Category Archives: MariaDB features

Improving InnoDB index statistics

The MySQL/MariaDB optimiser likes to know things like the cardinality of an index – that is, the number of distinct values the index holds. For a PRIMARY KEY, which only has unique values, the number is the same as the number of rows.  For an indexed column that is boolean (such as yes/no) the cardinality would be 2.

There’s more to it than that, but the point is that the optimiser needs some statistics from indexes in order to try and make somewhat sane decisions about which index to use for a particular query. The statistics also need to be updated when a significant number of rows have been added, deleted, or modified.

In MyISAM, ANALYZE TABLE does a tablescan where everything is tallied, and the index stats are updated. InnoDB, on the other hand, has always done “index dives”, looking at a small sample and deriving from that. That can be ok as a methodology, but unfortunately the history is awkward. The number used to be a constant in the code (4), and that is inadequate for larger tables. Later the number was made a server variable innodb_stats_sample_pages and its default is now 8 – but that’s still really not enough for big(ger) tables.

We recently encountered this issue again with a client, and this time it really needed addressing as no workarounds were effective across the number of servers and of course over time. Open Query engineer Daniel filed https://mariadb.atlassian.net/browse/MDEV-7084 which was picked up by MariaDB developed Jan Lindström.

Why not just set the innodb_stats_sample_pages much higher? Well, every operation takes time, so setting the number appropriate for your biggest table means that the sampling would take unnecessarily long for all the other (smaller, or even much smaller) tables. And that’s just unfortunate.

So why doesn’t InnoDB just scale the sample size along with the table size? Because, historically, it didn’t know the table size: InnoDB does not maintain a row count (this has to do with its multi-versioned architecture and other practicalities – as with everything, it’s a trade-off). However, these days we have persistent stats tables – rather than redoing the stats the first time a table is opened after server restart, they’re stored in a table. Good improvement. As part of that information, InnoDB now also knows how many index pages (and leaf nodes in its B+Tree) it has for each table. And while that’s not the same as a row count (rows have a variable length so there’s no fixed number of rows per index page), at least it grows along with the table. So now we have something to work with! The historical situation is no longer a hindrance.

In order to scale the sample size sanely, that is not have either too large a number for small tables, or a number for big tables that’s over the top, we’ll want some kind of logarithmic scale. For instance, log2(16 thousand) = 14, and log2(1 billion) = 30. That’s small enough to be workable. The new code as I suggested:

n_sample_pages = max(min(srv_stats_sample_pages, index->stat_index_size), log2(index->stat_index_size) * srv_stats_sample_pages);

This is a shorter construct (using min/max instead of ifs) of what was already there, combined with the logarithmic sample basis. For very small tables, either the innodb_stats_sample_pages number if used or the actual number of pages, whichever is smaller – for bigger tables, the log2 of the #indexpages is used, multiplied by the dynamic system variable innodb_stats_sample_pages. So we can still scale and thus influence the system in case we want more samples. Simple, but it seems effective – and it any case we get decent results in many more cases than before, so it’s a worthwhile improvement. Obviously, since it’s a statistical sample, it could still be wrong for an individual case.

Jan reckons that just like MyISAM, InnoDB should do a table scan and work things out properly – I agree, this makes sense now that we have persistent stats. So the above is a good fix for 5.5 and 10.0, and the more significant change to comprehensive stats can be in a near future major release. So then we have done away with the sampling altogether, instead basing the info on the full dataset. Excellent.

Another issue that needed to be dealt with is when InnoDB recalculates the statistics. You don’t want to do it on every change, but regularly if there has been some change is good as it might affect which indexes should be chosen for optimal query execution. The hardcoded rule was 1/16th of the table or 2 billion rows, whichever comes first. Again that’s unfortunate, because for a bigger table 1/16th still amounts to a very significant number. I’d tend towards setting the upper bound to say 100,000. Jan put in a new dynamic system variable for this, stat_modified_counter. It essentially replaces the old static value of 2 billion, providing us with a configurable upper bound. That should do nicely!

Once again horay for open source, but in particular responsive and open development. If someone reports a bug and there is no interaction between developer and the outside world until something is released, the discussion and iterative improvement process to come to a good solution cannot occur. The original code definitely worked as designed, but it was no longer suitable in the context of today’s usage/needs. The user-dev interaction allowed for a much better conclusion.

Optimising multi-threaded replication

Multi-threaded replication is a new feature introduced in MySQL 5.6 and MariaDB 10.0. In traditional single-threaded replication, the slaves have a disadvantage as they have to process in sequence what a master executed in parallel. This, plus the fact that slaves usually have a lot of read-only connections to deal with as well, can easily create performance problems. That is, a single-threaded slave needs to be set to allow fewer connections, otherwise there’s a higher risk of it not being able to keep up with the replication stream. There is no exact rule for this, as it relates to general I/O capacity and fsync latency, as well as general CPU and RAM considerations and query patterns.

Currently, it appears that the MariaDB implementation is a bit more mature in terms of design and effective implementation. For instance, MySQL 5.6 does not currently support retrying transactions while doing parallel replication. This can easily cause problems as commit conflicts are possible and obviously need to be handled. So for the purpose of this blog post, we’re going to focus on MariaDB 10.0, and it is what we currently use with some of our clients. MariaDB developer Kristian Nielsen has done awesome work and is very responsive to questions and bug reports. Rock on, Kristian!

The fundamental challenge for parallel replication is that some queries are safe to be executed in parallel, and some are not – and somehow, the server needs to know which is which. MariaDB employs two strategies to assist with this:

  1. Group commit. Since 5.5, transactions (remember, a standalone statement without START TRANSACTION/COMMIT is technically also a transaction) that happen around the same time in different connections are grouped in the binary log and effectively committed together. This is accomplished by the server trying to gather at least a certain number of transactions (binlog_commit_wait_count) and having individual connections wait just a fraction (binlog_commit_wait_usec) to increase the chances of gathering a nice number. This strategy reduces I/O and fsyncs, and thus helps quite a bit with write scaling. The miniscule delay that a transaction might incur because it has to wait is easily offset by the overall better performance. It’s good stuff. For the purpose of parallel replication, any transactions in the same group commit can in principle be executed in parallel on a slave – conflicts are possible, so deadlock handling and retries are essential.
  2. Global Transaction IDs (GTID) Domain IDs (gtid_domain_id) in MariaDB 10.0, which an application can set within a connection. Quite often, different applications and different components of applications use the same database server, but their actions are completely independent: no write operations will ever conflict between the different applications. GTID Domain IDs allows us to tell the server about this, allowing it to always run those transactions in parallel even if they weren’t part of the same group commit! Now that’s a real bonus!

Now, as a practicality, we’re not always able to modify applications to for instance set the GTID Domain ID. Plus, a magic (integer) number is required and so we need some planning/coordination between completely independent applications! Through database server consolidation, you may get applications on your server that were previously on a different one – strictly speaking having two applications use the same GTID Domain ID is harmless (after all, by default all transactions run in the same domain!) but obviously it doesn’t improve performance.

Open Query engineer Daniel Black and I came up with the following. It’s a combination of MySQL’s init_connect system variable (gets called when a user connects, except if they have SUPER privilege), a few stored procedures, and an event to keep the domain map reasonably up-to-date. The premise of this implementation is that each database username uniquely identifies an application, and that no two usernames refer to the same application. So, if you have for instance a general application user but also one for background scripts or one with special administrative privileges, then you need to modify the code in setdomain() a bit to take this into account. If you have transactions with a different GTID Domain ID execute in parallel on the same database, obviously this can cause conflicts. The MariaDB slave threads will retry, but in some cases conflicts cannot be resolved by retrying.

Obviously it’s not perfect, but it does resolve the issue for many situations. Feedback and improvements welcome!

# Automatic GTID Domain IDs for MariaDB 10.0
# Copyright (C) 2014 by Daniel Black & Arjen Lentz, Open Query Pty Ltd (http://openquery.com.au)
# Version 2014-11-18, initial publication via OQ blog (http://openquery.com.au/blog/)
#
# This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International
# http://creativecommons.org/licenses/by-sa/4.0/

USE mysql
DELIMITER //

DROP PROCEDURE IF EXISTS setdomain //
CREATE PROCEDURE setdomain(IN cuser varchar(140)) DETERMINISTIC READS SQL DATA SQL SECURITY DEFINER
BEGIN
  DECLARE EXIT HANDLER FOR NOT FOUND SET SESSION gtid_domain_id=10;
# modify this logic for your particular application/user naming convention
  SELECT domain INTO @l_gtid_domain_id
    FROM mysql.user_domain_map
   WHERE user=LEFT(cuser, LOCATE('@',cuser) -1 );

  SET SESSION gtid_domain_id=@l_gtid_domain_id;
END //

DROP PROCEDURE IF EXISTS create_user_domain_map //
CREATE PROCEDURE create_user_domain_map() MODIFIES SQL DATA
BEGIN
  DECLARE u CHAR(80);
  DECLARE h CHAR(60);
  DECLARE userhostcur CURSOR FOR SELECT user,host FROM mysql.user;
  DECLARE EXIT HANDLER FOR NOT FOUND FLUSH PRIVILEGES;

  CREATE TABLE IF NOT EXISTS mysql.user_domain_map
  (
    domain INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,
    user CHAR(80) COLLATE utf8_bin NOT NULL UNIQUE
  ) AUTO_INCREMENT=10, ENGINE=InnoDB;

  INSERT IGNORE INTO mysql.user_domain_map(user)
         SELECT user FROM mysql.user;

  OPEN userhostcur;
  LOOP FETCH userhostcur INTO u,h;
    INSERT IGNORE INTO mysql.procs_priv(Host,Db,User, Routine_name, Routine_type, Grantor, Proc_priv)
           VALUES(h, 'mysql', u, 'setdomain', 'PROCEDURE', CURRENT_USER(), 'Execute');
  END LOOP;
END;//

DELIMITER ; 

# (re)create the user domain map
CALL create_user_domain_map(); 

# set up event schedule
CREATE EVENT update_user_domain_map ON SCHEDULE EVERY 1 DAY DO CALL create_user_domain_map(); 

# also set this in my.cnf so it's persistent
# init_connect='CALL mysql.setdomain(current_user());'
SET GLOBAL init_connect='CALL mysql.setdomain(current_user());';

MariaDB 5.5 LIMIT ROWS EXAMINED

SELECT … LIMIT has always been very useful, particularly for web applications, restricting the number of rows in the result set to the amount that’s immediately required. To have web apps performing well, it’s always important to only retrieve as many rows as you need and no more.

The SQL_CALC_FOUND_ROWS option was added later, so that an application would be able to figure out (by using the FOUND_ROWS() function) how many more rows – and thus pages – would be available that can then be retrieved with the appropriate LIMIT … OFFSET … calls.

The problem with that construct was that while it kept the restriction of the number of rows in the result set, it required the server to keep retrieving rows even if the limit was already reached. Now, if the ORDER BY column is not the same as the indexes used for initial retrieval of table rows, the server naturally needs to first have all the matching rows, then order by, and then limit. There is no other way.

But from the above you can see that there is an interesting edge case: if the ORDER BY happens to be on the same column as the WHERE condition (which actually does happen quite a bit in the real world) and there is an index on that column, the server doesn’t necessarily have to do all the extra work, provided we get a way of  restricting that execution path. MariaDB 5.5 offers exactly that by adding a ROWS EXAMINED parameter to the LIMIT clause. For full syntax details, see https://kb.askmonty.org/en/limit-rows-examined/

Typically, what you’d do use use LIMIT, ROWS EXAMINED and SQL_CALC_FOUND_ROWS in an initial search or overview query, limiting to a maximum of a handful of pages. This way you can still indicate that there is more data available, and should the user select page 6, you just run a new query with a similar restriction but with a new LIMIT OFFSET boundary. This way you can vastly reduce the amount of work the server is required to do for paginated results.

We often see performance problems with search functionality on sites, and this is one of the ways that can be mitigated. Naturally that’s not the only thing, but it can really help.