SQLite Persistence

Overview

The SQLite sink writes the in-memory test_record_instance to a SQLite database so runs can be compared, regression-checked, or shipped to another tool. The schema is created lazily; an empty file is fine.

Python API

from je_load_density import (
    persist_records, list_runs, fetch_run_records,
)

run_id = persist_records(
    "loadtests.db",
    label="checkout-2026-04-28",
    metadata={"branch": "dev", "commit": "abc1234"},
)

for row in list_runs("loadtests.db", limit=10):
    print(row)

for record in fetch_run_records("loadtests.db", run_id):
    print(record)

Schema

• load_density_runs(id, started_at, label, metadata_json)

• load_density_records(id, run_id, outcome, method, test_url, name, status_code, response_time_ms, response_length, error)

Indexes are created on run_id and name to keep cross-run queries fast.

Action JSON

{"load_density": [
  ["LD_clear_records", {}],
  ["LD_start_test", {...}],
  ["LD_persist_records", {
    "database_path": "loadtests.db",
    "label": "checkout",
    "metadata": {"branch": "dev"}
  }]
]}

Use LD_list_runs and LD_fetch_run_records from later scripts to read back the data.