Production postmortem: The network latency and the I/O spikes
A customer called us about an elevated I/O load in their system after an upgrade to RavenDB 5.3 from RavenDB 4.2. We looked into that, and we saw a small (but very noticeable) rise that we just could not explain. Those sorts of issues are tough to crack, because there isn’t an error or a smoking gun to get you started.
Instead, we just saw a higher average I/O rate, but what is the reason for that? Maybe it is a seasonal change for the customer, with a higher load during the springtime? Or maybe it is related to a new index that was deployed?
We looked, but there hasn’t been anything that should cause higher I/O stress for the system that we could see. So we started diving deeper and deeper into the metrics. On Linux, you can check what files are being read or written to (and all of those that we could see represented reasonable values for their load, there wasn’t anything that wasn’t expected). You can also pull the I/O stats by thread, and we could see that the cluster threads were quite busy in terms of I/O, but that is a big cluster, with plenty of databases and cluster operations to manage, it seems reasonable.
What is going on? I just checked, and the timeline for this investigation is about four weeks, we tried a lot of things to figure it out. But we couldn’t find a smoking gun.
Separately, we got a few bug reports from the field about a cluster issue, sometimes the cluster connection between nodes would break for no reason. The connectivity was good, so there was no reason for the break. This is a transient (and expected) error, which RavenDB will gracefully recover from. But it was a new behavior, so we looked into that.
It turns out that during some refactoring, we moved a piece of code in such a way that under certain conditions, it would read too much from the buffer, but not consume all of it. Basically, this issue came back in some cases. In order to trigger this problem, we had to have a very specific network configuration with exact latencies compared to the CPU load on the server. When that behavior was triggered, we would discard some part of the message from the other side. In some cases, that just meant that we skipped an update (in a stream of them), no big deal, we’ll get the next one successfully. But depending on the size of the cluster in question and the latencies involved, we may get corrupted data (since we are missing the data). We properly detect and abort the connection in this case.
It turns out that when such a thing happens, RavenDB considers the other side to have failed, and the cluster takes the appropriate action to compensate. That means that it will re-assign the tasks across the cluster. A few seconds later, the connection would be resumed, the cluster would realize that the node is “up” again and move the tasks back to the node.
Those tasks include things like subscriptions, ETL processes, external replication, etc.
In other words, under a specific set of conditions, we’ll have a lot of jitters, for lack of a better term in the cluster. Some of the nodes will be moved in and out of rehab (a status that means that they aren’t fully functional). That led, in turn, to a high churn of tasks (and each of those has its own I/O costs).
There are other factors here, naturally, such as higher CPU and memory, but I/O is where we are typically most constrained, so it showed up there mostly. The bug was fixed (and it is in the latest stable) and we have confirmation from the customer that this indeed resolved their issue.
It just goes to show how complex systems are. A bug occurs on node A when reading from the network under specific latencies conditions has cascaded to a higher resource utilization on node C. Butterfly effect indeed.