Evan has a cool post showing some of our internal heat map stats and some interesting points on data visualization.
After a 3 year speaking hiatus from WordCamp SF, I am excited about speaking again this year. The most interesting part of my talks is usually the Q&A at the end, so this time we decided to get rid of the talk and go straight to the Q&A. It will focus on running large WordPress installations, but I’m sure there will be time to discuss other WordPress-related things. Bring your questions and make them difficult! If you have a question but won’t be able to attend, please ask in the comments and I will try to answer it during the session (which I think will be recorded).
Howdy – Iliya here again. Seems like I am taking over Barry’s blog. Hopefully this will motivate him to blog more.
On WordPress.com we have over 218 million tables and perform tens of thousands queries per second. To scale all of this, we shard our 24 million blogs across more than 550 MySQL servers. This allows us to cope with load bursts and to handle database servers failures.
For those who are unfamiliar, MySQL data replication is asynchronous and works as follows:
- [Master] Receives a query that modifies database structure or content (INSERT, UPDATE, ALTER etc.)
- [Master] The query is written to a log file (aka the binlog).
- [Master] The query is executed on the master.
- [Slaves] Create a “Slave I/O” thread that connects to the [Master] and requests all new queries from the mater’s binlog.
- [Master] Creates a “Binlog dump” thread for each connected slave, that reads the requested events from the binlog and sends them to the slave.
- [Slaves] Start a “Slave SQL” thread which reads queries from the log file written by the “Slave I/O” thread and executes them
There are a number of things to be considered in this scenario, which can lead to a condition known as replication lag where the slaves have older data then the master:
- Since only one thread on the slave executes write queries, and there are many execution threads on the master, there is no guarantee that the slave will be able to execute queries with the same speed as the master.
- Long running SELECTs or explicit locks on the slave, will cause the “Slave SQL” thread to wait, thus slowing it down.
- Long running queries on the master would take at least the same amount of time to run on the slave, causing it to fall behind the master
- I/O (disk or network) issues can prevent or slow down the slave from reading and replaying the binlog events
In order to deal with this, we needed a way to avoid connections to lagged slaves as long as there are slaves that are current. This would allow for the lagged ones to recover faster and avoid returning old data to our users. It also had to be something flexible enough, so we could have different settings for acceptable replication lag per dataset or stop tracking it altogether. Since we use the advanced database class, HyperDB, for all our database connections, it was the obvious place to integrate this.
We implemented it in the following steps:
- If a connection modifies data in a given table, then all subsequent SELECTs on the same connection for that table are sent to the master. Chances are replication won’t be fast enough to propagate the changes to the slaves on the same page load. This logic has existed in HyperDB for a while.
- Before we make a connection to a slave, we use a callback, to check if we have information for this slave’s lag in the cache and we skip it based on that, unless all slaves in the dataset are considered lagged. In case replication breaks on all slaves, we would rather return old data then overload the master with read queries and cause an outage.
- After a successful connection to a slave, if there was nothing in the cache regarding its lag status and not all slaves are considered lagged, we execute a second callback that checks whether this slave is lagged and updates the cache.
A slave is considered lagged when it has a “lag threshold” defined in it’s dataset configuration and the current lag is more than this threshold.
We considered the following options for checking if a slave is lagged. No MySQL patches are required for any of them:
- Checking the value of Seconds_Behind_Master from the
SHOW SLAVE STATUSstatement executed on the slave. It shows the difference between the timestamp of the currently executed query and the latest query we have received from the master. Although it is easy to implement and has low overhead, the main problem with using this option is that it is not completely reliable, as it can be tricked by IO latency and/or master connection problems.
- Tracking the “File” and “Position” on
SHOW MASTER STATUSexecuted on the master and comparing it to Relay_Master_Log_File and Exec_Master_Log_Pos of
SHOW SLAVE STATUSon the slave. This way we can wait until the slave executes the queries from binlog “file” and position “position” before send certain queries to that slave and thus effectively we wait for the data to be replicated to the point where we need it. While very reliable, this option is more complex, has lots of overhead and doesn’t give us clock time value which we can track and set between servers.
- Tracking the difference between the current time on the slave and the replication of a timestamp update from the master, which runs every second. This is basically what mk-heartbeat does. It requires proper time sync between the master and the slave servers but is otherwise very reliable.
The third option fit our needs best, however the code is flexible enough to easily support any of these. For caching, we decided to go with memcached, since it works well in our distributed, multi-server, multi-datacenter environment, but other methods (APC cache, shared memory, custom daemon etc.) would work just fine.
HyperDB is free, open-source and easy to integrate in your WordPress installation. You can download it here. We hope you enjoy this new functionality and please let us know if you have any questions in the comments.
This is a guest post by Iliya Polihronov. Iliya is the newest member of the global infrastructure, systems, and security team at Automattic and the first ever guest blogger here on barry.wordpress.com.
Hey, my name is Iliya and as a Systems Wrangler at Automattic, I am one of the people handling the server-side issues across the 2000 servers running WordPress.com and other Automattic services.
Last week, within two hours of each other, two of our MogileFS storage servers locked up with the following trace:
The next day, a few more servers crashed with similar traces.
We started searching for a common pattern. All hosts were running Debian kernels ranging from 2.6.32-21 to 2.6.32-24, some of them were in different data centers and had different purposes in our network.
One thing we noticed was that all of the servers crashed after having an uptime of a little more than 200 days. After some research and investigation, we found that the culprit appears to be a quite interesting kernel bug.
As part of the scheduler load balancing algorithm, the kernel searches for the busiest group within a given scheduling domain. In order to do that it has to take into account the average load for all groups. It is calculated in the function find_busiest_group() with:
sds.avg_load = (SCHED_LOAD_SCALE * sds.total_load) / sds.total_pwr;
sds.total_load is the sum of the load on all CPUs in the scheduling domain, based on the run queue tasks and their priority.
SCHED_LOAD_SCALE is a constant used to increase resolution.
sds.total_pwr is the sum of the power of all CPUs in the scheduling domain. This sum ends up being zero and that’s what causing the crash – division by zero.
The “CPU power” is used to take into account how much calculating capabilities a CPU has compared to the other CPUs and the main factors for calculating it are:
1. Whether the CPU is shared, for example by using multithreading.
2. How many real-time tasks the CPU is processing.
3. In newer kernels, how much time the CPU had spent processing IRQs.
The current suggested fix for this bug is relying on the theory that while taking into account the real-time tasks (#2 above), scale_rt_power() could return negative value, and thus the sum of all CPU powers may end up being zero.
This was merged into the 18.104.22.168 vanilla kernel, together with the IRQ accounting into the cpu_power (#3 above). It is also merged into the Debian 2.6.32-31 kernel.
Alternatively, the scheduling load balancing can be turned off, which would effectively skip the related code. This can be done using control groups, however it should be used with caution as it may cause performance issues:
mount -t cgroup -o cpuset cpuset /cgroups
echo 0 > /cgroups/cpuset.sched_load_balance
As it is yet not absolutely clear if the suggested fix really fixes the problem, we will try to post updates on any new developments as we observe them.
As you may have heard, on March 3rd and into the 4th, 2011, WordPress.com was targeted by a rather large Distributed Denial of Service Attack. I am part of the systems and infrastructure team at Automattic and it is our team’s responsibility to a) mitigate the attack, b) communicate status updates and details of the attack, and c) figure out how to better protect ourselves in the future. We are still working on the third part, but I wanted to share some details here.
One of our hosting partners, Peer1, provided us these InMon graphs to help illustrate the timeline. What we saw was not one single attack, but 6 separate attacks beginning at 2:10AM PST on March 3rd. All of these attacks were directed at a single site hosted on WordPress.com’s servers. The first graph shows the size of the attack in bits per second (bandwidth), and the second graph shows packets per second. The different colors represent source IP ranges.
The first 5 attacks caused minimal disruption to our infrastructure because they were smaller in size and shorter in duration. The largest attack began at 9:20AM PST and was mostly blocked by 10:20AM PST. The attacks were TCP floods directed at port 80 of our load balancers. These types of attacks try to fill the network links and overwhelm network routers, switches, and servers with “junk” packets which prevents legitimate requests from getting through.
The last TCP flood (the largest one on the graph) saturated the links of some of our providers and overwhelmed the core network routers in one of our data centers. In order to block the attack effectively, we had to work directly with our hosting partners and their Tier 1 bandwidth providers to filter the attacks upstream. This process took an hour or two.
Once the last attack was mitigated at around 10:20AM PST, we saw a lull in activity. On March 4th around 3AM PST, the attackers switched tactics. Rather than a TCP flood, they switched to a HTTP resource consumption attack. Enlisting a bot-net consisting of thousands of compromised PCs, they made many thousands of simultaneous HTTP requests in an attempt to overwhelm our servers. The source IPs were completely different than the previous attacks, but mostly still from China. Fortunately for us, the WordPress.com grid harnesses over 3,600 CPU cores in our web tier alone, so we were able to quickly mitigate this attack and identify the target.
We see denial of service attacks every day on WordPress.com and 99.9% of them have no user impact. This type of attack made it difficult to initially determine the target since the incoming DDoS traffic did not have any identifying information contained in the packets. WordPress.com hosts over 18 million sites, so finding the needle in the haystack is a challenge. This attack was large, in the 4-6Gbit range, but not the largest we have seen. For example, in 2008, we experienced a DDoS in the 8Gbit/sec range.
While it is true that some attacks are politically motivated, contrary to our initial suspicions, we have no reason to believe this one was. We are big proponents of free speech and aim to provide a platform that supports that freedom. We even have dedicated infrastructure for sites under active attack. Some of these attacks last for months, but this allows us to keep these sites online and not put our other users at risk.
We also don’t put all of our eggs in one basket. WordPress.com alone has 24 load balancers in 3 different data centers that serve production traffic. These load balancers are deployed across different network segments and different IP ranges. As a result, some sites were only affected for a couple minutes (when our provider’s core network infrastructure failed) throughout the duration of these attacks. We are working on ways to improve this segmentation even more.
If you have any questions, feel free to leave them in the comments and I will try to answer them.