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Moving a multi-million MAU site into the cloud in 3 weeks with minimal downtime and 40% savings

It was October 29th, 2014. We were making a routine visit to our datacenter in south San Francisco, where the servers powering were humming along as always.

In the hallway, we ran into a network engineer who worked at the datacenter.

“So you guys are moving out today, then?” he asked.

Puzzled, we asked what prompted the question.

“Didn’t you get the notice? Our provider was unable to renew the lease. Everyone needs to be out of here by the end of the year”

This is not a situation any company wants to be in. We had two months to move a multi-million MAU site with more than 180 million registered accounts (and all the data that comes with it). We were also in the process of building out the infrastructure for Brigade, which had acquired Causes in June. Panic was not a practical option, so we got to work.

Our team of three ops engineers came up with a plan and executed it. On November 19th, about 3 weeks after we received the notice, we had Causes running entirely within Rackspace’s cloud-based infrastructure, requiring less than two hours of accumulated downtime visible to users over that period.

Here’s how we got there.

Setting Expectations

With a migration of this size and urgency, we quickly met with the relevant stakeholders to relax the constraints of the problem we faced; an important tactic when facing a difficult challenge. We agreed that, if necessary:

  • We were comfortable with a day or so of intentional downtime
  • We could temporarily set services up in a non-HA configuration (coming back later to improve them)
  • We could drop support for old features and services (though we would need to propose each instance and discuss separately)

Respectively, the advantages were:

  • Allowing for downtime enabled us to avoid taking extra steps to gracefully move some services over
  • Rebuilding systems without HA redundancy incurs significantly less setup complexity
  • Removing entire features (like unused employee-only analytics dashboards or low-visibility user-facing features) might save us from having to migrate large quantities of data or entire subsystems

Creating a Plan

With the constraints clearly laid out, we now faced the task of figuring out our approach. At a high level, there were two obvious courses of action we could take:

  • Physically move all the servers to another location, or
  • Migrate everything to run alongside our existing infrastructure for Brigade in Rackspace.

The first option was potentially quicker; we could simply unplug the racks and roll them to their new location (likely a different floor in the same datacenter). However, it would require a complete shutdown of Causes for at least a day, and that would be a best-case scenario. Furthermore, renegotiating lease terms and maintaining the existing hardware (ranging in age from 2–7 years old) was undesirable and potentially risky.

The second option of migrating into Rackspace appealed to us for the following reasons:

  • Utilize existing systems: A number of the backend systems powering Causes were already running in Rackspace as part of Brigade’s infrastructure (services like Graphite, Sentry, etc.). We could piggy-back on these existing systems to save time.

  • Ditch the old Chef codebase: Brigade and Causes infrastructure had separate Chef servers and thus a largely different set of cookbook code. A lot of the Causes cookbook code had been written before many Chef best practices had been popularized, which we had addressed when writing the Brigade cookbooks. Furthermore, switching to using the Brigade cookbooks would save us having to maintain two codebases.

  • Switch to community cookbooks: For the cookbooks we hadn’t rewritten (since they were not services we used in Brigade), we could switch from using our custom-written Causes cookbooks to community cookbooks, giving us the benefit of using code actively maintained by others.

  • Upgrade all the things: There were a number of software components we could potentially upgrade that would normally be difficult or time-consuming (and that had not been upgraded for that very reason). This included upgrading our version of CentOS, Percona MySQL, Redis, and Beanstalk, among other services and packages. This would also mean we would only need to maintain one version of the respective software across our entire infrastructure, easing the maintenance burden on our team. By building Causes separately in Rackspace, we essentially had a giant integration test where we could test that all of these upgrades worked before we did the switchover.

  • Minimize downtime: We could build a copy of Causes in Rackspace while simultaneously leaving the existing Causes running in our data center, minimizing the amount of downtime we would need to take when switching over.

  • Reduce machine footprint: We could consolidate a large number of our servers into fewer machines in Rackspace, as the specifications on their servers were better than what we were running in our datacenter. Furthermore, we could combine our multiple database partitions into one. This would drastically reduce our footprint from ~150 machines to under 30!

  • Save $: Thanks to the reduced machine footprint, we could do all of this for less than it was currently costing to run Causes (on the order of 40% cheaper).

With all that in mind, we opted to try moving everything into Rackspace, with physically moving the machines as a backup plan if we saw ourselves unable to finish on time. We set an aggressive goal of having everything ready for switchover by December 1st, giving us enough time to re-evaluate the situation before the end-of-year deadline.

Executing the Plan

We didn’t take much time to make detailed plans, since we knew that ultimately the only way we would be able to tell if this would work would be if we tried it.

We completed the following set of tasks in this approximate order, parallelizing where possible (which was often the case):

  1. Set up a VPN tunnel from our datacenter to Rackspace. This would allow us to replicate data from the old datacenter as we were building the infrastructure in Rackspace.

  2. Built MySQL slaves to replicate data from datacenter into Rackspace. We had multiple database partitions, but focused on setting up replication on our main partition—the name we used for the primary partition with most of the tables for the application; we had two additional partitions containing tables which were queried heavily in order to improve performance.

    We set the replication master host using the IP address of the main partition instead of the hostname. This allowed us to avoid getting DNS resolution working between the two domains, as each had their own nameservers configured by a separate Chef server.

    These slaves were also running a newer version of Percona (5.6 versus 5.5). The upgrade process simply required restoring a backup made via xtrabackup and executing mysql_upgrade once the server was running.

  3. Combined other partitions into the main partition on the live site. This was done by syncing the tables from the other partitions to the main partition with Percona’s pt-table-sync. We were willing to temporarily forego the performance benefits of separate partitions for the convenience of having all data on a single partition, and the new server in Rackspace would be powerful enough to handle the full workload once we switched over anyway.

    This was accomplished for each partition in two passes: the first to copy the majority of the data over while web and async workers were still running (and thus still making changes to the original data, resulting in the copy straying from the original), and then a second after we stopped the workers so it could ensure they were both in sync.

    If we had stopped the workers before the first pass, we would have been down for hours at a time due to the large volume of data being synced. Introducing the second pass allowed us to minimize downtime, as only a fraction of the data would have been added/updated in the time between the end of the first pass and the shutting down of the workers in preparation for the second pass.

    We would then deploy a code change to start using the main partition instead of the old one, and then shut down the old partition. This process was repeated for each partition, with the downtime varying depending on the size of the data set, but was usually under 10 minutes.

  4. Upgraded the Beanstalk queues to the latest version. This required us to fix some issues in the AsyncObserver client that the workers used to fetch jobs, but otherwise was pretty straightforward.

  5. Replaced our use of memcached with Redis. This was relatively easy thanks to the abstraction provided by Rails.cache, but switching from DalliStore to RedisStore changed the signature of Rails.cache.increment which required some adjustments to fix. This wasn’t in our original high-level plan, but we saw an opportunity to further reduce the number of technologies we would need to support going forward.

  6. Stood up a new Solr cluster from scratch. Our old cookbook for provision Solr was written in-house and quite old, so we took the opportunity to switch to a wrapper cookbook using the community Solr cookbook while we were rebuilding the cluster.

    We weren’t able to import the indexes because they were sharded across multiple hosts, so we just reindexed everything with an async job, which took less than a day.

  7. Scrapped our custom deploy scripts in favor of Capistrano. This gave us the benefit of unifying the deployment technology we used for all Ruby code in our infrastructure and also allowed us to take advantage of the large number of community plugins Capistrano provides for projects like rbenv, bundler, Rails, unicorn, etc.

  8. Iterated. Throughout this process we developed on a separate rackspace branch in the Causes Rails app. This branch was what was deployed to Rackspace as we stood up the various services and tested them. We used copies of production data to give us better confidence that we had our ducks in a row without having to actually touch the live site.

    We managed to complete these steps far faster than we had originally anticipated. A lot of that was luck, but it was also thanks to the effort we had spent making it easy for us to provision new machines. The combination of Rackspace’s API and the cookbooks we had just written for Brigade’s infrastructure made building each of the backend Causes services take hours rather than days.

The Switchover

With a working copy of Causes now running in Rackspace using data replicated from production, we were now ready to perform the actual switchover.

Our efforts had us at a point where we had a single MySQL server replicating to a slave in Rackspace (which itself was replicating to another slave in Rackspace for HA), so there was an up-to-date replica of all production data in Rackspace. We didn’t need to pre-warm the new caches which meant the only tricky part of the switchover was changing which host was the MySQL master.

We carried out the following steps:

  1. Stopped all Causes web and async workers. This would stop any further writes from hitting the master.

  2. Waited for all writes to the current MySQL master to stop. Verified there were no connections to the current master by running:

  3. Set current master to read-only. SET GLOBAL read_only = 1; FLUSH TABLES WITH READ LOCK;

    This ensured we remained consistent and avoided the split-brain problem. We would have at most one master accepting writes at any time.

  4. Waited for the Rackspace slave to catch up to the master. We waited until the log position in the output of:


    …stopped incrementing. At this point, the slave and the master were in sync.

  5. Made the Rackspace slave writable. Executed the following on the slave:

    SET GLOBAL read_only = 0;

    The slave was now the new master, as it was the only server able to accept writes.

  6. Start all web and async workers in Rackspace. We had a commit ready that reconfigured the workers to connect to the newly promoted master, so deploying that commit and starting all the workers resulted in the new Causes application running entirely in Rackspace.

  7. Update public DNS to point to the new load balancers in Rackspace. It would take time for existing DNS caches to expire, so we still needed a way to forward traffic heading to our old load balancer to our new one in Rackspace.

  8. Set up reverse proxies in the old datacenter. These proxies would simply forward requests to the load balancer in Rackspace. This results in the site being slower for a while due to the datacenter hop, but is only a temporary measure until all existing DNS caches expired.

    We then monitored the proxy logs for the next hour while DNS updated. Eventually, requests stopped coming and we were good to shut the proxies down.

  9. Celebrate!

Closing Thoughts

This was a huge win for our team, not only because we finished way ahead of schedule, but we also:

  • Drastically reduced infrastructure-related technical debt and eliminated some services that we needed to maintain.
  • Unified our entire infrastructure to be running the same (newer) version of all software.
  • Minimized affecting our users by spreading out downtime over the 3 week process.
  • Saved ourselves 40% on the monthly costs for running Causes.

We didn’t panic when presented with a nasty situation; we set aggressive deadlines and worked hard to meet them; and we took calculated risks in order to allow ourselves to move quickly but effectively.

Overall, it was a productive November.

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Shane da Silva



Shane da  Silva

Coding by the woods

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