14 - Lifecycle Timing Benchmark

View source code

This example measures the embedded ArcadeDB lifecycle from Python with a mixed workload.

It reports timings for:

• JVM startup
• database create + schema setup
• database open
• transaction load (table + graph + vectors)
• query phase
• close
• reopen
• reopen query phase
• reopen close

Run

From bindings/python/examples:

python3 14_lifecycle_timing.py

With custom workload:

python3 14_lifecycle_timing.py \
  --runs 5 \
  --table-records 50000 \
  --graph-vertices 10000 \
  --vector-records 10000 \
  --vector-dimensions 64 \
  --query-runs 100 \
  --jvm-heap 2g

Notes

• The benchmark uses a random database path under /tmp by default.
• The path is removed at the end (cleanup is always on).
• The script is intended for benchmarking in examples, not deterministic CI assertions.

Expected Output (Desktop Baseline)

On a normal desktop CPU, this is a representative summary shape you can expect:

Averages
  jvm start:   0.242371s
  create:      0.065687s
  schema:      0.036061s
  open:        0.003828s
  transaction: 1.217344s
  load:        1.217346s
  query:       5.025231s
  close:       0.002140s
  reopen:      0.011762s
  reopen query:2.343519s
  reopen close:0.001561s

Actual timings vary with CPU, storage, memory, Python/JVM versions, and current system load.