Nevertheless Galera has some additional options.The two methods Total Order Isolation (TOI) and Rolling Schema Upgrade (RSU) have both their pros and cons.

TOI – This is the default DDL replication method. The node that originates the writeset detects DDL at parsing time and sends out a replication event for the SQL statement before even starting the DDL processing. Schema upgrades run on all cluster nodes in the same total order sequence, preventing other transactions from committing for the duration of the operation. This method is good when you want your online schema upgrades to replicate through the cluster and don’t mind locking the entire table (similar to how default schema changes happened in MySQL).

SET GLOBAL wsrep_OSU_method=’TOI’;

RSU – perfom the schema upgrades locally. In this method, your writes are affecting only the node on which they are run. The changes do not replicate to the rest of the cluster.This method is good for non-conflicting operations and it will not slow down the cluster.

SET GLOBAL wsrep_OSU_method=’RSU’;

While the node processes the schema upgrade, it desynchronizes with the cluster. When it finishes processing the schema upgrade, it applies delayed replication events and synchronizes itself with the cluster. This could be a good option to run heavy index creations.


We presented here several different methods that may help you with planning your schema changes. Of course it all depends on your application and business requirements. You can design your change plan, perform necessary tests, but there is still a small chance that something will go wrong. According to Murphy’s law – “things will go wrong in any given situation, if you give them a chance”. So make sure you try out different ways of performing these changes, and pick the one that you are the most comfortable with.


schema change
mysql replication
galera cluster

New Webinar: Disaster Recovery Planning for MySQL & MariaDB with ClusterControl

Everyone should have a disaster recovery plan for MySQL & MariaDB!

Join Vinay Joosery, CEO at Severalnines, on July 24th for our new webinar on Disaster Recovery Planning for MySQL & MariaDB with ClusterControl; especially if you find yourself wondering about disaster recovery planning for MySQL and MariaDB, if you’re unsure about RTO and RPO or whether you should you have a secondary datacenter, or are concerned about disaster recovery in the cloud…

Organizations need an appropriate disaster recovery plan in order to mitigate the impact of downtime. But how much should a business invest? Designing a highly available system comes at a cost, and not all businesses and certainly not all applications need five 9’s availability.

Vinay will explain key disaster recovery concepts and walk us through the relevant options from the MySQL & MariaDB ecosystem in order to meet different tiers of disaster recovery requirements; and demonstrate how ClusterControl can help fully automate an appropriate disaster recovery plan.

Sign up below to join the discussion!

Date, Time & Registration


Tuesday, July 24th at 09:00 BST / 10:00 CEST (Germany, France, Sweden)

Register Now

North America/LatAm

Tuesday, July 24th at 09:00 Pacific Time (US) / 12:00 Eastern Time (US)

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Business Considerations for DR

Is 100% uptime possible?
Analyzing risk
Assessing business impact

Defining DR

Outage Timeline
RTO + RPO = 0 ?

DR Tiers

No offsite data
Database backup with no Hot Site
Database backup with Hot Site
Asynchronous replication to Hot Site
Synchronous replication to Hot Site

Implementing DR with ClusterControl




Vinay Joosery, CEO & Co-Founder, Severalnines

Vinay Joosery, CEO, Severalnines, is a passionate advocate and builder of concepts and business around distributed database systems. Prior to co-founding Severalnines, Vinay held the post of Vice-President EMEA at Pentaho Corporation – the Open Source BI leader. He has also held senior management roles at MySQL / Sun Microsystems / Oracle, where he headed the Global MySQL Telecoms Unit, and built the business around MySQL’s High Availability and Clustering product lines. Prior to that, Vinay served as Director of Sales & Marketing at Ericsson Alzato, an Ericsson-owned venture focused on large scale real-time databases.

This webinar builds upon a related white paper written by Vinay on disaster recovery, which you can download here:

We look forward to “seeing” you there!


disaster recovery

New Whitepaper: Disaster Recovery Planning for MySQL & MariaDB

We’re happy to announce that our new whitepaper Disaster Recovery Planning for MySQL & MariaDB is now available to download for free!

Database outages are almost inevitable and understanding the timeline of an outage can help us better prepare, diagnose and recover from one. To mitigate the impact of downtime, organizations need an appropriate disaster recovery (DR) plan. However, it makes no business sense to abstract the cost of a DR solution from the design of it, so organizations have to implement the right level of protection at the lowest possible cost.

This white paper provides essential insights into how to build such a plan, discussing the database mechanisms involved as well as how these mechanisms can be fully automated with ClusterControl, a management platform for open source database systems.

Topics included in this whitepaper are…

Business Considerations for Disaster Recovery

Is 100% Uptime Possible?
Analysing Risk
Assessing Business Impact

Defining Disaster Recovery

Recovery Time Objectives
Recovery Point Objectives

Disaster Recovery Tiers

Offsite Data
Backups and Hot Sites

Download the whitepaper today!


Single Console for Your Entire Database Infrastructure

Find out what else is new in ClusterControl

Install ClusterControl for FREE

About the Author

Vinay Joosery, CEO & Co-Founder, Severalnines

Vinay Joosery, CEO, Severalnines, is a passionate advocate and builder of concepts and business around distributed database systems. Prior to co-founding Severalnines, Vinay held the post of Vice-President EMEA at Pentaho Corporation – the Open Source BI leader. He has also held senior management roles at MySQL / Sun Microsystems / Oracle, where he headed the Global MySQL Telecoms Unit, and built the business around MySQL’s High Availability and Clustering product lines. Prior to that, Vinay served as Director of Sales & Marketing at Ericsson Alzato, an Ericsson-owned venture focused on large scale real-time databases.

Related resources

 Download the Whitepaper

 Cloud Disaster Recovery for MariaDB and MySQL

 Several Ways to Intentionally Fail or Crash your MySQL Instances for Testing

About ClusterControl

ClusterControl is the all-inclusive open source database management system for users with mixed environments that removes the need for multiple management tools. ClusterControl provides advanced deployment, management, monitoring, and scaling functionality to get your MySQL, MongoDB, and PostgreSQL databases up-and-running using proven methodologies that you can depend on to work. At the core of ClusterControl is it’s automation functionality that lets you automate many of the database tasks you have to perform regularly like deploying new databases, adding and scaling new nodes, running backups and upgrades, and more.

To learn more about ClusterControl click here.


disaster recovery

How to Improve Performance of Galera Cluster for MySQL or MariaDB

Galera Cluster comes with many notable features that are not available in standard MySQL replication (or Group Replication); automatic node provisioning, true multi-master with conflict resolutions and automatic failover. There are also a number of limitations that could potentially impact cluster performance. Luckily, if you are not aware of these, there are workarounds. And if you do it right, you can minimize the impact of these limitations and improve overall performance.

We have previously covered many tips and tricks related to Galera Cluster, including running Galera on AWS Cloud. This blog post distinctly dives into the performance aspects, with examples on how to get the most out of Galera.

Replication Payload

A bit of introduction – Galera replicates writesets during the commit stage, transferring writesets from the originator node to the receiver nodes synchronously through the wsrep replication plugin. This plugin will also certify writesets on the receiver nodes. If the certification process passes, it returns OK to the client on the originator node and will be applied on the receiver nodes at a later time asynchronously. Else, the transaction will be rolled back on the originator node (returning error to the client) and the writesets that have been transferred to the receiver nodes will be discarded.

A writeset consists of write operations inside a transaction that changes the database state. In Galera Cluster, autocommit is default to 1 (enabled). Literally, any SQL statement executed in Galera Cluster will be enclosed as a transaction, unless you explicitly start with BEGIN, START TRANSACTION or SET autocommit=0. The following diagram illustrates the encapsulation of a single DML statement into a writeset:

For DML (INSERT, UPDATE, DELETE..), the writeset payload consists of the binary log events for a particular transaction while for DDLs (ALTER, GRANT, CREATE..), the writeset payload is the DDL statement itself. For DMLs, the writeset will have to be certified against conflicts on the receiver node while for DDLs (depending on wsrep_osu_method, default to TOI), the cluster cluster runs the DDL statement on all nodes in the same total order sequence, blocking other transactions from committing while the DDL is in progress (see also RSU). In simple words, Galera Cluster handles DDL and DML replication differently.

Round Trip Time

Generally, the following factors determine how fast Galera can replicate a writeset from an originator node to all receiver nodes:

Round trip time (RTT) to the farthest node in the cluster from the originator node.
The size of a writeset to be transferred and certified for conflict on the receiver node.

For example, if we have a three-node Galera Cluster and one of the nodes is located 10 milliseconds away (0.01 second), it’s very unlikely you might be able to write more than 100 times per second to the same row without conflicting. There is a popular quote from Mark Callaghan which describes this behaviour pretty well:

“[In a Galera cluster] a given row can’t be modified more than once per RTT”

To measure RTT value, simply perform ping on the originator node to the farthest node in the cluster:

$ ping # the farthest node

Wait for a couple of seconds (or minutes) and terminate the command. The last line of the ping statistic section is what we are looking for:

— ping statistics —
65 packets transmitted, 65 received, 0% packet loss, time 64019ms
rtt min/avg/max/mdev = 0.111/0.431/1.340/0.240 ms

The max value is 1.340 ms (0.00134s) and we should take this value when estimating the minimum transactions per second (tps) for this cluster. The average value is 0.431ms (0.000431s) and we can use to estimate the average tps while min value is 0.111ms (0.000111s) which we can use to estimate the maximum tps. The mdev means how the RTT samples were distributed from the average. Lower value means more stable RTT.

Hence, transactions per second can be estimated by dividing RTT (in second) into 1 second:


Minimum tps: 1 / 0.00134 (max RTT) = 746.26 ~ 746 tps
Average tps: 1 / 0.000431 (avg RTT) = 2320.19 ~ 2320 tps
Maximum tps: 1 / 0.000111 (min RTT) = 9009.01 ~ 9009 tps

Note that this is just an estimation to anticipate replication performance. There is not much we can do to improve this on the database side, once we have everything deployed and running. Except, if you move or migrate the database servers closer to each other to improve the RTT between nodes, or upgrade the network peripherals or infrastructure. This would require maintenance window and proper planning.

Chunk Up Big Transactions

Another factor is the transaction size. After the writeset is transferred, there will be a certification process. Certification is a process to determine whether or not the node can apply the writeset. Galera generates MD5 checksum pseudo keys from every full row. The cost of certification depends on the size of the writeset, which translates into a number of unique key lookups into the certification index (a hash table). If you update 500,000 rows in a single transaction, for example:

# a 500,000 rows table
mysql> UPDATE mydb.settings SET success = 1;

The above will generate a single writeset with 500,000 binary log events in it. This huge writeset does not exceed wsrep_max_ws_size (default to 2GB) so it will be transferred over by Galera replication plugin to all nodes in the cluster, certifying these 500,000 rows on the receiver nodes for any conflicting transactions that are still in the slave queue. Finally, the certification status is returned to the group replication plugin. The bigger the transaction size, the higher risk it will be conflicting with other transactions that come from another master. Conflicting transactions waste server resources, plus cause a huge rollback to the originator node. Note that a rollback operation in MySQL is way slower and less optimized than commit operation.

The above SQL statement can be re-written into a more Galera-friendly statement with the help of simple loop, like the example below:

(bash)$ for i in {1..500}; do \
mysql -uuser -ppassword -e “UPDATE mydb.settings SET success = 1 WHERE success != 1 LIMIT 1000″; \
sleep 2; \

The above shell command would update 1000 rows per transaction for 500 times and wait for 2 seconds between executions. You could also use a stored procedure or other means to achieve a similar result. If rewriting the SQL query is not an option, simply instruct the application to execute the big transaction during a maintenance window to reduce the risk of conflicts.

For huge deletes, consider using pt-archiver from the Percona Toolkit – a low-impact, forward-only job to nibble old data out of the table without impacting OLTP queries much.

Parallel Slave Threads

In Galera, the applier is a multithreaded process. Applier is a thread running within Galera to apply the incoming write-sets from another node. Which means, it is possible for all receivers to execute multiple DML operations that come right from the originator (master) node simultaneously. Galera parallel replication is only applied to transactions when it is safe to do so. It improves the probability of the node to sync up with the originator node. However, the replication speed is still limited to RTT and writeset size.

To get the best out of this, we need to know two things:

The number of cores the server has.
The value of wsrep_cert_deps_distance status.

The status wsrep_cert_deps_distance tells us the potential degree of parallelization. It is the value of the average distance between highest and lowest seqno values that can be possibly applied in parallel. You can use the wsrep_cert_deps_distance status variable to determine the maximum number of slave threads possible. Take note that this is an average value across time. Hence, in order get a good value, you have to hit the cluster with writes operations through test workload or benchmark until you see a stable value coming out.

To get the number of cores, you can simply use the following command:

$ grep -c processor /proc/cpuinfo

Ideally, 2, 3 or 4 threads of slave applier per CPU core is a good start. Thus, the minimum value for the slave threads should be 4 x number of CPU cores, and must not exceed the wsrep_cert_deps_distance value:

MariaDB [(none)]> SHOW STATUS LIKE ‘wsrep_cert_deps_distance’;
| Variable_name | Value |
| wsrep_cert_deps_distance | 48.16667 |

You can control the number of slave applier threads using wsrep_slave_thread variable. Even though this is a dynamic variable, only increasing the number would have an immediate effect. If you reduce the value dynamically, it would take some time, until the applier thread exits after it finishes applying. A recommended value is anywhere between 16 to 48:

mysql> SET GLOBAL wsrep_slave_threads = 48;

Take note that in order for parallel slave threads to work, the following must be set (which is usually pre-configured for Galera Cluster):


Galera Cache (gcache)

Galera uses a preallocated file with a specific size called gcache, where a Galera node keeps a copy of writesets in circular buffer style. By default, its size is 128MB, which is rather small. Incremental State Transfer (IST) is a method to prepare a joiner by sending only the missing writesets available in the donor’s gcache. IST is faster than state snapshot transfer (SST), it is non-blocking and has no significant performance impact on the donor. It should be the preferred option whenever possible.

IST can only be achieved if all changes missed by the joiner are still in the gcache file of the donor. The recommended setting for this is to be as big as the whole MySQL dataset. If disk space is limited or costly, determining the right size of the gcache size is crucial, as it can influence the data synchronization performance between Galera nodes.

The below statement will give us an idea of the amount of data replicated by Galera. Run the following statement on one of the Galera nodes during peak hours (tested on MariaDB >10.0 and PXC >5.6, galera >3.x):

mysql> SET @start := (SELECT SUM(VARIABLE_VALUE/1024/1024) FROM information_schema.global_status WHERE VARIABLE_NAME LIKE ‘WSREP%bytes’); do sleep(60); SET @end := (SELECT SUM(VARIABLE_VALUE/1024/1024) FROM information_schema.global_status WHERE VARIABLE_NAME LIKE ‘WSREP%bytes’); SET @gcache := (SELECT SUBSTRING_INDEX(SUBSTRING_INDEX(@@GLOBAL.wsrep_provider_options,’gcache.size = ‘,-1), ‘M’, 1)); SELECT ROUND((@end – @start),2) AS `MB/min`, ROUND((@end – @start),2) * 60 as `MB/hour`, @gcache as `gcache Size(MB)`, ROUND(@gcache/round((@end – @start),2),2) as `Time to full(minutes)`;
| MB/min | MB/hour | gcache Size(MB) | Time to full(minutes) |
| 7.95 | 477.00 | 128 | 16.10 |

We can estimate that the Galera node can have approximately 16 minutes of downtime, without requiring SST to join (unless Galera cannot determine the joiner state). If this is too short time and you have enough disk space on your nodes, you can change the wsrep_provider_options=”gcache.size=<value>” to a more appropriate value. In this example workload, setting gcache.size=1G allows us to have 2 hours of node downtime with high probability of IST when the node rejoins.

It’s also recommended to use gcache.recover=yes in wsrep_provider_options (Galera >3.19), where Galera will attempt to recover the gcache file to a usable state on startup rather than delete it, thus preserving the ability to have IST and avoiding SST as much as possible. Codership and Percona have covered this in details in their blogs. IST is always the best method to sync up after a node rejoins the cluster. It is 50% faster than xtrabackup or mariabackup and 5x faster than mysqldump.

Asynchronous Slave

Galera nodes are tightly-coupled, where the replication performance is as fast as the slowest node. Galera use a flow control mechanism, to control replication flow among members and eliminate any slave lag. The replication can be all fast or all slow on every node and is adjusted automatically by Galera. If you want to know about flow control, read this blog post by Jay Janssen from Percona.

In most cases, heavy operations like long running analytics (read-intensive) and backups (read-intensive, locking) are often inevitable, which could potentially degrade the cluster performance. The best way to execute this type of queries is by sending them to a loosely-coupled replica server, for instance, an asynchronous slave.

An asynchronous slave replicates from a Galera node using the standard MySQL asynchronous replication protocol. There is no limit on the number of slaves that can be connected to one Galera node, and chaining it out with an intermediate master is also possible. MySQL operations that execute on this server won’t impact the cluster performance, apart from the initial syncing phase where a full backup must be taken on the Galera node to stage the slave before establishing the replication link (although ClusterControl allows you to build the async slave from an existing backup first, before connecting it to the cluster).

GTID (Global Transaction Identifier) provides a better transactions mapping across nodes, and is supported in MySQL 5.6 and MariaDB 10.0. With GTID, the failover operation on a slave to another master (another Galera node) is simplified, without the need to figure out the exact log file and position. Galera also comes with its own GTID implementation but these two are independent to each other.

Scaling out an asynchronous slave is one-click away if you are using ClusterControl -> Add Replication Slave feature:

Take note that binary logs must be enabled on the master (the chosen Galera node) before we can proceed with this setup. We have also covered the manual way in this previous post.

The following screenshot from ClusterControl shows the cluster topology, it illustrates our Galera Cluster architecture with an asynchronous slave:

ClusterControl automatically discovers the topology and generates the super cool diagram like above. You can also perform administration tasks directly from this page by clicking on the top-right gear icon of each box.

SQL-aware Reverse Proxy

Related resources

 How to Benchmark Performance of MySQL & MariaDB using SysBench

 Galera Cluster Comparison – Codership vs Percona vs MariaDB

 Monitoring Galera Cluster for MySQL or MariaDB – Understanding metrics and their meaning

ProxySQL and MariaDB MaxScale are intelligent reverse-proxies which understand MySQL protocol and is capable of acting as a gateway, router, load balancer and firewall in front of your Galera nodes. With the help of Virtual IP Address provider like LVS or Keepalived, and combining this with Galera multi-master replication technology, we can have a highly available database service, eliminating all possible single-point-of-failures (SPOF) from the application point-of-view. This will surely improve the availability and reliability the architecture as whole.

Another advantage with this approach is you will have the ability to monitor, rewrite or re-route the incoming SQL queries based on a set of rules before they hit the actual database server, minimizing the changes on the application or client side and routing queries to a more suitable node for optimal performance. Risky queries for Galera like LOCK TABLES and FLUSH TABLES WITH READ LOCK can be prevented way ahead before they would cause havoc to the system, while impacting queries like “hotspot” queries (a row that different queries want to access at the same time) can be rewritten or being redirected to a single Galera node to reduce the risk of transaction conflicts. For heavy read-only queries like OLAP or backup, you can route them over to an asynchronous slave if you have any.

Reverse proxy also monitors the database state, queries and variables to understand the topology changes and produce an accurate routing decision to the backend servers. Indirectly, it centralizes the nodes monitoring and cluster overview without the need to check on each and every single Galera node regularly. The following screenshot shows the ProxySQL monitoring dashboard in ClusterControl:

There are also many other benefits that a load balancer can bring to improve Galera Cluster significantly, as covered in details in this blog post, Become a ClusterControl DBA: Making your DB components HA via Load Balancers.

Final Thoughts

With good understanding on how Galera Cluster internally works, we can work around some of the limitations and improve the database service. Happy clustering!



Watch the Webinar Replay: MySQL & MariaDB Performance Tuning for Dummies

Thanks to everyone who participated in this week’s webinar on Performance Tuning for MySQL & MariaDB!

If you’ve missed the live session or would like to watch it again, it is now available online to view; especially if any of the following questions sound familiar to you:

You’re running MySQL or MariaDB as backend database, how do you tune it to make best use of the hardware? How do you optimize the Operating System? How do you best configure MySQL or MariaDB for a specific database workload?

A database server needs CPU, memory, disk and network in order to function. Understanding these resources is important for anybody managing a production database. Any resource that is weak or overloaded can become a limiting factor and cause the database server to perform poorly.

In this webinar, we discuss some of the settings that are most often tweaked and which can bring you significant improvement in the performance of your MySQL or MariaDB database. We also cover some of the variables which are frequently modified even though they should not.

Performance tuning is not easy, especially if you’re not an experienced DBA, but you can go a surprisingly long way with a few basic guidelines.


What to tune and why?
Tuning process
Operating system tuning

I/O performance

MySQL configuration tuning

I/O performance

Useful tools
Do’s and do not’s of MySQL tuning
Changes in MySQL 8.0


Krzysztof Książek, Senior Support Engineer at Severalnines, is a MySQL DBA with experience managing complex database environments for companies like Zendesk, Chegg, Pinterest and Flipboard.

This webinar builds upon blog posts by Krzysztof from the ‘Become a MySQL DBA’ series.


performance tuning

Schema Management Tips for MySQL & MariaDB

Database schema is not something that is written in stone. It is designed for a given application, but then the requirements may and usually do change. New modules and functionalities are added to the application, more data is collected, code and data model refactoring is performed. Thereby the need to modify the database schema to adapt to these changes; adding or modifying columns, creating new tables or partitioning large ones. Queries change too as developers add new ways for users to interact with the data – new queries could use new, more efficient indexes so we rush to create them in order to provide the application with the best database performance.

So, how do we best approach a schema change? What tools are useful? How to minimize the impact on a production database? What are the most common issues with schema design? What tools can help you to stay on top of your schema? In this blog post we will give you a short overview of how to do schema changes in MySQL and MariaDB. Please note that we will not discuss schema changes in the context of Galera Cluster. We already discussed Total Order Isolation, Rolling Schema Upgrades and tips to minimize impact from RSU in previous blog posts. We will also discuss tips and tricks related to schema design and how ClusterControl can help you to stay on top of all schema changes.

Types of Schema Changes

Related resources

 Online schema change for MySQL & MariaDB – comparing GitHub’s gh-ost vs pt-online-schema-change

 How to perform online schema changes on MySQL using gh-ost

 Online schema change with gh-ost – throttling and changing configuration at runtime

 Become a MySQL DBA blog series – Common operations – Schema Changes

First things first. Before we dig into the topic, we have to understand how MySQL and MariaDB perform schema changes. You see, one schema change is not equal to another schema change.

You may have heard about online alters, instant alters or in-place alters. All of this is a result of work which is ongoing to minimize the impact of the schema changes on the production database. Historically, almost all schema changes were blocking. If you executed a schema change, all of the queries will start to pile up, waiting for the ALTER to complete. Obviously, this posed serious issues for production deployments. Sure, people immediately start to look for workarounds, and we will discuss them later in this blog, as even today those are still relevant. But also, work started to improve capability of MySQL to run DDL’s (Data Definition Language) without much impact to other queries.

Instant Changes

Sometimes it is not needed to touch any data in the tablespace, because all that has to be changed is the metadata. An example here will be dropping an index or renaming a column. Such operations are quick and efficient. Typically, their impact is limited. It is not without any impact, though. Sometimes it takes couple of seconds to perform the change in the metadata and such change requires a metadata lock to be acquired. This lock is on a per-table basis, and it may block other operations which are to be executed on this table. You’ll see this as “Waiting for table metadata lock” entries in the processlist.

An example of such change may be instant ADD COLUMN, introduced in MariaDB 10.3 and MySQL 8.0. It gives the possibility to execute this quite popular schema change without any delay. Both MariaDB and Oracle decided to include code from Tencent Game which allows to instantly add a new column to the table. This is under some specific conditions; column has to be added as the last one, full text indexes cannot exist in the table, row format cannot be compressed – you can find more information on how instant add column works in MariaDB documentation. For MySQL, the only official reference can be found on blog, although a bug exists to update the official documentation.

In Place Changes

Some of the changes require modification of the data in the tablespace. Such modifications can be performed on the data itself, and there’s no need to create a temporary table with a new data structure. Such changes, typically (although not always) allow other queries touching the table to be executed while the schema change is running. An example of such operation is to add a new secondary index to the table. This operation will take some time to perform but will allow DML’s to be executed.

Table Rebuild

If it is not possible to make a change in place, InnoDB will create a temporary table with the new, desired structure. It will then copy existing data to the new table. This operation is the most expensive one and it is likely (although it doesn’t always happen) to lock the DML’s. As a result, such schema change is very tricky to execute on a large table on a standalone server, without help of external tools – typically you cannot afford to have your database locked for long minutes or even hours. An example of such operation would be to change the column data type, for example from INT to VARCHAR.

Schema Changes and Replication

Ok, so we know that InnoDB allow online schema changes and if we consult MySQL documentation, we will see that the majority of the schema changes (at least among the most common ones) can be performed online. What is the reason behind dedicating hours of development to create online schema change tools like gh-ost? We can accept that pt-online-schema-change is a remnant of the old, bad times but gh-ost is a new software.

The answer is complex. There are two main issues.

For starters, once you start a schema change, you do not have control over it. You can abort it but you cannot pause it. You cannot throttle it. As you can imagine, rebuilding the table is an expensive operation and even if InnoDB allows DML’s to be executed, additional I/O workload from the DDL affects all other queries and there’s no way to limit this impact to a level that is acceptable to the application.

Second, even more serious issue, is replication. If you execute a non-blocking operation, which requires a table rebuild, it will indeed not lock DML’s but this is true only on the master. Let’s assume such DDL took 30 minutes to complete – ALTER speed depends on the hardware but it is fairly common to see such execution times on tables of 20GB size range. It is then replicated to all slaves and, from the moment DDL starts on those slaves, replication will wait for it to complete. It does not matter if you use MySQL or MariaDB, or if you have multi-threaded replication. Slaves will lag – they will wait those 30 minutes for the DDL to complete before the commence applying the remaining binlog events. As you can imagine, 30 minutes of lag (sometimes even 30 seconds will be not acceptable – it all depends on the application) is something which makes impossible to use those slaves for scale-out. Of course, there are workarounds – you can perform schema changes from the bottom to the top of the replication chain but this seriously limits your options. Especially if you use row-based replication, you can only execute compatible schema changes this way. Couple of examples of limitations of row-based replication; you cannot drop any column which is not the last one, you cannot add a column into a position other than the last one. You cannot also change column type (for example, INT -> VARCHAR).

As you can see, replication adds complexity into how you can perform schema changes. Operations which are non-blocking on the standalone host become blocking while executed on slaves. Let’s take a look at couple of methods you can use to minimize the impact of schema changes.

Online Schema Change Tools

As we mentioned earlier, there are tools, which are intended to perform schema changes. The most popular ones are pt-online-schema-change created by Percona and gh-ost, created by GitHub. In a series of blog posts we compared them and discussed how gh-ost can be used to perform schema changes and how you can throttle and reconfigure an undergoing migration. Here we will not go into details, but we would still like to mention some of the most important aspects of using those tools. For starters, a schema change executed through pt-osc or gh-ost will happen on all database nodes at once. There is no delay whatsoever in terms of when the change will be applied. This makes it possible to use those tools even for schema changes that are incompatible with row-based replication. The exact mechanisms about how those tools track changes on the table is different (triggers in pt-osc vs. binlog parsing in gh-ost) but the main idea is the same – a new table is created with the desired schema and existing data is copied from the old table. In the meantime, DML’s are tracked (one way or the other) and applied to the new table. Once all the data is migrated, tables are renamed and the new table replaces the old one. This is atomic operation so it is not visible to the application. Both tools have an option to throttle the load and pause the operations. Gh-ost can stop all of the activity, pt-osc only can stop the process of copying data between old and new table – triggers will stay active and they will continue duplicating data, which adds some overhead. Due to the rename table, both tools have some limitations regarding foreign keys – not supported by gh-ost, partially supported by pt-osc either through regular ALTER, which may cause replication lag (not feasible if the child table is large) or by dropping the old table before renaming the new one – it’s dangerous as there’s no way to rollback if, for some reason, data wasn’t copied to the new table correctly. Triggers are also tricky to support.

They are not supported in gh-ost, pt-osc in MySQL 5.7 and newer has limited support for tables with existing triggers. Other important limitations for online schema change tools is that unique or primary key has to exist in the table. It is used to identify rows to copy between old and new tables. Those tools are also much slower than direct ALTER – a change which takes hours while running ALTER may take days when performed using pt-osc or gh-ost.

On the other hand, as we mentioned, as long as the requirements are satisfied and limitations won’t come into play, you can run all schema changes utilizing one of the tools. All will happen at the same time on all hosts thus you don’t have to worry about compatibility. You have also some level of control over how the process is executed (less in pt-osc, much more in gh-ost).

You can reduce the impact of the schema change, you can pause them and let them run only under supervision, you can test the change before actually performing it. You can have them track replication lag and pause should an impact be detected. This makes those tools a really great addition to the DBA’s arsenal while working with MySQL replication.


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Rolling Schema Changes

Typically, a DBA will use one of the online schema change tools. But as we discussed earlier, under some circumstances, they cannot be used and a direct alter is the only viable option. If we are talking about standalone MySQL, you have no choice – if the change is non-blocking, that’s good. If it is not, well, there’s nothing you can do about it. But then, not that many people run MySQL as single instances, right? How about replication? As we discussed earlier, direct alter on the master is not feasible – most of the cases it will cause lag on the slave and this may not be acceptable. What can be done, though, is to execute the change in a rolling fashion. You can start with slaves and, once the change is applied on all of them, promote one of the slaves as a new master, demote the old master to a slave and execute the change on it. Sure, the change has to be compatible but, to tell the truth, the most common cases where you cannot use online schema changes is because of a lack of primary or unique key. For all other cases, there is some sort of workaround, especially in pt-online-schema-change as gh-ost has more hard limitations. It is a workaround you would call “so so” or “far from ideal”, but it will do the job if you have no other option to pick from. What is also important, most of the limitations can be avoided if you monitor your schema and catch the issues before the table grows. Even if someone creates a table without a primary key, it is not a problem to run a direct alter which takes half a second or less, as the table is almost empty.

If it will grow, this will become a serious problem but it is up to the DBA to catch this kind of issues before they actually start to create problems. We will cover some tips and tricks on how to make sure you will catch such issues on time. We will also share generic tips on how to design your schemas.

Tips and Tricks

Schema Design

As we showed in this post, online schema change tools are quite important when working with a replication setup therefore it is quite important to make sure your schema is designed in such a way that it will not limit your options for performing schema changes. There are three important aspects. First, primary or unique key has to exist – you need to make sure there are no tables without a primary key in your database. You should monitor this on a regular basis, otherwise it may become a serious problem in the future. Second, you should seriously consider if using foreign keys is a good idea. Sure, they have their uses but they also add overhead to your database and they can make it problematic to use online schema change tools. Relations can be enforced by the application. Even if it means more work, it still may be a better idea than to start using foreign keys and be severely limited to which types of schema changes can be performed. Third, triggers. Same story as with foreign keys. They are a nice feature to have, but they can become a burden. You need to seriously consider if the gains from using them outweight the limitations they pose.

Tracking Schema Changes

Schema change management is not only about running schema changes. You also have to stay on top of your schema structure, especially if you are not the only one doing the changes.

ClusterControl provides users with tools to track some of the most common schema design issues. It can help you to track tables which do not have primary keys:

As we discussed earlier, catching such tables early is very important as primary keys have to be added using direct alter.

ClusterControl can also help you track duplicate indexes. Typically, you don’t want to have multiple indexes which are redundant. In the example above, you can see that there is an index on (k, c) and there’s also an index on (k). Any query which can use index created on column ‘k’ can also use a composite index created on columns (k, c). There are cases where it is beneficial to keep redundant indexes but you have to approach it on case by case basis. Starting from MySQL 8.0, it is possible to quickly test if an index is really needed or not. You can make a redundant index ‘invisible’ by running:


This will make MySQL ignore that index and, through monitoring, you can check if there was any negative impact on the performance of the database. If everything works as planned for some time (couple of days or even weeks), you can plan on removing the redundant index. In case you detected something is not right, you can always re-enable this index by running:


Those operations are instant and the index is there all the time, and is still maintained – it’s only that it will not be taken into consideration by the optimizer. Thanks to this option, removing indexes in MySQL 8.0 will be much safer operation. In the previous versions, re-adding a wrongly removed index could take hours if not days on large tables.

ClusterControl can also let you know about MyISAM tables.

While MyISAM still may have its uses, you have to keep in mind that it is not a transactional storage engine. As such, it can easily introduce data inconsistency between nodes in a replication setup.

Another very useful feature of ClusterControl is one of the operational reports – a Schema Change Report.

In an ideal world, a DBA reviews, approves and implements all of the schema changes. Unfortunately, this is not always the case. Such review process just does not go well with agile development. In addition to that, Developer-to-DBA ratio typically is quite high which can also become a problem as DBA’s would struggle not to become a bottleneck. That’s why it is not uncommon to see schema changes performed outside of the DBA’s knowledge. Yet, the DBA is usually the one responsible for the database’s performance and stability. Thanks to the Schema Change Report, they can now keep track of the schema changes.

At first some configuration is needed. In a configuration file for a given cluster (/etc/cmon.d/cmon_X.cnf), you have to define on which host ClusterControl should track the changes and which schemas should be checked.


Once that’s done, you can schedule a report to be executed on a regular basis. An example output may be like below:

As you can see, two tables have changed since the previous run of the report. In the first one, a new composite index has been created on columns (k, c). In the second table, a column was added.

In the subsequent run we got information about new table, which was created without any index or primary key. Using this kind of info, we can easily act when it is needed and solve the issues before they actually start to become blockers.


schema changes

MySQL on Docker: Running a MariaDB Galera Cluster without Container Orchestration Tools – Part 1

Container orchestration tools simplify the running of a distributed system, by deploying and redeploying containers and handling any failures that occur. One might need to move applications around, e.g., to handle updates, scaling, or underlying host failures. While this sounds great, it does not always work well with a strongly consistent database cluster like Galera. You can’t just move database nodes around, they are not stateless applications. Also, the order in which you perform operations on a cluster has high significance. For instance, restarting a Galera cluster has to start from the most advanced node, or else you will lose data. Therefore, we’ll show you how to run Galera Cluster on Docker without a container orchestration tool, so you have total control.

Related resources

 MySQL on Docker – How to Containerize Your Database – New Whitepaper

 MySQL on Docker: Multi-Host Networking for MySQL Containers (Part 2 – Calico)

 MySQL on Docker: Running Galera Cluster on Kubernetes

In this blog post, we are going to look into how to run a MariaDB Galera Cluster on Docker containers using the standard Docker image on multiple Docker hosts, without the help of orchestration tools like Swarm or Kubernetes. This approach is similar to running a Galera Cluster on standard hosts, but the process management is configured through Docker.

Before we jump further into details, we assume you have installed Docker, disabled SElinux/AppArmor and cleared up the rules inside iptables, firewalld or ufw (whichever you are using). The following are three dedicated Docker hosts for our database cluster:

host1.local –
host2.local –
host3.local –

Multi-host Networking

First of all, the default Docker networking is bound to the local host. Docker Swarm introduces another networking layer called overlay network, which extends the container internetworking to multiple Docker hosts in a cluster called Swarm. Long before this integration came into place, there were many network plugins developed to support this – Flannel, Calico, Weave are some of them.

Here, we are going to use Weave as the Docker network plugin for multi-host networking. This is mainly due to its simplicity to get it installed and running, and support for DNS resolver (containers running under this network can resolve each other’s hostname). There are two ways to get Weave running – systemd or through Docker. We are going to install it as a systemd unit, so it’s independent from Docker daemon (otherwise, we would have to start Docker first before Weave gets activated).

Download and install Weave:

$ curl -L -o /usr/local/bin/weave
$ chmod a+x /usr/local/bin/weave

Create a systemd unit file for Weave:

$ cat > /etc/systemd/system/weave.service << EOF
Description=Weave Network
ExecStartPre=/usr/local/bin/weave launch –no-restart $PEERS
ExecStart=/usr/bin/docker attach weave
ExecStop=/usr/local/bin/weave stop

Define IP addresses or hostname of the peers inside /etc/sysconfig/weave:

$ echo ‘PEERS=”″‘ > /etc/sysconfig/weave

Start and enable Weave on boot:

$ systemctl start weave
$ systemctl enable weave

Repeat the above 4 steps on all Docker hosts. Verify with the following command once done:

$ weave status

The number of peers is what we are looking after. It should be 3:

Peers: 3 (with 6 established connections)

Running a Galera Cluster

Now the network is ready, it’s time to fire our database containers and form a cluster. The basic rules are:

Container must be created under –net=weave to have multi-host connectivity.
Container ports that need to be published are 3306, 4444, 4567, 4568.
The Docker image must support Galera. If you’d like to use Oracle MySQL, then get the Codership version. If you’d like Percona’s, use this image instead. In this blog post, we are using MariaDB’s.

The reasons we chose MariaDB as the Galera cluster vendor are:

Galera is embedded into MariaDB, starting from MariaDB 10.1.
The MariaDB image is maintained by the Docker and MariaDB teams.
One of the most popular Docker images out there.

Bootstrapping a Galera Cluster has to be performed in sequence. Firstly, the most up-to-date node must be started with “wsrep_cluster_address=gcomm://”. Then, start the remaining nodes with a full address consisting of all nodes in the cluster, e.g, “wsrep_cluster_address=gcomm://node1,node2,node3”. To accomplish these steps using container, we have to do some extra steps to ensure all containers are running homogeneously. So the plan is:

We would need to start with 4 containers in this order – mariadb0 (bootstrap), mariadb2, mariadb3, mariadb1.
Container mariadb0 will be using the same datadir and configdir with mariadb1.
Use mariadb0 on host1 for the first bootstrap, then start mariadb2 on host2, mariadb3 on host3.
Remove mariadb0 on host1 to give way for mariadb1.
Lastly, start mariadb1 on host1.

At the end of the day, you would have a three-node Galera Cluster (mariadb1, mariadb2, mariadb3). The first container (mariadb0) is a transient container for bootstrapping purposes only, using cluster address “gcomm://”. It shares the same datadir and configdir with mariadb1 and will be removed once the cluster is formed (mariadb2 and mariadb3 are up) and nodes are synced.

By default, Galera is turned off in MariaDB and needs to be enabled with a flag called wsrep_on (set to ON) and wsrep_provider (set to the Galera library path) plus a number of Galera-related parameters. Thus, we need to define a custom configuration file for the container to configure Galera correctly.

Let’s start with the first container, mariadb0. Create a file under /containers/mariadb0/conf.d/my.cnf and add the following lines:

$ mkdir -p /containers/mariadb0/conf.d
$ cat /containers/mariadb0/conf.d/my.cnf

default_storage_engine = InnoDB
binlog_format = ROW

innodb_flush_log_at_trx_commit = 0
innodb_flush_method = O_DIRECT
innodb_file_per_table = 1
innodb_autoinc_lock_mode = 2
innodb_lock_schedule_algorithm = FCFS # MariaDB >10.1.19 and >10.2.3 only

wsrep_on = ON
wsrep_provider = /usr/lib/galera/
wsrep_sst_method = xtrabackup-v2

Since the image doesn’t come with MariaDB Backup (which is the preferred SST method for MariaDB 10.1 and MariaDB 10.2), we are going to stick with xtrabackup-v2 for the time being.

To perform the first bootstrap for the cluster, run the bootstrap container (mariadb0) on host1:

$ docker run -d \
–name mariadb0 \
–hostname mariadb0.weave.local \
–net weave \
–publish “3306” \
–publish “4444” \
–publish “4567” \
–publish “4568” \
$(weave dns-args) \
–env MYSQL_ROOT_PASSWORD=”PM7%cB43$sd@^1″ \
–env MYSQL_USER=proxysql \
–env MYSQL_PASSWORD=proxysqlpassword \
–volume /containers/mariadb1/datadir:/var/lib/mysql \
–volume /containers/mariadb1/conf.d:/etc/mysql/mariadb.conf.d \
mariadb:10.2.15 \
–wsrep_cluster_address=gcomm:// \
–wsrep_sst_auth=”root:PM7%cB43$sd@^1″ \

The parameters used in the the above command are:

–name, creates the container named “mariadb0”,

–hostname, assigns the container a hostname “mariadb0.weave.local”,

–net, places the container in the weave network for multi-host networing support,

–publish, exposes ports 3306, 4444, 4567, 4568 on the container to the host,

$(weave dns-args), configures DNS resolver for this container. This command can be translated into Docker run as “–dns= –dns-search=weave.local.”,

–env MYSQL_ROOT_PASSWORD, the MySQL root password,

–env MYSQL_USER, creates “proxysql” user to be used later with ProxySQL for database routing,

–env MYSQL_PASSWORD, the “proxysql” user password,

–volume /containers/mariadb1/datadir:/var/lib/mysql, creates /containers/mariadb1/datadir if does not exist and map it with /var/lib/mysql (MySQL datadir) of the container (for bootstrap node, this could be skipped),

–volume /containers/mariadb1/conf.d:/etc/mysql/mariadb.conf.d, mounts the files under directory /containers/mariadb1/conf.d of the Docker host, into the container at /etc/mysql/mariadb.conf.d.

mariadb:10.2.15, uses MariaDB 10.2.15 image from here,

–wsrep_cluster_address, Galera connection string for the cluster. “gcomm://” means bootstrap. For the rest of the containers, we are going to use a full address instead.

–wsrep_sst_auth, authentication string for SST user. Use the same user as root,

–wsrep_node_address, the node hostname, in this case we are going to use the FQDN provided by Weave.

The bootstrap container contains several key things:

The name, hostname and wsrep_node_address is mariadb0, but it uses the volumes of mariadb1.
The cluster address is “gcomm://”
There are two additional –env parameters – MYSQL_USER and MYSQL_PASSWORD. This parameters will create additional user for our proxysql monitoring purpose.

Verify with the following command:

$ docker ps
$ docker logs -f mariadb0

Once you see the following line, it indicates the bootstrap process is completed and Galera is active:

2018-05-30 23:19:30 139816524539648 [Note] WSREP: Synchronized with group, ready for connections

Create the directory to load our custom configuration file in the remaining hosts:

$ mkdir -p /containers/mariadb2/conf.d # on host2
$ mkdir -p /containers/mariadb3/conf.d # on host3

Then, copy the my.cnf that we’ve created for mariadb0 and mariadb1 to mariadb2 and mariadb3 respectively:

$ scp /containers/mariadb1/conf.d/my.cnf /containers/mariadb2/conf.d/ # on host1
$ scp /containers/mariadb1/conf.d/my.cnf /containers/mariadb3/conf.d/ # on host1

Next, create another 2 database containers (mariadb2 and mariadb3) on host2 and host3 respectively:

$ docker run -d \
–name ${NAME} \
–hostname ${NAME}.weave.local \
–net weave \
–publish “3306:3306” \
–publish “4444” \
–publish “4567” \
–publish “4568” \
$(weave dns-args) \
–env MYSQL_ROOT_PASSWORD=”PM7%cB43$sd@^1″ \
–volume /containers/${NAME}/datadir:/var/lib/mysql \
–volume /containers/${NAME}/conf.d:/etc/mysql/mariadb.conf.d \
mariadb:10.2.15 \

–wsrep_cluster_address=gcomm://mariadb0.weave.local,mariadb1.weave.local,mariadb2.weave.local,mariadb3.weave.local \
–wsrep_sst_auth=”root:PM7%cB43$sd@^1″ \

** Replace ${NAME} with mariadb2 or mariadb3 respectively.

However, there is a catch. The entrypoint script checks the mysqld service in the background after database initialization by using MySQL root user without password. Since Galera automatically performs synchronization through SST or IST when starting up, the MySQL root user password will change, mirroring the bootstrapped node. Thus, you would see the following error during the first start up:

018-05-30 23:27:13 140003794790144 [Warning] Access denied for user ‘root’@’localhost’ (using password: NO)
MySQL init process in progress…
MySQL init process failed.

The trick is to restart the failed containers once more, because this time, the MySQL datadir would have been created (in the first run attempt) and it would skip the database initialization part:

$ docker start mariadb2 # on host2
$ docker start mariadb3 # on host3

Once started, verify by looking at the following line:

$ docker logs -f mariadb2

2018-05-30 23:28:39 139808069601024 [Note] WSREP: Synchronized with group, ready for connections

At this point, there are 3 containers running, mariadb0, mariadb2 and mariadb3. Take note that mariadb0 is started using the bootstrap command (gcomm://), which means if the container is automatically restarted by Docker in the future, it could potentially become disjointed with the primary component. Thus, we need to remove this container and replace it with mariadb1, using the same Galera connection string with the rest and use the same datadir and configdir with mariadb0.

First, stop mariadb0 by sending SIGTERM (to ensure the node is going to be shutdown gracefully):

$ docker kill -s 15 mariadb0

Then, start mariadb1 on host1 using similar command as mariadb2 or mariadb3:

$ docker run -d \
–name mariadb1 \
–hostname mariadb1.weave.local \
–net weave \
–publish “3306:3306” \
–publish “4444” \
–publish “4567” \
–publish “4568” \
$(weave dns-args) \
–env MYSQL_ROOT_PASSWORD=”PM7%cB43$sd@^1″ \
–volume /containers/mariadb1/datadir:/var/lib/mysql \
–volume /containers/mariadb1/conf.d:/etc/mysql/mariadb.conf.d \
mariadb:10.2.15 \

–wsrep_cluster_address=gcomm://mariadb0.weave.local,mariadb1.weave.local,mariadb2.weave.local,mariadb3.weave.local \
–wsrep_sst_auth=”root:PM7%cB43$sd@^1″ \

This time, you don’t need to do the restart trick because MySQL datadir already exists (created by mariadb0). Once the container is started, verify the cluster size is 3, the status must be in Primary and the local state is synced:

$ docker exec -it mariadb3 mysql -uroot “-pPM7%cB43$sd@^1” -e ‘select variable_name, variable_value from information_schema.global_status where variable_name in (“wsrep_cluster_size”, “wsrep_local_state_comment”, “wsrep_cluster_status”, “wsrep_incoming_addresses”)’
| variable_name | variable_value |
| WSREP_INCOMING_ADDRESSES | mariadb1.weave.local:3306,mariadb3.weave.local:3306,mariadb2.weave.local:3306 |

At this point, our architecture is looking something like this:

Although the run command is pretty long, it well describes the container’s characteristics. It’s probably a good idea to wrap the command in a script to simplify the execution steps, or use a compose file instead.

Database Routing with ProxySQL

Now we have three database containers running. The only way to access to the cluster now is to access the individual Docker host’s published port of MySQL, which is 3306 (map to 3306 to the container). So what happens if one of the database containers fails? You have to manually failover the client’s connection to the next available node. Depending on the application connector, you could also specify a list of nodes and let the connector do the failover and query routing for you (Connector/J, PHP mysqlnd). Otherwise, it would be a good idea to unify the database resources into a single resource, that can be called a service.

This is where ProxySQL comes into the picture. ProxySQL can act as the query router, load balancing the database connections similar to what “Service” in Swarm or Kubernetes world can do. We have built a ProxySQL Docker image for this purpose and will maintain the image for every new version with our best effort.

Before we run the ProxySQL container, we have to prepare the configuration file. The following is what we have configured for proxysql1. We create a custom configuration file under /containers/proxysql1/proxysql.cnf on host1:

$ cat /containers/proxysql1/proxysql.cnf
mysql_servers =
{ address=”mariadb1.weave.local” , port=3306 , hostgroup=10, max_connections=100 },
{ address=”mariadb2.weave.local” , port=3306 , hostgroup=10, max_connections=100 },
{ address=”mariadb3.weave.local” , port=3306 , hostgroup=10, max_connections=100 },
{ address=”mariadb1.weave.local” , port=3306 , hostgroup=20, max_connections=100 },
{ address=”mariadb2.weave.local” , port=3306 , hostgroup=20, max_connections=100 },
{ address=”mariadb3.weave.local” , port=3306 , hostgroup=20, max_connections=100 }
mysql_users =
{ username = “sbtest” , password = “password” , default_hostgroup = 10 , active = 1 }
mysql_query_rules =
match_pattern=”^SELECT .* FOR UPDATE”
match_pattern=”^SELECT .*”
scheduler =
id = 1
filename = “/usr/share/proxysql/tools/”
active = 1
interval_ms = 2000
arg1 = “10”
arg2 = “20”
arg3 = “1”
arg4 = “1”
arg5 = “/var/lib/proxysql/proxysql_galera_checker.log”

The above configuration will:

configure two host groups, the single-writer and multi-writer group, as defined under “mysql_servers” section,
send reads to all Galera nodes (hostgroup 20) while write operations will go to a single Galera server (hostgroup 10),
schedule the,
use monitor_username and monitor_password as the monitoring credentials created when we first bootstrapped the cluster (mariadb0).

Copy the configuration file to host2, for ProxySQL redundancy:

$ mkdir -p /containers/proxysql2/ # on host2
$ scp /containers/proxysql1/proxysql.cnf /container/proxysql2/ # on host1

Then, run the ProxySQL containers on host1 and host2 respectively:

$ docker run -d \
–name=${NAME} \
–publish 6033 \
–publish 6032 \
–restart always \
–net=weave \
$(weave dns-args) \
–hostname ${NAME}.weave.local \
-v /containers/${NAME}/proxysql.cnf:/etc/proxysql.cnf \
-v /containers/${NAME}/data:/var/lib/proxysql \

** Replace ${NAME} with proxysql1 or proxysql2 respectively.

We specified –restart=always to make it always available regardless of the exit status, as well as automatic startup when Docker daemon starts. This will make sure the ProxySQL containers act like a daemon.

Verify the MySQL servers status monitored by both ProxySQL instances (OFFLINE_SOFT is expected for the single-writer host group):

$ docker exec -it proxysql1 mysql -uadmin -padmin -h127.0.0.1 -P6032 -e ‘select hostgroup_id,hostname,status from mysql_servers’
| hostgroup_id | hostname | status |
| 10 | mariadb1.weave.local | ONLINE |
| 10 | mariadb2.weave.local | OFFLINE_SOFT |
| 10 | mariadb3.weave.local | OFFLINE_SOFT |
| 20 | mariadb1.weave.local | ONLINE |
| 20 | mariadb2.weave.local | ONLINE |
| 20 | mariadb3.weave.local | ONLINE |

At this point, our architecture is looking something like this:

All connections coming from 6033 (either from the host1, host2 or container’s network) will be load balanced to the backend database containers using ProxySQL. If you would like to access an individual database server, use port 3306 of the physical host instead. There is no virtual IP address as single endpoint configured for the ProxySQL service, but we could have that by using Keepalived, which is explained in the next section.

Virtual IP Address with Keepalived

Since we configured ProxySQL containers to be running on host1 and host2, we are going to use Keepalived containers to tie these hosts together and provide virtual IP address via the host network. This allows a single endpoint for applications or clients to connect to the load balancing layer backed by ProxySQL.

As usual, create a custom configuration file for our Keepalived service. Here is the content of /containers/keepalived1/keepalived.conf:

vrrp_instance VI_DOCKER {
interface ens33 # interface to monitor
state MASTER
virtual_router_id 52 # Assign one ID for this route
priority 101
unicast_peer {
virtual_ipaddress { # the virtual IP

Copy the configuration file to host2 for the second instance:

$ mkdir -p /containers/keepalived2/ # on host2
$ scp /containers/keepalived1/keepalived.conf /container/keepalived2/ # on host1

Change the priority from 101 to 100 inside the copied configuration file on host2:

$ sed -i ‘s/101/100/g’ /containers/keepalived2/keepalived.conf

**The higher priority instance will hold the virtual IP address (in this case is host1), until the VRRP communication is interrupted (in case host1 goes down).

Then, run the following command on host1 and host2 respectively:

$ docker run -d \
–name=${NAME} \
–cap-add=NET_ADMIN \
–net=host \
–restart=always \
–volume /containers/${NAME}/keepalived.conf:/usr/local/etc/keepalived/keepalived.conf \ osixia/keepalived:1.4.4

** Replace ${NAME} with keepalived1 and keepalived2.

The run command tells Docker to:

–name, create a container with

–cap-add=NET_ADMIN, add Linux capabilities for network admin scope

–net=host, attach the container into the host network. This will provide virtual IP address on the host interface, ens33

–restart=always, always keep the container running,

–volume=/containers/${NAME}/keepalived.conf:/usr/local/etc/keepalived/keepalived.conf, map the custom configuration file for container’s usage.

After both containers are started, verify the virtual IP address existence by looking at the physical network interface of the MASTER node:

$ ip a | grep ens33
2: ens33: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UP qlen 1000
inet brd scope global ens33
inet scope global ens33

The clients and applications may now use the virtual IP address, to access the database service. This virtual IP address exists on host1 at this moment. If host1 goes down, keepalived2 will take over the IP address and bring it up on host2. Take note that the configuration for this keepalived does not monitor the ProxySQL containers. It only monitors the VRRP advertisement of the Keepalived peers.

At this point, our architecture is looking something like this:


So, now we have a MariaDB Galera Cluster fronted by a highly available ProxySQL service, all running on Docker containers.

In part two, we are going to look into how to manage this setup. We’ll look at how to perform operations like graceful shutdown, bootstrapping, detecting the most advanced node, failover, recovery, scaling up/down, upgrades, backup and so on. We will also discuss the pros and cons of having this setup for our clustered database service.

Happy containerizing!



Watch the Replay: How to Migrate to Galera Cluster for MySQL & MariaDB

Watch the replay of this webinar with Severalnines Support Engineer Bart Oles, as he walks us through what you need to know in order to migrate from standalone or a master-slave MySQL/MariaDB setup to Galera Cluster.

When considering such a migration, plenty of questions typically come up, such as: how do we migrate? Does the schema or application change? What are the limitations? Can a migration be done online, without service interruption? What are the potential risks?

Galera Cluster has become a mainstream option for high availability MySQL and MariaDB. And though it is now known as a credible replacement for traditional MySQL master-slave architectures, it is not a drop-in replacement.

It has some characteristics that make it unsuitable for certain use cases, however, most applications can still be adapted to run on it.

The benefits are clear: multi-master InnoDB setup with built-in failover and read scalability.

Check out this walk-through on how to migrate to Galera Cluster for MySQL and MariaDB.

Watch the replay and browse through the slides!


Application use cases for Galera
Schema design
Events and Triggers
Query design
Migrating the schema
Load balancer and VIP
Loading initial data into the cluster

Cluster technology
Application vendor support

Performing Online Migration to Galera
Operational management checklist
Belts and suspenders: Plan B


Bartlomiej Oles is a MySQL and Oracle DBA, with over 15 years experience in managing highly available production systems at IBM, Nordea Bank, Acxiom, Lufthansa, and other Fortune 500 companies. In the past five years, his focus has been on building and applying automation tools to manage multi-datacenter database environments.


mysql cluster
galera cluster
percona xtradb

Cloud Disaster Recovery for MariaDB and MySQL

MySQL has a long tradition in geographic replication. Distributing clusters to remote data centers reduces the effects of geographic latency by pushing data closer to the user. It also provides a capability for disaster recovery. Due to the significant cost of duplicating hardware in a separate site, not many companies were able to afford it in the past. Another cost is skilled staff who is able to design, implement and maintain a sophisticated multiple data centers environment.

With the Cloud and DevOps automation revolution, having distributed datacenter has never been more accessible to the masses. Cloud providers are increasing the range of services they offer for a better price.One can build cross-cloud, hybrid environments with data spread all over the world. One can make flexible and scalable DR plans to approach a broad range of disruption scenarios. In some cases, that can just be a backup stored offsite. In other cases, it can be a 1 to 1 copy of a production environment running somewhere else.

Related blog posts

 Become a ClusterControl DBA: Safeguarding your Data

 How to do Point-in-Time Recovery of MySQL & MariaDB Data using ClusterControl

 Zero Downtime Network Migration with MySQL Galera Cluster using Relay Node

In this blog we will take a look at some of these cases, and address common scenarios.

Storing Backups in the Cloud

A DR plan is a general term that describes a process to recover disrupted IT systems and other critical assets an organization uses. Backup is the primary method to achieve this. When a backup is in the same data center as your production servers, you risk that all data may be wiped out in case you lose that data center. To avoid that, you should have the policy to create a copy in another physical location. It’s still a good practice to keep a backup on disk to reduce the time needed to restore. In most cases, you will keep your primary backup in the same data center (to minimize restore time), but you should also have a backup that can be used to restore business procedures when primary datacenter is down.

ClusterControl: Upload Backup to the cloud

ClusterControl allows seamless integration between your database environment and the cloud. It provides options for migrating data to the cloud. We offer a full combination of database backups for Amazon Web Services (AWS), Google Cloud Services or Microsoft Azure. Backups can now be executed, scheduled, downloaded and restored directly from your cloud provider of choice. This ability provides increased redundancy, better disaster recovery options, and benefits in both performance and cost savings.

ClusterControl: Managing Cloud Credentials

The first step to set up “data center failure – proof backup” is to provide credentials for your cloud operator. You can choose from multiple vendors here. Let’s take a look at the process set up for the most popular cloud operator – AWS.

ClusterControl: adding cloud credentials

All you need is the AWS Key ID and the secret for the region where you want to store your backup. You can get that from AWS console. You can follow a few steps to get it.

Use your AWS account email address and password to sign in to the AWS Management Console as the AWS account root user.
On the IAM Dashboard page, choose your account name in the navigation bar, and then select My Security Credentials.
If you see a warning about accessing the security credentials for your AWS account, choose to Continue to Security Credentials.
Expand the Access keys (access key ID and secret access key) section.
Choose to Create New Access Key. Then choose Download Key File to save the access key ID and secret access key to a file on your computer. After you close the dialog box, you will not be able to retrieve this secret access key again.

ClusterControl: Hybrid cloud backup

When all is set, you can adjust your backup schedule and enable backup to cloud option. To reduce network traffic make sure to enable data compression. It makes backups smaller and minimizes the time needed for upload. Another good practice is to encrypt the backup. ClusterControl creates a key automatically and uses it if you decide to restore it. Advanced backup policies should have different keep times for backups stored on servers in the same datacenter, and the backups stored in another physical location. You should set a more extended retention period for cloud-based backups, and shorter period for backups stored near the production environment, as the probability of restore drops with the backup lifetime.

ClusterControl: backup retention policy

Extend your cluster with asynchronous replication

Galera with asynchronous replication can be an excellent solution to build an active DR node in a remote data center. There are a few good reasons to attach an asynchronous slave to a Galera Cluster. Long-running OLAP type queries on a Galera node might slow down a whole cluster. With delay apply option, delayed replication can save you from human errors so all those golden enters will be not immediately applied to your backup node.

ClusterControl: delayed replication

In ClusterControl, extending a Galera node group with asynchronous replication is done in a single page wizard. You need to provide the necessary information about your future or existing slave server. The slave will be set up from an existing backup, or a freshly streamed XtraBackup from the master to the slave.

Load balancers in multi-datacenter

Load balancers are a crucial component in MySQL and MariaDB database high availability. It’s not enough to have a cluster spanning across multiple data centers. You still need your services to access them. A failure of a load balancer that is available in one data center will make your entire environment unreachable.

Web proxies in cluster environment

One of the popular methods to hide the complexity of the database layer from an application is to use a proxy. Proxies act as an entry point to the databases, they track the state of the database nodes and should always direct traffic to only the nodes that are available. ClusterControl makes it easy to deploy and configure several different load balancing technologies for MySQL and MariaDB, including ProxySQL, HAProxy, with a point-and-click graphical interface.

ClusterControl: load balancer HA

Related webinar

 How to Get Started with Open Source Database Management

It also allows making this component redundant by adding keepalived on top of it. To prevent your load balancers from being a single point of failure, one would set up two identical (one active and one in different DC as standby) HAProxy, ProxySQL or MariaDB Maxscale instances and use Keepalived to run Virtual Router Redundancy Protocol (VRRP) between them. VRRP provides a Virtual IP address to the active load balancer and transfers the Virtual IP to the standby HAProxy in case of failure. It is seamless because the two proxy instances need no shared state.

Of course, there are many things to consider to make your databases immune to data center failures.
Proper planning and automation will make it work! Happy Clustering!


disaster recovery

Deploying Cloud Databases with ClusterControl 1.6

ClusterControl 1.6 comes with tighter integration with AWS, Azure and Google Cloud, so it is now possible to launch new instances and deploy MySQL, MariaDB, MongoDB and PostgreSQL directly from the ClusterControl user interface. In this blog, we will show you how to deploy a cluster on Amazon Web Services.

Note that this new feature requires two modules called clustercontrol-cloud and clustercontrol-clud. The former is a helper daemon which extends CMON capability of cloud communication, while the latter is a file manager client to upload and download files on cloud instances. Both packages are dependencies of the clustercontrol UI package, which will be installed automatically if they do not exist. See the Components documentation page for details.

Cloud Credentials

ClusterControl allows you to store and manage your cloud credentials under Integrations (side menu) -> Cloud Providers:

The supported cloud platforms in this release are Amazon Web Services, Google Cloud Platform and Microsoft Azure. On this page, you can add new cloud credentials, manage existing ones and also connect to your cloud platform to manage resources.

The credentials that have been set up here can be used to:

Manage cloud resources
Deploy databases in the cloud
Upload backup to cloud storage

The following is what you would see if you clicked on “Manage AWS” button:

You can perform simple management tasks on your cloud instances. You can also check the VPC settings under “AWS VPC” tab, as shown in the following screenshot:

The above features are useful as reference, especially when preparing your cloud instances before you start the database deployments.

Database Deployment on Cloud

In previous versions of ClusterControl, database deployment on cloud would be treated similarly to deployment on standard hosts, where you had to create the cloud instances beforehand and then supply the instance details and credentials in the “Deploy Database Cluster” wizard. The deployment procedure was unaware of any extra functionality and flexibility in the cloud environment, like dynamic IP and hostname allocation, NAT-ed public IP address, storage elasticity, virtual private cloud network configuration and so on.

With version 1.6, you just need to supply the cloud credentials, which can be managed via the “Cloud Providers” interface and follow the “Deploy in the Cloud” deployment wizard. From ClusterControl UI, click Deploy and you will be presented with the following options:

At the moment, the supported cloud providers are the three big players – Amazon Web Service (AWS), Google Cloud and Microsoft Azure. We are going to integrate more providers in the future release.

In the first page, you will be presented with the Cluster Details options:

In this section, you would need to select the supported cluster type, MySQL Galera Cluster, MongoDB Replica Set or PostgreSQL Streaming Replication. The next step is to choose the supported vendor for the selected cluster type. At the moment, the following vendors and versions are supported:

MySQL Galera Cluster – Percona XtraDB Cluster 5.7, MariaDB 10.2
MongoDB Cluster – MongoDB 3.4 by MongoDB, Inc and Percona Server for MongoDB 3.4 by Percona (replica set only).
PostgreSQL Cluster – PostgreSQL 10.0 (streaming replication only).

In the next step, you will be presented with the following dialog:

Here you can configure the selected cluster type accordingly. Pick the number of nodes. The Cluster Name will be used as the instance tag, so you can easily recognize this deployment in your cloud provider dashboard. No space is allowed in the cluster name. My.cnf Template is the template configuration file that ClusterControl will use to deploy the cluster. It must be located under /usr/share/cmon/templates on the ClusterControl host. The rest of the fields are pretty self-explanatory.

The next dialog is to select the cloud credentials:

You can choose the existing cloud credentials or create a new one by clicking on the “Add New Credential” button. The next step is to choose the virtual machine configuration:

Most of the settings in this step are dynamically populated from the cloud provider by the chosen credentials. You can configure the operating system, instance size, VPC setting, storage type and size and also specify the SSH key location on the ClusterControl host. You can also let ClusterControl generate a new key specifically for these instances. When clicking on “Add New” button next to Virtual Private Cloud, you will be presented with a form to create a new VPC:

VPC is a logical network infrastructure you have within your cloud platform. You can configure your VPC by modifying its IP address range, create subnets, configure route tables, network gateways, and security settings. It’s recommended to deploy your database infrastructure in this network for isolation, security and routing control.

When creating a new VPC, specify the VPC name and IPv4 address block with subnet. Then, choose whether IPv6 should be part of the network and the tenancy option. You can then use this virtual network for your database infrastructure.


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The last step is the deployment summary:

In this stage, you need to choose which subnet under the chosen virtual network that you want the database to be running on. Take note that the chosen subnet MUST have auto-assign public IPv4 address enabled. You can also create a new subnet under this VPC by clicking on “Add New Subnet” button. Verify if everything is correct and hit the “Deploy Cluster” button to start the deployment.

You can then monitor the progress by clicking on the Activity -> Jobs -> Create Cluster -> Full Job Details:

Depending on the connections, it could take 10 to 20 minutes to complete. Once done, you will see a new database cluster listed under the ClusterControl dashboard. For PostgreSQL streaming replication cluster, you might need to know the master and slave IP addresses once the deployment completes. Simply go to Nodes tab and you would see the public and private IP addresses on the node list on the left:

Your database cluster is now deployed and running on AWS.

At the moment, the scaling up works similar to the standard host, where you need to create a cloud instance manually beforehand and specify the host under ClusterControl -> pick the cluster -> Add Node.

Under the hood, the deployment process does the following:

Create cloud instances
Configure security groups and networking
Verify the SSH connectivity from ClusterControl to all created instances
Deploy database on every instance
Configure the clustering or replication links
Register the deployment into ClusterControl

Take note that this feature is still in beta. Nevertheless, you can use this feature to speed up your development and testing environment by controlling and managing the database cluster in different cloud providers from a single user interface.

Database Backup on Cloud

This feature has been around since ClusterControl 1.5.0, and now we added support for Azure Cloud Storage. This means that you can now upload and download the created backup on all three major cloud providers (AWS, GCP and Azure). The upload process happens right after the backup is successfully created (if you toggle “Upload Backup to the Cloud”) or you can manually click on the cloud icon button of the backup list:

Related resources

 Cloud Database Features Comparison – Amazon RDS vs Google Cloud SQL

 Comparing Cloud Database Options for PostgreSQL

 Announcing ClusterControl 1.6 – automation and management of open source databases in the cloud

You can then download and restore backups from the cloud, in case you lost your local backup storage, or if you need to reduce local disk space usage for your backups.

Current Limitations

There are some known limitations for the cloud deployment feature, as stated below:

There is currently no ‘accounting’ in place for the cloud instances. You will need to manually remove the cloud instances if you remove a database cluster.
You cannot add or remove a node automatically with cloud instances.
You cannot deploy a load balancer automatically with a cloud instance.

We have extensively tested the feature in many environments and setups but there are always corner cases that we might have missed out upon. For more information, please take a look at the change log.

Happy clustering in the cloud!



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