Mammoth transactions, which involve long-running operations that access many items, are common in graph workloads. Graph analytics tasks, including pattern matching and graph algorithms, can generate large read-write operations that impact significant portions of data, which makes their execution challenging under strict isolation guarantees. Consequently, we face an apparent trade-off between ensuring high isolation and achieving high performance, forcing users to choose between the two. In this work, we present TuskFlow, an experimental graph database based on Neo4j, designed to efficiently handle mammoth transactions on graphs (the technique is applicable to other models such as relational) while maintaining existing transactional semantics. TuskFlow employs a deterministic protocol that safely reorders regular transactions around mammoths within an epoch. Our protocol supports parallel mammoth execution inspired by graph-parallel algorithms. To minimize conflicts with regular transactions, TuskFlow introduces query- and workload-aware optimizations, including graph entity tagging and partitioning. Our experiments demonstrate that, unlike traditional protocols like two-phase locking or MVCC, TuskFlow avoids blocking write transactions and improves tail latency by up to 45×.

BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It dynamically optimizes queries according to meta-level changes in the underlying graph (i.e. changes in topology) without the need for any explicit schema. This is possible by using state-of-the-art techniques from managed programming languages, such as self-optimizing ASTs and deoptimization, to combine query optimization and compilation. The approach provides high fidelity for even highly irregular labeled property graphs and gives good performance when compared to other systems that depend on fixed schemas for query planning and optimization.