Tag Archives: mariadb

Mixing databases usually not optimal

Dan McKinley (Etsy) wrote an [IMHO] insightful article Why MongoDB Never Worked at Etsy.

First off, it’s important to realise that it’s not a snipe at MongoDB – it’s a fine tool.

The lessons are related to mixing multiple databases in a deployment (administration and monitoring overhead) and the acknowledgement that issues of schema design, scalability and maintenance need attention regardless of which brand or technology you pick for your database. That comes back to the old insight that migrations are rarely worth it (regardless of what you migrate to what).

I think these are indeed important considerations as they have a major impact on the ongoing costs of your entire environment (production as well as development and testing) – these days we encounter the “we’re doing this part of our application using MongoDB” approach quite often, so it’s useful to read about and learn from other people’s experience.

With MongoDB there is a particular extra issue to consider, and Dan McKinley also mentions it in his post. NoSQL databases are often also schema-less. However, to keep your data manageable when it grows to significance, you do need to structure it somehow – that is, you need to make sure that (and I’ll just use generic terminology here) in a specific set of records each record contains the required fields. If you don’t, at some point things become unmanageable (or your data ends up as a pile of unusable bits).

Thus, you’re dealing with some form of schema, whether you call it that or not. And you might deal with it in application logic or through some toolkit, rather than in the database itself, but it can’t just be ignored or disregarded. And that’s critical, as often going to a schema-less database is presented as a “then you don’t need to worry about that” change. You do need to “worry” about it: you can pick where the most suitable place is for your needs. If you look at it in that way, you can make an appropriate choice for the particular application at hand.

Luxbet, MariaDB and Melbourne Cup

Yesterday was Melbourne Cup day in Australia – the biggest annual horse race event in the country, and in the state of Victoria it’s even a public holiday.

Open Query does work for Luxbet (part of Tabcorp), and Melbourne Cup day is by far their biggest day of the year in terms of traffic. It’s not just a big spike, there’s orders of magnitude difference so you can really say that the rest of the year is downright quiet (in relative terms). So, a very interesting load pattern.

Since last year Luxbet has upgraded from stock MySQL to MariaDB, and with our input made some other infrastructure modifications including moving to a pure solid state storage (FusionIO) solution as a SAN just won’t deliver the resilience and performance required. This may seem odd, but remember that a) a SAN is also a single point of failure (so when the SAN fails, multiple db servers will be “out” – not desirable even though a failover to another datacenter is possible), and b) MariaDB/XtraDB (InnoDB) already have all recent data and indexes in RAM, so whatever I/O is required won’t benefit from a SAN cache. Thus, the SAN will have to actually do a physical disk seek and read to get what is needed, and we all know seeks are slow. A write or fsync also incurs some latency, regardless of the storage array speed.

So those are the reasons for the local storage solution. While there are aspects of RAID and other redundancy in that setup, the main resilience in the infrastructure comes from having more machines, rather than necessarily having more redundancy in each machine.

Grant is working on a more comprehensive version of this story.

MySQL Connector/Arduino

Chuck Bell, one of my former colleague from MySQL AB, has created a connector for Arduino to MySQL. So this allows Arduino code to be a direct client of a MySQL or MariaDB server, with Ethernet and WiFi shields supported.

With Arduino boards being used more and more, this can come in really handy – not only for retrieving (for instance) centralised configuration data, but also for logging. Useful stuff. Thanks Chuck!


 Introducing MySQL Connector/Arduino 1.0.0 beta

Hint of the day: noatime and relatime in fstab

It’s been written about everywhere, but since we keep spotting installations in the wild where people don’t know about it, it probably deserves another mention.

By default, Linux uses the atime option on a disk mount, which means it writes a timestamp (e.g. a write to the drive) every time it reads anything. So in this case, reads cause writes – and also disk seeks, because a read from a file will then trigger having to write to the directory that contains the file. This even occurs if a file is read from the file system’s page cache (reading from the machine’s memory rather than the drive).

Unless you require an audit trail of users reading files, you generally you don’t want this. Thus, you want to add the noatime option to the disk mount in /etc/fstab. If you have just the defaults in there, you just make it defaults,noatime. It’ll doesn’t necesarily require a reboot as you can use umount/mount, but that gets tricky when dealing with the root filesystem so a reboot is generally easier. Setting these options is one of the first things we do when configuring a server.

Some user applications, such as Mutt (mail reader) do use the read access time. In that case, you can use the relatime option instead, which only writes a timestamp when a file or directory is written to. This is just for completeness of this story, as it’s still sub-optimal for a database server.

If you require read details for auditing (security) of the operating system, make sure all database-related files (database directories, InnoDB log files, binary logs, etc) are on a separate mount where you can use noatime.

Using noatime also makes a lot of sense on a web server, as it does a lot of reads. Remember, the fact that most files are in the filesystem cache doesn’t make a difference. As a general guide, it makes sense to set on most server installations. Quick win.

LEVENSHTEIN MySQL stored function

At Open Query we steer clear of code development for clients. We sometimes advise on code, but as a company we don’t want to be in the programmer role. Naturally we do write scripts and other necessities to do our job.

Assisting with an Open Source project, I encountered three old UDFs. User Defined Functions are native functions that are compiled and then loaded by the server similar to a plugin. As with plugins, compiling can be a pest as it requires some of the server MySQL header files and matching build switches to the server it’s going to be loaded in. Consequentially, binaries cannot be considered safely portable and that means that you don’t really want to have a project rely on UDFs as it can hinder adoption quite severely.

Since MySQL 5.0 we can also use SQL stored functions and procedures. Slower, of course, but functional and portable. By the way, there’s one thing you can do with UDFs that you (at least currently) can’t do with stored functions, and that’s create a new aggregate function (like SUM or COUNT).

The other two functions were very specific to the app, but the one was a basic levenshtein implementation. A quick google showed that there were existing SQL and even MySQL stored function implementations, most derived from a single origin which was actually broken (and the link is now dead, as well). I grabbed one that appeared functional, and reformatted it for readability then cleaned it up a bit as it was doing some things in a convoluted way. Given that the stored function is going to be much slower than a native function anyway, doing things inefficiently inside loops can really hurt.

The result is below. Feel free to use, and if you spot a bug or can improve the code further, please let me know!
Given the speed issue, I’m actually thinking this should perhaps be added as a native function in MariaDB. What do you think?

-- core levenshtein function adapted from
-- function by Jason Rust (http://sushiduy.plesk3.freepgs.com/levenshtein.sql)
-- originally from http://codejanitor.com/wp/2007/02/10/levenshtein-distance-as-a-mysql-stored-function/
-- rewritten by Arjen Lentz for utf8, code/logic cleanup and removing HEX()/UNHEX() in favour of ORD()/CHAR()
-- Levenshtein reference: http://en.wikipedia.org/wiki/Levenshtein_distance

-- Arjen note: because the levenshtein value is encoded in a byte array, distance cannot exceed 255;
-- thus the maximum string length this implementation can handle is also limited to 255 characters.

    DECLARE s1_len, s2_len, i, j, c, c_temp, cost INT;
    -- max strlen=255 for this function
    DECLARE cv0, cv1 VARBINARY(256);

    SET s1_len = CHAR_LENGTH(s1),
        s2_len = CHAR_LENGTH(s2),
        cv1 = 0x00,
        j = 1,
        i = 1,
        c = 0;

    IF (s1 = s2) THEN
      RETURN (0);
    ELSEIF (s1_len = 0) THEN
      RETURN (s2_len);
    ELSEIF (s2_len = 0) THEN
      RETURN (s1_len);
    END IF;

    WHILE (j <= s2_len) DO
      SET cv1 = CONCAT(cv1, CHAR(j)),
          j = j + 1;

    WHILE (i <= s1_len) DO
      SET s1_char = SUBSTRING(s1, i, 1),
          c = i,
          cv0 = CHAR(i),
          j = 1;

      WHILE (j <= s2_len) DO
        SET c = c + 1,
            cost = IF(s1_char = SUBSTRING(s2, j, 1), 0, 1);

        SET c_temp = ORD(SUBSTRING(cv1, j, 1)) + cost;
        IF (c > c_temp) THEN
          SET c = c_temp;
        END IF;

        SET c_temp = ORD(SUBSTRING(cv1, j+1, 1)) + 1;
        IF (c > c_temp) THEN
          SET c = c_temp;
        END IF;

        SET cv0 = CONCAT(cv0, CHAR(c)),
            j = j + 1;
      END WHILE;

      SET cv1 = cv0,
          i = i + 1;

    RETURN (c);
  END $$



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.


Storage caching options in Linux 3.9 kernel

dm-cache is (albeit still classified “experimental”) is in the just released Linux 3.9 kernel. It deals with generic block devices and uses the device mapper framework. While there have been a few other similar tools flying around, since this one has been adopted into the kernel it looks like this will be the one that you’ll be seeing the most in to the future. It saves sysadmins the hassle of compiling extra stuff for a system.

A typical use is for an SSD to cache a HDD. Similar to a battery backed RAID controller, the objective is to insulate the application from latency caused by the mechanical device, the most laggy part of which is seek time (measured in milliseconds). Giventhe  relatively high storage capacity of an SSD (in the hundreds of GBs), this allows you to mostly disregard the mechanical latency for writes and that’s very useful for database systems such as MariaDB.

That covers writes (for the moment), but what about reads? Can MariaDB benefit from the read-caching? For the MyISAM storage engine, yes (as it relies on filesystem caching for speeding up row data access). For InnoDB, much less so. But let’s explore this, because it’s not quite a yes/no story – it depends. For typical systems with a correctly dimensioned system and InnoDB buffer pool, most of the active dataset will reside in RAM. For a system using a cached RAID controller that means that an actual disk read is not likely to be in the cache. With an SSD cache you might get lucky as it’s bigger – so stuff that has been read or written in some recent past may still be there. What we have found from testing with hdlatency (on actual client/hosting infra) is that SANs typically don’t have enough cache to pull that off – they too may have SSD caches now, but remember they get accessed by many more users with different data needs as well. The result of SSD filesystem caching for reads is actually similar to InnoDB tweaks that implement a secondary buffer pool on SSD storage, it creates a relatively large and cheap space for “lukewarm” pages (ones that haven’t been recently accessed).

So why does it depend? Because your active dataset might be too large, and/or your combined reads/writes are still more than the physical disks can handle. It’s very important to consider the latter: write caching insulates you from the seeks and allows an intermediate layer to re-order writes to optimise the head movement, but the writes still need to be done and thus ultimately you remain bound by an upper end physical limit. Insulation is not complete separation. If your active dataset is larger than RAM+SSD, then the reads also also need to be taken into account for seek capacity.

So right now you could say that at decent prices, if your active dataset is in the range of a few hundred GB to even a few TB, RAM with the optional addition of SSD caching can all work out nicely – what can still make it go sour is the rate of writes. Conclusion: this type of setup provides you with more headroom than a battery backed RAID controller, should you need that.

Separating reporting to distinct database servers (typically slaves, configured for relatively few connections and large queries) actually still helps quite a bit as it really changes what’s in the buffer pool and other caches. Or, differently put, looking at the access patterns of the different parts of your application is important – there are numerous variation on this basic pattern. It’s a form of functional sharding.

You’ll have noticed I didn’t mention any benchmarks when discussing all this (and most other topics). Many if not most benchmarks have artificial aspects to them, which makes them problematic when dealing with the real world. As shown above, applying background knowledge of the systems and structures, logic, and maths gets you a very long way (either independently or in consultation with us). It can get you through important decision processes quicker. Testing can still play an important part, but then it’s either part of or very close to your real world environment, not a lab activity. It will be specific to you. Don’t get trapped having to deliver on numbers from benchmarks.

Fedora 19 – MariaDB Test Day 2013-04-30

From https://fedoraproject.org/wiki/Test_Day:2013-04-30_MariaDB, this installment of Fedora’s Test Day focuses on the replacement of MySQL with MariaDB. If you’re a Fedora (or RHEL or CentOS user), do take a peek at the page and see if you can pitch in – it might be a little bit of work for you, but with great benefits in terms of getting the MariaDB performance and features, and specifically on the day the Fedora crowd have extra people on the case to track and address issues you might find, so it’s an ideal opportunity to upgrade on a development or test-prod environment!

MariaDB Foundation

You may have already seen the announcement MariaDB Foundation to Safeguard Leading Open Source Database. We at Open Query wholeheartedly support this (r)evolution of the MySQL ecosystem, which appears to be increasingly necessary as Oracle Corp is seriously dropping the ball with security updates and actually just general development and innovation. Oracle has actually done some very good work, I happily acknowledge that – but security issues are critical, having crashing bugs and incorrect query results in a .28 of a GA release is uncool, and not incorporating awesome development efforts by the community is just astonishing.

MariaDB is where the Sphinx fulltext search and OQGraph engines are integrated, the community developed virtual columns and Galera synchronous replication are added, and the fabulous developers at Monty Program have taken the time to fix up the previously ghastly performance of subqueries so that they now rock. And that’s apart from the many little bugs Monty Program has fixed along the way. Awesome work, guys!

MariaDB still merges from stock MySQL, but it’s important to not be dependent on that – the MariaDB Foundation can help support that already existing effort and raise the profile of MariaDB to – as far as I’m concerned – move on from MySQL.

From our end, apart from our continued active involvement in the community, Open Query will be adjusting and augmenting its fee structure so that our clients co-fund MariaDB Foundation and contributing companies such as Codership (Galera). MySQL and MariaDB are mission critical for businesses. It’s not (and never has been) a case of volunteers and mere charity. We all p(l)ay our part.

It’s time to upgrade.

MariaDB security updates

Important Security Fix for a Buffer Overflow Bug: MariaDB 5.5.28a, 5.3.11, 5.2.13 and 5.1.66 include a fix for CVE-2012-5579, a vulnerability that allowed an authenticated user to crash MariaDB server or to execute arbitrary code with the privileges of the mysqld process. This is a serious security issue. We recommend upgrading from older versions as soon as possible.

MariaDB 5.5.28a, 5.3.11, 5.2.13 and 5.1.66 (GA) binaries, packages, and source tarballs are now available for download from http://downloads.mariadb.org. So you can upgrade within your own major series.

Note that while this fix has just been published, some other vulnerabilities have been noted over the weekend also. Below a summary of these other CVEs as documented by Red Hat Security Response Team, with annotations by Sergei Gulubchik who is the Security Coordinator for MariaDB.

See http://seclists.org/oss-sec/2012/q4/388 for Sergei’s full response.

Note that stock MySQL is also affected – in this post we’re just referring to the specific MariaDB fixes/releases/responses. It appears that Oracle has not yet made any releases for this security issue, which is unfortunate as the issues have been published and can therefore be easily exploited by malicious users. In the same thread referenced above it is stated that Oracle has been made aware of the issues so a fix should be forthcoming for people who use stock MySQL also.

Naturally these security advisories also affect anyone still running a 5.0 OurDelta or early 5.1 OurDelta version. Please upgrade urgently to the latest MariaDB 5.1 (5.1.66) or above. If you require any assistance with this, please contact Open Query. This advisory is also noted on the front page of ourdelta.org.