Load balancing is a critical component of any scalable software system. By distributing traffic intelligently across a set of endpoints—even as that set changes dynamically, and as endpoints fail or slow down—good load balancing can reduce tail latencies and increase reliability. Linkerd offers a variety of powerful load-balancing algorithms, including least loaded, EWMA, and aperture. These algorithms have been tested at scale at Twitter and other companies.
Because Linkerd operates at the RPC layer, it can balance load based on observed RPC latencies and queue sizes, rather than heuristics such as LRU or TCP activity. This means it can optimize traffic flow and reduce tail latencies across the application. Because Linkerd is built on top of Finagle, it can take advantage of a variety of load-balancing algorithms, which are designed to maximize the number of requests that are served successfully while minimizing latencies.
The load-balancing algorithms available to Linkerd are described below. They’re also covered in more detail in Finagle’s load balancing documentation.
Picking the right load balancer for your setup depends at least partly on your application topology. Linkerd provides a sane default for most installs and allows you to swap in other balancers as needed.
This is Linkerd’s default load balancer, and it is surprisingly simple. When determining where to send a request, Linkerd picks two replicas from the load balancer pool at random, and it selects the one that is the least loaded of the two. Load is determined by the number of outstanding requests to each replica. This algorithm provides a manageable upper bound on load for individual replicas. It does so using less overhead than other algorithms with similar performance.
This load balancer is a variation on the P2C: Least Loaded strategy from above, in that it is still picking between two replicas when sending a request. To make that decision, it maintains a moving average of observed latencies, and uses that to weight the number of outstanding requests to each replica. This approach is more sensitive to fluctuations in latency and allows slower backends time to recover by sending them fewer requests. The latency window is configurable.
The aperture load balancer is appropriate for clients that are sending a relatively small amount of traffic to a backend with a relatively large amount of replicas. It constrains the number of replicas to which it sends traffic to some subset of the full replica set, and then picks the replica that has the least number of outstanding requests. This approach ensures a higher rate of connection reuse at low traffic volumes.
This load balancer uses a min heap to track the number of outstanding requests to all replicas. It sends all requests to the top replica in the heap, which is the one that is the least loaded at time of request, and will change over time. One notable limitation of this approach is that the heap is a shared resource across all requests, and therefore will cause contention issues with high volumes of traffic.
If you’d like to learn more about how various different load-balancing algorithms compare performance-wise, check out Buoyant’s blog post on the topic: Beyond Round Robin: Load Balancing for Latency.
If you’re ready to start configuring load balancing in your setup, see the Load Balancer section of the Linkerd configuration reference.