> Consider a system with 1000 nodes where each node sends heartbeats to a central monitor every 500 milliseconds. This results in 2000 heartbeat messages per second just for health monitoring. In a busy production environment, this overhead can interfere with actual application traffic.
If your 1000-node busy production environment is run so close to the edge that 2000 heartbeat messages per second, push it into overload, that's impressive resource scheduling.
Really, setting the interval balances speed of detection/cost of slow detection vs cost of reacting to a momentary interruption. If the node actually dies, you'd like to react as soon as possible; but if it's something like a link flap or system pause (GC or otherwise), most applications would prefer to wait and not transition state; some applications like live broadcast are better served by moving very rapidly and 500 ms might be too long.
Re: network partitioning, the author left out the really fun splits. Say you have servers in DC, TX, and CA. If there's a damaged (but not severed) link between TX and CA, there's a good chance that DC can talk to everyone, but TX and CA can't communicate. You can have that inside a datacenter too, maybe each node can only reach 75% of the other nodes, but A can reach B and B can reach C does not indicate A can reach C. Lots of fun times there.
When systems were smaller I tried to push for the realization that I don’t need a heartbeat from a machine that is currently returning status 200 messages from 60 req/s. The evidence of work is already there, and more meaningful than the status check.
We end up adding real work to the status checks often enough anyway, to make sure the database is still visible and other services. So inference has a lot of power that a heartbeat does not.
I've dealt with exactly this. We had a couple thousand webapp server instances that had connections to a MySQL database. Each one only polled its connection for liveliness once per second, but those were little interruptions that were poking at the servers and showed up on top time consuming request charts.
Indeed, which is why I've heard of failover setups where the backup has a means to make very sure that the main system is off before it takes over (often by cutting the power).
I did this systematically: at the first sign of outlier in performance one system would move itself to another platform and shut itself down. The shutdown meant turn all services off and let someone log in to investigate and rearrange it again. This system allowed different roles to be assigned to different platform. The platform was bare metal or bhyve vm. It worked perfect.
I've been noodling a lot on how IP/ARP works as a "distributed system". Are there any reference distributed systems that have a similar setup of "optimistic"/best effort delivery? IPv6 and NDP seem like they could scale a lot, what would be the negatives about using a similar design for RPC?
Top shelf would be noticing an anomaly in behavior for a node and then interrogating it to see what’s wrong.
Automatic load balancing always gets weird, because it can end up sending more traffic to the sick server instead of less, because the results come back faster. So you have to be careful with status codes.
Some fuzzy thinking in here. "A heartbeat sent from a node in California to a monitor in Virginia might take 80 milliseconds under normal conditions, but could spike to 200 milliseconds during periods of congestion." This is not really the effect of congestion, or at best this sentence misleads the reader. The mechanism that causes high latency during congestion is dropped frames, which are retried at the protocol level based on timers. You can get a 200ms delay between two nodes even if they are adjacent, because the TCP minimum RTO is 200ms.
> Consider a system with 1000 nodes where each node sends heartbeats to a central monitor every 500 milliseconds. This results in 2000 heartbeat messages per second just for health monitoring. In a busy production environment, this overhead can interfere with actual application traffic.
If your 1000-node busy production environment is run so close to the edge that 2000 heartbeat messages per second, push it into overload, that's impressive resource scheduling.
Really, setting the interval balances speed of detection/cost of slow detection vs cost of reacting to a momentary interruption. If the node actually dies, you'd like to react as soon as possible; but if it's something like a link flap or system pause (GC or otherwise), most applications would prefer to wait and not transition state; some applications like live broadcast are better served by moving very rapidly and 500 ms might be too long.
Re: network partitioning, the author left out the really fun splits. Say you have servers in DC, TX, and CA. If there's a damaged (but not severed) link between TX and CA, there's a good chance that DC can talk to everyone, but TX and CA can't communicate. You can have that inside a datacenter too, maybe each node can only reach 75% of the other nodes, but A can reach B and B can reach C does not indicate A can reach C. Lots of fun times there.
When systems were smaller I tried to push for the realization that I don’t need a heartbeat from a machine that is currently returning status 200 messages from 60 req/s. The evidence of work is already there, and more meaningful than the status check.
We end up adding real work to the status checks often enough anyway, to make sure the database is still visible and other services. So inference has a lot of power that a heartbeat does not.
Kingsbury & Bailis's paper on the topic of network partitions: https://github.com/aphyr/partitions-post
I've dealt with exactly this. We had a couple thousand webapp server instances that had connections to a MySQL database. Each one only polled its connection for liveliness once per second, but those were little interruptions that were poking at the servers and showed up on top time consuming request charts.
Related advice based on my days working at Basho: find a way to recognize, and terminate, slow-running (or erratically-behaving) servers.
A dead server is much better for a distributed system than a misbehaving one. The latter can bring down your entire application.
Indeed, which is why I've heard of failover setups where the backup has a means to make very sure that the main system is off before it takes over (often by cutting the power).
I did this systematically: at the first sign of outlier in performance one system would move itself to another platform and shut itself down. The shutdown meant turn all services off and let someone log in to investigate and rearrange it again. This system allowed different roles to be assigned to different platform. The platform was bare metal or bhyve vm. It worked perfect.
Docker and Kubernetes have health check mechanisms to help solve for this;
Docker docs > Dockerfile HEALTHCHECK instruction: https://docs.docker.com/reference/dockerfile/#healthcheck
Podman docs > podman-healthcheck-run, docker-healthcheck-run: https://docs.podman.io/en/v5.4.0/markdown/podman-healthcheck...
Kubernetes docs > "Configure Liveness, Readiness and Startup Probes" https://kubernetes.io/docs/tasks/configure-pod-container/con...
I've been noodling a lot on how IP/ARP works as a "distributed system". Are there any reference distributed systems that have a similar setup of "optimistic"/best effort delivery? IPv6 and NDP seem like they could scale a lot, what would be the negatives about using a similar design for RPC?
Does anyone have recommendations on books/papers/articles which cover gossip protocols?
I have been more interested in learning about gossip protocols and how they are used, different tradeoffs, etc.
Two interesting papers:
* Epidemic broadcast trees: https://asc.di.fct.unl.pt/~jleitao/pdf/srds07-leitao.pdf
* HyParView: https://asc.di.fct.unl.pt/~jleitao/pdf/dsn07-leitao.pdf
The iroh-gossip implementation is based on those: https://docs.rs/iroh-gossip/latest/iroh_gossip/
Thank you
https://thesecretlivesofdata.com/raft/
> https://thesecretlivesofdata.com/raft/
Are you suggesting to use raft as a gossip protocol? Run a replicated state machine with leader election, replicated logs and stable storage?
Raft is a consensus protocol, which is very different from a gossip protocol.
I'm sorry, I got confused.
Why can't network time synchronization services like SPTP and WhiteRabbit also solve for heartbeats in distributed systems?
> When a system uses very short intervals, such as sending heartbeats every 500 milliseconds
500 milliseconds is a very long interval, on a CPU timescale. Funny how we all tend to judge intervals based on human timescales
Of course the best way to choose heartbeat intervals is based on metrics like transaction failure rate or latency
Top shelf would be noticing an anomaly in behavior for a node and then interrogating it to see what’s wrong.
Automatic load balancing always gets weird, because it can end up sending more traffic to the sick server instead of less, because the results come back faster. So you have to be careful with status codes.
Well, it is called a heartbeat after all, not a oscillator beat :-)
Some fuzzy thinking in here. "A heartbeat sent from a node in California to a monitor in Virginia might take 80 milliseconds under normal conditions, but could spike to 200 milliseconds during periods of congestion." This is not really the effect of congestion, or at best this sentence misleads the reader. The mechanism that causes high latency during congestion is dropped frames, which are retried at the protocol level based on timers. You can get a 200ms delay between two nodes even if they are adjacent, because the TCP minimum RTO is 200ms.