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Database talks at OSDC 2014 Gold Coast

Open Query Engineer Daniel Black and Engineer/Trainer Peter Lock will be presenting sessions at the upcoming Open Source Developers’ Conference which is hosted at Griffith University Gold Coast Campus, 4-7 November 2014.

I also spotted

which should be very interesting as well. There many be more still, there are lots of sessions!

Full conference tickets cost less than $300 and include the lunches as well as the conference dinner, and all the tutorials/workshops in the main conference. Speaking from experience, OSDC is always great with good talks and excellent people to chat with.

With the conference over, the session videos are now online!

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GROUP BY fixed

Friend and former colleague Roland Bouwman has written an excellent update on the GROUP BY implementation in MySQL.

MySQL’s implementation of GROUP BY has historically been quirky. Sometimes that quirkiness has been useful, but often it causes grief as SQL authors can make mistakes that are executed but don’t produce the results they want (or expect).

Simple example:

SELECT cat, COUNT(val) as cnt, othercol FROM tbl GROUP BY cat

The ‘cat‘ column is in the GROUP BY clause, the COUNT(val) is an aggregate, but the ‘othercol‘ column is … well… neither. What used to effectively happen is that the server would pick one othercol value from within each group. As I noted before, sometimes useful but often a pest as the server wouldn’t know if you just made a mistake or whether you actually intended to use this ‘feature’.

The long existent sql_mode option ONLY_FULL_GROUP_BY mitigated some of that (but not fully in all cases – see Roland’s explanation).

With the sql_mode option enabled, the server chucks an error for faulty constructs, the most common one being:

Error: 1055 SQLSTATE: 42000 (ER_WRONG_FIELD_WITH_GROUP)
Message: ‘col’ isn’t in GROUP BY

MySQL 5.7.5 and above have ONLY_FULL_GROUP_BY enabled by default, with an updated implementation, so GROUP BY will now generally behave properly.

We know that many existing applications often rely on the previously “standard” but technically quirky/faulty behaviour. So when upgrading to this version or beyond, it’s important to

  • use and test in development and staging environment first. This also means upgrading your dev environment first.
  • scan the code for GROUP BY use, and ensure that the queries are correct.
  • have proper error handling and reporting in your application.
  • in some cases disable the sql_mode ONLY_FULL_GROUP_BY option, but that’s really a last resort. It’s best to fix the application.

 

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Hard Drive Reliability

Cloud service provider Backblaze has updated its earlier study of hard drive failure rates (Nov 2013) in its own infrastructure – from 27,000 to more than 34,000 drives, and the new report (Sep 2014) is quite informative. Hitachi comes out pretty high, Western Digital has produced some good drives, but Seagate tends to come out worst. Each brand does have good and not-so-good models so there’s no single right answer, and for any new model you’ll always be dealing with an unknown factor.

Backblaze also found that consumer drives actually perform well compared to enterprise grade drives, and once price is taking into account the enterprise drives just lose. We’ve been noting this to our clients for a number of years – based on actual performance (rather than specs). It’s useful to see this backed up by reliability data, also.

Typically, enterprise drives are SAS, consumer drives are SATA. Comparing SAS and SATA, there’s actually very little difference. SAS has a longer command queue, allowing it to be more efficient in seeks by re-ordering commands. That’s nice, but a RAID or other storage controller and even your operating system would be doing that as well now. So it’s pretty much a moot point.

Higher RPM or even data transfer rate tend to be minor factors as well – your database server will already have all recent pages in memory anyway. When it asks the physical storage for some pages, it won’t hit any cache and it’ll require a seek. It can ask for more nearby pages (this is configurable in MySQL/MariaDB), but those pages may or may not actually be used. With a higher RPM and higher data transfer rate, such reads would be a bit faster. But compared to the overhead of a seek operation, which is measured, in milliseconds, it’s really minimal.

These days, if you have issues with (spinning) hard drive performance, the solution is not caching (other than database server caching which is hugely important!), not higher RPM, higher transfer rate, not higher grade drives, but solid state. And probably local, as latency then becomes the critical factor: if the access path to your fast storage device is slow (again, in terms of latency for reading or writing a specific block, not burst transfer rate), the end result is going to be slow.

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Tracing down a problem, finding sloppy code

Daniel was tracking down what appeared to be a networking problem….

  • server reported 113 (No route to host)
  • However, an strace did not reveal the networking stack ever returning that.
  • On the other side, IP packets were actually received.
  • When confronted with mysteries like this, I get suspicious – mainly of (fellow) programmers.
  • I suggested a grep through the source code, which revealed  return -EHOSTUNREACH;
  • Mystery solved, which allowed us to find what was actually going on.

Lessons:

  1. Don’t just believe or presume the supposed origin of an error.
  2. Programmers often take shortcuts that cause grief later. I fully appreciate how the above code came about, but I still think it was wrong. Mapping a “similar” situation onto an existing error code is convenient. But when an error occurs, the most important thing is for people to be able to track down what the root cause is. Reporting this error outside of its original context (error code reported by network stack) is clearly unhelpful, it actually misdirects and requires people to essentially waste time to track it down (as above).
  3. Horay once again for Open Source, which makes it so much easier to figure these things out. While possibly briefly embarrassing for the programmer, more eyes allows code to improve better and faster – and, perhaps, also entices towards better coding practices from the outset (I can hope!).

What do you think?

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Munin graphing of MySQL

While there are many graphing tools out there and we’ve used Munin for a while now.

The MySQL plugin for Munin had fallen out of date and the show engine innodb status output changed in 5.5 making some bits of the plugin simply not work any more. Also the show global status has some extra variables so there was a need to create new graphs.

All of these are now in the 2.1.8+ development releases of Munin.

Here are samples of the new/updated graphs.

mysql2_tables-day
Tables
Munin table definations
Table Definitions
mysql2_innodb_bpool_act-day

Innodb Buffer Pool Activity
mysql_innodb_bpool_internal_breakdown-day
Innodb Buffer Pool Internal Breakdown
Innodb Insert Buffer
Innodb Insert Buffer
mysql_innodb_bpool-day
Innodb Buffer Pool
Innodb Semaphores
Innodb Semaphores
Innodb Master Thread
Innodb Master Thread
mysql_innodb_adaptive_hash-day
Innodb Adaptive Hash Index
Innodb Queries and Transactions
Innodb Queries and Transactions
Innodb Read Views
Innodb Read Views
Innodb Descriptors
Innodb Descriptors
Performance Schema Losses
Performance Schema Losses
Query Cache
Query Cache
Maximum Memory of MySQL
Maximum Memory of MySQL
Rows
Rows
Handler Read
Handler Read
Handler Transaction
Handler Transaction
Handler Write
Handler Write
Handler Temporary Write/Updates
Handler Temporary Write/Updates
mysql2_execution-day
Execution (triggers and events)
mysql_icp-day
Index Condition Pushdown
Multi Range Read Optimizations
Multi Range Read Optimizations

Some of these above graphs may miss a variable or two with MariaDB-10 because of variable name changes. These will be corrected when we get to those. In MariaDB-10 there is useful transition to information schema tables for status information which will make it significantly easier to parse.

Individual buffer pool information also has been parsed out however we haven’t worked out how to graphing this correctly. Also not yet merged is a bunch of Galera graphs which are currently waiting on some Galera provider changes.

We’ll continue to work with the Munin developers to keep this MySQL plugin up to date and useful.

There’s other graphs in the MySQL Munin plugins that we haven’t changed so aren’t included here.

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Password rules

The below comes from an Australian government site (formatting is mine, for readability):

“Your password must be a minimum length of nine characters, consisting of three of the following – lowercase (a-z) and uppercase (A-Z) alphabetic characters,
numeric characters (0-9) or
special characters (! $ # %).
It cannot contain any 2 consecutive characters that appear in your user ID, first name or last name.
It must not be one of your 8 previous passwords.”

That’s a serious looking ruleset. But does it actually make things safer?

I doubt it. What do you think?

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innodb_flush_logs_on_trx_commit and Galera Cluster

We deploy Galera Cluster (in MariaDB) for some clients, and innodb_flush_logs_on_trx_commit is one of the settings we’ve been playing with. The options according to the manual:

  • =0 don’t write or flush at commit, write and flush once per second
  • =1 write and flush at trx commit
  • =2 write log, but only flush once per second

The flush (fsync) refers to the mechanism the filesystem uses to try and guarantee that written data is actually on the physical medium/device and not just in a buffer (of course cached RAID controllers, SANs and other devices use some different logic there, but it’s definitely written beyond the OS space).

In a non-cluster setup, you’d always want it to be =1 in order to be ACID compliant and that’s also InnoDB’s default. So far so good. For cluster setups, you could be more lenient with this as you require ACID on the cluster as a whole, not each individual machine – after all, if one machine drops out at any point, you don’t lose any data.

Codership docu recommended =2, so that’s what Open Query engineer Peter Lock initially used for some tests that he was conducting. However, performance wasn’t particularly shiny – actually not much higher than =1. That in itself is interesting, because typically we regard the # of fsyncs/second a storage system can deal with as a key indicator of performance capacity. That is, as our HD Latency tool shows when you run it on a storage device (even your local laptop harddisk), the most prominent aspect of what limits the # of writes you can do per second appears to be the fsyncs.

I then happened to chat with Oli Sennhauser (former colleague from MySQL AB) who now runs the FromDual MySQL/MariaDB consulting firm in Switzerland, and he’s been working with Galera for quite a long time. He recognised the pattern and said that he too had that experience, and he thought =0 might be the better option.

I delved into the InnoDB source code to see what was actually happening, and the code indeed concurs with what’s described in the manual (that hasn’t always been the case ;-). I also verified this with Jeremy Cole whom we may happily regard as guru on “how InnoDB actually works”. The once-per-second flush (and optional preceding write) is performed by the InnoDB master thread. Take a peek in log/log0log.c and trx/trx0trx.c, specifically trx_commit_off_kernel() and srv_sync_log_buffer_in_background().

In conclusion:

  1. Even with =0, the log does get written and flushed once per second. This is done in the background so connection threads don’t have to wait for it.
  2. There is no setting where there is never a flush/fsync.
  3. With =2, the writing of the log takes place in the connection thread and this appears to incur a significant overhead, at least relative to =0. Aside from the writing of the log at transaction commit, there doesn’t appear to be a difference.
  4. Based on the preceding points, I would say that if you don’t want =1, you might as well set =0 in order to get the performance you’re after. There is of course a slight practical difference between =0 and =2. With =2 the log is immediately written. If the mysqld process were to crash within a second after that, the OS would close the file and have that log write stored. With =0 that log data wouldn’t have been written. If the OS or machine fails, that log write is lost either way.

In production environments, we tend to mainly want to mitigate trouble from system failures, so =0 appears to be a suitable/appropriate option – for a Galera cluster environment.

What remains is the question of why the log write operation appears to reduce transaction commit performance so much, in a way more so than the flush/fsync. Something to investigate further!
Your thoughts?

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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.

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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.

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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!

Links

 Introducing MySQL Connector/Arduino 1.0.0 beta

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