跨节点负载均衡与故障转移
在 Erlang 节点之间均匀分配工作，并通过分布式集群中的自动故障转移保持服务可用。
跨节点负载均衡与故障转移 是 CoddyKit 上的免费 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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
常见问题解答
「跨节点负载均衡与故障转移」课时是免费的吗？
是的 — 「跨节点负载均衡与故障转移」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课程的其余内容，请升级到 CoddyKit PRO。 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课程共包含 4 节课。
「跨节点负载均衡与故障转移」这节课中我会学到什么？
在 Erlang 节点之间均匀分配工作，并通过分布式集群中的自动故障转移保持服务可用。 你通过在浏览器中直接运行的动手代码来练习 Erlang OTP: Distributed & Fault-Tolerant Systems Programming，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「跨节点负载均衡与故障转移」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课中编写并运行代码吗？
能。每节 Erlang OTP: Distributed & Fault-Tolerant Systems Programming 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 处理网络分区
- 使用 ETS 与 Mnesia 处理分布式数据
- 可扩展性与弹性设计
- 跨节点负载均衡与故障转移