NeuronDB: A self-evolving cloud database system — NextLogica research

NeuronDB is a neuroplastic database that treats the database engine as a differentiable system: the optimizer, storage layout, and execution paths evolve through reinforcement learning from real workload telemetry. Unlike traditional databases with static architectures, NeuronDB’s core idea is that the database should rewrite itself based on observed access patterns.

Core innovation: a Write-Optimized Adaptive Memory Fabric (WOAMF) storage engine combining log-structured storage with learned predictive compaction; RDMA-native page management with disaggregated memory; AI-driven adaptive indexing that morphs between B-tree, LSM, and learned structures per table/partition; and predictive prefetching using transformer-based access pattern models.

Performance thesis: up to 10x lower p99 latency via predictive query routing and learned index structures; up to 5x higher throughput through adaptive parallelism and zero-copy networking; up to 3x cost reduction via intelligent tiering and compression; and a large reduction in operational tasks through autonomous optimization.

The system includes a neural query optimizer that learns from execution telemetry, polymorphic learned indexes chosen per partition, predictive scaling and compaction, and a control plane with observability and cost intelligence. This NextLogica research outlines a path toward databases that adapt continuously to workload rather than relying on manual tuning.