UC Berkeley

Debugging large-scale, data-intensive, distributed applications running in a datacenter ("datacenter applications") is complex and time-consuming. The key obstacle is non-deterministic failures--hard-to-reproduce program misbehaviors that are immune to traditional cyclic debugging techniques. Datacenter applications are rife with such failures because they operate in highly non-deterministic environments: a typical setup employs thousands of nodes, spread across multiple datacenters, to process terabytes of data per day. In these environments, existing methods for debugging non-deterministic failures are of limited use. They either incur excessive production overheads or don't scale to multi-node, terabyte-scale processing.

To help remedy the situation, we have built a new deterministic replay tool. Our tool, called DCR, enables the reproduction and debugging of non-deterministic failures in production datacenter runs. The key observation behind DCR is that debugging does not always require a precise replica of the original datacenter run. Instead, it often suffices to produce some run that exhibits the original behavior of the control-plane--the most error-prone component of datacenter applications. DCR leverages this observation to relax the determinism guarantees offered by the system, and consequently, to address key requirements of production datacenter applications: lightweight recording of long-running programs, causally consistent replay of large-scale clusters, and out-of-the box operation with existing, real-world applications running on commodity multiprocessors.