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arXiv2026

Building An Integrated Vector Database System in PostgreSQL

This paper presents PostgreSQL-V 2.0, a scalable integrated vector database system inside PostgreSQL. Existing PostgreSQL-based vector search systems such as pgvector embed vector indexes into PostgreSQL's page-oriented storage engine, incurring significant overhead that leads to a huge performance gap with specialized vector databases. In our earlier work, we introduced PostgreSQL-V 1.0, which addresses this issue by separating vector index structures from PostgreSQL's storage engine, enabling vector search performance close to that of native vector index libraries while preserving SQL compatibility. However, we find that PostgreSQL-V 1.0 has three limitations that matter for real-world workloads: it only supports a single connection (without concurrency), recovery time grows with index size, and physical replication is unsupported. We further present PostgreSQL-V 2.0, which closes all three gaps. PostgreSQL-V 2.0's concurrency support enables fully concurrent vector searches and updates across PostgreSQL's multi-process backends, delivering up to 36.4x the throughput of PostgreSQL-V 1.0 while serving 32 concurrent clients. PostgreSQL-V 2.0's fast crash recovery keeps cost independent of total index size, remaining near 20 ms while PostgreSQL-V 1.0's grows into seconds-scale. PostgreSQL-V 2.0's physical replication support extends physical replication to the decoupled index, preserving index consistency on standbys without burdening the primary node. Together, these advances make PostgreSQL-V 2.0 a fully concurrent, crash-resilient, and replication-ready vector database inside PostgreSQL.

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arXiv2026

O^3-LSM: Maximizing Disaggregated LSM Write Performance via Three-Layer Offloading

Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) have been optimized and redesigned for disaggregated storage via techniques such as compaction offloading to reduce the network I/Os between compute and storage. However, the constrained memory space and slow flush at the compute node severely limit the overall write throughput of existing optimizations. In this paper, we propose O3-LSM, a fundamental new LSM-KVS architecture, that leverages the shared Disaggregated Memory (DM) to support a three-layer offloading, i.e., memtable Offloading, flush Offloading, and the existing compaction Offloading. Compared to the existing disaggregated LSM-KVS with compaction offloading only, O3-LSM maximizes the write performance by addressing the above issues. O3-LSM first leverages a novel DM-Optimized Memtable to achieve dynamic memtable offloading, which extends the write buffer while enabling fast, asynchronous, and parallel memtable transmission. Second, we propose Collaborative Flush Offloading that decouples the flush control plane from execution and supports memtable flush offloading at any node with dedicated scheduling and global optimizations. Third, O3-LSM is further improved with the Shard-Level Optimization, which partitions the memtable into shards based on disjoint key-ranges that can be transferred and flushed independently, unlocking parallelism across shards. Besides, to mitigate slow lookups in the disaggregated setting, O3-LSM also employs an adaptive Cache-Enhanced Read Delegation mechanism to combine a compact local cache with DM-assisted memtable delegated read. Our evaluation shows that O3-LSM achieves up to 4.5X write, 5.2X range query, and 1.8X point lookup throughput improvement, and up to 76% P99 latency reduction compared with Disaggregated-RocksDB, CaaS-LSM, and Nova-LSM.

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