The Cantal is a monitoring system designed specifically for real-time measuring and load-balancing distributed computing clusters.

The essential part of cantal is a Cantal Agent it does the following things:

  • Scans for local metrics at 2 second interval
  • Preserves one hour history of all metrics data with 100% precision (compressed)
  • Provides web interface for viewing local metrics
  • Has peer to peer discovery mechanism
  • On demand provides aggregated statistics over cluster

Additional features:

  • Cantal is aware of linux containers
  • Almost zero-cost connunication between processes and agent
  • Written in rust, so can track thousands of metrics with 2 second precision in less than couple of percents of a single CPU core

The project also consists of:

  • Protocol to submit data to agent at nearly zero cost
  • Command-line tool to view data locally without running agent


Since Cantal is designed for real-time load balancing, it has very strong and very specific requirements:

  1. Very high precision (cantal scans at 2 second interval, while common case is about a minute interval, very rarely interval is 10 seconds or less)
  2. Similarly very fast collection of metrics across large cluster
  3. Discovering trends quickly (i.e. having 30 value snapshots per minute we can find out load growth in a fraction of minute)
  4. High availability (no master, quorum or similar)
  5. Being able to observe individual nodes in case of partitioning
  6. Lightweight

Design Decisions

Here is short roundup of all the important design decisions. Some of them are described in detail in the following sections.

Agent has embedded web server. So you can point your browser to:

..and see all the statistics on the node.

Agent stores history locally. So we don’t loose stats in case of network failure

Agent has peer to peer gossip-like discovery with UDP. So we don’t rely on any other discovery mechanism when time comes to gather metrics over cluster. Note: we do use UDP only for discovery, so we don’t loose statistics when network is lossy.

You can ask any instance of agent to get metrics for whole cluster. This is how we allow to get data over whole cluster with a single HTTP request. But we do it lazily, so that we don’t have full mesh of connections. I.e. when first client asks, we connect to every node by TCP and subcribe for metrics.


We have gossip-like peer to peer discovery. You need to add a peer address at any node and every node will know it.


We use UDP for peer discovery. It works by sending ping packets between nodes. Each packet contains some critical info about current node and info about few randomly selected known peers.


Each node sends 5 ping packets with 10 neighbour nodes each second. Each ping packet receives pong packet with other 10 nodes. Overall it’s not very large number of packets, and packets are distributed uniformly across the nodes. This allows to discover even large network with thousand of nodes in few dozens of seconds.

In the future we plan to discover physical topology using UDP packets. In turn this allows to display graph and provide diagnostics for different kinds of network partitions (including assymmetric partitions, bridge nodes, etc.)

Note that we use UDP exclusively for peer discovery. This allows us to avoid having a full mesh of TCP connections. But we don’t use UDP for transferring metrics, so we don’t lose statistics when network suddenly becomes lossy. Not being able to reach some nodes via UDP in lossy network is definitely the expected outcome and will help diagnose problems too.

Aggregated Metrics

To have efficient cluster management we consider imporant two points:

  1. Resource manager don’t have to gather from each node, they must be pushed as fast as possible
  2. We don’t want to constrain failover and/or lower the availability of the resource manager. I.e. cantal’s data should be virtually always available for resource manager.

When we talk about resource manager (RM) we talk about any software which consumes metrics and implements some resource allocation decisions. Obviously resource management is out of scope of the cantal itself.

So to get metrics RM connects to any cantal agent and requests statistics for all it’s peers.


On first request for some monitoring data Cantal Agent does the following:

  1. Enables remote peer publish-subscribe subsystem
  2. Connects to every known peer via bidirectional protocol (WebSockets in the current implementation)
  3. Subscribes on each node for the subset of data requested by client
  4. Fetches chunk of history for these metrics from every node


UDP-based peer tracking and dicovery works always. So every agent does know all it’s peers. We just activate TCP-based reliable publish-subscribe for metrics at request.

Other client might ask another node and that node will seamlessly provide the stats and historical data.


The only practical limitation of it is the that running a full-mesh of TCP connections is quite inefficient. So you should poll a single node while it’s still available and switch to another one only when it’s not.


It’s hard to overstate that you should not poll every node in turn, otherwise you will have a full mesh of connections and every node will send updates to each other every two seconds.

Viewing web interface for local metrics and polling for them is perfectly OK on any and every node.

In perfect world we expect that resource manager will poll an agent on localhost, and has failover resource manager node with own cantal agent.