We’ve recently updated our server platform at Turalt to a new approach, to make it smaller, simpler, and easier to manage – while saving us a little money too. We don’t use Kubernetes, and we’re unlikely to need it for a while. In fact, virtually all our infrastructure runs on a small number of 1Gb servers. So this is how we do it.
First, a little history. Initially, we used MariaDB because (a) it wasn’t Oracle, and (b) it was fast and easy. But… as soon as you need replication, it can become impossibly hard. There are few good and easy solutions. It wasn’t as if we were committed to MariaDB – all our database logic is written using knexjs so switching to a new dialect was always going to be pretty quick and easy.
The problem is, we needed replication early on. Our analysis endpoints need to work 24/7, so we need to be able to apply an update while still running. This means we need more than one server, so we can progressively remove one from the service, update it, and then add it back. Coupled with the need for a database system that is solid and reliable and also available 24/7, this was a lot to ask.
And then, our hosting service offered a managed PostgreSQL service, which was perfect. As a startup in Digital Ocean’s (excellent) Hatch program, we didn’t even need to pay for it for a year. This meant we didn’t need to worry about the database at all, and all our servers could be simple, identical, application servers.
Fast forward, and after we graduated from Hatch, we now get billed. It’s not a lot for a wealthy startup, but for us and those who bootstrap, it’s enough we’d rather not pay for it. So, the question was: how could we use standard droplets to build something that was close enough. That meant: replication, automatic failover, and safe backups.
Unlike MariaDB, pretty much everything you need for replication is standard in PostgreSQL. So let’s dive in.
The usual model for PostgreSQL is two servers: a primary and a hot standby. To make for true high availability, we need one more thing: a monitoring system that tracks the primary, and if it fails, we promote the hot standby to a new primary, and then set up a new hot standby to take its place. Alternatively, if the hot standby fails, it can be dropped and a new hot standby set up directly. This is pretty much what Digital Ocean offered us – on a well-configured server which was frankly far in excess of our actual needs.
Another significant change is that we used a separate logging server, running Graylog, which was huge! Our one logging server was more powerful and used more resources than the whole of the rest of our server platform. Not a big surprise since these things typically use ElasticSearch and MongoDB and Java virtual machines. All we really needed from our logging system was aggregation, so that we could scan a single log source for all servers, rather than having to grep through multiple machines every time. So dropped all that nonsense too, and simply used some rather cunning rsyslog configurations to have replicated logging and aggregation. This is not only much lighter in resources, it’s better, as we no longer have a single point of failure in our logging systems.
Our deployment consists of these components.
Both the PostgreSQL and redis platforms use replication across our small cluster, with automatic failover provided (in the case of PostgreSQL through repmgrd and pgbouncer, in the case of redis through redis-sentinel).
It’s true that running PostgreSQL on 1Gb servers, alongside the application, might seem like a really bad idea. For us, it isn’t especially. We use redis a lot for write caching, so virtually all the fast-changing data is done through redis anyway, with background tasks periodically writing in batches to PostgreSQL.
Switching from single inserts to batch inserts made a significant reduction in our need for server performance, so we do not really need a very fast database. We can write blocks of a hundred records or so when we need to, and we don’t even need to wait for the insert to complete most of the time.
To make any cluster handle outages, you need monitoring processes that keep an eye on other servers, and remove them if something goes wrong. The two common choices for PostgreSQL are repmgrd and keepalived. repmgrd only works for PostgreSQL, but contains builtin monitoring for it; keepalived is more general-purpose, and the price you pay for that it that it doesn’t know as much about the innards, so you need to put more into your scripts for tracking and management.
The choice between them was not a simple one, and after some trial and error (and pain) we found that repmgrd was easier to automate the failover process in a consistent way. keepalived is certainly a lot more powerful, and we’re very likely to use it again some day, but for now, and for us, repmgrd works fine.
The main advantage of repmgrd is that it is nicely integrated with and designed for PostgreSQL, so it doesn’t need anything like as much configuration as keepalived, which, being more general purpose, can handle cluster failover for many other different services. For example, it provides pre-built scripts that allow a new standby node to synchronize data from an upstream primary. With keepalived, that’s something you’d need to configure yourself.
The dynamic parts of our application run as Express services through Node.js. In development, these run from the command line, but for deployment, we use systemd unit files to manage each of them. And we install yarn on all the servers so that we can deploy the dependencies correctly.
I know that systemd is somewhat controversial. But frankly, I don’t care. For normal everyday
use it is significantly easier (and safer) than writing your own
init.d files. There’s
significantly less to go wrong.
All we want is for a command line to be able to start stable processes that restart automatically if they go down, and to have these processes start on boot. Actually, even that isn’t especially important, as the whole cluster nature means a restart always needs some kind of outside supervision, whether it’s human or not, to make sure that servers don’t restart and create parallel database versions or what have you. But the restart of the individual worker processes absolutely needs to be automatic and clean, so that after, e.g., a minor version tweak, we can bounce the process.
A whole lot of our services are set up through some complex nginx configuration. nginx does a lot for us, including:
We do still use a Digital Ocean load balancer, so web requests that come into our servers go through that, and get relayed to the private IP addresses for our servers, not the public IP addresses (which literally block every single thing but public-key ssh for management reasons).
Normally, all our web servers require HTTPS, so SSL is essential to our nginx setup. However, the load balancer also does health checks, and those prefer HTTP, so we allow one single HTTP endpoint for the health checks. Everything else gets SSL checked, and either served as a static file or proxied over HTTP internally to one of the service processes.
Obviously we have a firewall, and iptables works fine for us. I totally hate its command line, but for deployment, it was very much simpler to template out a complete rule file and then import it when we need to.
We also use fail2ban, with some very extensive rules to detect various attacks, both direct through ssh and indirect through web requests. Obviously, none of our ssh accounts use passwords at all, but attackers don’t know that, and it’s quite horrifying to see how many ssh brute force attacks happen in a single day. We have a pretty strict policy of blocking any IP address that attacks us for a considerable time.
Our other fail2ban rules block nginx attacks, looking for home requests, proxying, bots, and so on. Again, too many attacks and you get sent directly to jail through iptables. There’s a lot more we can do with fail2ban, and in an ideal world we’d have a full web application firewall, but the reality is, what we have is plenty good enough and very sound and secure.
Logging is handled in two parts: we have logging clients and logging servers. Every server is set up as a logging client, and we choose a subset to run as logging servers. On every client, we use rsyslog to forward logging events if and only if they originate on the local machine to all of the logging servers.
Logging servers are slightly different. They add an action to listen for
incoming logging events, and then drop these into a new file in
with an appropriate log rotation setup. It really is that simple.
The only downside is that instead of having a nice GUI and indexed searching, we need to use grep, but in practice, that is just fine. 99% of the time we know exactly when an event happened and can filter accordingly. And rsyslog is extremely flexible and allows us to add additional reporting and notifications very easily, any time we need it.
We also use a tweaked version of jsonperfmon, a perfect little C program that simply writes out a very complete assessment of the local machine’s state every n seconds. Our tweaked version only has one significant change, which writes the output to syslog rather than standard output. This means we can neatly run it as a service through systemd, and now, in our aggregated logs, we get a complete track of performance across the entire cluster.
That about describes the technical infrastructure, so how do we get it onto the servers?
We use Ansible for that. Instead of worrying about Puppet, Chef, or Kubernetes, Ansible simply allows coordinated ssh access to more-or-less bare servers, with a whole bunch of idempotent actions that do things like create directories, copy files, templating, executing shell commands when needed, and so on.
In many cases, the actions are pretty simple. The application itself is run as a Node.js service, so we simply need to copy files over, run yarn to install the dependencies, and deploy a service file for systemd, then we can start the service and there will be a new worker process running, which nginx can proxy to.
However, the clusters, both redis and PostgreSQL, are a lot more complex, because the nodes need to know about each other. We have a relatively complex set of Ansible plays that deploys the PostgreSQL cluster, with repmgrd, and will repair a cluster when we need to. In essence, this looks at the current state (if servers are running) and either nominates a primary and a standby, or uses ones that exist, and then does the minimum changes necessary to make the cluster operational.
redis is simpler, because redis-sentinel makes it pretty easy. We run a sentinel on every application node, alongside a redis instance, and all application nodes have both. The only difference is that one of them will be a primary.
Apart from redis, PostgreSQL, and logging, everything else is identical on all servers, which makes Ansible nice and simple.
Ansible is something of an acquired taste. The concept is a nice one, but the messy use of YAML files for everything is not as clean as it might be.
We now have a small, lean, server setup, where we can afford to lose a machine at any time without interruption or loss of data or service. It’s cheap and fairly easy to maintain, without the layers of complexity of a Kubernetes setup.
The main loss? Digital Ocean’s database service was very very good, and pro-actively dealt with issues before we even had to worry about them.
We have gained in a few places, though: we now have redundancy in our logging system, and direct access to our database servers, which means we can implement our own backup strategies a little more easily than is usually allowed by cloud-native services. It’s just as easy to scale, as we can add more application servers very simply. And if the database service does need to expand, it’s very easy to use Ansible to segment off the database management to a few larger and more dedicated servers.
And to cap it all, it costs around 25% of the amount we were paying before, by eliminating the need for the dedicated database service and logging server.