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Displaying posts with tag: io (reset)
linux memory management for servers

We’ve been learning for many years how to run Linux for databases, but over time we realized that many of our lessons learned apply to many other server workloads. Generally, server process will have to interact with network clients, access memory, do some storage operations and do some processing work – all under supervision of the kernel.

Unfortunately, from what I learned, there’re various problems in pretty much every area of server operation. By keeping the operational knowledge in narrow camps we did not help others. Finding out about these problems requires quite intimate understanding of how things work and slightly more than beginner kernel knowledge.

Many different choices could be made by doing empiric tests, sometimes with outcomes that guide or misguide direction for many years. In our work we try to understand the reasons behind differences that we observe in random poking at a problem.

In order …

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Log Buffer #426: A Carnival of the Vanities for DBAs

This Log Buffer edition transcends beyond ordinary and loop in few of the very good blog posts from Oracle, SQL Server and MySQL.


  • Variable selection also known as feature or attribute selection is an important technique for data mining and predictive analytics.
  • The Oracle Utilities SDK V4. has been released and is available from My Oracle Support for download.
  • This article provides a high level list of the new features that exist in HFM and details the changes/differences between HFM and previous releases.
  • In recent …
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on io scheduling again

Most of database engines have to deal with underlying layers – operating systems, device drivers, firmware and physical devices, albeit different camps choose different methods.
In MySQL world people believe that InnoDB should be handling all the memory management and physical storage operations – maximized buffer pool space, adaptive/fuzzy flushing, crash recovery getting faster, etc. That can result in lots of efficiency wins, as managing everything with data problem in mind allows to tune for efficiency and performance.

Other storage systems (though I hear it from engineers on different types of problems too) like PostgreSQL or MongoDB consider OS to be much smarter and let it do caching or buffering. Which means that in top Postgres expert presentations you will hear much more about operating systems than in MySQL talks. This results in OS knowledge attrition in MySQL world (all you have to know is “use O_DIRECT, XFS and …

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On SSDs – Lifespans, Health Measurement and RAID

Solid State Drive (SSD) have made it big and have made their way not only in desktop computing but also in mission-critical servers. SSDs have proved to be a break-through in IO performance and leave HDD far far behind in terms of Random IO performance. Random IO is what most of the database administrators would be concerned about as that is 90% of the IO pattern visible on database servers like MySQL. I have found Intel 520-series and Intel 910-series to be quite popular and they do give very good numbers in terms of Random IOPS. However, its not just performance that you should be concerned about, failure predictions and health gauges are also very important, as loss of data is a big NO-NO. There is a great deal of misconception about the endurance level of SSD, as its mostly compared to rotating disks even when measuring endurance levels, however, there is a big difference in how both SSD and HDD work, and that has a direct impact on the endurance …

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aio-max-nr in general and “InnoDB: Error: io_setup() failed with EAGAIN” in particular

The problem many MySQL/MariaDB 5.5+ users are painfully aware of:

InnoDB: Using Linux native AIO
InnoDB: Warning: io_setup() failed with EAGAIN. Will make 5 attempts before giving up.
InnoDB: Warning: io_setup() attempt 1 failed.
InnoDB: Warning: io_setup() attempt 2 failed.
InnoDB: Warning: io_setup() attempt 3 failed.
InnoDB: Warning: io_setup() attempt 4 failed.
InnoDB: Warning: io_setup() attempt 5 failed.
InnoDB: Error: io_setup() failed with EAGAIN after 5 attempts.
InnoDB: You can disable Linux Native AIO by setting innodb_native_aio = off in my.cnf
InnoDB: Initializing buffer pool, size = 128.0M
InnoDB: Completed initialization of buffer pool
mysqld got signal 11 ;

There is no news that disabling InnoDB native AIO is not exactly the best possible option. It’s also not a secret that the alternative is increasing aio-max-nr if …

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On binlogs and datacenters

Once MySQL is deployed inside a datacenter environment (i.e. forms a cloud ;-), major feature in it becomes replication. It is used to maintain hot copies, standby copies, read-only copies, invalidate external systems, replicate to external systems, etc. If this functionality is broken, datacenter is broken – components are not synchronized anymore, invalidations not done, data not consistent.

From performance perspective, replication not working properly results in unusable slaves so load cannot be spread. This results in higher load on other machines, including master (especially on master, if environment needs stronger consistency guarantees).

Judging on replication importance in MySQL deployments, it should attract performance engineering as much as InnoDB and other critical pieces. Though slave replication performance is being increased in 5.6, master side is not (well, group commit may help a bit, but not as much).

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Join Optimizations in MySQL 5.6 and MariaDB 5.5

This is the third blog post in the series of blog posts leading up to the talk comparing the optimizer enhancements in MySQL 5.6 and MariaDB 5.5. This blog post is targeted at the join related optimizations introduced in the optimizer. These optimizations are available in both MySQL 5.6 and MariaDB 5.5, and MariaDB 5.5 has introduced some additional optimizations which we will also look at, in this post.

Now let me briefly explain these optimizations.

Batched Key Access

Traditionally, MySQL always uses Nested Loop Join to join two or more tables. What this means is that, select rows from first table participating in the joins are read, and then for each of these rows an index lookup is performed on the second table. This means many point queries, say for example if table1 yields 1000 …

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InnoDB locking makes me sad

Vadim and others have pointed at the index->lock problems before, but I think they didn’t good job enough at pointing out how bad it can get (the actual problematic was hidden somewhere as some odd edge case). What ‘index lock’ means is generally the fact that InnoDB has table-level locking which will kill performance on big tables miserably.

InnoDB is a huge pie of layers, that have various locking behaviors, and are layered on top of each other, and are structured nicely as subdirectories in your innodb_plugin directory. Low level storage interfaces are done via os/ routines, then on top of that there’s some file space manager, fsp/, which allocates space for btr/ to live in, where individual page/ entities live, with multiple row/ pieces. There’re few other subsystems around, that got …

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Logs memory pressure

Warning, this may be kernel version specific, albeit this kernel is used by many database systems

Lately I’ve been working on getting more memory used by InnoDB buffer pool – besides obvious things like InnoDB memory tax there were seemingly external factors that were pushing out MySQL into swap (even with swappiness=0). We were working a lot on getting low hanging fruits like scripts that use too much memory, but they seem to be all somewhat gone, but MySQL has way too much memory pressure from outside.

I grabbed my uncache utility to assist with the investigation and started uncaching various bits on two systems, one that had larger buffer pool (60G), which was already being sent to swap, and a conservatively allocated (55G) machine, both 72G boxes. Initial finds were somewhat …

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on performance stalls

We quite often say, that benchmark performance is usually different from real world performance – so performance engineering usually has to cover both – benchmarks allow to understand sustained performance bottlenecks, and real world analysis usually concentrates on something what would be considered ‘exceptional’ and not important in benchmarks – stalls of various kind. They are extremely important, as the state when our performance is lowest is the state of performance we provide to our platform users.

On a machine that is doing 5000qps, stalling for 100ms means that 500 queries were not served as fast as they could, or even hit application timeouts or exceptional MySQL conditions (like 1023 transaction limit). Of course, stalling for a second means 5000 queries were not served in time…

We have multiple methods to approach this – one is our …

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