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Grove is a concurrent separation logic library for verifying distributed systems. Grove is the first to handle time-based leases, including their interaction with reconfiguration, crash recovery, thread-level concurrency, and unreliable networks. This paper uses Grove to verify several distributed system components written in Go, including GroveKV, a realistic distributed multi-threaded key-value store. GroveKV supports reconfiguration, primary/backup replication, and crash recovery, and uses leases to execute read-only requests on any replica. GroveKV achieves high performance (67-73% of Redis on a single core), scales with more cores and more backup replicas (achieving about 2× the throughput when going from 1 to 3 servers), and can safely execute reads while reconfiguring. 

Understanding and debugging the performance of distributed systems is a notoriously hard task, but a critical one. Traditional techniques like logging, tracing, and benchmarking represent a best-effort way to find performance bugs, but they either require a full deployment to be effective or can only find bugs after they manifest. Even with such techniques in place, real deployments often exhibit performance bugs that cause unwanted behavior. In this paper, we present Performal, a novel methodology that leverages the recent advances in formal verification to provide rigorous latency guarantees for real, complex distributed systems. The task is not an easy one: it requires carefully decoupling the formal proofs from the execution environment, formally defining latency properties, and proving them on real, distributed implementations. We used Performal to prove rigorous upper bounds for the latency of three applications: a distributed lock, ZooKeeper and a MultiPaxos-based State Machine Replication system. Our experimental evaluation shows that these bounds are a good proxy for the behavior of the deployed system and can be used to identify performance bugs in real-world systems. 

Multi-version concurrency control (MVCC) is a widely used, sophisticated approach for handling concurrent transactions. vMVCC is the first MVCC-based transaction library that comes with a machine-checked proof of correctness, providing clients with a guarantee that it will correctly handle all transactions despite a complicated design and implementation that might otherwise be error-prone. vMVCC is implemented in Go, stores data in memory, and uses several optimizations, such as RDTSC-based timestamps, to achieve high performance (25–96% the throughput of Silo, a state-of-the-art in-memory database, for YCSB and TPC-C workloads). Formally specifying and verifying vMVCC required adopting advanced proof techniques, such as logical atomicity and prophecy variables, owing to the fact that MVCC transactions can linearize at timestamp generation prior to transaction execution.