TL;DR
Recent benchmarks compare SurrealDB 3.x with Postgres, MongoDB, Neo4j, and Redis, revealing significant performance improvements and competitive advantages in durability. SurrealDB 3.x shows notable gains in CRUD operations, full-table scans, and latency. The tests used production-grade configurations with full disk durability enabled, providing a realistic performance picture.
Benchmark results released in March 2024 demonstrate that SurrealDB 3.x significantly outperforms earlier versions and rivals established databases such as Postgres, MongoDB, Neo4j, and Redis in various workloads, using production-grade configurations with full disk durability enabled.
The benchmarks were conducted on identical hardware: an AMD Ryzen Threadripper 9970X with 128 GiB DDR5 RAM, NVMe storage, running Ubuntu 24.04. The testing harness, crud-bench, translated workloads into each database’s native query language, ensuring fairness. All databases were configured for production use, with fsync and snapshot isolation enabled, avoiding in-memory shortcuts.
SurrealDB 3.x showed substantial performance gains: a 164-fold increase in full-table scan speed compared to version 1.x, with 11 ops/sec. CRUD throughput rose from 78,000 to 141,000 operations per second, and query latency improved across all metrics—up to 99% faster in scans. These improvements are attributed to internal rewrites of the query, parser, and storage layers, and the elimination of per-row decoding overhead.
When compared to PostgreSQL, SurrealDB demonstrated approximately 1.5 times faster write operations (creates, updates, deletes) and about 1.3 times faster reads. Against MySQL, SurrealDB was 5 to 7 times faster on write operations. In the document database category, SurrealDB outperformed MongoDB in read and filter scan operations, especially on unindexed workloads, being roughly 2.7 times faster in filter scans.
Why It Matters
The benchmarks highlight SurrealDB 3.x’s significant advancements in performance and durability, positioning it as a competitive multi-model database option for production deployments. The improvements in CRUD throughput and full-table scans suggest better handling of complex workloads, which can influence database selection decisions for enterprises seeking a durable, high-performance solution.
These results are especially relevant as SurrealDB continues to evolve from its earlier versions, demonstrating its potential to challenge more established databases in both transactional and analytical tasks. The full disk durability configuration underscores its readiness for real-world, resilient deployment scenarios.
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Background
Previous benchmark rounds with fsync disabled showed higher throughput but less realistic performance, as most production environments require full durability. SurrealDB’s internal overhaul over three major releases has focused on improving query performance and storage efficiency. Historically, databases like Postgres and MySQL have set performance standards, but emerging multi-model solutions like SurrealDB are closing the gap, especially in mixed workloads.
The benchmarking methodology used identical hardware and configurations, reflecting real-world production settings. Prior to this, SurrealDB’s performance was less competitive, but recent releases have significantly improved its capabilities, as evidenced by the latest results.
“SurrealDB 3.x demonstrates remarkable performance gains, especially in full-table scans and CRUD operations, thanks to internal architecture improvements.”
— Benchmarking team
“The full disk durability configuration provides a realistic view of SurrealDB’s capabilities in production environments, setting it apart from earlier benchmarks.”
— Database expert
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What Remains Unclear
It remains unclear how SurrealDB 3.x will perform under different workloads, such as high concurrency with complex joins or in distributed deployments. Further testing is needed to verify scalability and stability in diverse environments.
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What’s Next
Next steps include testing SurrealDB 3.x in real-world production scenarios, evaluating its scalability across distributed systems, and benchmarking with more complex workloads. Updates to documentation and further tuning are expected as the database matures.
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Key Questions
How does SurrealDB 3.x compare to other databases in real-world use?
While benchmark results are promising, real-world performance depends on workload specifics, hardware, and configuration. Further testing is needed to confirm its advantages in production environments.
What are the main improvements in SurrealDB 3.x?
Significant gains in query speed, especially in full-table scans and CRUD operations, due to internal architecture rewrites and elimination of decoding overhead.
Is SurrealDB suitable for large-scale, high-availability deployments?
Current benchmarks with full durability are promising, but additional testing is required to assess scalability and resilience in distributed environments.
Will SurrealDB close the performance gap with traditional relational databases?
It is making substantial progress, especially in transactional workloads, but some gaps remain, particularly in complex query planning and filtering.
Source: Hacker News
