Chronos speeds up state changes across multiple data systems
Chronos: Efficient Bolt-on Branching Across Data Stores for Stateful Agentic Applications
Databases
Summary
Many computer programs today need to try out different possible steps that change information across several different types of databases at once. This can get complicated and slow because managing these changes in sync is hard. The authors made a tool called Chronos that helps these programs create and manage branches of data changes quickly and safely, without needing to change the databases themselves. Chronos can handle several popular data systems and makes certain types of explorations run up to 16 times faster while keeping everything consistent.
What this means in practice
- •For data engineers: Manage consistent branching and merging of state changes efficiently across multiple database types in data-centric applications.
- •For machine learning platform teams: Accelerate MCTS-style exploration workflows that require rapid state branching and merging over different data systems.
Authors
Xinjing Zhou, Jason Mohoney, Samuel Madden, Michael Stonebraker, Lei Cao
Abstract
Data-centric applications increasingly use speculative execution to explore multiple candidate paths where each path modifies state distributed across heterogeneous data stores. This trend is intensified by the rise of tool-calling agents. Hence, applications need data systems that can create branches quickly, isolate state-modifying paths, and merge changes consistently across stores without imposing substantial query overhead. Existing systems provide only partial support, forcing applications to coordinate branches and merges manually, which increases overhead and risks inconsistent cross-store state. To solve this problem, we introduce Chronos, a bolt-on system that provides branching capability across heterogeneous data stores. We make two contributions. First, Chronos introduces a compact interval-based versioning technique that enables efficient branching and data sharing through simple query rewrite. Second, Chronos introduces a bolt-on architecture that separates branch management from data path within each store. Combined with interval-based versioning, this separation provides atomic cross-store visibility for merges and enables Chronos to support diverse data stores without modifying their engines. We implement Chronos for PostgreSQL, SQLite, DuckDB, Qdrant, and a DBMS-backed filesystem. We evaluate it using cross-store agent workflows, MCTS-style exploration, and per-store benchmarks. Chronos runs MCTS-style exploration up to 16.7x faster than existing approaches while maintaining practical query performance across the underlying stores. Under concurrent cross-store workflows, Chronos prevents partially visible merges while substantially outperforming serialized execution.