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OrioleDB beta7: Benchmarks

· 7 min read
Alexander Korotkov
Alexander Korotkov
Creator of OrioleDB
Pavel Borisov
Pavel Borisov
PostgreSQL contributor

OrioleDB is a storage extension for PostgreSQL which uses PostgreSQL's pluggable storage system. Designed as a drop-in replacement for PostgreSQL's existing Heap storage, OrioleDB aims to overcome scalability bottlenecks and fully utilize modern hardware capabilities. By integrating seamlessly with PostgreSQL, it offers improved performance, efficiency, and scalability without sacrificing the robustness and reliability that PostgreSQL is known for.

Today we’re releasing OrioleDB version beta7. This marks a significant step in delivering a high-performance, next-generation storage engine for Postgres users. OrioleDB is designed to extract the full potential of modern hardware, offering superior performance, efficiency, and scalability.

OrioleDB design choices​

OrioleDB is built from the ground up to leverage modern hardware, reduce maintenance needs, and enhance distributed capabilities. The key technical decisions forming the foundation of OrioleDB are:

  1. Elimination of Buffer Mapping and Lock-less Page Reading: In OrioleDB, in-memory pages are directly linked to storage pages, eliminating the need for buffer mapping and its associated bottlenecks. Additionally, in-memory page reading is performed without atomic operations, enabling lock-less access. Together, these design choices significantly elevate vertical scalability for PostgreSQL.
  2. MVCC Based on the UNDO Log Concept: OrioleDB employs a Multi-Version Concurrency Control (MVCC) mechanism based on an undo log. Old versions of tuples are evicted into undo logs—forming undo chains—rather than causing bloat in the main storage system. Page-level undo records allow the system to promptly reclaim space occupied by deleted tuples. Combined with page merging, these mechanisms eliminate bloat in most cases. As a result, dedicated vacuuming of tables is unnecessary, removing a common cause of system performance degradation and database outages.
  3. Copy-on-Write Checkpoints and Row-Level WAL: OrioleDB utilizes copy-on-write checkpoints to provide structurally consistent snapshots of data at all times. This approach is friendly to modern SSDs and enables row-level Write-Ahead Logging (WAL). Row-level WAL is easy to parallelize (already implemented), compact, and suitable for active-active multi-master configurations (planned).

Benchmarking OrioleDB vs PostgreSQL Heap​

To illustrate the performance characteristics of OrioleDB we used TPC-C benchmarking, a complex test simulating real database workloads that is considered a modern standard in database applications.