This paper presents our experience specifying and verifying the correctness of ZooKeeper, a complex and evolving distributed coordination system. We use TLA+ to model finegrained behaviors of ZooKeeper and use the TLC model checker to verify its correctness properties; we also check conformance between the model and code. The fundamental challenge is to balance the granularity of specifications and the scalability of model checking---fine-grained specifications lead to state-space explosion, while coarse-grained specifications introduce model-code gaps. To address this challenge, we write specifications with different granularities for composable modules, and compose them into mixed-grained specifications based on specific scenarios. For example, to verify code changes, we compose fine-grained specifications of changed modules and coarse-grained specifications that abstract away details of unchanged code with preserved interactions. We show that writing multi-grained specifications is a viable practice and can cope with model-code gaps without untenable state space, especially for evolving software where changes are typically local and incremental. We detected six severe bugs that violate five types of invariants and verified their code fixes; the fixes have been merged to ZooKeeper. We also improve the protocol design to make it easy to implement correctly.

Implementation-level distributed system model checkers (DMCKs) have proven valuable in verifying the correctness of real distributed systems. However, they primarily focus on state space reduction, and often have a bottleneck on another crucial dimension: exploration speed. To scale DMCK, we introduce SandTable, a technique for lifting state-space exploration from the implementation level to the specification level, and confirming bugs at the implementation level. We made SandTable practical through a methodology consisting of four essential parts: (1) writing specifications that adhere to the implementation, (2) checking conformance to enhance specification quality and reduce false positives and false negatives, (3) exploring the state space with heuristics for effectiveness and efficiency, and (4) confirming bugs and verifying their fixes in the implementation.