How to avoid even more of the common (but deadly) MySQL development mistakes

On July 16 I’ll be presenting my next webinar focusing on common mistakes committed by MySQL users.

How to Avoid Even More of the Common (but Deadly) MySQL Development Mistakes

“Why can’t I just save my data to a file?”

Using an SQL database seems so complex to get right, and for good reason. The variety of data-driven applications is practically limitless, and as project requirements change, we find ourselves taking shortcuts and adopting bad habits. But there are proven methods to understanding how to develop and manage data in a scalable and reliable way. This talk shows you some of these methods, including:

  • How to optimize a database application with partitioning and sharding.
  • How to avoid the secret security vulnerability that you’re probably guilty of.
  • How to use optimizer hints effectively.

Percona MySQL webinars: How to avoid even more of the common (but deadly) MySQL development mistakesAt the end of this webinar, you’ll be more productive and confident as you develop database-driven applications.

Please register for this webinar and join me on July 16!

You might also like to view recordings of my past “deadly mistakes” webinars:

The post How to avoid even more of the common (but deadly) MySQL development mistakes appeared first on MySQL Performance Blog.

Percona Server 5.6.19-67.0 with TokuDB (GA) now available

Percona ServerPercona is glad to announce the release of Percona Server 5.6.19-67.0 on July 1, 2014. Download the latest version from the Percona web site or from the Percona Software Repositories.

Based on MySQL 5.6.19, including all the bug fixes in it, Percona Server 5.6.19-67.0 is the current GA release in the Percona Server 5.6 series. All of Percona’s software is open-source and free. Complete details of this release can be found in the 5.6.19-67.0 milestone on Launchpad.

New Features:

  • Percona has merged a contributed patch by Kostja Osipov implementing the Multiple user level locks per connection feature. This feature fixes the upstream bugs: #1118 and #67806.
  • TokuDB storage engine support is now considered general availability (GA) quality. The TokuDB storage engine from Tokutek improves scalability and the operational efficiency of MySQL with faster performance and increased compression. It is available as a separate package and can be installed along with the Percona Server by following the instructions in the release documentation.
  • Percona Server now supports the MTR --valgrind option for a server that is either statically or dynamically linked with jemalloc.

Bugs Fixed:

  • The libperconaserverclient18.1 package was missing the library files. Bug fixed #1329911.
  • Percona Server introduced a regression in 5.6.17-66.0 when support for TokuDB storage engine was initially introduced. This regression caused spurious “wrong table structure” errors for PERFORMANCE_SCHEMA tables. Bug fixed #1329772.
  • Race condition in group commit code could lead to a race condition in PFS instrumentation code resulting in a server crash. Bug fixed #1309026 (upstream #72681).

Other bugs fixed: #1326348 and #1167486.

NOTE: There was no Percona Server 5.6.18 release because there was no MySQL Community Server 5.6.18 release. That version number was used for a MySQL Enterprise Edition release to address the OpenSSL “Heartbleed” issue.

Release notes for Percona Server 5.6.19-67.0 are available in the online documentation. Please report any bugs on the launchpad bug tracker.

The post Percona Server 5.6.19-67.0 with TokuDB (GA) now available appeared first on MySQL Performance Blog.

Using MySQL triggers and views in Amazon RDS

I recently had an opportunity to migrate a customer from a physical server into Amazon’s RDS environment. In this particular case the customers’ platform makes extensive use of MySQL triggers and views.  I came across two significant issues that prevented me from following Amazon’s documentation, which basically states “use mysqldump” but doesn’t call out a specific method of dealing with MySQL triggers and views.

Amazon Relational Database Service (Amazon RDS) is a great platform if you’re looking for complete hands-off management of your MySQL environment, but comes at a cost in the area of flexibility, i.e. you don’t have SUPER privilege and this brings up additional challenges.

  1. You need to ensure you set log_bin_trust_function_creators=1 ( by default this is off, 0).
  2. You need to clean up your mysqldump syntax.

#1 is easy, you simply make a configuration change within the Amazon RDS GUI on the node’s Parameter Group to set log_bin_trust_function_creators=1 and then a restart of your Amazon RDS node.  The restart is required since without the SUPER privilege you lose access to changing DYNAMIC variables on the fly.
#2 is a little more complex.  If you go with vanilla mysqldump (from say a 5.5 mysqldump binary) on a schema that has triggers and views, you will see error 1227, something like this:

ERROR 1227 (42000) at line 27311: Access denied; you need (at least one of) the SUPER privilege(s) for this operation

You’re seeing this message because MySQL in Amazon RDS doesn’t provide the SUPER privilege, and thus you cannot set up a trigger or view to run as a different user — only a user with SUPER can do that.

mysqldump will generate syntax for a trigger like this:

DELIMITER ;;
/*!50003 CREATE*/ /*!50017 DEFINER=`root`@`%`*/ /*!50003 TRIGGER `after_insert_lead` AFTER INSERT ON `leads` FOR EACH ROW BEGIN
UPDATE analytics.mapping SET id_lead = NEW.id_lead WHERE mc_email = NEW.email;
END */;;
DELIMITER ;

and for a view like this:

/*!50001 CREATE ALGORITHM=UNDEFINED */
/*!50013 DEFINER=`web`@`%` SQL SECURITY DEFINER */
/*!50001 VIEW `admin_user_view` AS SELECT ...

The problem is in the “DEFINER” lines.

Here’s one method that worked for me:

  1. Identify all the DEFINER lines in your schema. I found it helpful to dump out a –no-data and then weed through that to get a unique list of the DEFINER lines
  2. Create a sed line for each unique DEFINER line (see my example in a moment)
  3. Include this sed line in your dump/load script

Here’s what my sed matches looked like:

sed
-e 's//*!50017 DEFINER=`root`@`localhost`*///'
-e 's//*!50017 DEFINER=`root`@`%`*///'
-e 's//*!50017 DEFINER=`web`@`%`*///'
-e 's//*!50017 DEFINER=`cron`@`%`*///'
-e 's//*!50013 DEFINER=`cron`@`%` SQL SECURITY DEFINER *///'
-e 's//*!50013 DEFINER=`root`@`localhost` SQL SECURITY DEFINER *///'
-e 's//*!50013 DEFINER=`root`@`%` SQL SECURITY DEFINER *///'
-e 's//*!50013 DEFINER=`web`@`%` SQL SECURITY DEFINER *///'

Note: the example above won’t directly work due to WordPress “helpfully” stripping my text… you need to escape the forward slashes and asterisks.

A big caveat: this method is akin to a brute force method of getting your data into Amazon RDS — you’ve lost the elegance & security of running your triggers and views as separate defined users within the database — they are all now going to run as the user you loaded them in as. If this is a show-stopper for you, contact Percona and I’d be happy to take on your case and develop a more comprehensive solution.  :)

Now all that’s left is to integrate this into your dump flow.  Something like this should work:

mysqldump
--host=source
| sed
-e ... lots of lines
| mysql
--host=destination

I hope this helps someone!

The post Using MySQL triggers and views in Amazon RDS appeared first on MySQL Performance Blog.

Percona Server 5.5.38-35.2 is now available

Percona ServerPercona is glad to announce the release of Percona Server 5.5.38-35.2 on July 2, 2014 (Downloads are available here and from the Percona Software Repositories). Based on MySQL 5.5.38, including all the bug fixes in it, Percona Server 5.5.38-35.2 is now the current stable release in the 5.5 series. All of Percona‘s software is open-source and free, all the details of the release can be found in the 5.5.38-35.2 milestone at Launchpad.

Bugs Fixed:

Release notes for Percona Server 5.5.38-35.2 are available in our online documentation. Bugs can be reported on the launchpad bug tracker.

The post Percona Server 5.5.38-35.2 is now available appeared first on MySQL Performance Blog.

Failover with the MySQL Utilities: Part 2 – mysqlfailover

In the previous post of this series we saw how you could use mysqlrpladmin to perform manual failover/switchover when GTID replication is enabled in MySQL 5.6. Now we will review mysqlfailover (version 1.4.3), another tool from the MySQL Utilities that can be used for automatic failover.

Summary

  • mysqlfailover can perform automatic failover if MySQL 5.6′s GTID-replication is enabled.
  • All slaves must use --master-info-repository=TABLE.
  • The monitoring node is a single point of failure: don’t forget to monitor it!
  • Detection of errant transactions works well, but you have to use the --pedantic option to make sure failover will never happen if there is an errant transaction.
  • There are a few limitations such as the inability to only fail over once, or excessive CPU utilization, but they are probably not showstoppers for most setups.

Setup

We will use the same setup as last time: one master and two slaves, all using GTID replication. We can see the topology using mysqlfailover with the health command:

$ mysqlfailover --master=root@localhost:13001 --discover-slaves-login=root health
[...]
MySQL Replication Failover Utility
Failover Mode = auto     Next Interval = Tue Jul  1 10:01:22 2014
Master Information
------------------
Binary Log File   Position  Binlog_Do_DB  Binlog_Ignore_DB
mysql-bin.000003  700
GTID Executed Set
a9a396c6-00f3-11e4-8e66-9cebe8067a3f:1-3
Replication Health Status
+------------+--------+---------+--------+------------+---------+
| host       | port   | role    | state  | gtid_mode  | health  |
+------------+--------+---------+--------+------------+---------+
| localhost  | 13001  | MASTER  | UP     | ON         | OK      |
| localhost  | 13002  | SLAVE   | UP     | ON         | OK      |
| localhost  | 13003  | SLAVE   | UP     | ON         | OK      |
+------------+--------+---------+--------+------------+---------+

Note that --master-info-repository=TABLE needs to be configured on all slaves or the tool will exit with an error message:

2014-07-01 10:18:55 AM CRITICAL Failover requires --master-info-repository=TABLE for all slaves.
ERROR: Failover requires --master-info-repository=TABLE for all slaves.

Failover

You can use 2 commands to trigger automatic failover:

  • auto: the tool tries to find a candidate in the list of servers specified with --candidates, and if no good server is found in this list, it will look at the other slaves to see if one can be a good candidate. This is the default command
  • elect: same as auto, but if no good candidate is found in the list of candidates, other slaves will not be checked and the tool will exit with an error.

Let’s start the tool with auto:

$ mysqlfailover --master=root@localhost:13001 --discover-slaves-login=root auto

The monitoring console is visible and is refreshed every --interval seconds (default: 15). Its output is similar to what you get when using the health command.

Then let’s kill -9 the master to see what happens once the master is detected as down:

Failed to reconnect to the master after 3 attemps.
Failover starting in 'auto' mode...
# Candidate slave localhost:13002 will become the new master.
# Checking slaves status (before failover).
# Preparing candidate for failover.
# Creating replication user if it does not exist.
# Stopping slaves.
# Performing STOP on all slaves.
# Switching slaves to new master.
# Disconnecting new master as slave.
# Starting slaves.
# Performing START on all slaves.
# Checking slaves for errors.
# Failover complete.
# Discovering slaves for master at localhost:13002
Failover console will restart in 5 seconds.
MySQL Replication Failover Utility
Failover Mode = auto     Next Interval = Tue Jul  1 10:59:47 2014
Master Information
------------------
Binary Log File   Position  Binlog_Do_DB  Binlog_Ignore_DB
mysql-bin.000005  191
GTID Executed Set
a9a396c6-00f3-11e4-8e66-9cebe8067a3f:1-3
Replication Health Status
+------------+--------+---------+--------+------------+---------+
| host       | port   | role    | state  | gtid_mode  | health  |
+------------+--------+---------+--------+------------+---------+
| localhost  | 13002  | MASTER  | UP     | ON         | OK      |
| localhost  | 13003  | SLAVE   | UP     | ON         | OK      |
+------------+--------+---------+--------+------------+---------+

Looks good! The tool is then ready to fail over to another slave if the new master becomes unavailable.

You can also run custom scripts at several points of execution with the --exec-before, --exec-after, --exec-fail-check, --exec-post-failover options.

However it would be great to have a --failover-and-exit option to avoid flapping: the tool would detect master failure, promote one of the slaves, reconfigure replication and then exit (this is what MHA does for instance).

Tool registration

When the tool is started, it registers itself on the master by writing a few things in the specific table:

mysql> SELECT * FROM mysql.failover_console;
+-----------+-------+
| host      | port  |
+-----------+-------+
| localhost | 13001 |
+-----------+-------+

This is nice as it avoids that you start several instances of mysqlfailover to monitor the same master. If we try, this is what we get:

$ mysqlfailover --master=root@localhost:13001 --discover-slaves-login=root auto
[...]
Multiple instances of failover console found for master localhost:13001.
If this is an error, restart the console with --force.
Failover mode changed to 'FAIL' for this instance.
Console will start in 10 seconds..........starting Console.

With the fail command, mysqlfailover will monitor replication health and exit in the case of a master failure, without actually performing failover.

Running in the background

In all previous examples, mysqlfailover was running in the foreground. This is very good for demo, but in a production environment you are likely to prefer running it in the background. This can be done with the --daemon option:

$ mysqlfailover --master=root@localhost:13001 --discover-slaves-login=root auto --daemon=start --log=/var/log/mysqlfailover.log

and it can be stopped with:

$ mysqlfailover --daemon=stop

Errant transactions

If we create an errant transaction on one of the slaves, it will be detected:

MySQL Replication Failover Utility
Failover Mode = auto     Next Interval = Tue Jul  1 16:29:44 2014
[...]
WARNING: Errant transaction(s) found on slave(s).
Replication Health Status
[...]

However this does not prevent failover from occurring! You have to use --pedantic:

$ mysqlfailover --master=root@localhost:13001 --discover-slaves-login=root --pedantic auto
[...]
# WARNING: Errant transaction(s) found on slave(s).
#  - For slave 'localhost@13003': db906eee-012d-11e4-8fe1-9cebe8067a3f:1
2014-07-01 16:44:49 PM CRITICAL Errant transaction(s) found on slave(s). Note: If you want to ignore this issue, please do not use the --pedantic option.
ERROR: Errant transaction(s) found on slave(s). Note: If you want to ignore this issue, please do not use the --pedantic option.

Limitations

  • Like for mysqlrpladmin, the slave election process is not very sophisticated and it cannot be tuned.
  • The server on which mysqlfailover is running is a single point of failure.
  • Excessive CPU utilization: once it is running, mysqlfailover hogs one core. This is quite surprising.

Conclusion

mysqlfailover is a good tool to automate failover in clusters using GTID replication. It is flexible and looks reliable. Its main drawback is that there is no easy way to make it highly available itself: if mysqlfailover crashes, you will have to manually restart it.

The post Failover with the MySQL Utilities: Part 2 – mysqlfailover appeared first on MySQL Performance Blog.

Looking out for max values in integer-based columns in MySQL

Yay! My first blog post! As long as at least 1 person finds it useful, I’ve done my job. ;) Recently, one of my long-term clients was noticing that while their INSERTs were succeeding, a particular column counter was not incrementing. A quick investigation determined the column was of type int(11) and they had reached the maximum value of 2147483647. We fixed this by using pt-online-schema-change to change the column to int(10) unsigned, thus allowing values up to 4294967295.

My client was now concerned about all his other integer-based columns and wanted me to check them all. So I wrote a quick-n-dirty script in Go to check all integer-based columns on their current value compared to the maximum allowed for that column type.

You can find the full source code in my git repo.

Here’s a quick overview; the code is pretty simple.

First we connect to MySQL and verify the connection:

db, err := sql.Open("mysql", fmt.Sprintf("%s:%s@tcp(%s:3306)/%s", mysqlUn, mysqlPw, hostToCheck, dbToCheck))
if err != nil {
	fmt.Printf("Error connecting to MySQL on '%s': n", hostToCheck, err)
	db.Close()
	os.Exit(1)
}
// Check connection is alive.
err = db.Ping()
if err != nil {
	fmt.Printf("Unable to ping mysql at '%s': %sn", hostToCheck, err)
	db.Close()
	os.Exit(1)
}

Next, we query the information_schema.columns table for the names of all integer-based columns and calculate what their maximum value can be (credit for the clever SQL goes to Peter Boros).

// Construct our base i_s query
var tableExtraSql string
if tableToCheck != "" {
	tableExtraSql = fmt.Sprintf("AND TABLE_NAME = '%s'", tableToCheck)
}
baseSql := fmt.Sprintf(`
	SELECT TABLE_NAME, COLUMN_NAME, COLUMN_TYPE, (CASE DATA_TYPE
   	  WHEN 'tinyint' THEN 255
    	  WHEN 'smallint' THEN 65535
    	  WHEN 'mediumint' THEN 16777215
    	  WHEN 'int' THEN 4294967295
    	  WHEN 'bigint' THEN 18446744073709551615
   	END >> IF(LOCATE('unsigned', COLUMN_TYPE) > 0, 0, 1)) AS MAX_VALUE
	FROM information_schema.columns
	WHERE TABLE_SCHEMA = '%s' %s
	AND DATA_TYPE IN ('tinyint', 'int', 'mediumint', 'bigint')`, dbToCheck, tableExtraSql)

Now that we have this list of columns to check, we simply loop over this result set, get the MAX() of each column and print a pretty report.

// Loop over rows received from i_s query above.
for columnsToCheck.Next() {
	err := columnsToCheck.Scan(&tableName, &columnName, &columnType, &maxValue)
	if err != nil {
		log.Fatal("Scanning Row Error: ", err)
	}
	// Check this column
	query := fmt.Sprintf("SELECT MAX(%s), ROUND((MAX(%s)/%d)*100, 2) AS ratio FROM %s.%s",
		columnName, columnName, maxValue, dbToCheck, tableName)
	err = db.QueryRow(query).Scan(&currentValue, &ratio)
	if err != nil {
		fmt.Printf("Couldn't get MAX(%s.%s): %sn", tableName, columnName, err)
		fmt.Println("SQL: ", query)
		continue
	}
	// Print report
	if ratio.Valid && ratio.Float64 >= float64(reportPct) {
		fmt.Printf("'%s'.'%s' - Type: '%s' - ", tableName, columnName, columnType)
		fmt.Printf("ColumMax: '%d'", maxValue)
		fmt.Printf(" - CurVal: '%d'", currentValue.Int64)
		fmt.Printf(" - FillRatio: '%.2f'n", ratio.Float64)
	}
}

There are more options to the app that allow you to silence some of the verbosity and to only print report lines where the value-to-max ratio is > a user-defined threshold. If you have frequently changing schemas, this should allow you to cron the app and only receive email reports when there is a potential problem. Otherwise, this tool could be useful to run once a month/quarter, just to verify things are in good standing.

Like I said before, hopefully this helps at least 1 person catch a potential problem sooner rather than later.

The post Looking out for max values in integer-based columns in MySQL appeared first on MySQL Performance Blog.

TIMESTAMP Columns, Amazon RDS 5.6, and You

This comes from an issue that I worked on recently, wherein a customer reported that their application was working fine under stock MySQL 5.6 but producing erroneous results when they tried running it on Amazon RDS 5.6. They had a table which, on the working server, contained two TIMESTAMP columns, one which defaulted to CURRENT_TIMESTAMP and the other which defaulted to ’0000-00-00 00:00:00′, like so:

CREATE TABLE mysql56 (
  id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
  ts1 TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
  ts2 TIMESTAMP NOT NULL DEFAULT '0000-00-00 00:00:00',
);

However, under Amazon RDS, the same table looked like this:

CREATE TABLE rds56 ( 
  id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
  ts1 TIMESTAMP NULL DEFAULT NULL,
  ts2 TIMESTAMP NULL DEFAULT NULL, 
);

They mentioned that their schema contains TIMESTAMP column definitions without any modifiers for nullability or default values. In other words, they were doing something like this:

CREATE TABLE foo56 (
    id NOT NULL AUTO_INCREMENT PRIMARY KEY,
    ts1 TIMESTAMP,
    ts2 TIMESTAMP
);

It’s a known issue (or change, or difference, whatever we choose to call it) that MySQL is deprecating defaults for TIMESTAMP columns that don’t have any nullability or default-value specifiers; this is covered in the 5.6 documentation. However, the docs also mention that the default value for this setting is OFF – i.e., if you create a table with TIMESTAMP columns without any defaults, it will fill them in for you, similarly to what I’ve described above.

As it turns out, the RDS default for this setting is ON, hence the “NULL DEFAULT NULL” modifiers when creating the table under RDS. We changed the parameter group, restarted the instance (note that this variable is NOT dynamic), and their schema-creation script created the tables in the proper way.

So, what have we learned here?

  • Migrating from standalone MySQL to Amazon RDS sometimes has hidden pitfalls that aren’t always readily apparent. Many times it will “just work” – but sometimes it doesn’t. Percona is, of course, happy to help review your configurations and assist with any Amazon RDS implementation plans you might have.
  • When in doubt, fully-specify your TIMESTAMP columns. If you want them NOT NULL, say so. If you want a default value or an on-updated value, set it. Even the configuration variable explicit_defaults_for_timestamp is deprecated and slated for removal in a future version, so eventually it won’t be possible to get the old pre-5.6 behavior at all.

The post TIMESTAMP Columns, Amazon RDS 5.6, and You appeared first on MySQL Performance Blog.

A conversation with 5 Facebook MySQL gurus

A conversation with 6 Facebook MySQL gurusFacebook, the undisputed king of online social networks, has 1.23 billion monthly active users collectively contributing to an ocean of data-intensive tasks – making the company one of the world’s top MySQL users.

A small army of Facebook MySQL experts will be converging on Santa Clara, Calif. next week where several of them are leading sessions at the Percona Live MySQL Conference and Expo. I had the chance to chat virtually with four of them about their sessions: Steaphan Greene, Evan Elias, Shlomo Priymak and Yoshinori MatsunobuMark Callaghan, who spoke at Percona Live last year, also joined our conversation which included a discussion of Facebook’s use of MySQL and other open source technologies.


Tom: What’s Facebook’s view of Open Source?

Steaphan: Facebook was built on open source software, and we still invest heavily in open source today. We understand the power these communities have to drive innovation – they allow us to focus on new challenges, as opposed to reinventing the wheel over and over again. And contributing as much as possible back to open projects is in everyone’s best interest.


Tom: Why MySQL? Wouldn’t NoSQL databases, for example, be better suited for the massive workloads seen at Facebook?

Mark: MySQL is great for many of our important workloads. We make it even better with our expertise in MySQL operations and engineering, and by working with the community and learning from their experience.

Yoshinori: I have not been able to find a transactional NoSQL database better than InnoDB. And it’s easy to understand how MySQL Replication works, which makes much easier to fix problems in production.


 Tom: How does Facebook make MySQL scale?

Steaphan: Sharding, automation, monitoring, and heavy investment in operations and performance engineering.


 Tom: What other things help Facebook run smoothly?

Steaphan: Our completely open culture, and the freedom all engineers here have to try any idea they have.


Tom: What is the top scaling challenge(s) Facebook faces in 2014 – and beyond?

Mark: Our biggest challenge is to make things better (performance, efficiency, availability) in the future at the rate we made things better in the past.

Yoshinori: Availability has improved a lot so far for us. Come to my session at Percona Live to hear about that. For me personally, efficiency is the biggest challenge for 2014 and 2015. This includes reducing space and optimizing for newer-generation hardware.


Tom: Facebook deployed MySQL 5.6 last year – including on critical environments – long before many other large organizations. What prompted such a move so soon? And where there any major concerns?

Steaphan: The same thing that prompts most efforts on the Facebook infra team: We will consider any technology that will help us improve performance, efficiency, or reliability, and we’re willing to accept the risk that sometimes comes along with adopting things like 5.6 very early on. But that’s only half the story here. The other half is that Facebook encourages its engineers to go after big bets like these — in this case it was just one engineer who made this happen. And we had the MySQL engineering talent we needed to work with the Oracle team to get 5.6 ready for production at our scale.

Yoshinori: At Facebook, we have three MySQL teams — Operations, Performance and Engineering. Facebook is one of the very few MySQL users that has internal MySQL developers. We all worked hard to adapt 5.6 to our scale and ensure that it would be production-ready. We found some issues after production deployment, but in many cases we could fix the problem and deployed new MySQL binary within one or two days. When deploying in production, we expected that we encountered MySQL 5.6 specific issues, which was typical when releasing new software. We were just confident that we could fix issues immediately.

Our 5.6 deployment step was not all at once. At first rollout, we disabled most major 5.6 features, such as GTID and binlog checksum. We gradually enabled such features in production.


Tom: Where there any significant issues in that move to MySQL 5.6? Any lessons learned you like to share – along with best practices you’d like to share?

Yoshinori: Performance regression of the CPU intensive replication was a main blocker for some of our applications. I wrote a blog post about this last year. We have several design plans to fix the problem on MySQL side. One of the most effective plans is grouping multiple transactions into one, since the most expensive part is writing to InnoDB system table at transaction commit. This optimization would be done when writing binary log, or by SQL thread. It may take longer time to test and deploy in production. For existing applications, we optimized to group multiple transactions from application side to mitigate the problem.


Tom: Performance monitoring is usually challenging at any organization. How do you do that at Facebook, which has tens of thousands of MySQL instances?

Yoshinori: Top-N monitoring is very important for managing a huge number of instances. Average statistics (for example: average innodb_rows_read across all instances) is not always useful since ~1% of problematic instances won’t noticeably change average numbers. p99 gives better indicators, but in our environment we typically have fewer than 0.1% instances causing problems, in which case p99 is not helpful either. We have several graphical and command-line tools to efficiently list up top-N bad behaving hosts. After listing up bad instances, the way to investigate root cause is pretty straightforward, like what MySQL consultants usually do. Server failure is something we expect and plan for at Facebook. For example, typical MySQL DBA at small companies may not encounter master instance failure during employment, because recent mysqld and H/W are stable enough. At Facebook, master failure is a norm and something the system can accommodate.


Tom: Evan, you and Yoshinori will present on Global Transaction ID (GTID) at Facebook. GTID is very tricky to deploy to an existing large-scale environment – how, and why, did you decide on adopting it?

Evan: Our primary motivations for adopting GTID all relate to either failover or binlog backups. When a master fails, getting replicas in sync with GTID is substantially simpler, faster, and less error-prone than previous methods of diffing binlogs. For backups, GTID is a cornerstone in building cross-datacenter point-in-time recovery, without needing redundant binlog streams from every region.

The “how” question is a bit more involved, and we’ll be covering this in detail during the session. The GTID project was a joint effort between three of Facebook’s MySQL teams. Santosh added new functionality to the MySQL server to make online rollout possible, and Yoshinori improved MHA to seamlessly support GTID-based failover. I added GTID support to all of our other in-house automation, and also scripted the rollout procedure across our many thousands of replica sets. A lot of validation logic and monitoring functionality was involved to ensure the safety of the rollout.


Tom: Shlomo, your session is titled “Under the Hood – MySQL Pool Scanner (MPS).” As you point out in your talk, Facebook has one of the largest MySQL database clusters in the world, comprising thousands of servers across multiple data centers. You must have an army of DBAs – or is there some secret you’d like to share? 

Shlomo: We do have an army, yes — it’s an “army of one.” We have one person on call on the MySQL Operations team at a given time, and they don’t even need to do all that much most days. We built “robots” to do our day to day jobs. The largest and most complex robot we have is MPS, an automated system to do most of the work a DBA might otherwise spend time on, such as replacement of broken or overflowing servers. Among other things, MPS also allows a human to initiate complex bulk operations with a few keystrokes, and it will follow up and complete the operations over the course of days or weeks.

I’ll be describing some of the complex MySQL automation systems we have at Facebook, and how they fit together during my talk.


Tom: Shlomo, what does a typical day look like for you there at Facebook?

Shlomo: The team’s work mostly focuses on maintaining those robots I’ve mentioned, as well as developing new ways to improve the reliability of our databases for Facebook’s users. This year the team also spent a lot of time making sure the new MySQL features such as GTIDs and semi-sync are deeply integrated in our automation. Every day, we work hard to to make ourselves obsolete, but we haven’t gotten there just yet!

On a typical day, I probably spend much of the time coding, mainly in Python. I also spend a significant amount of time working on capacity-related projects, such as thinking of ways to optimize the way we distribute the data across our fleet of servers.
Even after 2.5 years at Facebook, I am still in awe of the number of servers we manage. The typical small-scale maintenance operation at Facebook probably involves more servers than all the companies I’ve previously worked for had, combined. It really is pretty amazing!


Tom:  What are you looking forward to the most at this year’s conference?

Evan: There are plenty of fascinating sessions this year. Just to mention a handful: Jeremy Cole and Davi Arnaut’s session on innodb_ruby, since it’s a very unique way to interactively learn about InnoDB’s internals. Baron Schwartz’s session on using Go with MySQL, as VividCortex is blazing the trail here. Peter Boros and Kenny Gryp’s talk on scalability and benchmarking, which I’m hoping will include recent developments of Percona Playback. Tom Christ’s session on my former project Jetpants, to see how it has evolved over the past year at Tumblr. And several talks by Oracle engineers about upcoming functionality in MySQL 5.7.

Steaphan: In addition to the conference sessions, I look forward to the birds of a feather session with the MySQL team.  Last year, it proved to be a valuable opportunity to engage with those upstream developers who make the changes we care about, and I expect the same this year.


Tom: If you could talk to a DBA or developer on the fence about attending this year’s conference, what would be your top 3-5 reasons for making it over to Santa Clara for this event?

Evan: I’m based in NYC, so I’m traveling a bit further than many of my colleagues, but I can still confidently say that Percona Live is well worth the trip. The MySQL ecosystem is very healthy and constantly evolving, and the conference is the best place to learn about ongoing developments across a wide spectrum of companies and contributors. It’s also a perfect opportunity to personally connect with all of the amazing engineers, DBAs, users, and vendors that make MySQL so unique and compelling.


 The Percona Live MySQL Conference and Expo 2014 runs April 1-4 in Santa Clara, Calif. Use the code “SeeMeSpeak” when registering and save 10 percent. The inaugural Open Source Appreciation Day is on March 31 – this full-day event is free but because space is limited I suggest registering now to reserve your spot.

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Percona XtraBackup 2.2.1 alpha release is now available

Percona XtraBackup for MySQL

Percona is glad to announce the release of Percona XtraBackup 2.2.1-alpha1 on March 28th 2014. Downloads are available from our download site here. This ALPHA release, will be available in our Debian experimental and CentOS testing repositories.

This is an ALPHA quality release and it is not intended for production. If you want a high quality, Generally Available release, the current Stable version should be used (Percona XtraBackup 2.1.8 in the 2.1 series at the time of writing).

This release contains all of the features and bug fixes in Percona XtraBackup 2.1.8, plus the following:

New Features:

  • Percona XtraBackup has removed the multiple binaries (xtrabackup_56, xtrabackup_55, xtrabackup) and now uses single xtrabackup binary instead for handling backups. Single binary implementation removed the requirement to download server source tarballs and removed different patches which resulted in cleaner code.
  • Percona XtraBackup source layout has been changed to implement the single binary. Percona XtraBackup code can now be found in storage/innobase/xtrabackup.
  • Percona XtraBackup implemented support for Backup Locks.
  • Percona XtraBackup can now store backup history on the server itself in a special table created for that purpose.
  • innobackupex-1.5.1 symlink has been removed, instead innobackupex binary should be used.
  • Percona XtraBackup has removed the build.sh script and it’s now built with CMake.
  • Percona XtraBackup has been rebased on MySQL 5.6.16.

Bugs Fixed:

  • Information about tool version used to take the backup was added by implementing backup history feature. Bug fixed #1133017.
  • If an XtraDB-based binary was used to a backup an InnoDB database, it would convert it to XtraDB by adding the XTRADB_1 marker in the dictionary header page and by adding the SYS_STATS table. Bug fixed #988310.

Release notes with all the bugfixes for Percona XtraBackup 2.2.1 are available in our online documentation. All of Percona‘s software is open source and free, all the details of the release can be found in the 2.2.1 milestone at Launchpad. Bugs can be reported on the launchpad bug tracker.

The post Percona XtraBackup 2.2.1 alpha release is now available appeared first on MySQL Performance Blog.

Innodb redo log archiving

Innodb redo log archivingPercona Server 5.6.11-60.3 introduces a new “log archiving” feature. Percona XtraBackup 2.1.5 supports “apply archived logs.” What does it mean and how it can be used?

Percona products propose three kinds of incremental backups. The first is full scan of data files and comparison the data with backup data to find some delta. This approach provides a history of changes and saves disk space by storing only data deltas. But the disadvantage is a full-data file scan that adds load to the disk subsystem. The second kind of incremental backup avoids extra disk load during data file scans.

The idea is in reading only changed data pages. The information about what specific pages were changed is provided by the server itself which writes files with the information during work. It’s a good alternative but changed-pages tracking adds some small load. And Percona XtraBackup’s delta reading leads to non-sequential disk io. This is good alternative but there is one more option.

The Innodb engine has a data log. It writes all operations which modify database pages to log files. This log is used in the case of unexpected server terminating to recover data. The Innodb log consists of the several log files which are filled sequentially in circular. The idea is to save those files somewhere and apply all modifications from archived logs to backup data files. The disadvantage of this approach is in using extra disk space. The advantage is there is no need to do an “explicit” backup on the host server. A simple script could sit and wait for logs to appear then scp/netcat them over to another machine.

But why not use good-old replication? Maybe replication does not have such performance as logs recovering but it is more controlled and well-known. Archived logs allows you to do any number of things with them from just storing them to doing periodic log applying. You can not recover from a ‘DROP TABLE’, etc with replication. But with this framework one could maintain the idea of “point in time” backups.

So the “archived logs” feature is one more option to organize incremental backups. It is not widely used as it was issued not so far and there is not A good understanding of how it works and how it can be used. We are open to any suggestions about its suggest improvements and use cases. The subject of this post is to describe how it works in depth. As log archiving is closely tied with innodb redo logs the internals of redo logs will be covered too. This post would be useful not only for DBA but also for Software Engineers because not only common principles are considered but the specific code too, and knowledge from this post can be used for further MySQL code exploring and patching.

What is the innodb log and how it is written?

Let’s remember what are innodb logs, why they are written, what they are used for.

The Innodb engine has buffer pool. This is a cache of database pages. Any changes are done on page in buffer pool, then page is considered as “dirty,” which means it must be flushed, and pushed to the flush list which is processed periodically by special thread. If pages are not flushed to disk and server is terminated unexpectedly the changes will be lost. To avoid this innodb writes changes to redo log and recover data from redo log during start. This technique allows to delay buffer pool pages flushing. It can increase performance because several changes of one page can be accumulated in memory and then flushed by one io. Except that flushed pages can be grouped to decrease the number of non-sequential io’s. But the down-side of this approach is time for data recovering. Let’s consider how this log is stored, generated and used for data recovering.

Log files

Redo log consists of a several log files which are treated as a circular buffer. The number and the size of log files can be configured. Each log file has a header. The description of this header can be found in “storage/innobase/include/log0log.h” by “LOG_GROUP_ID” keyword.

Each log file contains log records. Redo log records are written sequentially by log blocks of OS_FILE_LOG_BLOCK_SIZE size which is equal to 512 bytes by default and can be changed with innodb option. Each record has its LSN. LSN is a “Log Sequence Number” – the number of bytes written to log from the log creation to the certain log record. Each block consists of header, trailer and log records.

Log blocks

Let’s consider log block header. The first 4 bytes of the header is log block number. The block number is very similar as LSN but LSN is measured in bytes and block number is measured by OS_FILE_LOG_BLOCK_SIZE. Here is the simple formula how LSN is converted to block number:

return(((ulint) (lsn / OS_FILE_LOG_BLOCK_SIZE) & 0x3FFFFFFFUL) + 1);

This formula can be found in log_block_convert_lsn_to_no() function. The next two bytes is the number of bytes used in the block. The next two bytes is the offset of the first MTR log record in this block. What is MTR will be described below. Currently it can be considered as a synonym of bunch of log records which are gathered together as a description of some logical operation. For example it can be a group of log records for inserting new row to some table. This field is used when there are records of several MTR’s in one block. The next four bytes is a checkpoint number. The trailer is four bytes of log block checksum. The above description can be found in “storage/innobase/include/log0log.h” by “LOG_BLOCK_HDR_NO” keyword.

Before writing to disk log blocks must be somehow formed and stored. And the question is:

How log blocks are stored in memory and on disk?

Where log blocks are stored before flushing to disk and how they are written and flushed?

Global log object and log buffer

The answer to the first part of the question is log buffer. Server holds very important global object log_sys in memory. It contains a lot of useful information about logging state. Log buffer is pointed by log_sys->buf pointer which is initialized in log_init(). I would highlight the following log_sys fields that are used for work with log buffer and flushing:

log_sys->buf_size – the size of log buffer, can be set with innodb-log-buffer-size variable, the default value is 8M;
log_sys->buf_free – the offset from which the next log record will be written;
log_sys->max_buf_free – if log_sys->buf_free is greater then this value log buffer must be flushed, see log_free_check();
log_sys->buf_next_to_write – the offset of the next log record to write to disk;
log_sys->write_lsn – the LSN up to which log is written;
log_sys->flushed_to_disk_lsn – the LSN up to which log is written and flushed;
log_sys->lsn – the last LSN in log buffer;

So log_sys->buf_next_to_write is between 0 and log_sys->buf_free, log_sys->write_lsn is equal or less log_sys->lsn, log_sys->flushed_to_disk_lsn is less or equal to log_sys->write_lsn.

The relationships for those fields can be easily traced with debugged by setting up watchpoints.

Ok, we have log buffer, but how do log records come to this buffer?

Where log records come from?

Innodb has special objects that allow you to gather redo log records for some operations in one bunch before writing them to log buffer. These objects are called “mini-transactions” and corresponding functions and data types have “mtr” prefix in the code. The objects itself are described in mtr_t “c” structure. The most interesting fields of this structure are the following:

mtr_t::log – contains log records for the mini-transaction,
mtr_t::memo – contains pointers to pages which are changed or locked by the mini-transaction, it is used to push pages to flush list and release locks after logs records are copied to log buffer in mtr_commit() (see mtr_memo_pop_all() called in mtr_commit()).

mtr_start() function initializes an object of mtr_t type and mtr_commit() writes log records from mtr_t::log to log_sys->buf + log_sys->buf_free. So the typical sequence of any operation which changes data is the following:


mtr_start(); // initialize mtr object
some_ops... // operations on data which are logged in mtr_t::log
mtr_commit(); // write logged operations from mtr_t::log to log buffer log_sys->buf

page_cur_insert_rec_write_log() is a good example of how mtr records can be written and mtr::memo can be filled. The low-level function which writes data to log buffer is log_write_low(). This function is invoked inside of mtr_commit() and not only copy the log records from mtr_t object to log buffer log_sys->buf but also creates a new log blocks inside of log_sys->buf, fills their header, trailer, calculates checksum.

So log buffer contains log blocks which are sequentially filled with log records which are grouped in “mini-transactions” which logically can be treated as some logical operation over data which consists of a sequence of mini-operations(log records).

As log records are written sequentially in log buffer one mini-transaction and even one log record can be written in two neighbour blocks. That is why the header field which would contain the offset of the first MTR in the block is necessary to calculate the point from which log records parsing can be started. This field was described in 2.2.

So we have a buffer of log blocks in a memory. How is data from this buffer written to disk? The mysql documentation says that this depends on innodb_flush_log_at_trx_commit option. There can be three cases depending on the value of this option. Let’s consider each of them.

Writing log buffer to disk: innodb_flush_log_at_trx_commit is 1 or 2.

The first two cases is when innodb_flush_log_at_trx_commit is 1 or 2. In these cases flush log records are written for 2 and flushed for 1 on each transaction commit. If innodb_flush_log_at_trx_commit is 2 log records are flushed periodically by special thread which will be considered later. The low-level function which writes log records from buffer to file is log_group_write_buf(). But in the most cases it is not called directly but it is called from more high level log_write_up_to(). For the current case the calling stack is the following:


(trx_commit_in_memory() or
trx_commit_complete_for_mysql() or
trx_prepare() e.t.c)->
trx_flush_log_if_needed()->
trx_flush_log_if_needed_low()->
log_write_up_to()->
log_group_write_buf().

It is quite easy to find the higher levels of calling stack, just set up breakpoint on log_group_write_buf() and execute any sql query that modifies innodb data. For example for the simple “insert” sql query the higher levels of calling stack are the following:


mysql_execute_command()->
trans_commit_stmt()->
ha_commit_trans()->
TC_LOG_DUMMY::commit()->
ha_commit_low()->
innobase_commit()->
trx_commit_complete_for_mysql()->
trx_flush_log_if_needed()-> ... .

log_io_complete() callback is invoked when i/o is finished for log files (see fil_aio_wait()). log_io_complete() flushes log files if this is not forbidden by innodb_flush_method or innodb_flush_log_at_trx_commit options.

Writing log buffer to disk: innodb_flush_log_at_trx_commit is equal to 0

The third case is when innodb_flush_log_at_trx_commit is equal to 0. For this case log buffer is NOT written to disk on transaction commit, it is written and flushed periodically by separate thread “srv_master_thread”. If innodb_flush_log_at_trx_commit = 0 log files are flushed in the same thread by the same calls. The calling stack is the following:


srv_master_thread()->
(srv_master_do_active_tasks() or srv_master_do_idle_tasks() or srv_master_do_shutdown_tasks())->
srv_sync_log_buffer_in_background()->
log_buffer_sync_in_background()->log_write_up_to()->... .

Special cases for logs flushing

While log_io_complete() do flushing depending on innodb_flush_log_at_trx_commit value among others log_write_up_to() has it’s own flushing criteria. This is flush_to_disk function argument. So it is possible to force log files flushing even if innodb_flush_log_at_trx_commit = 0. Here are examples of such cases:

1) buf_flush_write_block_low()
Each page contains information about the last applied LSN(buf_flush_write_block_low::newest_modification), each log record is a description of change on certain page. Imagine we flushed some changed pages but log records for these pages were not flushed and server goes down. After starting the server some pages will have the newest modifications, but some of them were not flushed and the correspondent log records are lost too. We will have inconsistent database in this case. That is why log records must be flushed before the pages they refer.

2) srv_sync_log_buffer_in_background()
As it was described above this function is called periodically by special thread and forces flushing.

3) log_checkpoint()
When checkpoint is made log files must be reliably flushed.

4) The special handlerton innobase_flush_logs() which can be called through ha_flush_logs() from mysql server.
For example ha_flush_logs() is called from MYSQL_BIN_LOG::reset_logs() when “RESET MASTER” or “RESET SLAVE” are executed.

5) srv_master_do_shutdown_tasks() – on shutdown, ha_innobase::create() – on table creating, ha_innobase::delete_table() – on table removing, innobase_drop_database() – on all database tables removing, innobase_rename_table() – on table rename e.t.c

If log files are treated as circular buffer what happens when the buffer is overflown?

Briefly. Innodb has a mechanism which allows you to avoid overflowing. It is called “checkpoints.” The checkpoint is a state when log files are synchronized with data files. In this case there is no need to keep the history of changes before checkpoint because all pages with the last modifications LSN less or equal to checkpoint LSN are flushed and the log files space from the last written LSN to the last checkpoint LSN can be reused. We will not describe a checkpoint process here because it is a separate interesting subject. The only thing we need to know is when checkpoint happens all pages with modification LSN less or equal to checkpoint LSN are reliably flushed.

How archived logs are written by server.

So the log contains information about page changes. But as we said, log files are the circular buffer. This means that they occupy fixed disk size and the oldest records can be rewritten by the newest ones as there are points when data files are synchronized with log files called checkpoints and there is no need to store the previous history of log records to guarantee database consistency. The idea is to save somewhere all log records to have the possibility of applying them to backuped data to have some kind if incremental backup. For example if we want to have an archive of log records. As log consists of log files it is reasonable to store log records in such files too, and these files are called “archived logs.”

Archived log files are written to the directory which can be set with special innodb option. Each file has the same size as innodb log size and the suffix of each archived file is the LSN from which it is started.

As well as log writing system log archiving system stores its data in global log_sys object. Here are the most valuable fields in log_sys from my point of view:

log_sys->archive_buf, log_sys->archive_buf_size – logs archive buffer and its size, log records are copied from log buffer log_sys->buf to this buffer before writing to disk;
log_sys->archiving_phase – the current phase of log archiving: LOG_ARCHIVE_READ when log records are being copied from log_sys->buf to log_sys->archive_buf, LOG_ARCHIVE_WRITE when log_sys->archive_buf is being written to disk;
log_sys->archived_lsn – the LSN to which log files are written;
log_sys->next_archived_lsn – the LSN to which write operations was invoked but not yet finished;
log_sys->max_archived_lsn_age – the maximum difference between log_sys->lsn and log_sys->archived_lsn, if this difference exceeds the log are being archived synchronously, i.e. the difference is decreased;
log_sys->archive_lock – this is rw-lock which is used for synchronizing LOG_ARCHIVE_WRITE and LOG_ARCHIVE_READ phases, it is x-locked on LOG_ARCHIVE_WRITE phase.

So how is data copied from log_sys->buf to log_sys->archived_buf? log_archive_do() is used for this. It is not only set the proper state for archived log fields in log_sys but also invokes log_group_read_log_seg() with corresponding arguments which not only copy data from log buffer to archived log buffer but also invokes asynchronous write operation for archived log buffer. log_archive_do() can wait until io operations are finished using log_sys->archive_lock if corresponding function parameter is set.

The main question is on what circumstances log_archive_do() is invoked, i.e. when log records are being written to archived log files. The first call stack is the following:


log_free_check()->
log_check_margins()->
log_archive_margin()->
log_archive_do().

Here is text of log_free_check() with comments:


/*********************************************************************//
Checks if there is need for a log buffer flush or a new checkpoint, and does
this if yes. Any database operation should call this when it has modified
more than about 4 pages. NOTE that this function may only be called when the
OS thread owns no synchronization objects except the dictionary mutex. */
UNIV_INLINE
void
log_free_check(void)
/*================*/
{

#ifdef UNIV_SYNC_DEBUG
ut_ad(sync_thread_levels_empty_except_dict());
#endif /* UNIV_SYNC_DEBUG */

if (log_sys->check_flush_or_checkpoint) {

log_check_margins();
}
}

log_sys->check_flush_or_checkpoint is set when there is no enough free space in log buffer or it is time to do checkpoint or any other bound case. log_archive_margin() is invoked only if the limit if the difference between log_sys->lsn and log_sys->archived_lsn is exceeded. Let’s refer to this difference as archived lsn age.

One more call log_archive_do() is from log_open() when archived lsn age exceeds some limit. log_open() is called on each mtr_commit(). And for this case archived logs are written synchronously.

The next synchronous call is from log_archive_all() during shutdown.

Summarizing all above archived logs begins to be written when the log buffer is full enough to be written or when checkpoint happens or when the server is in the process of shut down. And there is no any delay between writing to archive log buffer and writing to disk. I mean there is no way to say that archived logs must be written once a second as it is possible for redo logs with innodb_flush_log_at_trx_commit = 0. As soon as data is copied to the buffer the write operation is invoked immediately for this buffer. Archived log buffer is not filled on each mtr_commit() so it does not slow down the usual logging process. The exception is when there are a lot of io operations what can be the reason of archive log age is too big. The result of big archive log age is the synchronous archived logs writing during mtr_commit(). Memory to memory copying is quite fast operation that is why the data is copied to archived log buffer and is written to disk asynchronously minimizing delays which can be caused by logs archiving.

PS: Here is another call stack for writing archived log buffer to archived log files:

log_io_complete()->log_io_complete_archive()->log_archive_check_completion_low()->log_archive_groups().

I propose to explore this stack yourself.

Logs recovery process, how it is started and works inside. Archived logs applying.

So we discovered how innodb redo logging works, and how redo logs are archived. And the last uncovered thing is how recovery works and how archived logs are applied. These two processes are very similar – that is why they are discussed in one section of this post.

The story begins with innobase_start_or_create_for_mysql() which is invoked from innobase_init(). The following trident in innobase_start_or_create_for_mysql() can be used to search the relevant code:


if (create_new_db) {
...
} else if (srv_archive_recovery) {
...
} else {
...
}

The second condition and the last one is the place from which archived logs applying and innodb logs recovery processes correspondingly start. These two blocks wrap two pairs of functions:


recv_recovery_from_archive_start()
recv_recovery_from_archive_finish()

and

recv_recovery_from_checkpoint_start()
recv_recovery_from_checkpoint_finish()

And all the magic happens in these pairs. As well as global log_sys object for redo logging there is global recv_sys object for innodb recovery and archived logs applying. It is created and initialized in recv_sys_create() and recv_sys_init() functions correspondingly. The following fields if recv_sys object are the most important from my point:

recv_sys->limit_lsn – the LSN up to which recovery should be made, this value is initialized with the maximum value of uint64_t(see #define LSN_MAX) for the recovery process and with certain value which is passed as an argument of recv_recovery_from_archive_start() function and can be set via xtrabackup option for log applying;
recv_sys->parse_start_lsn – the LSN from which logs parsing is started, for the the logs recovery this value equals to the last checkpoint LSN, for logs applying this is last applied LSN;
recv_sys->scanned_lsn – the LSN up to which log files are scanned;
recv_sys->recovered_lsn – the LSN up to which log records are applied, this value <= recv_sys->scanned_lsn;

The first thing that must be done for starting recovery process is to find out the point in log files where the recovery must be started from. This is the last checkpoint LSN. recv_find_max_checkpoint() proceed this. As we can see in log_group_checkpoint() the following code writes checkpoint info into two places in the first log file depending on the checkpoint number:


/* We alternate the physical place of the checkpoint info in the first
log file */

 

if ((log_sys->next_checkpoint_no & 1) == 0) {
write_offset = LOG_CHECKPOINT_1;
} else {
write_offset = LOG_CHECKPOINT_2;
}

So recv_find_max_checkpoint() reads checkpoint info from both places and selects the latest checkpoint.

The same idea is applied for logs, too, but the last applied LSN instead of last checkpoint LSN must be found. Here is the call stack for reading last applied LSN:


innobase_start_or_create_for_mysql()->
open_or_create_data_files()->
fil_read_first_page().

The last applied LSN is stored in the first page of data files in (min|max)_flushed_lsn fields(see FIL_PAGE_FILE_FLUSH_LSN offset). These values are written in fil_write_flushed_lsn_to_data_files() function on server shutdown.

So the main difference between logs applying and recovery process at this stage is the manner of calculating LSN from which log records will be read. For logs applying the last flushed LSN is used but for recovery process it is the last checkpoint LSN. Why does this difference take place? Logs can be applied periodically. Assume we gather archived logs and apply them once an hour to have fresh backup. After applying the previous bunch of log files there can be unfinished transactions. For the recovery process any unfinished transactions are rolled back to have consistent db state at server starting. But for the logs applying process there is no need to roll back them because any unfinished transactions can be finished during the next logs applying.

After calculating the start LSN the sequence of actions is the same for both recovering and applying. The next step is reading and parsing log records. See recv_group_scan_log_recs() which is invoked from recv_recovery_from_checkpoint_start_func() for logs recovering and recv_recovery_from_archive_start()->log_group_recover_from_archive_file() for logs applying.

The first we read log records to some buffer and then invoke recv_scan_log_recs() to parse them. recv_scan_log_recs() checks each log block on consistency(checksum + comparing the log block number written in log block with log block number calculated from log block LSN) and other edge cases and copy it to parsing buffer recv_sys->buf with recv_sys_add_to_parsing_buf() function. The parsing buffer is then parsed by recv_parse_log_recs(). Log records are stored in hash table recv_sys->addr_hash. The key for this hash table is calculated basing on space id and page number pair. This pair refers to the page to which log records must be applied. The value of the hash table is object of recv_addr_t type. recv_addr_t type contains rec_list field which is the list of log records for applying to the (space id, page num) page (see recv_add_to_hash_table().

After parsing and storing log record in hash table recv_sys->addr_hash log records are applied. The function which is responsible for log records applying is recv_apply_hashed_log_recs(). It is invoked from recv_scan_log_recs() if there is no enough memory to store log records and at the end of recovering/applying process. For each element of recv_sys->addr_hash, i.e. for each DB page which must be changed with log records recv_recover_page() is invoked. It can be invoked as from recv_apply_hashed_log_recs() in the case if page is already in buffer pool of from buf_page_io_complete() on io completion, i.e. just after page was read from storage. Applying log records on page read completion is necessary and very convenient. Assume log records have not yet applied as we had enough memory to store the whole recovery log records. But we want for example to boot DB dictionary. I this case any records that concern to the pages of the dictionary will be applied to those pages just after reading them from storage to buffer pool.

The function which applies log records to the certain page is recv_recover_page_func(). It gets the list of log records for the certain page from recv_sys->addr_hash hash table, for each element of this list it compares the lsn of last page changes with the LSN of the record, and if the former is greater the later it applies log record to the page.

After applying all log records from archived logs xtrabackup writes last applied LSN to (min|max)_flushed LSN fields of each data file and finishes execution. The logs recovery process rollbacks all unfinished transactions unless this is forbidden with innodb-force-recovery parameter.

Conclusion

We covered the processes of redo logs writing and recovery in depth. These are very important processes as they provide data consistency on crashes. These two processes became a base for logs archiving and applying features. As log records can describe any data changes the idea is to store these records somewhere and then apply them to backups for organizing some kind of incremental backup.

The features were implemented a short time ago and currently they are not widely used. So if you have something to say about them you are welcome to comment for discussion.

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