Load Balancing & Failover Across Nodes
Distribute work evenly across Erlang nodes and keep services available through automatic failover in a distributed cluster.
Load Balancing & Failover Across Nodes is a free Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Erlang OTP: Distributed & Fault-Tolerant Systems Programming learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Load Balancing?
In a cluster, spreading work across nodes prevents any one node from becoming a bottleneck and lets you scale by adding machines. Failover keeps the system available when a node dies.
Listing Cluster Nodes
A node sees its connected peers with nodes/0. This list is the pool you balance work over.
nodes().
% => ['worker1@host', 'worker2@host']Round-Robin Dispatch
A simple strategy cycles through nodes in order, giving each an equal share of tasks.
pick_node(Index) ->
Nodes = [node() | nodes()],
lists:nth((Index rem length(Nodes)) + 1, Nodes).Spawning on a Remote Node
spawn/4 can start a process on a chosen node, sending the work where you want it.
spawn(TargetNode, my_worker, run, [Task]).The pg Process Groups
The pg module maintains named groups of processes spread across nodes. You can fetch group members and dispatch to them.
pg:join(workers, self()).
Members = pg:get_members(workers).Choosing a Local Member
For low latency, prefer a worker on the local node when one exists, falling back to remote members otherwise.
case pg:get_local_members(workers) of
[] -> pick_remote();
Local -> hd(Local)
end.Detecting Node Failure
Call net_kernel:monitor_nodes(true) to receive {nodedown, Node} messages when a peer disconnects, the trigger for failover.
net_kernel:monitor_nodes(true).
% receive {nodedown, Node} -> handle_failure(Node) end.Failover Strategy
On nodedown, redistribute that node's pending work to surviving nodes and remove it from your dispatch pool.
handle_failure(Node) ->
Tasks = pending_tasks(Node),
[reassign(T) || T <- Tasks].Stateless vs Stateful
Stateless work is trivial to reassign. Stateful work needs its state replicated (e.g. via Mnesia) so a survivor can resume it. Design state placement up front.
Avoiding Thundering Herds
When a node returns, do not dump all reconnection or rebalancing work at once. Stagger it to avoid overwhelming the cluster.
Putting It Together
A resilient setup: register workers in a pg group, dispatch round-robin with local preference, monitor nodes, and reassign work on failure with state backed by replicated storage.
Quick Check
Test your failover knowledge.
Recap
You learned distributed load balancing and failover.
- Dispatch work across
nodes/0with round-robin and local preference - Use
pggroups to track workers - Monitor nodes and reassign work on
nodedown - Replicate state for stateful failover
Frequently asked questions
Is the “Load Balancing & Failover Across Nodes” lesson free?
Yes — the full text of “Load Balancing & Failover Across Nodes” is free to read here on the web, and the Erlang OTP: Distributed & Fault-Tolerant Systems Programming course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Erlang OTP: Distributed & Fault-Tolerant Systems Programming course, upgrade to CoddyKit PRO.
What will I learn in “Load Balancing & Failover Across Nodes”?
Distribute work evenly across Erlang nodes and keep services available through automatic failover in a distributed cluster. You practise Erlang OTP: Distributed & Fault-Tolerant Systems Programming with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Erlang OTP: Distributed & Fault-Tolerant Systems Programming?
No prior experience is required. Erlang OTP: Distributed & Fault-Tolerant Systems Programming on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Load Balancing & Failover Across Nodes” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson?
Yes. Every Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Handling Network Partitions
- Distributed Data with ETS & Mnesia
- Scalability & Resilience Design
- Load Balancing & Failover Across Nodes