Distributed databases are surging in popularity with the growing need for performance and fault tolerance. However, implementing transaction isolation models on distributed databases is challenging due to their sharding and replication. As a result, they can produce executions that violate their claimed isolation guarantees. We propose a novel, system-agnostic methodology centered around the usage of graph databases to efficiently detect isolation violations expressed as anti-patterns in transactional dependency graphs. To illustrate, we introduce the GRAIL framework, implemented on top of the popular ArangoDB and Neo4j graph databases. GRAIL combines soundness and high performance with understandable counter-examples. We show that GRAIL outperforms the state-of-the-art checkers through an extensive experimental analysis of real execution histories collected in both the single-node and cluster settings of ArangoDB as a system under test.