TL;DR
Recent benchmarks tested SurrealDB 3.x against leading databases on identical hardware with full durability enabled. SurrealDB shows significant performance gains over Postgres and MongoDB, especially in write operations, and substantial improvements over earlier SurrealDB versions.
Benchmark tests conducted on the same hardware environment have shown that SurrealDB 3.x significantly outperforms previous versions and rivals, and in some cases surpasses, traditional databases like Postgres and MongoDB in various workload metrics, with full disk durability enabled.
The benchmarks used a high-end AMD Ryzen Threadripper 9970X system with 128 GiB RAM, NVMe storage, and Ubuntu 24.04. All databases were configured for production-grade durability, with fsync enabled and transactions committed to disk, ensuring realistic performance metrics. SurrealDB 3.x, after three major releases, demonstrated substantial improvements in throughput and latency, notably achieving 141,000 operations per second in mixed CRUD workloads, a 31% increase over SurrealDB 2.x and a 164-fold increase in full-table scan performance.
Compared to PostgreSQL, SurrealDB 3.x is roughly 1.5 times faster in create, update, and delete operations, though PostgreSQL maintains an edge in single-record read performance. Against MongoDB, SurrealDB outperforms in read and heavier write workloads, being approximately 1.3 times faster on reads and 5-7 times faster on writes. The benchmarks also included comparisons with Neo4j and Redis, though detailed numbers for these are still emerging. The testing methodology involved tuning all databases for optimal performance, including adjusting buffer pools, connection limits, and storage engines, to emulate real-world production setups.
Why It Matters
This benchmarking effort provides a clearer view of SurrealDB 3.x’s capabilities in real-world scenarios, especially under full durability conditions that are critical for production environments. The notable improvements in throughput and latency suggest SurrealDB’s potential as a multi-model, durable database alternative for workloads traditionally dominated by relational or document stores. For developers and organizations evaluating database options, these results highlight SurrealDB’s growing maturity and performance competitiveness.
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Background
Previous benchmark rounds with SurrealDB used relaxed durability settings, which favored cache-based performance. This latest round emphasizes full durability, aligning with production deployment standards. SurrealDB’s rapid development over three major releases has focused on improving query parsing, storage efficiency, and overall throughput, making it a more viable option for enterprise use. Comparisons with established databases like Postgres, MongoDB, Neo4j, and Redis are particularly relevant as they represent the primary options for different data models and workloads.
“The latest benchmarks demonstrate how SurrealDB 3.x has closed the performance gap and delivered substantial improvements across all major workload types.”
— SurrealDB team
“Running all databases with full durability and optimized configurations provides a fair comparison, revealing SurrealDB’s true performance potential.”
— Benchmark author
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What Remains Unclear
It is not yet clear how SurrealDB 3.x will perform under different hardware configurations or with more complex multi-table workloads. The comparisons with Neo4j and Redis are still preliminary, and detailed numbers are expected in future releases.
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What’s Next
Further testing on diverse hardware and workload types is anticipated to validate these results. The SurrealDB team is likely to release updates aimed at closing remaining gaps in query planning and read performance. Additionally, comparisons with more complex, multi-table or distributed setups are expected to follow.
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Key Questions
How does SurrealDB 3.x performance compare to other databases in real-world applications?
While benchmark results are promising, real-world performance depends on workload specifics. SurrealDB shows strong potential, especially in write-heavy and mixed workloads, but further testing is needed for comprehensive evaluation.
Are these benchmarks applicable to production environments?
Yes, the tests used production-grade configurations with full durability enabled, making the results relevant for real-world deployment considerations.
What are the main improvements in SurrealDB 3.x over earlier versions?
Major improvements include faster query execution, elimination of per-row decoding overhead, and significantly enhanced full-table scan performance, resulting in higher throughput and lower latency.
Will SurrealDB be able to catch up with or surpass PostgreSQL and MongoDB in all areas?
SurrealDB demonstrates competitive performance, especially in write-heavy workloads, but it still trails PostgreSQL in some query optimization areas. Ongoing development aims to close these gaps.
Source: Hacker News
