VibeSQL Performance Vs. SQLite, MySQL, And DuckDB
Achieving competitive performance with leading databases like SQLite, MySQL, and DuckDB is crucial for VibeSQL's success. This article delves into a detailed performance analysis, comparing VibeSQL's speed and efficiency across various workloads. We will explore the current performance baselines, identify key bottlenecks, and outline strategies for optimization, ensuring VibeSQL can stand toe-to-toe with its competitors in both OLAP and OLTP scenarios.
Current Performance Baseline (as of November 29, 2025)
To understand VibeSQL's current standing, we've conducted benchmarks across different workloads, including TPC-H (OLAP) and TPC-C (OLTP). These benchmarks provide a clear picture of VibeSQL's strengths and weaknesses, highlighting areas that require immediate attention and long-term optimization.
TPC-H (OLAP) - Scale Factor 0.01
In the realm of Online Analytical Processing (OLAP), the TPC-H benchmark serves as a standard for measuring decision support performance. Our tests covered all 22 queries, providing a comprehensive view of VibeSQL's analytical capabilities. However, a detailed comparison of total execution time against other databases is essential to contextualize these results. Let's dive into the query-by-query breakdown:
| Query | VibeSQL | DuckDB Est. | Gap |
|---|---|---|---|
| Q1 | 435ms | ~1.4ms | ~310x |
| Q2 | 27ms | ~1.9ms | ~14x |
| Q3 | 460ms | ~1.7ms | ~270x |
| Q4 | 27ms | ~1.8ms | ~15x |
| Q5 | 112ms | ~2.1ms | ~53x |
| Q6 | 78ms | ~0.3ms | ~260x |
| Q7 | 486ms | ~3.0ms | ~162x |
| Q8 | 120ms | ~3.4ms | ~35x |
| Q9 | 148ms | ~3.6ms | ~41x |
| Q10 | 409ms | ~3.3ms | ~124x |
| Q11 | 11ms | ~1.7ms | ~6x |
| Q12 | 319ms | ~2.5ms | ~128x |
| Q13 | 63ms | ~2.9ms | ~22x |
| Q14 | 16ms | ~1.0ms | ~16x |
| Q15 | 27ms | ~1.2ms | ~23x |
| Q16 | 27ms | ~2.6ms | ~10x |
| Q17 | 23ms | ~1.0ms | ~23x |
| Q18 | 128ms | ~3.1ms | ~41x |
| Q19 | 364ms | ~3.4ms | ~107x |
| Q20 | 24ms | ~2.5ms | ~10x |
| Q21 | 129ms | ~4.3ms | ~30x |
| Q22 | 6ms | ~1.9ms | ~3x |
The results reveal a mixed bag. While some queries show promise, others indicate significant performance gaps compared to DuckDB. Specifically, queries Q22, Q11, Q16, and Q20 demonstrate relatively strong performance, with VibeSQL's execution times being only a fraction slower than DuckDB's. However, queries like Q1, Q3, Q6, and Q7 lag considerably, running 100 to 300 times slower. Understanding the underlying causes for these discrepancies is paramount.
TPC-C (OLTP) - 1 Warehouse
For Online Transaction Processing (OLTP) workloads, the TPC-C benchmark is the gold standard. It simulates a complete order-entry environment, measuring a database's ability to handle a high volume of concurrent transactions. Here's how VibeSQL stacks up against SQLite and DuckDB in this crucial area:
| Database | TPS | Avg Latency |
|---|---|---|
| SQLite | 994 TPS | 1.0ms |
| DuckDB | 448 TPS | 2.2ms |
| VibeSQL | 58 TPS | 17.2ms |
The numbers paint a stark picture. VibeSQL's performance in OLTP workloads is significantly behind SQLite, achieving only 58 transactions per second (TPS) compared to SQLite's impressive 994 TPS. This translates to VibeSQL being 17 times slower than SQLite, a gap that needs to be urgently addressed. The average latency of 17.2ms further underscores the need for optimization. A detailed transaction breakdown reveals that the New-Order (35ms avg) and Stock-Level (34ms avg) transactions are the primary bottlenecks, suggesting specific areas for focused improvement.
Sysbench OLTP - 10K rows
To gain a more granular understanding of VibeSQL's performance across individual operations, we utilized Sysbench, a versatile benchmarking tool. Sysbench allows us to isolate and measure the performance of specific database operations, such as point selects, inserts, deletes, and updates. The results are illuminating:
| Operation | VibeSQL | SQLite | DuckDB | Gap to SQLite |
|---|---|---|---|---|
| Point Select | 103µs | 328ns | 66µs | 314x |
| Insert | 1.9µs | 2.3µs | 188µs | 0.8x (faster!) |
| Delete | 2.1ms | 26µs | 1.3ms | 81x |
| Update (indexed) | 1.5ms | 1.7µs | - | 882x |
| Update (non-indexed) | 1.7ms | 1.6µs | 183µs | 1062x |
The Sysbench results highlight a critical performance bottleneck: point lookups. VibeSQL's point select operation is a staggering 314 times slower than SQLite. This is a P0 (Priority 0) issue because it impacts almost every other operation. Conversely, VibeSQL demonstrates competitive performance in insert operations, even outperforming SQLite slightly. However, delete and update operations, both indexed and non-indexed, are significantly slower, with gaps ranging from 81x to over 1000x compared to SQLite. These results strongly suggest that VibeSQL's indexing and data retrieval mechanisms require substantial optimization.
TPC-DS - Limited Results
The TPC-DS benchmark is designed to evaluate the performance of decision support systems that handle complex analytical queries. Due to parser limitations, our TPC-DS testing yielded limited results. Query 1 (Q1) completed with a runtime of 4.4ms, but subsequent queries failed due to parser issues. This indicates that VibeSQL's parser needs enhancements to fully support the TPC-DS query syntax. Addressing these parser limitations is crucial for a comprehensive evaluation of VibeSQL's analytical capabilities.
Priority Analysis: Identifying the Bottlenecks
Based on the benchmark results, we've identified several key areas that require immediate attention. These areas are prioritized based on their impact on overall performance and their potential for quick wins.
P0: Point Lookups (Blocking Issue)
The most critical issue is the 314x performance gap in point SELECT operations compared to SQLite. This bottleneck has a cascading effect, impacting virtually every other operation. Every JOIN operation involves point lookups, and every UPDATE/DELETE statement must first locate the rows to be modified. This explains, in part, the 17x performance gap observed in the TPC-C benchmark. The root cause is hypothesized to be either B-tree traversal overhead or value materialization costs. Resolving this issue is paramount for improving overall performance.
P1: Table Scan Performance
Queries Q1, Q3, Q6, and Q7 in the TPC-H benchmark all involve scanning the lineitem table, which contains 60,000 rows at Scale Factor 0.01. The fact that Q6, a simple SUM aggregation over lineitem, is 260 times slower than DuckDB is concerning. While columnar processing is expected to improve table scan performance, it doesn't seem to be providing sufficient benefit in these cases. The root cause is hypothesized to be row iteration overhead, suggesting that the current implementation may not be effectively leveraging vectorization techniques.
P2: Join Performance
Multi-table joins, as seen in TPC-H queries Q7, Q10, and Q19, exhibit significant performance gaps. Several factors could be contributing to this, including suboptimal join order selection, slow hash join build/probe operations, and excessive intermediate result materialization. A thorough investigation into these areas is necessary to optimize join performance.
P3: Aggregation Performance
GROUP BY queries, such as Q1 and Q13 in TPC-H, are slower than expected despite the use of SIMD aggregation techniques. This suggests that overhead associated with hash table management and final result construction may be limiting performance. Further analysis is needed to pinpoint the specific bottlenecks in aggregation operations.
Target Metrics: Setting Performance Goals
To guide our optimization efforts, we've established clear performance goals for the next 6 to 12 months. These goals are ambitious yet achievable, providing a roadmap for VibeSQL to become a truly competitive database.
6-Month Goals
- [ ] Reduce the point SELECT performance gap to within 10x of SQLite (currently 314x).
- [ ] Improve TPC-C performance to within 5x of SQLite (currently 17x).
- [ ] Achieve an average TPC-H performance gap under 30x compared to DuckDB (currently ~80x).
- [ ] Ensure all TPC-DS sanity queries are passing.
12-Month Goals
- [ ] Further reduce the point SELECT performance gap to within 3x of SQLite.
- [ ] Improve TPC-C performance to within 2x of SQLite.
- [ ] Achieve TPC-H query performance within 10x of DuckDB on average.
- [ ] Become competitive with MySQL on OLTP workloads.
Investigation Tasks: A Phased Approach to Optimization
To achieve our performance goals, we've outlined a phased approach to investigation and optimization. This approach focuses on identifying the root causes of performance bottlenecks and implementing targeted solutions.
Phase 1: Profiling
The first phase involves in-depth profiling to pinpoint performance hotspots. This will involve:
- [ ] Profiling point SELECT operations to identify specific areas of overhead.
- [ ] Profiling Q6 (the simplest slow query) using flamegraphs to visualize performance bottlenecks.
- [ ] Profiling the TPC-C New-Order transaction to understand its performance characteristics.
- [ ] Adding microbenchmarks for B-tree operations to isolate and measure their performance.
Phase 2: Quick Wins
This phase focuses on implementing quick, high-impact optimizations based on the profiling results. Potential quick wins include:
- [ ] Optimizing B-tree key comparison algorithms.
- [ ] Reducing
SqlValueallocation and cloning overhead. - [ ] Implementing prepared statement caching.
- [ ] Implementing connection pooling for benchmarks.
Phase 3: Architectural Improvements
The final phase involves more substantial architectural changes to address fundamental performance limitations. This may include:
- [ ] Evaluating the prepared statement execution path for inefficiencies.
- [ ] Considering arena allocation for queries to reduce memory management overhead.
- [ ] Reviewing the tradeoffs between iterator-based and batch processing approaches.
- [ ] Exploring columnar storage format improvements to enhance analytical query performance.
Running Benchmarks: Ensuring Reproducibility
To ensure the validity and reproducibility of our performance results, we've established a standardized benchmarking process. The following commands can be used to run the benchmark suite:
# Full benchmark suite
make benchmark
# Individual benchmarks
./scripts/bench-tpch.sh 30 # TPC-H with 30s timeout
make benchmark-tpcc # TPC-C comparison
cargo bench --bench sysbench_oltp # Sysbench OLTP
References and Further Reading
For additional context and related information, please refer to the following resources:
- Previous: #2804 (TPC-H Phase 2 - completed)
- Previous: #2490 (TPC-H Phase 1 - completed)
- Benchmark infrastructure:
scripts/bench-*.sh,benches/
Scale Factors: TPC-H SF 0.01, TPC-C SF 1, Sysbench 10K rows
Last Updated: 2025-11-29
This article provides a comprehensive overview of VibeSQL's performance compared to leading databases. By focusing on the identified priorities and systematically implementing optimizations, we are confident that VibeSQL can achieve its performance goals and become a competitive force in the database landscape. For more information on database performance and optimization, check out Database Performance Blog. (This is a placeholder link; please replace it with an actual relevant resource.)
