DivergenceV3 3.2.0
divergence/divergence · mean 86.0 ms · 144 samples
- sqlite-memory 86.0 ms
- Started
- Generated
- Measured samples
- 144
- Combinations
- 1
Machine
Captured from the runner's system manifest.
Host
eimu
Linux · kernel 6.19.6
PHP
8.5.3
Processor
AMD Ryzen 9 5950X 16-Core Processor
32 cores · x86_64
Memory
31.2 GB
DIMM_A2 16 GiB 2666 MT/s DDR4 F4-4266C19-16GVK
DIMM_B2 16 GiB 2666 MT/s DDR4 F4-4266C19-16GVK
Disk
3.5 TB
Device: /dev/nvme0n1
Capacity: 3.5 TB
Runtime envelope
End-to-end time per scenario run across all non-warmup samples on sqlite-memory.
- Min sample
- 10.6 ms
- Mean sample
- 86.0 ms
- Max sample
- 330 ms
- Cumulative time
- 12.384s
- Throughput
- 11.628/s
Runtime comparison
Mean time per scenario run in this upload, warmup runs excluded, sorted by runtime.
Where the time goes
Each bar is one framework's mean scenario run on sqlite-memory, split into its phases and sorted by total. Bars share a scale within each engine. Hover a bar for the numbers.
Init
Insert
Read
Update
Delete
Mean split across 1 combinations
sqlite-memory
- DivergenceV33.2.0 86.0 ms
Exact numbers
| DivergenceV3 3.2.0 | sqlite-memory | 144 | 86.0 ms | 19.2 ms | 16.7 ms | 45.3 ms | 3.97 ms |
|---|
Scenario families
Mean across every framework and engine in this upload.
What the workloads measure
The row shape and operation mix decide where each framework spends its time.
Scenario families
- simple baseline row shape
- Shows framework overhead with minimal field complexity, so object creation and query setup dominate.
- canary mixed field and nullable stress
- Wide mixed records amplify casting, hydration, dirty tracking, and adapter metadata overhead.
- int_fixed pure scalar writes
- A low-noise floor. Slow results here usually mean framework machinery is expensive even before text and cast-heavy payloads show up.
- float_fixed numeric conversion paths
- Decimal and float normalization can widen update and verification cost, especially for ORMs that compare serialized values.
- string_fixed predictable text hydration
- Column count and text assignment matter more than payload variability, which makes hydration strategy easier to isolate.
- string_variable application-like text workload
- Variable strings and note fields make this the closest to real CRUD traffic and usually punish heavier active-record layers the most.
Operation shapes
- Warmup vs hot runs
- Run 1 is warmup. Hot-path averages exclude it so boot and metadata setup do not dominate steady-state rankings.
- Insert and indexed schemas
- Indexed tables make writes cost more because every insert and many updates must maintain secondary indexes as well as the row itself.
- Read all vs read probes
- Read All rewards bulk hydration efficiency; random and tail probes expose per-query setup, lookup overhead, and tiny-result inefficiency.
- Update with verification
- Each update shape is followed by a verification read, so dirty-checking, write SQL generation, hydration, and comparison all affect the final time.
- Delete with confirmation
- Deletes are checked afterward. Frameworks that pay extra for follow-up lookups or teardown lose more time here than a bare SQL path.
- Total runtime
- The total runtime metric is effectively a benchmark of the machine plus adapter behavior across the full workload, not just one CRUD verb.