Task ID: task_e_682e109d40e483238e1d8b82fe4332b0
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
idna ------------------------------ 68.79 KiB/68.79 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 76.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 30.87 KiB/145.08 KiB
certifi ------------------------------ 155.88 KiB/155.88 KiB
werkzeug ------------------------------ 75.19 KiB/219.24 KiB
pytest ------------------------------ 75.59 KiB/335.58 KiB
greenlet ------------------------------ 30.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 127.83 KiB/11.02 MiB
duckdb ------------------------------ 144.00 KiB/19.27 MiB
playwright ------------------------------ 30.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
blinker ------------------------------ 8.26 KiB/8.26 KiB
python-slugify ------------------------------ 9.82 KiB/9.82 KiB
pyee ------------------------------ 15.36 KiB/15.36 KiB
pytest-playwright ------------------------------ 16.23 KiB/16.23 KiB
pluggy ------------------------------ 16.00 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 92.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 30.87 KiB/145.08 KiB
certifi ------------------------------ 155.88 KiB/155.88 KiB
werkzeug ------------------------------ 75.19 KiB/219.24 KiB
pytest ------------------------------ 75.59 KiB/335.58 KiB
greenlet ------------------------------ 30.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 143.83 KiB/11.02 MiB
duckdb ------------------------------ 176.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
python-slugify ------------------------------ 9.82 KiB/9.82 KiB
pyee ------------------------------ 15.36 KiB/15.36 KiB
pytest-playwright ------------------------------ 16.23 KiB/16.23 KiB
pluggy ------------------------------ 16.00 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 92.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 30.87 KiB/145.08 KiB
certifi ------------------------------ 155.88 KiB/155.88 KiB
werkzeug ------------------------------ 75.19 KiB/219.24 KiB
pytest ------------------------------ 91.59 KiB/335.58 KiB
greenlet ------------------------------ 30.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 159.83 KiB/11.02 MiB
duckdb ------------------------------ 176.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
python-slugify ------------------------------ 9.82 KiB/9.82 KiB
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 16.00 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 92.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 30.87 KiB/145.08 KiB
certifi ------------------------------ 155.88 KiB/155.88 KiB
werkzeug ------------------------------ 75.19 KiB/219.24 KiB
pytest ------------------------------ 91.59 KiB/335.58 KiB
greenlet ------------------------------ 30.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 175.83 KiB/11.02 MiB
duckdb ------------------------------ 192.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 20.06 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 92.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 46.87 KiB/145.08 KiB
certifi ------------------------------ 155.88 KiB/155.88 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 91.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 191.83 KiB/11.02 MiB
duckdb ------------------------------ 208.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 20.06 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 92.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 46.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 91.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 191.83 KiB/11.02 MiB
duckdb ------------------------------ 208.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 20.06 KiB/20.06 KiB
markupsafe ------------------------------ 22.58 KiB/22.58 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 62.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 108.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 78.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 107.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 223.83 KiB/11.02 MiB
duckdb ------------------------------ 256.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 20.06 KiB/20.06 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
requests ------------------------------ 63.41 KiB/63.41 KiB
click ------------------------------ 78.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 108.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 110.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 107.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 255.83 KiB/11.02 MiB
duckdb ------------------------------ 272.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
pluggy ------------------------------ 20.06 KiB/20.06 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
click ------------------------------ 78.88 KiB/99.76 KiB
flask ------------------------------ 62.88 KiB/100.88 KiB
jinja2 ------------------------------ 108.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 110.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 107.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 255.83 KiB/11.02 MiB
duckdb ------------------------------ 272.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
pyee ------------------------------ 15.36 KiB/15.36 KiB
execnet ------------------------------ 32.00 KiB/39.66 KiB
click ------------------------------ 94.88 KiB/99.76 KiB
flask ------------------------------ 78.88 KiB/100.88 KiB
jinja2 ------------------------------ 124.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 126.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 123.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 287.83 KiB/11.02 MiB
duckdb ------------------------------ 320.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
execnet ------------------------------ 32.00 KiB/39.66 KiB
click ------------------------------ 94.88 KiB/99.76 KiB
flask ------------------------------ 78.88 KiB/100.88 KiB
jinja2 ------------------------------ 124.28 KiB/131.74 KiB
charset-normalizer ------------------------------ 126.87 KiB/145.08 KiB
werkzeug ------------------------------ 91.19 KiB/219.24 KiB
pytest ------------------------------ 123.59 KiB/335.58 KiB
greenlet ------------------------------ 46.91 KiB/589.71 KiB
pyright ------------------------------ 14.91 KiB/5.31 MiB
ruff ------------------------------ 287.83 KiB/11.02 MiB
duckdb ------------------------------ 320.00 KiB/19.27 MiB
playwright ------------------------------ 46.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
execnet ------------------------------ 39.66 KiB/39.66 KiB
flask ------------------------------ 94.88 KiB/100.88 KiB
jinja2 ------------------------------ 131.74 KiB/131.74 KiB
charset-normalizer ------------------------------ 142.87 KiB/145.08 KiB
werkzeug ------------------------------ 107.19 KiB/219.24 KiB
pytest ------------------------------ 171.59 KiB/335.58 KiB
greenlet ------------------------------ 173.89 KiB/589.71 KiB
pyright ------------------------------ 94.79 KiB/5.31 MiB
ruff ------------------------------ 495.83 KiB/11.02 MiB
duckdb ------------------------------ 512.00 KiB/19.27 MiB
playwright ------------------------------ 62.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
flask ------------------------------ 100.88 KiB/100.88 KiB
jinja2 ------------------------------ 131.74 KiB/131.74 KiB
charset-normalizer ------------------------------ 142.87 KiB/145.08 KiB
werkzeug ------------------------------ 123.19 KiB/219.24 KiB
pytest ------------------------------ 187.59 KiB/335.58 KiB
greenlet ------------------------------ 205.89 KiB/589.71 KiB
pyright ------------------------------ 126.79 KiB/5.31 MiB
ruff ------------------------------ 527.83 KiB/11.02 MiB
duckdb ------------------------------ 544.00 KiB/19.27 MiB
playwright ------------------------------ 62.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
flask ------------------------------ 100.88 KiB/100.88 KiB
jinja2 ------------------------------ 131.74 KiB/131.74 KiB
charset-normalizer ------------------------------ 142.87 KiB/145.08 KiB
werkzeug ------------------------------ 123.19 KiB/219.24 KiB
pytest ------------------------------ 187.59 KiB/335.58 KiB
greenlet ------------------------------ 269.89 KiB/589.71 KiB
pyright ------------------------------ 174.79 KiB/5.31 MiB
ruff ------------------------------ 575.83 KiB/11.02 MiB
duckdb ------------------------------ 587.00 KiB/19.27 MiB
playwright ------------------------------ 62.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
flask ------------------------------ 100.88 KiB/100.88 KiB
charset-normalizer ------------------------------ 142.87 KiB/145.08 KiB
werkzeug ------------------------------ 123.19 KiB/219.24 KiB
pytest ------------------------------ 187.59 KiB/335.58 KiB
greenlet ------------------------------ 269.89 KiB/589.71 KiB
pyright ------------------------------ 190.79 KiB/5.31 MiB
ruff ------------------------------ 591.83 KiB/11.02 MiB
duckdb ------------------------------ 603.00 KiB/19.27 MiB
playwright ------------------------------ 62.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
flask ------------------------------ 100.88 KiB/100.88 KiB
werkzeug ------------------------------ 139.19 KiB/219.24 KiB
pytest ------------------------------ 203.59 KiB/335.58 KiB
greenlet ------------------------------ 365.89 KiB/589.71 KiB
pyright ------------------------------ 286.79 KiB/5.31 MiB
ruff ------------------------------ 687.83 KiB/11.02 MiB
duckdb ------------------------------ 683.00 KiB/19.27 MiB
playwright ------------------------------ 78.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
werkzeug ------------------------------ 139.19 KiB/219.24 KiB
pytest ------------------------------ 203.59 KiB/335.58 KiB
greenlet ------------------------------ 365.89 KiB/589.71 KiB
pyright ------------------------------ 286.79 KiB/5.31 MiB
ruff ------------------------------ 687.83 KiB/11.02 MiB
duckdb ------------------------------ 699.00 KiB/19.27 MiB
playwright ------------------------------ 78.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
werkzeug ------------------------------ 155.19 KiB/219.24 KiB
pytest ------------------------------ 235.59 KiB/335.58 KiB
greenlet ------------------------------ 461.89 KiB/589.71 KiB
pyright ------------------------------ 590.79 KiB/5.31 MiB
ruff ------------------------------ 991.83 KiB/11.02 MiB
duckdb ------------------------------ 1003.00 KiB/19.27 MiB
playwright ------------------------------ 110.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠹ Preparing packages... (5/33)
werkzeug ------------------------------ 219.24 KiB/219.24 KiB
pytest ------------------------------ 315.59 KiB/335.58 KiB
greenlet ------------------------------ 493.89 KiB/589.71 KiB
pyright ------------------------------ 1.08 MiB/5.31 MiB
ruff ------------------------------ 1.48 MiB/11.02 MiB
duckdb ------------------------------ 1.48 MiB/19.27 MiB
playwright ------------------------------ 286.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
pytest ------------------------------ 315.59 KiB/335.58 KiB
greenlet ------------------------------ 493.89 KiB/589.71 KiB
pyright ------------------------------ 1.20 MiB/5.31 MiB
ruff ------------------------------ 1.59 MiB/11.02 MiB
duckdb ------------------------------ 1.59 MiB/19.27 MiB
playwright ------------------------------ 398.91 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
greenlet ------------------------------ 509.89 KiB/589.71 KiB
pyright ------------------------------ 1.42 MiB/5.31 MiB
ruff ------------------------------ 1.94 MiB/11.02 MiB
duckdb ------------------------------ 1.95 MiB/19.27 MiB
playwright ------------------------------ 760.56 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
greenlet ------------------------------ 541.89 KiB/589.71 KiB
pyright ------------------------------ 1.53 MiB/5.31 MiB
ruff ------------------------------ 2.05 MiB/11.02 MiB
duckdb ------------------------------ 2.05 MiB/19.27 MiB
playwright ------------------------------ 888.56 KiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
greenlet ------------------------------ 573.89 KiB/589.71 KiB
pyright ------------------------------ 1.79 MiB/5.31 MiB
ruff ------------------------------ 2.70 MiB/11.02 MiB
duckdb ------------------------------ 2.68 MiB/19.27 MiB
playwright ------------------------------ 1.50 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
pyright ------------------------------ 1.81 MiB/5.31 MiB
ruff ------------------------------ 3.12 MiB/11.02 MiB
duckdb ------------------------------ 3.08 MiB/19.27 MiB
playwright ------------------------------ 1.91 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠸ Preparing packages... (25/33)
pyright ------------------------------ 1.81 MiB/5.31 MiB
ruff ------------------------------ 3.48 MiB/11.02 MiB
duckdb ------------------------------ 3.43 MiB/19.27 MiB
playwright ------------------------------ 2.24 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠼ Preparing packages... (28/33)
pyright ------------------------------ 1.84 MiB/5.31 MiB
ruff ------------------------------ 4.19 MiB/11.02 MiB
duckdb ------------------------------ 4.09 MiB/19.27 MiB
playwright ------------------------------ 2.95 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠼ Preparing packages... (28/33)
pyright ------------------------------ 1.89 MiB/5.31 MiB
ruff ------------------------------ 4.87 MiB/11.02 MiB
duckdb ------------------------------ 4.77 MiB/19.27 MiB
playwright ------------------------------ 3.63 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠼ Preparing packages... (28/33)
pyright ------------------------------ 1.92 MiB/5.31 MiB
ruff ------------------------------ 5.65 MiB/11.02 MiB
duckdb ------------------------------ 5.56 MiB/19.27 MiB
playwright ------------------------------ 4.40 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠼ Preparing packages... (28/33)
pyright ------------------------------ 1.97 MiB/5.31 MiB
ruff ------------------------------ 6.43 MiB/11.02 MiB
duckdb ------------------------------ 6.34 MiB/19.27 MiB
playwright ------------------------------ 5.18 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠼ Preparing packages... (28/33)
pyright ------------------------------ 2.00 MiB/5.31 MiB
ruff ------------------------------ 7.21 MiB/11.02 MiB
duckdb ------------------------------ 7.14 MiB/19.27 MiB
playwright ------------------------------ 5.95 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠴ Preparing packages... (28/33)
pyright ------------------------------ 2.03 MiB/5.31 MiB
ruff ------------------------------ 7.94 MiB/11.02 MiB
duckdb ------------------------------ 7.88 MiB/19.27 MiB
playwright ------------------------------ 6.69 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠴ Preparing packages... (28/33)
pyright ------------------------------ 2.08 MiB/5.31 MiB
ruff ------------------------------ 8.75 MiB/11.02 MiB
duckdb ------------------------------ 8.69 MiB/19.27 MiB
playwright ------------------------------ 7.51 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠴ Preparing packages... (28/33)
pyright ------------------------------ 2.11 MiB/5.31 MiB
ruff ------------------------------ 9.51 MiB/11.02 MiB
duckdb ------------------------------ 9.46 MiB/19.27 MiB
playwright ------------------------------ 8.28 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠴ Preparing packages... (28/33)
pyright ------------------------------ 2.12 MiB/5.31 MiB
ruff ------------------------------ 10.34 MiB/11.02 MiB
duckdb ------------------------------ 10.25 MiB/19.27 MiB
playwright ------------------------------ 9.08 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠦ Preparing packages... (28/33)
pyright ------------------------------ 2.14 MiB/5.31 MiB
duckdb ------------------------------ 11.00 MiB/19.27 MiB
playwright ------------------------------ 9.83 MiB/43.05 MiB
Building scubaduck @ file:///workspace/scubaduck
⠦ Preparing packages... (28/33)
pyright ------------------------------ 2.16 MiB/5.31 MiB
duckdb ------------------------------ 11.12 MiB/19.27 MiB
playwright ------------------------------ 9.97 MiB/43.05 MiB
Built scubaduck @ file:///workspace/scubaduck
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pyright ------------------------------ 2.16 MiB/5.31 MiB
duckdb ------------------------------ 11.14 MiB/19.27 MiB
playwright ------------------------------ 9.97 MiB/43.05 MiB
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pyright ------------------------------ 2.17 MiB/5.31 MiB
duckdb ------------------------------ 12.25 MiB/19.27 MiB
playwright ------------------------------ 11.09 MiB/43.05 MiB
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pyright ------------------------------ 2.19 MiB/5.31 MiB
duckdb ------------------------------ 13.55 MiB/19.27 MiB
playwright ------------------------------ 12.39 MiB/43.05 MiB
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pyright ------------------------------ 2.20 MiB/5.31 MiB
duckdb ------------------------------ 14.77 MiB/19.27 MiB
playwright ------------------------------ 13.57 MiB/43.05 MiB
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pyright ------------------------------ 2.22 MiB/5.31 MiB
duckdb ------------------------------ 16.00 MiB/19.27 MiB
playwright ------------------------------ 14.76 MiB/43.05 MiB
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pyright ------------------------------ 2.23 MiB/5.31 MiB
duckdb ------------------------------ 17.27 MiB/19.27 MiB
playwright ------------------------------ 16.02 MiB/43.05 MiB
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pyright ------------------------------ 2.25 MiB/5.31 MiB
duckdb ------------------------------ 18.48 MiB/19.27 MiB
playwright ------------------------------ 17.27 MiB/43.05 MiB
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pyright ------------------------------ 2.28 MiB/5.31 MiB
duckdb ------------------------------ 19.20 MiB/19.27 MiB
playwright ------------------------------ 18.65 MiB/43.05 MiB
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pyright ------------------------------ 2.33 MiB/5.31 MiB
playwright ------------------------------ 19.56 MiB/43.05 MiB
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pyright ------------------------------ 2.36 MiB/5.31 MiB
playwright ------------------------------ 20.70 MiB/43.05 MiB
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pyright ------------------------------ 2.40 MiB/5.31 MiB
playwright ------------------------------ 23.13 MiB/43.05 MiB
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pyright ------------------------------ 2.44 MiB/5.31 MiB
playwright ------------------------------ 25.65 MiB/43.05 MiB
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pyright ------------------------------ 2.45 MiB/5.31 MiB
playwright ------------------------------ 28.03 MiB/43.05 MiB
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playwright ------------------------------ 30.73 MiB/43.05 MiB
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pyright ------------------------------ 2.51 MiB/5.31 MiB
playwright ------------------------------ 33.22 MiB/43.05 MiB
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playwright ------------------------------ 35.42 MiB/43.05 MiB
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pyright ------------------------------ 2.59 MiB/5.31 MiB
playwright ------------------------------ 37.51 MiB/43.05 MiB
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pyright ------------------------------ 2.64 MiB/5.31 MiB
playwright ------------------------------ 39.31 MiB/43.05 MiB
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playwright ------------------------------ 40.92 MiB/43.05 MiB
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pyright ------------------------------ 2.76 MiB/5.31 MiB
playwright ------------------------------ 41.47 MiB/43.05 MiB
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pyright ------------------------------ 2.81 MiB/5.31 MiB
playwright ------------------------------ 42.76 MiB/43.05 MiB
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pyright ------------------------------ 4.56 MiB/5.31 MiB
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Prepared 33 packages in 2.52s
░░░░░░░░░░░░░░░░░░░░ [0/0] Installing wheels...
░░░░░░░░░░░░░░░░░░░░ [0/33] Installing wheels...
░░░░░░░░░░░░░░░░░░░░ [0/33] markupsafe==3.0.2
░░░░░░░░░░░░░░░░░░░░ [1/33] markupsafe==3.0.2
░░░░░░░░░░░░░░░░░░░░ [1/33] text-unidecode==1.3
█░░░░░░░░░░░░░░░░░░░ [2/33] text-unidecode==1.3
█░░░░░░░░░░░░░░░░░░░ [2/33] click==8.2.0
█░░░░░░░░░░░░░░░░░░░ [3/33] click==8.2.0
█░░░░░░░░░░░░░░░░░░░ [3/33] flask==3.1.1
██░░░░░░░░░░░░░░░░░░ [4/33] flask==3.1.1
██░░░░░░░░░░░░░░░░░░ [4/33] requests==2.32.3
███░░░░░░░░░░░░░░░░░ [5/33] requests==2.32.3
███░░░░░░░░░░░░░░░░░ [5/33] execnet==2.1.1
███░░░░░░░░░░░░░░░░░ [6/33] execnet==2.1.1
███░░░░░░░░░░░░░░░░░ [6/33] pluggy==1.6.0
████░░░░░░░░░░░░░░░░ [7/33] pluggy==1.6.0
████░░░░░░░░░░░░░░░░ [7/33] pytest-xdist==3.6.1
████░░░░░░░░░░░░░░░░ [8/33] pytest-xdist==3.6.1
████░░░░░░░░░░░░░░░░ [8/33] jinja2==3.1.6
█████░░░░░░░░░░░░░░░ [9/33] jinja2==3.1.6
███████████████████░ [32/33] pyright==1.1.400
████████████████████ [33/33] pyright==1.1.400
Installed 33 packages in 109ms
+ blinker==1.9.0
+ certifi==2025.4.26
+ charset-normalizer==3.4.2
+ click==8.2.0
+ duckdb==1.2.2
+ execnet==2.1.1
+ flask==3.1.1
+ greenlet==3.2.2
+ idna==3.10
+ iniconfig==2.1.0
+ itsdangerous==2.2.0
+ jinja2==3.1.6
+ markupsafe==3.0.2
+ nodeenv==1.9.1
+ packaging==25.0
+ playwright==1.52.0
+ pluggy==1.6.0
+ pyee==13.0.0
+ pyright==1.1.400
+ pytest==8.3.5
+ pytest-base-url==2.1.0
+ pytest-playwright==0.7.0
+ pytest-xdist==3.6.1
+ python-dateutil==2.9.0.post0
+ python-slugify==8.0.4
+ requests==2.32.3
+ ruff==0.11.10
+ scubaduck==0.1.0 (from file:///workspace/scubaduck)
+ six==1.17.0
+ text-unidecode==1.3
+ typing-extensions==4.13.2
+ urllib3==2.4.0
+ werkzeug==3.1.3
++ source .venv/bin/activate
+++ '[' -z '' ']'
+++ '[' -n x ']'
+++ SCRIPT_PATH=.venv/bin/activate
+++ '[' .venv/bin/activate = /tmp/1rHLeo-setup_script.sh ']'
+++ deactivate nondestructive
+++ unset -f pydoc
+++ '[' -z '' ']'
+++ '[' -z '' ']'
+++ hash -r
+++ '[' -z '' ']'
+++ unset VIRTUAL_ENV
+++ unset VIRTUAL_ENV_PROMPT
+++ '[' '!' nondestructive = nondestructive ']'
+++ VIRTUAL_ENV=/workspace/scubaduck/.venv
+++ '[' linux-gnu = cygwin ']'
+++ '[' linux-gnu = msys ']'
+++ export VIRTUAL_ENV
+++ '[' -z '' ']'
+++ unset SCRIPT_PATH
+++ _OLD_VIRTUAL_PATH=/root/.cargo/bin:/root/.rbenv/shims:/root/.rbenv/bin:/root/.rbenv/shims:/root/.local/share/swiftly/bin:/root/.bun/bin:/root/.nvm/versions/node/v22.15.1/bin:/root/.pyenv/shims:3441PYENV_ROOT/shims:/root/.pyenv/bin:/usr/local/go/bin:/root/go/bin:/root/.rbenv/bin:/root/.rbenv/shims:/root/.bun/bin:/root/.local/bin:/root/.pyenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+++ PATH=/workspace/scubaduck/.venv/bin:/root/.cargo/bin:/root/.rbenv/shims:/root/.rbenv/bin:/root/.rbenv/shims:/root/.local/share/swiftly/bin:/root/.bun/bin:/root/.nvm/versions/node/v22.15.1/bin:/root/.pyenv/shims:3441PYENV_ROOT/shims:/root/.pyenv/bin:/usr/local/go/bin:/root/go/bin:/root/.rbenv/bin:/root/.rbenv/shims:/root/.bun/bin:/root/.local/bin:/root/.pyenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+++ export PATH
+++ '[' xscubaduck '!=' x ']'
+++ VIRTUAL_ENV_PROMPT='(scubaduck) '
+++ export VIRTUAL_ENV_PROMPT
+++ '[' -z '' ']'
+++ '[' -z '' ']'
+++ _OLD_VIRTUAL_PS1=
+++ PS1='(scubaduck) '
+++ export PS1
+++ alias pydoc
+++ true
+++ hash -r
++ playwright install chromium
Downloading Chromium 136.0.7103.25 (playwright build v1169) from https://cdn.playwright.dev/dbazure/download/playwright/builds/chromium/1169/chromium-linux.zip
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Chromium 136.0.7103.25 (playwright build v1169) downloaded to /root/.cache/ms-playwright/chromium-1169
Downloading FFMPEG playwright build v1011 from https://cdn.playwright.dev/dbazure/download/playwright/builds/ffmpeg/1011/ffmpeg-linux.zip
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FFMPEG playwright build v1011 downloaded to /root/.cache/ms-playwright/ffmpeg-1011
Downloading Chromium Headless Shell 136.0.7103.25 (playwright build v1169) from https://cdn.playwright.dev/dbazure/download/playwright/builds/chromium/1169/chromium-headless-shell-linux.zip
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Chromium Headless Shell 136.0.7103.25 (playwright build v1169) downloaded to /root/.cache/ms-playwright/chromium_headless_shell-1169
++ echo 'source .venv/bin/activate'I'll start by looking at AGENTS.md to understand what the instructions are for this task.
bash: .venv/bin/activate: No such file or directory
root@cd76d0add7bd:/workspace# ls
scubaduckroot@cd76d0add7bd:/workspace# cd scubaduck
root@cd76d0add7bd:/workspace/scubaduck# ls
AGENTS.md codex_setup.sh scubaduck tests
README.md pyproject.toml stubs uv.lockroot@cd76d0add7bd:/workspace/scubaduck# cat AGENTS.md
- Use "source .venv/bin/activate" to activate the virtual environment before
running any commands
- ONLY use non-interactive commands like cat, sed, apply_patch to do edits.
Do NOT use interactive editors.
- Do NOT attempt to install packages. Only the packages specified in
pyproject.toml are available. You cannot add new packages. If you
desperately want another package, make a note of it in the final PR
description.
- Use conventional commits to format PR title
- There are no nested AGENTS.md files, this is the only agents file
- When using Playwright, ONLY use chromium browser. This browser is already
installed.
- Use "ruff check" to check lint, "ruff format" to autoformat files and
"pyright" to typecheck.
- When writing the PR description, include the original prompt supplied by the
user.
- When you add functionality to the server, add server tests to
tests/test_server.py. When you add features to the frontend, add frontend
tests to tests/test_web.pyI see the AGENTS file has setup steps, testing guidance, and tooling instructions. Let's proceed!
root@cd76d0add7bd:/workspace/scubaduck# ls scubaduck
__init__.py sample.csv server.py staticroot@cd76d0add7bd:/workspace/scubaduck# ls scubaduck/static
index.htmlI’ll take a look at the README now.
root@cd76d0add7bd:/workspace/scubaduck# cat README.md
I really like Scuba (Meta's internal real-time database system). The distributed
, real-time database part of Scuba is quite difficult (and expensive) to replica
te, but I also really like Scuba's UI for doing queries, and I have found myself
wishing that I have access to it even for "small" databases, e.g., I have a sql
ite dataset I want to explore.
Pivotal ideas:
* Time series by default. In the dedicated "time series" view, there are many fe
atures specifically oriented towards working towards tables that represent event
s that occurred over time: the start, end, compare, aggregate and granularity fi
elds all specially privilege the timestamp field. In fact, you can't log events
to Scuba's backing data store without a timestamp, they always come with one. (S
cuba also supports other views that don't presuppose a time series, but the time
series is the most beloved and well used view.) This is in contrast to typical
software which tries to generalize to arbitrary data first, with time series bei
ng added on later.
* It's all about exploration. Scuba is predicated on the idea that you don't kno
w what you're looking for, that you are going to spend time tweaking queries and
changing filters/grouping as part of an investigation to figure out why a syste
m behaves the way it is. So the filters/comparisons/groupings you want to edit a
re always visible on the left sidebar, with the expectation that you're going to
tweak the query to look at something else. Similarly, all the parameters of you
r query get saved into your URL, so your browser history can double up as a quer
y history / you can easily share a query with someone else. This is contrast to
typical software which is often oriented to making pretty dashboards and reports
. (This function is important too, but it's not what I want in exploration mode!
)
* You can fix data problems in the query editor. It's pretty common to have mess
ed up and ended up with a database that doesn't have exactly the columns you nee
d, or some columns that are corrupted in some way. Scuba has pretty robust suppo
rt for defining custom columns with arbitrary SQL functions, grouping over them
as if they were native functions, and doing so with minimal runtime cost (Scuba
aims to turn around your query in milliseconds!) Having to go and run a huge dat
a pipeline to fix your data is a big impediment to exploration; quick and easy c
ustom columns means you can patch over problems when you're investigating and fi
x them for real later.
We're going to build a exploratory data analysis tool like Scuba for time series
database (i.e., a database with a mandatory timestamp representing the time an
event occurred). We'll use DuckDB as the underlying SQL engine served from a Py
thon server, and render the GUI/results as a webpage with vanilla HTML and JS. W
e'll use choices.js to support token inputs. We define a token input to mean a
text input element where as you type a dropdown displays with valid values, and
if you select one or press enter, the selection turns into a token/chip that can
only be deleted as one unit.
To start, we are going to support one views: samples. The samples view only all
ows you to view individual samples from the database, subject to a filter. Our m
ain UI concept is that there is a left sidebar that is the query editor, and the
right side that shows the view. The sidebar is always visible and defaults to
the query parameters of the current view. After you make changes to the query,
clicking the "Dive" button updates the view. The URL of the page encodes all of
the values of the query (and gets updated when you Dive), so the browser's back
button lets you view previous queries.
The query editor's job is to generate a SQL query, which then is applied on the
database, and then the result visualized according to the view.
Here are the settings you can apply to the query. The help text should show up w
hen you mouse over the field name:
* Start/End - Help text: "Sets the start/end of the time range to query. Can be
any kind of datetime string. For example: 'April 23, 2014' or 'yesterday'." The
UI for this selector supports both relative selections (now, -1 hour, -3 hours,
-12 hours, -1 day, -3 days, -1 week, -1 fortnight, -30 days, -90 days) as well a
s specifying an absolute date. The way this field is rendered is there is a fre
e form text box, a drop down arrow (for the relative selectors), and then a cale
ndar button (for date selection).
* Order By - Help text: "Choose a column to sort results by." There is an ASC/D
ESC toggle next to it.
* Limit - Help text: "Choose the maximum number of results to show in the chart
after any aggregations have been applied. For example, a limit of 10 will show
no more than 10 rows for a table, etc."
* Filters - You can create as many filters as you want. You can either write a f
ilter using a UI or manual SQL. In the UI, filter consists of a column name, a r
elation (e.g., =, !=, <, >) and then a text field. The text field is a token inp
ut. It accepts multiple tokens for = relation, in which case we match using an O
R for all options.
There is also a "Columns" tab which lets you view all fields in the table, organ
ized by their type. You can also define derived columns, by specifying a column
name and SQL expression. Derived columns can be used for all parts of the UI, in
cluding filters/group by/etc. Columns have checkboxes indicating if we should SE
LECT them or not. Each selected column shows up in the graph. There is an All/N
one link which can be used to select/deselect all checkboxes.
The query UI constructs a SQL query that intuitively has this form:
```
SELECT column, column, ...,
FROM table
WHERE time >= min-timestamp
AND time <= max-timestamp
[AND condition ...]
ORDER BY aggregate(column)
LIMIT number
```
You should write tests for the server backend, demonstrating that at specific qu
ery values we get back the correct rows of data.AGENTS.md says no interactive editors. I'll source the virtual environment and check pyproject.
root@cd76d0add7bd:/workspace/scubaduck# cat pyproject.toml
[project]
name = "scubaduck"
version = "0.1.0"
description = "Scuba on DuckDB"
readme = "README.md"
requires-python = ">=3.12"
license = {text = "MIT"}
dependencies = [
"duckdb",
"flask",
"python-dateutil>=2.9.0.post0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
addopts = "-n auto --tb=native"
[tool.pyright]
# Pyright configuration with strict settings
include = ["scubaduck", "tests"]
exclude = ["**/__pycache__", "dist"]
stubPath = "stubs"
venvPath = "."
venv = ".venv"
reportMissingImports = true
reportMissingTypeStubs = true
pythonVersion = "3.12"
pythonPlatform = "All"
typeCheckingMode = "strict"
reportUnknownMemberType = true
reportUnknownParameterType = true
reportUnknownVariableType = true
reportUnknownArgumentType = true
reportPrivateImportUsage = true
reportUntypedFunctionDecorator = true
reportFunctionMemberAccess = true
reportIncompatibleMethodOverride = true
[tool.ruff]
target-version = "py312"
[tool.uv]
dev-dependencies = [
"pytest>=8.3.5",
"ruff>=0.11.2",
"pytest-xdist>=3.6.1",
"playwright>=1.52.0",
"pyright>=1.1.350",
"pytest-playwright>=0.7.0",
]root@cd76d0add7bd:/workspace/scubaduck# ls tests
conftest.py test_server.py test_web.pyroot@cd76d0add7bd:/workspace/scubaduck# cat tests/test_server.py
from __future__ import annotations
import json
from pathlib import Path
import duckdb
from scubaduck import server
import pytest
def test_basic_query() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-02 00:00:00",
"order_by": "timestamp",
"order_dir": "ASC",
"limit": 10,
"columns": ["timestamp", "event", "value", "user"],
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert data
rows = data["rows"]
# We expect first three rows (until 2024-01-02 00:00:00)
assert len(rows) == 3
assert rows[0][1] == "login"
assert rows[1][1] == "logout"
def test_filter_multi_token() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-02 03:00:00",
"order_by": "timestamp",
"limit": 10,
"columns": ["timestamp", "event", "value", "user"],
"filters": [{"column": "user", "op": "=", "value": ["alice", "charlie"]}
],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert data
rows = data["rows"]
# Should only return rows for alice and charlie
assert len(rows) == 3
assert rows[0][3] == "alice"
assert rows[-1][3] == "charlie"
def test_empty_filter_is_noop() -> None:
app = server.app
client = app.test_client()
base_payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"limit": 100,
"columns": ["timestamp", "event", "value", "user"],
}
no_filter = {**base_payload, "filters": []}
empty_filter = {
**base_payload,
"filters": [{"column": "user", "op": "=", "value": None}],
}
rv1 = client.post(
"/api/query", data=json.dumps(no_filter), content_type="application/json
"
)
rv2 = client.post(
"/api/query", data=json.dumps(empty_filter), content_type="application/j
son"
)
rows1 = rv1.get_json()["rows"]
rows2 = rv2.get_json()["rows"]
assert rows1 == rows2
def test_select_columns() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"order_by": "timestamp",
"limit": 10,
"columns": ["timestamp", "user"],
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert data
rows = data["rows"]
assert len(rows[0]) == 2
assert rows[0][1] == "alice"
def test_string_filter_ops() -> None:
app = server.app
client = app.test_client()
base = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"order_by": "timestamp",
"limit": 100,
"columns": ["timestamp", "event", "value", "user"],
}
contains = {
**base,
"filters": [{"column": "user", "op": "contains", "value": "ali"}],
}
rv = client.post(
...
def test_table_unknown_column_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "table",
"order_by": "timestamp",
"limit": 100,
"columns": ["user", "Hits", "value"],
"group_by": ["user"],
"aggregate": "Count",
"show_hits": True,
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "Unknown column" in data["error"]
def test_samples_view_rejects_group_by() -> None:
app = server.app
client = app.test_client()
payload = {
"graph_type": "samples",
"group_by": ["user"],
"columns": ["timestamp"],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "only valid" in data["error"]
def test_table_avg_with_timestamp() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "table",
"order_by": "user",
"limit": 100,
"columns": ["user", "timestamp", "value"],
"group_by": ["user"],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert "error" not in data
rows = data["rows"]
assert rows[0][0] == "alice"
from dateutil import parser
ts = parser.parse(rows[0][1]).replace(tzinfo=None)
assert ts == parser.parse("2024-01-01 12:00:00")
def test_timeseries_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 100,
"group_by": ["user"],
"aggregate": "Count",
"columns": ["value"],
"x_axis": "timestamp",
"granularity": "1 day",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert len(data["rows"]) == 4
def test_timeseries_string_column_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 7,
"columns": ["timestamp", "event", "value", "user"],
"x_axis": "timestamp",
"granularity": "1 hour",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "Aggregate" in data["error"]
def test_derived_column_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"limit": 10,
"columns": ["timestamp"],
"derived_columns": {"val2": "value * 2"},
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert data["rows"][0][1] == 20root@cd76d0add7bd:/workspace/scubaduck# sed -n '101,200p' tests/test_server.py
assert len(rows[0]) == 2
assert rows[0][1] == "alice"
def test_string_filter_ops() -> None:
app = server.app
client = app.test_client()
base = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"order_by": "timestamp",
"limit": 100,
"columns": ["timestamp", "event", "value", "user"],
}
contains = {
**base,
"filters": [{"column": "user", "op": "contains", "value": "ali"}],
}
rv = client.post(
"/api/query", data=json.dumps(contains), content_type="application/json"
)
rows = rv.get_json()["rows"]
assert all("ali" in r[3] for r in rows)
regex = {
**base,
"filters": [{"column": "user", "op": "~", "value": "^a.*"}],
}
rv = client.post(
"/api/query", data=json.dumps(regex), content_type="application/json"
)
rows = rv.get_json()["rows"]
assert all(r[3].startswith("a") for r in rows)
assert len(rows) == 2
not_empty = {**base, "filters": [{"column": "user", "op": "!empty"}]}
rv = client.post(
"/api/query", data=json.dumps(not_empty), content_type="application/json
"
)
assert len(rv.get_json()["rows"]) == 4
def _make_payload() -> dict[str, object]:
return {
"start": "2024-01-01 00:00:00",
"end": "2024-01-02 00:00:00",
"order_by": "timestamp",
"order_dir": "ASC",
"limit": 10,
"columns": ["timestamp", "event", "value", "user"],
"filters": [],
}
def test_database_types(tmp_path: Path) -> None:
csv_file = tmp_path / "events.csv"
csv_file.write_text(Path("scubaduck/sample.csv").read_text())
sqlite_file = tmp_path / "events.sqlite"
import sqlite3
conn = sqlite3.connect(sqlite_file)
conn.execute(
"CREATE TABLE events (timestamp TEXT, event TEXT, value INTEGER, user TE
XT)"
)
with open(csv_file) as f:
next(f)
for line in f:
ts, ev, val, user = line.strip().split(",")
conn.execute(
"INSERT INTO events VALUES (?, ?, ?, ?)", (ts, ev, int(val), use
r)
)
conn.commit()
conn.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcce
ssIssue]
duckdb_file = tmp_path / "events.duckdb"
con = duckdb.connect(duckdb_file)
con.execute(
f"CREATE TABLE events AS SELECT * FROM read_csv_auto('{csv_file.as_posix
()}')"
)
con.close() # pyright: ignore[reportUnknownMemberType, reportAttributeAcces
sIssue]
for db in (csv_file, sqlite_file, duckdb_file):
app = server.create_app(db)
client = app.test_client()
payload = _make_payload()
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/js
on"
)
rows = rv.get_json()["rows"]
assert len(rows) == 3
def test_group_by_table() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",I need to keep going to capture the remaining section.
root@cd76d0add7bd:/workspace/scubaduck# sed -n '200,400p' tests/test_server.py
"end": "2024-01-03 00:00:00",
"graph_type": "table",
"order_by": "user",
"limit": 10,
"columns": ["value"],
"group_by": ["user"],
"aggregate": "Sum",
"show_hits": True,
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
rows = rv.get_json()["rows"]
assert rows[0][0] == "alice"
assert rows[0][1] == 2
assert rows[0][2] == 40
def test_relative_time_query(monkeypatch: pytest.MonkeyPatch) -> None:
app = server.app
client = app.test_client()
from datetime import datetime
fixed_now = datetime(2024, 1, 2, 4, 0, 0)
class FixedDateTime(datetime):
@classmethod
def now(cls, tz=None): # type: ignore[override]
return fixed_now if tz is None else fixed_now.astimezone(tz)
monkeypatch.setattr(server, "datetime", FixedDateTime)
payload = {
"start": "-1 hour",
"end": "now",
"order_by": "timestamp",
"limit": 100,
"columns": ["timestamp", "event", "value", "user"],
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert len(data["rows"]) == 1
assert data["rows"][0][3] == "charlie"
def test_invalid_time_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "nonsense",
"end": "now",
"order_by": "timestamp",
"limit": 10,
"columns": ["timestamp"],
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "error" in data
def test_query_error_returns_sql_and_traceback() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"columns": ["event"],
"group_by": ["user"],
"aggregate": "avg",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "error" in data
def test_table_unknown_column_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "table",
"order_by": "timestamp",
"limit": 100,
"columns": ["user", "Hits", "value"],
"group_by": ["user"],
"aggregate": "Count",
"show_hits": True,
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "Unknown column" in data["error"]
def test_samples_view_rejects_group_by() -> None:
app = server.app
client = app.test_client()
payload = {
"graph_type": "samples",
"group_by": ["user"],
"columns": ["timestamp"],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "only valid" in data["error"]
def test_table_avg_with_timestamp() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "table",
"order_by": "user",
"limit": 100,
"columns": ["user", "timestamp", "value"],
"group_by": ["user"],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert "error" not in data
rows = data["rows"]
assert rows[0][0] == "alice"
from dateutil import parser
ts = parser.parse(rows[0][1]).replace(tzinfo=None)
assert ts == parser.parse("2024-01-01 12:00:00")
def test_timeseries_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 100,
"group_by": ["user"],
"aggregate": "Count",
"columns": ["value"],
"x_axis": "timestamp",
"granularity": "1 day",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert len(data["rows"]) == 4
def test_timeseries_string_column_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 7,
"columns": ["timestamp", "event", "value", "user"],
"x_axis": "timestamp",
"granularity": "1 hour",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "Aggregate" in data["error"]
def test_derived_column_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"limit": 10,
"columns": ["timestamp"],I’ll keep scrolling to make sure everything is covered in the tests.
root@cd76d0add7bd:/workspace/scubaduck# sed -n '400,800p' tests/test_server.py
"columns": ["timestamp"],
"derived_columns": {"val2": "value * 2"},
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert data["rows"][0][1] == 20I'll check the tests/test_web.py file since it ends around 428 lines.
group_by,
)
if aggregate is not None:
select_value(page, "#graph_type", "table")
select_value(page, "#aggregate", aggregate)
page.evaluate("window.lastResults = undefined")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
return page.evaluate("window.lastResults")
def test_range_filters(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-02 00:00:00",
end="2024-01-02 04:00:00",
order_by="timestamp",
limit=100,
)
assert len(data["rows"]) == 2
from dateutil import parser
timestamps = [parser.parse(row[0]).replace(tzinfo=None) for row in data["row
s"]]
assert timestamps == [
parser.parse("2024-01-02 00:00:00"),
parser.parse("2024-01-02 03:00:00"),
]
def test_order_by(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-01 00:00:00",
end="2024-01-03 00:00:00",
order_by="value",
order_dir="DESC",
limit=100,
)
values = [row[2] for row in data["rows"]]
assert values == sorted(values, reverse=True)
def test_limit(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-01 00:00:00",
end="2024-01-03 00:00:00",
order_by="timestamp",
limit=2,
)
assert len(data["rows"]) == 2
def test_simple_filter(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
page.click("text=Add Filter")
filter_el = page.query_selector("#filters .filter:last-child")
assert filter_el
page.evaluate(
"arg => setSelectValue(arg.el.querySelector('.f-col'), arg.val)",
{"el": filter_el, "val": "user"},
)
val_input = filter_el.query_selector(".f-val")
val_input.click()
page.keyboard.type("alice")
page.keyboard.press("Enter")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
data = page.evaluate("window.lastResults")
assert len(data["rows"]) == 2
assert all(row[3] == "alice" for row in data["rows"])
def test_default_filter_and_layout(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
count = page.evaluate("document.querySelectorAll('#filters .filter').length"
)
assert count == 1
last_is_button = page.evaluate(
"document.querySelector('#filters').lastElementChild.id === 'add_filter'
"
)
assert last_is_button
position = page.evaluate(
"getComputedStyle(document.querySelector('#filters .filter button.remove
')).position"
)
assert position == "static"
def test_filter_remove_alignment(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
diff = page.evaluate(
"() => { const r=document.querySelector('#filters .filter-row').getBound
ingClientRect(); const x=document.querySelector('#filters .filter-row button.rem
ove').getBoundingClientRect(); return Math.abs(r.right - x.right); }"
)
assert diff <= 1
def test_header_and_tabs(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
header = page.text_content("#header")
assert "sample.csv" in header
assert "events" in header
assert page.is_visible("#settings")
assert page.is_hidden("#columns")
page.click("text=Columns")
assert page.is_visible("#columns")
cols = [c.strip() for c in page.locator("#column_groups li").all_inner_texts
()]
assert "timestamp" in cols
assert "event" in cols
page.click("text=View Settings")
...
data = run_query(
page,
server_url,
start="2025-01-01 00:00:00",
end="2025-01-02 00:00:00",
order_by="timestamp",
limit=100,
)
assert data["rows"] == []
msg = page.text_content("#view")
assert "Empty data provided to table" in msg
def test_group_by_chip_from_url(page: Any, server_url: str) -> None:
url = f"{server_url}?graph_type=table&group_by=user&order_by=user&limit=10"
page.goto(url)
page.wait_for_selector("#group_by_field .chip", state="attached")
chips = page.evaluate(
"Array.from(document.querySelectorAll('#group_by_field .chip')).map(c =>
c.firstChild.textContent)"
)
assert chips == ["user"]
def test_group_by_autocomplete(page: Any, server_url: str) -> None:
page.goto(f"{server_url}?graph_type=table")
page.wait_for_selector("#group_by_field", state="visible")
inp = page.query_selector("#group_by_field .f-val")
assert inp
inp.click()
page.keyboard.type("us")
page.wait_for_selector("#group_by_field .chip-dropdown div")
options = page.locator("#group_by_field .chip-dropdown div").all_inner_texts
()
assert "user" in options
def test_group_by_copy_icon(page: Any, server_url: str) -> None:
page.goto(f"{server_url}?graph_type=table")
page.wait_for_selector("#group_by_field", state="visible")
icon = page.text_content("#group_by_field .chip-copy")
assert icon == "⎘"
def test_group_by_input_no_border(page: Any, server_url: str) -> None:
page.goto(f"{server_url}?graph_type=table")
page.wait_for_selector("#group_by_field", state="visible")
border = page.evaluate(
"getComputedStyle(document.querySelector('#group_by_field .f-val')).bord
erStyle"
)
assert border == "none"
def test_table_group_by_query(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-01 00:00:00",
end="2024-01-03 00:00:00",
order_by="timestamp",
limit=100,
group_by=["user"],
aggregate="Count",
)
assert "error" not in data
assert len(data["rows"]) == 3
def test_table_avg_no_group_by(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
aggregate="Avg",
)
assert len(data["rows"]) == 1
row = data["rows"][0]
assert row[0] == 4
from dateutil import parser
ts = parser.parse(row[1]).replace(tzinfo=None)
assert ts == parser.parse("2024-01-01 13:00:00")
assert row[2] == 25
def test_table_headers_show_aggregate(page: Any, server_url: str) -> None:
run_query(
page,
server_url,
aggregate="Avg",
)
headers = page.locator("#results th").all_inner_texts()
assert "Hits" in headers
assert "timestamp (avg)" in headers
assert "value (avg)" in headers
def test_format_number_function(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
vals = page.evaluate(
"() => [formatNumber(815210), formatNumber(999.999), formatNumber(0.0004
), formatNumber(0)]"
)
assert vals == ["815.21 K", "999.999", "0.000", "0"]
def test_derived_column_query(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
page.click("text=Columns")
page.click("text=Add Derived")
expr = page.query_selector("#derived_list .derived textarea")
assert expr
expr.fill("value * 2")
page.click("text=View Settings")
page.fill("#start", "2024-01-01 00:00:00")
page.fill("#end", "2024-01-03 00:00:00")
page.fill("#limit", "10")
page.evaluate("window.lastResults = undefined")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
data = page.evaluate("window.lastResults")
assert data["rows"][0][-1] == 20I see there's a test_web.py. Instructions suggest sorting results by the x-axis—let's check the server.
import time
from pathlib import Path
import sqlite3
import traceback
import duckdb
from dateutil import parser as dtparser
from flask import Flask, jsonify, request, send_from_directory
@dataclass
class Filter:
column: str
op: str
value: str | int | float | list[str] | None
@dataclass
class QueryParams:
start: str | None = None
end: str | None = None
order_by: str | None = None
order_dir: str = "ASC"
limit: int | None = None
columns: list[str] = field(default_factory=lambda: [])
filters: list[Filter] = field(default_factory=lambda: [])
derived_columns: dict[str, str] = field(default_factory=lambda: {})
graph_type: str = "samples"
group_by: list[str] = field(default_factory=lambda: [])
aggregate: str | None = None
show_hits: bool = False
x_axis: str | None = None
granularity: str = "Auto"
fill: str = "0"
def _load_database(path: Path) -> duckdb.DuckDBPyConnection:
ext = path.suffix.lower()
if ext == ".csv":
con = duckdb.connect()
con.execute(
f"CREATE TABLE events AS SELECT * FROM read_csv_auto('{path.as_posix
()}')"
)
elif ext in {".db", ".sqlite"}:
con = duckdb.connect()
sconn = sqlite3.connect(path)
info = sconn.execute("PRAGMA table_info(events)").fetchall()
col_defs = ", ".join(f"{r[1]} {r[2]}" for r in info)
con.execute(f"CREATE TABLE events ({col_defs})")
placeholders = ",".join("?" for _ in info)
for row in sconn.execute("SELECT * FROM events"):
con.execute(f"INSERT INTO events VALUES ({placeholders})", row)
sconn.close()
else:
con = duckdb.connect(path)
return con
_REL_RE = re.compile(
r"([+-]?\d+(?:\.\d*)?)\s*(hour|hours|day|days|week|weeks|fortnight|fortnight
s)",
re.IGNORECASE,
)
def parse_time(val: str | None) -> str | None:
"""Parse an absolute or relative time string into ``YYYY-MM-DD HH:MM:SS``.""
"
if val is None or val == "":
return None
s = val.strip()
if s.lower() == "now":
dt = datetime.now(timezone.utc)
return dt.replace(microsecond=0).strftime("%Y-%m-%d %H:%M:%S")
m = _REL_RE.fullmatch(s)
if m:
qty = float(m.group(1))
unit = m.group(2).lower()
delta: timedelta
if unit.startswith("hour"):
delta = timedelta(hours=qty)
elif unit.startswith("day"):
delta = timedelta(days=qty)
elif unit.startswith("week"):
delta = timedelta(weeks=qty)
elif unit.startswith("fortnight"):
delta = timedelta(weeks=2 * qty)
else: # pragma: no cover - defensive
raise ValueError(f"Unsupported unit: {unit}")
dt = datetime.now(timezone.utc) + delta
return dt.replace(microsecond=0).strftime("%Y-%m-%d %H:%M:%S")
dt = dtparser.parse(s)
return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H:%M:%S")
def _granularity_seconds(granularity: str, start: str | None, end: str | None) -
> int:
gran = granularity.lower()
mapping = {
"1 second": 1,
"5 seconds": 5,
"10 seconds": 10,
"30 seconds": 30,
"1 minute": 60,
"4 minutes": 240,
"5 minutes": 300,
"10 minutes": 600,
"15 minutes": 900,
"30 minutes": 1800,
"1 hour": 3600,
"3 hours": 10800,
"6 hours": 21600,
"1 day": 86400,
"1 week": 604800,
"30 days": 2592000,
}
if gran in mapping:
return mapping[gran]
if gran in {"auto", "fine"} and start and end:
try:
s = dtparser.parse(start)
...
end=end,
order_by=payload.get("order_by"),
order_dir=payload.get("order_dir", "ASC"),
limit=payload.get("limit"),
columns=payload.get("columns", []),
derived_columns=payload.get("derived_columns", {}),
graph_type=payload.get("graph_type", "samples"),
group_by=payload.get("group_by", []),
aggregate=payload.get("aggregate"),
show_hits=payload.get("show_hits", False),
x_axis=payload.get("x_axis"),
granularity=payload.get("granularity", "Auto"),
fill=payload.get("fill", "0"),
)
for f in payload.get("filters", []):
params.filters.append(Filter(f["column"], f["op"], f.get("value")))
if params.graph_type not in {"table", "timeseries"} and (
params.group_by or params.aggregate or params.show_hits
):
return (
jsonify(
{
"error": "group_by, aggregate and show_hits are only val
id for table or timeseries view"
}
),
400,
)
valid_cols = set(column_types.keys())
valid_cols.update(params.derived_columns.keys())
if params.graph_type == "timeseries":
if params.x_axis is None:
for cand in ["time", "timestamp"]:
if cand in valid_cols:
params.x_axis = cand
break
if params.x_axis is None or params.x_axis not in valid_cols:
return jsonify({"error": "Invalid x_axis"}), 400
ctype = column_types.get(params.x_axis, "").upper()
if not any(t in ctype for t in ["TIMESTAMP", "DATE", "TIME"]):
return jsonify({"error": "x_axis must be a time column"}), 400
for col in params.columns:
if col not in valid_cols:
return jsonify({"error": f"Unknown column: {col}"}), 400
for col in params.group_by:
if col not in valid_cols:
return jsonify({"error": f"Unknown column: {col}"}), 400
if params.order_by and params.order_by not in valid_cols:
return jsonify({"error": f"Unknown column: {params.order_by}"}), 400
if params.group_by or params.graph_type == "timeseries":
agg = (params.aggregate or "avg").lower()
if agg.startswith("p") or agg == "sum":
need_numeric = True
allow_time = False
elif agg == "avg" or agg in {"min", "max"}:
need_numeric = False
allow_time = True
else:
need_numeric = False
allow_time = False
if need_numeric or allow_time:
for c in params.columns:
if c in params.group_by or c == params.x_axis:
continue
if c not in column_types:
continue
ctype = column_types.get(c, "").upper()
is_numeric = any(
t in ctype
for t in [
"INT",
"DECIMAL",
"REAL",
"DOUBLE",
"FLOAT",
"NUMERIC",
"HUGEINT",
]
)
is_time = "TIMESTAMP" in ctype or "DATE" in ctype or "TIME"
in ctype
if need_numeric and not is_numeric:
return (
jsonify(
{
"error": f"Aggregate {agg} cannot be applied
to column {c}",
}
),
400,
)
if allow_time and not (is_numeric or is_time):
return (
jsonify(
{
"error": f"Aggregate {agg} cannot be applied
to column {c}",
}
),
400,
)
sql = build_query(params, column_types)
try:
rows = con.execute(sql).fetchall()
except Exception as exc:
tb = traceback.format_exc()
print(f"Query failed:\n{sql}\n{tb}")
return (
jsonify({"sql": sql, "error": str(exc), "traceback": tb}),
400,
)
return jsonify({"sql": sql, "rows": rows})
return app
app = create_app()
if __name__ == "__main__":
app.run(debug=True)I'm going back to check the build_query function. Opening it now.
root@cd76d0add7bd:/workspace/scubaduck# grep -n "build_query" -n scubaduck/serve
r.py
138:def build_query(params: QueryParams, column_types: Dict[str, str] | None = N
one) -> str:
398: sql = build_query(params, column_types)-> str:
select_parts: list[str] = []
group_cols = params.group_by[:]
if params.graph_type == "timeseries":
sec = _granularity_seconds(params.granularity, params.start, params.end)
x_axis = params.x_axis or "timestamp"
bucket_expr = (
f"TIMESTAMP 'epoch' + INTERVAL '{sec} second' * "
f"CAST(floor(epoch({x_axis})/{sec}) AS BIGINT)"
)
select_parts.append(f"{bucket_expr} AS bucket")
group_cols = ["bucket"] + group_cols
has_agg = bool(group_cols) or params.aggregate is not None
if has_agg:
select_cols = (
group_cols[1:] if params.graph_type == "timeseries" else group_cols
)
select_parts.extend(select_cols)
agg = (params.aggregate or "avg").lower()
def agg_expr(col: str) -> str:
if agg.startswith("p"):
quant = float(agg[1:]) / 100
return f"quantile({col}, {quant})"
if agg == "count distinct":
return f"count(DISTINCT {col})"
if agg == "avg" and column_types is not None:
ctype = column_types.get(col, "").upper()
if "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype:
return (
"TIMESTAMP 'epoch' + INTERVAL '1 second' * "
f"CAST(avg(epoch({col})) AS BIGINT)"
)
return f"{agg}({col})"
for col in params.columns:
if col in group_cols:
continue
select_parts.append(f"{agg_expr(col)} AS {col}")
if params.show_hits:
select_parts.insert(len(group_cols), "count(*) AS Hits")
else:
select_parts.extend(params.columns)
for name, expr in params.derived_columns.items():
select_parts.append(f"{expr} AS {name}")
select_clause = ", ".join(select_parts) if select_parts else "*"
query = f"SELECT {select_clause} FROM events"
where_parts: list[str] = []
if params.start:
where_parts.append(f"timestamp >= '{params.start}'")
if params.end:
where_parts.append(f"timestamp <= '{params.end}'")
for f in params.filters:
op = f.op
if op in {"empty", "!empty"}:
val = "''"
else:
if f.value is None:
continue
if isinstance(f.value, list):
if not f.value:
continue
if op == "=":
vals = " OR ".join(f"{f.column} = '{v}'" for v in f.value)
where_parts.append(f"({vals})")
continue
val = f"'{f.value}'" if isinstance(f.value, str) else str(f.value)
if op == "contains":
where_parts.append(f"{f.column} ILIKE '%' || {val} || '%'")
elif op == "!contains":
where_parts.append(f"{f.column} NOT ILIKE '%' || {val} || '%'")
elif op == "empty":
where_parts.append(f"{f.column} = {val}")
elif op == "!empty":
where_parts.append(f"{f.column} != {val}")
else:
where_parts.append(f"{f.column} {op} {val}")
if where_parts:
query += " WHERE " + " AND ".join(where_parts)
if group_cols:
query += " GROUP BY " + ", ".join(group_cols)
if params.order_by:
query += f" ORDER BY {params.order_by} {params.order_dir}"
if params.limit is not None:
query += f" LIMIT {params.limit}"
return query
def create_app(db_file: str | Path | None = None) -> Flask:
app = Flask(__name__, static_folder="static")
db_path = Path(db_file or Path(__file__).with_name("sample.csv")).resolve()
con = _load_database(db_path)I’ll check near the top where the imports should be, as the code’s truncated.
from datetime import datetime, timedelta, timezone
import time
from pathlib import Path
import sqlite3
import traceback
import duckdb
from dateutil import parser as dtparser
from flask import Flask, jsonify, request, send_from_directory
@dataclass
class Filter:
column: str
op: str
value: str | int | float | list[str] | None
@dataclass
class QueryParams:
start: str | None = None
end: str | None = None
order_by: str | None = None
order_dir: str = "ASC"
limit: int | None = None
columns: list[str] = field(default_factory=lambda: [])
filters: list[Filter] = field(default_factory=lambda: [])
derived_columns: dict[str, str] = field(default_factory=lambda: {})
graph_type: str = "samples"
group_by: list[str] = field(default_factory=lambda: [])
aggregate: str | None = None
show_hits: bool = False
x_axis: str | None = None
granularity: str = "Auto"
fill: str = "0"
def _load_database(path: Path) -> duckdb.DuckDBPyConnection:
ext = path.suffix.lower()
if ext == ".csv":
con = duckdb.connect()
con.execute(
f"CREATE TABLE events AS SELECT * FROM read_csv_auto('{path.as_posix
()}')"
)
elif ext in {".db", ".sqlite"}:
con = duckdb.connect()
sconn = sqlite3.connect(path)
info = sconn.execute("PRAGMA table_info(events)").fetchall()
col_defs = ", ".join(f"{r[1]} {r[2]}" for r in info)
con.execute(f"CREATE TABLE events ({col_defs})")
placeholders = ",".join("?" for _ in info)
for row in sconn.execute("SELECT * FROM events"):
con.execute(f"INSERT INTO events VALUES ({placeholders})", row)
sconn.close()
else:
con = duckdb.connect(path)
return con
_REL_RE = re.compile(
r"([+-]?\d+(?:\.\d*)?)\s*(hour|hours|day|days|week|weeks|fortnight|fortnight
s)",
re.IGNORECASE,
)
def parse_time(val: str | None) -> str | None:
"""Parse an absolute or relative time string into ``YYYY-MM-DD HH:MM:SS``.""
"
if val is None or val == "":
return None
s = val.strip()
if s.lower() == "now":
dt = datetime.now(timezone.utc)
return dt.replace(microsecond=0).strftime("%Y-%m-%d %H:%M:%S")
m = _REL_RE.fullmatch(s)
if m:
qty = float(m.group(1))
unit = m.group(2).lower()
delta: timedelta
if unit.startswith("hour"):
delta = timedelta(hours=qty)
elif unit.startswith("day"):
delta = timedelta(days=qty)
elif unit.startswith("week"):
delta = timedelta(weeks=qty)
elif unit.startswith("fortnight"):
delta = timedelta(weeks=2 * qty)
else: # pragma: no cover - defensive
raise ValueError(f"Unsupported unit: {unit}")
dt = datetime.now(timezone.utc) + delta
return dt.replace(microsecond=0).strftime("%Y-%m-%d %H:%M:%S")
dt = dtparser.parse(s)
return dt.replace(microsecond=0, tzinfo=None).strftime("%Y-%m-%d %H:%M:%S")
def _granularity_seconds(granularity: str, start: str | None, end: str | None) -
> int:
gran = granularity.lower()
mapping = {
"1 second": 1,
"5 seconds": 5,
"10 seconds": 10,
"30 seconds": 30,
"1 minute": 60,
"4 minutes": 240,
"5 minutes": 300,
"10 minutes": 600,
"15 minutes": 900,
"30 minutes": 1800,
"1 hour": 3600,
"3 hours": 10800,
"6 hours": 21600,
"1 day": 86400,
"1 week": 604800,
"30 days": 2592000,
}
if gran in mapping:
return mapping[gran]
if gran in {"auto", "fine"} and start and end:
try:
s = dtparser.parse(start)
e = dtparser.parse(end)
except Exception:
return 3600
total = max((e - s).total_seconds(), 1)
buckets = 100 if gran == "auto" else 500
return max(int(total // buckets), 1)
return 3600
def build_query(params: QueryParams, column_types: Dict[str, str] | None = None)
-> str:
select_parts: list[str] = []
group_cols = params.group_by[:]I'm looking for the final lines to wrap up everything.
-> str:
select_parts: list[str] = []
group_cols = params.group_by[:]
if params.graph_type == "timeseries":
sec = _granularity_seconds(params.granularity, params.start, params.end)
x_axis = params.x_axis or "timestamp"
bucket_expr = (
f"TIMESTAMP 'epoch' + INTERVAL '{sec} second' * "
f"CAST(floor(epoch({x_axis})/{sec}) AS BIGINT)"
)
select_parts.append(f"{bucket_expr} AS bucket")
group_cols = ["bucket"] + group_cols
has_agg = bool(group_cols) or params.aggregate is not None
if has_agg:
select_cols = (
group_cols[1:] if params.graph_type == "timeseries" else group_cols
)
select_parts.extend(select_cols)
agg = (params.aggregate or "avg").lower()
def agg_expr(col: str) -> str:
if agg.startswith("p"):
quant = float(agg[1:]) / 100
return f"quantile({col}, {quant})"
if agg == "count distinct":
return f"count(DISTINCT {col})"
if agg == "avg" and column_types is not None:
ctype = column_types.get(col, "").upper()
if "TIMESTAMP" in ctype or "DATE" in ctype or "TIME" in ctype:
return (
"TIMESTAMP 'epoch' + INTERVAL '1 second' * "
f"CAST(avg(epoch({col})) AS BIGINT)"
)
return f"{agg}({col})"
for col in params.columns:
if col in group_cols:
continue
select_parts.append(f"{agg_expr(col)} AS {col}")
if params.show_hits:
select_parts.insert(len(group_cols), "count(*) AS Hits")
else:
select_parts.extend(params.columns)
for name, expr in params.derived_columns.items():
select_parts.append(f"{expr} AS {name}")
select_clause = ", ".join(select_parts) if select_parts else "*"
query = f"SELECT {select_clause} FROM events"
where_parts: list[str] = []
if params.start:
where_parts.append(f"timestamp >= '{params.start}'")
if params.end:
where_parts.append(f"timestamp <= '{params.end}'")
for f in params.filters:
op = f.op
if op in {"empty", "!empty"}:
val = "''"
else:
if f.value is None:
continue
if isinstance(f.value, list):
if not f.value:
continue
if op == "=":
vals = " OR ".join(f"{f.column} = '{v}'" for v in f.value)
where_parts.append(f"({vals})")
continue
val = f"'{f.value}'" if isinstance(f.value, str) else str(f.value)
if op == "contains":
where_parts.append(f"{f.column} ILIKE '%' || {val} || '%'")
elif op == "!contains":
where_parts.append(f"{f.column} NOT ILIKE '%' || {val} || '%'")
elif op == "empty":
where_parts.append(f"{f.column} = {val}")
elif op == "!empty":
where_parts.append(f"{f.column} != {val}")
else:
where_parts.append(f"{f.column} {op} {val}")
if where_parts:
query += " WHERE " + " AND ".join(where_parts)
if group_cols:
query += " GROUP BY " + ", ".join(group_cols)
if params.order_by:
query += f" ORDER BY {params.order_by} {params.order_dir}"
if params.limit is not None:
query += f" LIMIT {params.limit}"
return query
def create_app(db_file: str | Path | None = None) -> Flask:
app = Flask(__name__, static_folder="static")
db_path = Path(db_file or Path(__file__).with_name("sample.csv")).resolve()
con = _load_database(db_path)
column_types: Dict[str, str] = {
r[1]: r[2] for r in con.execute("PRAGMA table_info(events)").fetchall()
}
sample_cache: Dict[Tuple[str, str], Tuple[List[str], float]] = {}
CACHE_TTL = 60.0
CACHE_LIMIT = 200
@app.route("/")
def index() -> Any: # pyright: ignore[reportUnusedFunction]
assert app.static_folder is not None
return send_from_directory(app.static_folder, "index.html")
@app.route("/api/columns")
def columns() -> Any: # pyright: ignore[reportUnusedFunction]
rows = con.execute("PRAGMA table_info(events)").fetchall()
return jsonify([{"name": r[1], "type": r[2]} for r in rows])
def _cache_get(key: Tuple[str, str]) -> List[str] | None:
item = sample_cache.get(key)
if item is None:I’m going to review the functions around the close of create_app.
root@cd76d0add7bd:/workspace/scubaduck# sed -n '300,450p' scubaduck/server.py
order_dir=payload.get("order_dir", "ASC"),
limit=payload.get("limit"),
columns=payload.get("columns", []),
derived_columns=payload.get("derived_columns", {}),
graph_type=payload.get("graph_type", "samples"),
group_by=payload.get("group_by", []),
aggregate=payload.get("aggregate"),
show_hits=payload.get("show_hits", False),
x_axis=payload.get("x_axis"),
granularity=payload.get("granularity", "Auto"),
fill=payload.get("fill", "0"),
)
for f in payload.get("filters", []):
params.filters.append(Filter(f["column"], f["op"], f.get("value")))
if params.graph_type not in {"table", "timeseries"} and (
params.group_by or params.aggregate or params.show_hits
):
return (
jsonify(
{
"error": "group_by, aggregate and show_hits are only val
id for table or timeseries view"
}
),
400,
)
valid_cols = set(column_types.keys())
valid_cols.update(params.derived_columns.keys())
if params.graph_type == "timeseries":
if params.x_axis is None:
for cand in ["time", "timestamp"]:
if cand in valid_cols:
params.x_axis = cand
break
if params.x_axis is None or params.x_axis not in valid_cols:
return jsonify({"error": "Invalid x_axis"}), 400
ctype = column_types.get(params.x_axis, "").upper()
if not any(t in ctype for t in ["TIMESTAMP", "DATE", "TIME"]):
return jsonify({"error": "x_axis must be a time column"}), 400
for col in params.columns:
if col not in valid_cols:
return jsonify({"error": f"Unknown column: {col}"}), 400
for col in params.group_by:
if col not in valid_cols:
return jsonify({"error": f"Unknown column: {col}"}), 400
if params.order_by and params.order_by not in valid_cols:
return jsonify({"error": f"Unknown column: {params.order_by}"}), 400
if params.group_by or params.graph_type == "timeseries":
agg = (params.aggregate or "avg").lower()
if agg.startswith("p") or agg == "sum":
need_numeric = True
allow_time = False
elif agg == "avg" or agg in {"min", "max"}:
need_numeric = False
allow_time = True
else:
need_numeric = False
allow_time = False
if need_numeric or allow_time:
for c in params.columns:
if c in params.group_by or c == params.x_axis:
continue
if c not in column_types:
continue
ctype = column_types.get(c, "").upper()
is_numeric = any(
t in ctype
for t in [
"INT",
"DECIMAL",
"REAL",
"DOUBLE",
"FLOAT",
"NUMERIC",
"HUGEINT",
]
)
is_time = "TIMESTAMP" in ctype or "DATE" in ctype or "TIME"
in ctype
if need_numeric and not is_numeric:
return (
jsonify(
{
"error": f"Aggregate {agg} cannot be applied
to column {c}",
}
),
400,
)
if allow_time and not (is_numeric or is_time):
return (
jsonify(
{
"error": f"Aggregate {agg} cannot be applied
to column {c}",
}
),
400,
)
sql = build_query(params, column_types)
try:
rows = con.execute(sql).fetchall()
except Exception as exc:
tb = traceback.format_exc()
print(f"Query failed:\n{sql}\n{tb}")
return (
jsonify({"sql": sql, "error": str(exc), "traceback": tb}),
400,
)
return jsonify({"sql": sql, "rows": rows})
return app
app = create_app()
if __name__ == "__main__":
app.run(debug=True)I’ll adjust the query to default ordering by x-axis for timeseries graph rendering.
root@cd76d0add7bd:/workspace/scubaduck# cat scubaduck/sample.csv
timestamp,event,value,user
2024-01-01 00:00:00,login,10,alice
2024-01-01 01:00:00,logout,20,bob
2024-01-02 00:00:00,login,30,alice
2024-01-02 03:00:00,login,40,charlieI see that the query may return unsorted groups. I'll set a default 'ORDER BY' on the x-axis or bucket for timeseries graphs. Let's review the tests.
root@cd76d0add7bd:/workspace/scubaduck# grep -n "timeseries" -n tests/test_serve
r.py
351:def test_timeseries_basic() -> None:
357: "graph_type": "timeseries",
373:def test_timeseries_string_column_error() -> None:
379: "graph_type": "timeseries",I’ll add a test to ensure time-series results are ordered by x-axis. The code in build_query can automatically set the order if none is provided. I’ll verify it works even with group_by or aggregate.
I'll check tests/test_web for relevant timeseries tests, especially for cases without group_by or aggregate.
y
188:def test_graph_type_timeseries_fields(page: Any, server_url: str) -> None:
191: select_value(page, "#graph_type", "timeseries")
199:def test_timeseries_default_query(page: Any, server_url: str) -> None:
202: select_value(page, "#graph_type", "timeseries")
213:def test_timeseries_single_bucket(page: Any, server_url: str) -> None:
218: select_value(page, "#graph_type", "timeseries")assert page.is_visible("#aggregate_field")
assert page.is_visible("#show_hits_field")
page.click("text=Columns")
assert not page.is_visible("text=Strings:")
def test_graph_type_timeseries_fields(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#graph_type", state="attached")
select_value(page, "#graph_type", "timeseries")
assert page.is_visible("#group_by_field")
assert page.is_visible("#aggregate_field")
assert page.is_visible("#x_axis_field")
assert page.is_visible("#granularity_field")
assert page.is_visible("#fill_field")
def test_timeseries_default_query(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#graph_type", state="attached")
select_value(page, "#graph_type", "timeseries")
page.evaluate("window.lastResults = undefined")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
data = page.evaluate("window.lastResults")
assert "error" not in data
assert page.is_visible("#chart")
page.click("text=Columns")
assert not page.is_checked("#column_groups input[value='timestamp']")
def test_timeseries_single_bucket(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#graph_type", state="attached")
page.fill("#start", "2024-01-01 00:00:00")
page.fill("#end", "2024-01-01 00:00:00")
select_value(page, "#graph_type", "timeseries")
page.evaluate("window.lastResults = undefined")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
path = page.get_attribute("#chart path", "d")
assert path is not None and "NaN" not in path
def test_help_and_alignment(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
titles = page.evaluate(
"Array.from(document.querySelectorAll('#settings .help')).map(e => e.tit
le)"I'll add a test to verify ordering since there's no test for it yet.
page.evaluate(
"arg => setSelectValue(arg.sel, arg.val)",
{"sel": selector, "val": value},
)
def run_query(
page: Any,
url: str,
*,
start: str | None = None,
end: str | None = None,
order_by: str | None = None,
order_dir: str | None = "ASC",
limit: int | None = None,
group_by: list[str] | None = None,
aggregate: str | None = None,
) -> dict[str, Any]:
page.goto(url)
page.wait_for_selector("#order_by option", state="attached")
page.wait_for_selector("#order_dir", state="attached")
page.wait_for_function("window.lastResults !== undefined")
if start is not None:
page.fill("#start", start)
if end is not None:
page.fill("#end", end)
if order_by is not None:
select_value(page, "#order_by", order_by)
if order_dir is not None and order_dir == "DESC":
page.click("#order_dir")
if limit is not None:
page.fill("#limit", str(limit))
if group_by is not None:
select_value(page, "#graph_type", "table")
page.evaluate(
"g => { groupBy.chips = g; groupBy.renderChips(); }",
group_by,
)
if aggregate is not None:
select_value(page, "#graph_type", "table")
select_value(page, "#aggregate", aggregate)
page.evaluate("window.lastResults = undefined")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
return page.evaluate("window.lastResults")
def test_range_filters(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-02 00:00:00",
end="2024-01-02 04:00:00",
order_by="timestamp",
limit=100,
)
assert len(data["rows"]) == 2
from dateutil import parser
timestamps = [parser.parse(row[0]).replace(tzinfo=None) for row in data["row
s"]]
assert timestamps == [
parser.parse("2024-01-02 00:00:00"),
parser.parse("2024-01-02 03:00:00"),
]
def test_order_by(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-01 00:00:00",
end="2024-01-03 00:00:00",
order_by="value",
order_dir="DESC",
limit=100,
)
values = [row[2] for row in data["rows"]]
assert values == sorted(values, reverse=True)
def test_limit(page: Any, server_url: str) -> None:
data = run_query(
page,
server_url,
start="2024-01-01 00:00:00",
end="2024-01-03 00:00:00",
order_by="timestamp",
limit=2,
)
assert len(data["rows"]) == 2
def test_simple_filter(page: Any, server_url: str) -> None:
page.goto(server_url)
page.wait_for_selector("#order_by option", state="attached")
page.click("text=Add Filter")
filter_el = page.query_selector("#filters .filter:last-child")
assert filter_el
page.evaluate(
"arg => setSelectValue(arg.el.querySelector('.f-col'), arg.val)",
{"el": filter_el, "val": "user"},
)
val_input = filter_el.query_selector(".f-val")
val_input.click()
page.keyboard.type("alice")
page.keyboard.press("Enter")
page.click("text=Dive")
page.wait_for_function("window.lastResults !== undefined")
data = page.evaluate("window.lastResults")
assert len(data["rows"]) == 2
assert all(row[3] == "alice" for row in data["rows"])
def test_default_filter_and_layout(page: Any, server_url: str) -> None:I’ll create a test for timeseries ordering, checking the default behavior and output order.
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>ScubaDuck</title>
<style>
body { margin: 0; font-family: sans-serif; height: 100vh; display: flex; fle
x-direction: column; }
#header { padding: 10px; font-weight: bold; border-bottom: 1px solid #ccc; }
#content { flex: 1; display: flex; height: calc(100vh - 42px); overflow: hid
den; }
#sidebar { width: 450px; padding: 10px; border-right: 3px solid #ccc; overfl
ow-y: auto; display: flex; flex-direction: column; box-sizing: border-box; }
#sidebar-resizer { width: 5px; cursor: col-resize; background: #ccc; }
#view { flex: 1; padding: 10px; overflow-y: auto; overflow-x: auto; }
.field { display: flex; align-items: center; margin-bottom: 10px; }
.field label { width: 80px; text-align: right; margin-right: 5px; }
.help { margin-left: 4px; cursor: help; }
.rel-btn { margin-left: 4px; }
#tabs { display: flex; align-items: center; margin-bottom: 10px; }
#tabs .tab { margin-right: 5px; background: none; border: 1px solid #ccc; pa
dding: 4px 8px; cursor: pointer; width: 120px; text-align: center; box-sizing: b
order-box; }
#tabs .tab.active { background: #eee; font-weight: bold; }
#dive { margin-left: auto; background: green; color: white; border: none; pa
dding: 5px 10px; cursor: pointer; }
.tab-content { display: none; }
.tab-content.active { display: block; }
#filter_list { display: flex; flex-direction: column; }
#filters .filter {
border: 1px solid #ccc;
padding: 5px;
margin-bottom: 5px;
position: relative;
display: flex;
flex-direction: column;
}
#derived_columns .derived {
border: 1px solid #ccc;
padding: 5px;
margin-bottom: 5px;
display: flex;
flex-direction: column;
}
#derived_columns .derived-row {
display: flex;I'll look for the default query or any JavaScript files related to this.
root@cd76d0add7bd:/workspace/scubaduck# grep -n "order_by" -n scubaduck/static/i
ndex.html | head -n 20
181: <select id="order_by"></select>
441: document.getElementById('order_by').value = '';
453: const orderSelect = document.getElementById('order_by');
991: order_by: document.getElementById('order_by').value,
1032: if (params.order_by) sp.set('order_by', params.order_by);
1056: if (params.order_by) {
1057: document.getElementById('order_by').value = params.order_by;
1113: if (sp.has('order_by')) params.order_by = sp.get('order_by');I'm checking lines from 991 to 1113 to gather the necessary context.
root@cd76d0add7bd:/workspace/scubaduck# sed -n '970,1130p' scubaduck/static/inde
x.html
queryStart = performance.now();
fetch('/api/query', {method:'POST', headers:{'Content-Type':'application/json'
}, body:JSON.stringify(payload)})
.then(async r => {
const data = await r.json();
if (!r.ok) throw data;
return data;
})
.then(data => {
lastQueryTime = Math.round(performance.now() - queryStart);
showResults(data);
})
.catch(err => {
showError(err);
});
}
function collectParams() {
updateSelectedColumns();
const payload = {
start: document.getElementById('start').value,
end: document.getElementById('end').value,
order_by: document.getElementById('order_by').value,
order_dir: orderDir,
limit: parseInt(document.getElementById('limit').value, 10),
columns: selectedColumns.filter(c =>
c !== 'Hits' && !derivedColumns.some(dc => dc.name === c)
),
graph_type: graphTypeSel.value,
filters: Array.from(document.querySelectorAll('#filters .filter')).map(f =>
{
const chips = f.chips || [];
const op = f.querySelector('.f-op').value;
let value = null;
if (op !== 'empty' && op !== '!empty') {
value = chips.length === 0 ? null : (chips.length === 1 ? chips[0] : chi
ps);
}
const colSel = f.querySelector('.f-col select') || f.querySelector('.f-col
');
return {column: colSel.value, op, value};
}),
derived_columns: Array.from(document.querySelectorAll('#derived_list .derive
d')).map(d => ({
type: d.querySelector('.d-type').value,
name: d.querySelector('.d-name').value,
expr: d.querySelector('.d-expr').value,
include: d.querySelector('.d-use').checked,
}))
};
if (graphTypeSel.value === 'table' || graphTypeSel.value === 'timeseries') {
payload.group_by = groupBy.chips || [];
payload.aggregate = document.getElementById('aggregate').value;
payload.show_hits = document.getElementById('show_hits').checked;
}
if (graphTypeSel.value === 'timeseries') {
payload.x_axis = document.getElementById('x_axis').value;
payload.granularity = document.getElementById('granularity').value;
payload.fill = document.getElementById('fill').value;
}
return payload;
}
function paramsToSearch(params) {
const sp = new URLSearchParams();
if (params.start) sp.set('start', params.start);
if (params.end) sp.set('end', params.end);
if (params.order_by) sp.set('order_by', params.order_by);
if (params.order_dir) sp.set('order_dir', params.order_dir);
if (params.limit !== null && params.limit !== undefined) sp.set('limit', param
s.limit);
if (params.columns && params.columns.length) sp.set('columns', params.columns.
join(','));
if (params.filters && params.filters.length) sp.set('filters', JSON.stringify(
params.filters));
if (params.derived_columns && params.derived_columns.length) sp.set('derived_c
olumns', JSON.stringify(params.derived_columns));
if (params.graph_type) sp.set('graph_type', params.graph_type);
if (params.graph_type === 'table' || params.graph_type === 'timeseries') {
if (params.group_by && params.group_by.length) sp.set('group_by', params.gro
up_by.join(','));
if (params.aggregate) sp.set('aggregate', params.aggregate);
if (params.show_hits) sp.set('show_hits', '1');
}
if (params.graph_type === 'timeseries') {
if (params.x_axis) sp.set('x_axis', params.x_axis);
if (params.granularity) sp.set('granularity', params.granularity);
if (params.fill) sp.set('fill', params.fill);
}
const qs = sp.toString();
return qs ? '?' + qs : '';
}
function applyParams(params) {
document.getElementById('start').value = params.start || '';
document.getElementById('end').value = params.end || '';
if (params.order_by) {
document.getElementById('order_by').value = params.order_by;
}
orderDir = params.order_dir || 'ASC';
updateOrderDirButton();
if (params.limit !== undefined && params.limit !== null) {
document.getElementById('limit').value = params.limit;
}
graphTypeSel.value = params.graph_type || 'samples';
updateDisplayTypeUI();
if (params.x_axis) document.getElementById('x_axis').value = params.x_axis;
if (params.granularity) document.getElementById('granularity').value = params.
granularity;
if (params.fill) document.getElementById('fill').value = params.fill;
if (params.group_by) {
groupBy.chips.splice(0, groupBy.chips.length, ...params.group_by);
groupBy.renderChips();
}
if (params.aggregate) document.getElementById('aggregate').value = params.aggr
egate;
document.getElementById('show_hits').checked = params.show_hits ?? true;
document.querySelectorAll('#column_groups input').forEach(cb => {
cb.checked = !params.columns || params.columns.includes(cb.value);
});
updateSelectedColumns();
const dlist = document.getElementById('derived_list');
dlist.innerHTML = '';
derivedColumns.splice(0, derivedColumns.length);
if (params.derived_columns && params.derived_columns.length) {
params.derived_columns.forEach(dc => addDerived(dc));
}
refreshDerivedColumns();
const list = document.getElementById('filter_list');
list.innerHTML = '';
if (params.filters && params.filters.length) {
params.filters.forEach(f => {
addFilter();
const el = list.lastElementChild;
const colSel = el.querySelector('.f-col select') || el.querySelector('.f-c
ol');
colSel.value = f.column;
colSel.dispatchEvent(new Event('change'));
el.querySelector('.f-op').value = f.op;
el.querySelector('.f-op').dispatchEvent(new Event('change'));
if (f.value !== null && f.op !== 'empty' && f.op !== '!empty') {
const values = Array.isArray(f.value) ? f.value : [f.value];
values.forEach(v => el.addChip(v));
el.renderChips();
}
});
} else {
addFilter();
}
}
function parseSearch() {
const sp = new URLSearchParams(window.location.search);
const params = {};
if (sp.has('start')) params.start = sp.get('start');
if (sp.has('end')) params.end = sp.get('end');
if (sp.has('order_by')) params.order_by = sp.get('order_by');
if (sp.has('order_dir')) params.order_dir = sp.get('order_dir');
if (sp.has('limit')) params.limit = parseInt(sp.get('limit'), 10);
if (sp.has('columns')) params.columns = sp.get('columns').split(',').filter(c
=> c);
if (sp.has('filters')) {
try { params.filters = JSON.parse(sp.get('filters')); } catch(e) { params.fi
lters = []; }
}
if (sp.has('graph_type')) params.graph_type = sp.get('graph_type');
if (sp.has('group_by')) params.group_by = sp.get('group_by').split(',').filter
(c => c);
if (sp.has('aggregate')) params.aggregate = sp.get('aggregate');
if (sp.has('show_hits')) params.show_hits = sp.get('show_hits') === '1';
if (sp.has('x_axis')) params.x_axis = sp.get('x_axis');
if (sp.has('granularity')) params.granularity = sp.get('granularity');
if (sp.has('fill')) params.fill = sp.get('fill');
if (sp.has('derived_columns')) {
try { params.derived_columns = JSON.parse(sp.get('derived_columns')); } catc
h(e) { params.derived_columns = []; }
}
return params;I'm continuing to scroll to capture the full information I need.
root@cd76d0add7bd:/workspace/scubaduck# sed -n '1130,1330p' scubaduck/static/ind
ex.html
return params;
}
function initFromUrl() {
const params = parseSearch();
history.replaceState(params, '', paramsToSearch(params));
applyParams(params);
dive(false);
}
window.addEventListener('popstate', e => {
const params = e.state || parseSearch();
applyParams(params);
dive(false);
});
let originalRows = [];
let sortState = {index: null, dir: null};
function renderTable(rows) {
const table = document.getElementById('results');
table.innerHTML = '';
if (rows.length === 0) return;
let hitsIndex = selectedColumns.indexOf('Hits');
let totalHits = 0;
if (hitsIndex !== -1) {
totalHits = rows.reduce((s, r) => s + Number(r[hitsIndex]), 0);
}
const header = document.createElement('tr');
selectedColumns.forEach((col, i) => {
const th = document.createElement('th');
let label = col;
if (
displayType === 'table' &&
col !== 'Hits' &&
!(groupBy.chips || []).includes(col)
) {
const agg = document.getElementById('aggregate').value.toLowerCase();
label += ` (${agg})`;
}
th.textContent = label;
th.dataset.index = i;
th.addEventListener('click', handleSort);
if (sortState.index === i) {
th.classList.add('sorted');
th.textContent =
label + (sortState.dir === 'desc' ? ' \u25BC' : ' \u25B2');
}
if (!isStringColumn(col)) th.style.textAlign = 'right';
header.appendChild(th);
});
table.appendChild(header);
rows.forEach(row => {
const tr = document.createElement('tr');
tr.addEventListener('click', () => {
tr.classList.toggle('selected');
});
row.forEach((v, i) => {
const col = selectedColumns[i];
const td = document.createElement('td');
if (isTimeColumn(col)) {
const d = new Date(v);
td.textContent = d.toLocaleString('en-US', {
weekday: 'short',
month: 'short',
day: 'numeric',
year: 'numeric',
hour: 'numeric',
minute: 'numeric',
second: 'numeric',
hour12: true,
timeZoneName: 'short'
});
} else {
if (col === 'Hits') {
const pct = totalHits ? ((v / totalHits) * 100).toFixed(1) : '0';
td.textContent = `${formatNumber(v)} (${pct}%)`;
} else {
td.textContent = isStringColumn(col) ? v : formatNumber(v);
}
}
td.style.textAlign = isStringColumn(col) ? 'left' : 'right';
tr.appendChild(td);
});
table.appendChild(tr);
});
// ensure table does not overflow unless necessary
const view = document.getElementById('view');
if (table.scrollWidth <= view.clientWidth) {
table.style.width = '100%';
}
}
function handleSort(e) {
const idx = parseInt(e.target.dataset.index, 10);
if (sortState.index !== idx) {
sortState.index = idx;
sortState.dir = 'asc';
} else if (sortState.dir === 'asc') {
sortState.dir = 'desc';
} else if (sortState.dir === 'desc') {
sortState.index = null;
sortState.dir = null;
} else {
sortState.dir = 'asc';
}
let rows = originalRows.slice();
if (sortState.index !== null) {
rows.sort((a, b) => {
const va = a[sortState.index];
const vb = b[sortState.index];
if (va === vb) return 0;
if (sortState.dir === 'desc') return va < vb ? 1 : -1;
return va > vb ? 1 : -1;
});
}
renderTable(rows);
}
function showTimeSeries(data) {
const view = document.getElementById('view');
if (data.rows.length === 0) {
view.innerHTML = '<p id="empty-message">Empty data provided to table</p>';
return;
}
const width = 600;
const height = 400;
view.innerHTML = '<div id="legend"></div><svg id="chart" width="' + width + '"
height="' + height + '"></svg>';
const svg = document.getElementById('chart');
const legend = document.getElementById('legend');
const groups = groupBy.chips || [];
const hasHits = document.getElementById('show_hits').checked ? 1 : 0;
const series = {};
let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity;
data.rows.forEach(r => {
const ts = new Date(r[0]).getTime();
const key = groups.map((_, i) => r[1 + i]).join(':') || 'all';
const val = Number(r[1 + groups.length + hasHits]);
if (!series[key]) series[key] = [];
series[key].push({x: ts, y: val});
if (ts < minX) minX = ts;
if (ts > maxX) maxX = ts;
if (val < minY) minY = val;
if (val > maxY) maxY = val;
});
const colors = ['#1f77b4','#ff7f0e','#2ca02c','#d62728','#9467bd','#8c564b','#
e377c2'];
let colorIndex = 0;
const xRange = maxX - minX || 1;
const yRange = maxY - minY || 1;
const xScale = x => ((x - minX) / xRange) * (width - 60) + 50;
const yScale = y => height - 30 - ((y - minY) / yRange) * (height - 60);
Object.keys(series).forEach(key => {
const pts = series[key];
const color = colors[colorIndex++ % colors.length];
const path = pts.map((p,i)=> (i===0? 'M':'L') + xScale(p.x) + ' ' + yScale(p
.y)).join(' ');
const el = document.createElementNS('http://www.w3.org/2000/svg','path');
el.setAttribute('d', path);
el.setAttribute('fill','none');
el.setAttribute('stroke', color);
svg.appendChild(el);
const item = document.createElement('div');
item.textContent = key;
item.style.color = color;
legend.appendChild(item);
});
}
function showResults(data) {
window.lastResults = data;
const view = document.getElementById('view');
if (graphTypeSel.value === 'timeseries') {
showTimeSeries(data);
} else {
if (data.rows.length === 0) {
view.innerHTML =
'<p id="empty-message">Empty data provided to table</p><table id="result
s"></table>';
} else {
view.innerHTML = '<table id="results"></table>';
}
originalRows = data.rows.slice();
sortState = {index: null, dir: null};
renderTable(originalRows);
}
document.getElementById('query_info').textContent =
`Your query took about ${lastQueryTime} ms`;
}
function showError(err) {
window.lastResults = err;
const view = document.getElementById('view');
let msg = '';
if (typeof err === 'string') {
msg = err;
} else if (err) {
msg = err.error || 'Error';
if (err.sql) {
msg += '\nSQL: ' + err.sql;
}
if (err.traceback) {
msg += '\n' + err.traceback;
}I'll update build_query to order timeseries results by bucket for better chart rendering.
I'll add a test to validate timeseries order by x-axis for ascending bucket sorting.
I'll structure a timeseries query, ensuring results are ordered by the earliest timestamp bucket.
I'll run timeseries_basic to check order before making any modifications.
root@cd76d0add7bd:/workspace/scubaduck# git status --shortroot@cd76d0add7bd:/workspace/scubaduck# source .venv/bin/activate
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# pytest -k test_timeseries_ba
sic -vvv============================= test session starts ==============================
platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /workspace/scubad
uck/.venv/bin/python
cachedir: .pytest_cache
rootdir: /workspace/scubaduck
configfile: pyproject.toml
plugins: xdist-3.6.1, base-url-2.1.0, playwright-0.7.0
5 workers [1 item]
scheduling tests via LoadScheduling
tests/test_server.py::test_timeseries_basic
[gw0] [100%] PASSED tests/test_server.py::test_timeseries_basic
============================== 1 passed in 0.98s ===============================
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll print the results of the test to check if the order is correct.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# python - <<'EOF'
> import json
> from scubaduck import server
> app=server.app
> client=app.test_client()
> payload = {
> "start": "2024-01-01 00:00:00",
> "end": "2024-01-03 00:00:00",
> "graph_type": "timeseries",
> "limit": 100,
> "group_by": ["user"],
> "aggregate": "Count",
> "columns": ["value"],
> "x_axis": "timestamp",
> "granularity": "1 day",
> }
> rv=client.post("/api/query", data=json.dumps(payload), content_type='applicati
on/json')
> print('status',rv.status_code)
> print('data', rv.get_json())
> EOF
status 200
data {'rows': [['Mon, 01 Jan 2024 00:00:00 GMT', 'bob', 1], ['Mon, 01 Jan 2024 0
0:00:00 GMT', 'alice', 1], ['Tue, 02 Jan 2024 00:00:00 GMT', 'alice', 1], ['Tue,
02 Jan 2024 00:00:00 GMT', 'charlie', 1]], 'sql': "SELECT TIMESTAMP 'epoch' + I
NTERVAL '86400 second' * CAST(floor(epoch(timestamp)/86400) AS BIGINT) AS bucket
, user, count(value) AS value FROM events WHERE timestamp >= '2024-01-01 00:00:0
0' AND timestamp <= '2024-01-03 00:00:00' GROUP BY bucket, user LIMIT 100"}
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#The rows are grouped by user and date, but not sorted by bucket. I'll fix that.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# python - <<'EOF'
> import json
> from scubaduck import server
> app=server.app
> client=app.test_client()
> payload = {
> "start": "2024-01-01 00:00:00",
> "end": "2024-01-03 00:00:00",
> "graph_type": "timeseries",
> "limit": 100,
> "group_by": ["user"],
> "aggregate": "Count",
> "columns": ["value"],
> "x_axis": "timestamp",
> "granularity": "1 day",
> "order_by": "bucket"
> }
> rv=client.post("/api/query", data=json.dumps(payload), content_type='applicati
on/json')
> print(rv.get_json())
> EOF
{'error': 'Unknown column: bucket'}
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I can't let the user specify "bucket" for order_by, but I'll add it by default in the query. I'll check if it's part of group_cols and handle the alias accordingly. I'll also verify DuckDB compatibility.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# python - <<'EOF'
> import duckdb
> con=duckdb.connect()
> con.execute("SELECT 'a' as col1, 1 as val UNION ALL SELECT 'b', 2");
> print(con.execute("SELECT 'a' as col1, 1 as val UNION ALL SELECT 'b', 2 ORDER
BY col1").fetchall())
> EOF
[('a', 1), ('b', 2)]
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll test the alias 'bucket' to see if it works in ORDER BY.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# python - <<'EOF'
> import duckdb
> con=duckdb.connect()
> con.execute("CREATE TABLE t(v TIMESTAMP)")
> con.execute("INSERT INTO t VALUES ('2024-01-01'), ('2024-01-02')")
> print(con.execute("SELECT v, v AS bucket FROM t ORDER BY bucket DESC").fetchal
l())
> EOF
[(datetime.datetime(2024, 1, 2, 0, 0), datetime.datetime(2024, 1, 2, 0, 0)), (da
tetime.datetime(2024, 1, 1, 0, 0), datetime.datetime(2024, 1, 1, 0, 0))]
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#The alias is working! Let’s add the new test to ensure correct ordering by default.
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert "error" not in data
rows = data["rows"]
assert rows[0][0] == "alice"
from dateutil import parser
ts = parser.parse(rows[0][1]).replace(tzinfo=None)
assert ts == parser.parse("2024-01-01 12:00:00")
def test_timeseries_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 100,
"group_by": ["user"],
"aggregate": "Count",
"columns": ["value"],
"x_axis": "timestamp",
"granularity": "1 day",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert len(data["rows"]) == 4
def test_timeseries_string_column_error() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"graph_type": "timeseries",
"limit": 7,
"columns": ["timestamp", "event", "value", "user"],
"x_axis": "timestamp",
"granularity": "1 hour",
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 400
assert "Aggregate" in data["error"]
def test_derived_column_basic() -> None:
app = server.app
client = app.test_client()
payload = {
"start": "2024-01-01 00:00:00",
"end": "2024-01-03 00:00:00",
"limit": 10,
"columns": ["timestamp"],
"derived_columns": {"val2": "value * 2"},
"filters": [],
}
rv = client.post(
"/api/query", data=json.dumps(payload), content_type="application/json"
)
data = rv.get_json()
assert rv.status_code == 200
assert data["rows"][0][1] == 20
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll locate the section near test_timeseries_basic to add the new test.
s/test_server.py | head -n 20
351:def test_timeseries_basic() -> None:
357: "graph_type": "timeseries",
373:def test_timeseries_string_column_error() -> None:
379: "graph_type": "timeseries",
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll add the new test after test_timeseries_basic or string column error.
> *** Begin Patch
> *** Update File: tests/test_server.py
> @@
> def test_timeseries_basic() -> None:
> @@
> assert len(data["rows"]) == 4
> +
> +
> +def test_timeseries_orders_by_xaxis() -> None:
> + app = server.app
> + client = app.test_client()
> + payload = {
> + "start": "2024-01-01 00:00:00",
> + "end": "2024-01-03 00:00:00",
> + "graph_type": "timeseries",
> + "limit": 100,
> + "columns": ["value"],
> + "x_axis": "timestamp",
> + "granularity": "1 day",
> + }
> + rv = client.post(
> + "/api/query", data=json.dumps(payload), content_type="application/jso
n"
> + )
> + data = rv.get_json()
> + assert rv.status_code == 200
> + rows = data["rows"]
> + from dateutil import parser
> +
> + timestamps = [parser.parse(r[0]).replace(tzinfo=None) for r in rows]
> + assert timestamps == sorted(timestamps)
> *** End Patch
> PATCH
Success. Updated the following files:
M tests/test_server.py
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#Patch worked, but I need to verify if the final lines were printed correctly.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# grep -n "timeseries_orders_b
y_xaxis" -n tests/test_server.py
373:def test_timeseries_orders_by_xaxis() -> None:
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#Time to make some changes to server.build_query. I'll start by patching.
server.py
for f in params.filters:
op = f.op
if op in {"empty", "!empty"}:
val = "''"
else:
if f.value is None:
continue
if isinstance(f.value, list):
if not f.value:
continue
if op == "=":
vals = " OR ".join(f"{f.column} = '{v}'" for v in f.value)
where_parts.append(f"({vals})")
continue
val = f"'{f.value}'" if isinstance(f.value, str) else str(f.value)
if op == "contains":
where_parts.append(f"{f.column} ILIKE '%' || {val} || '%'")
elif op == "!contains":
where_parts.append(f"{f.column} NOT ILIKE '%' || {val} || '%'")
elif op == "empty":
where_parts.append(f"{f.column} = {val}")
elif op == "!empty":
where_parts.append(f"{f.column} != {val}")
else:
where_parts.append(f"{f.column} {op} {val}")
if where_parts:
query += " WHERE " + " AND ".join(where_parts)
if group_cols:
query += " GROUP BY " + ", ".join(group_cols)
if params.order_by:
query += f" ORDER BY {params.order_by} {params.order_dir}"
if params.limit is not None:
query += f" LIMIT {params.limit}"
return query
def create_app(db_file: str | Path | None = None) -> Flask:
app = Flask(__name__, static_folder="static")
db_path = Path(db_file or Path(__file__).with_name("sample.csv")).resolve()
con = _load_database(db_path)
column_types: Dict[str, str] = {
r[1]: r[2] for r in con.execute("PRAGMA table_info(events)").fetchall()
}
sample_cache: Dict[Tuple[str, str], Tuple[List[str], float]] = {}
CACHE_TTL = 60.0
CACHE_LIMIT = 200
@app.route("/")
def index() -> Any: # pyright: ignore[reportUnusedFunction]
assert app.static_folder is not None
return send_from_directory(app.static_folder, "index.html")
@app.route("/api/columns")
def columns() -> Any: # pyright: ignore[reportUnusedFunction]
rows = con.execute("PRAGMA table_info(events)").fetchall()
return jsonify([{"name": r[1], "type": r[2]} for r in rows])
def _cache_get(key: Tuple[str, str]) -> List[str] | None:
item = sample_cache.get(key)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll patch the code right after the "if group_cols" condition in the query-building logic.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# grep -n "GROUP BY" -n scubad
uck/server.py
219: query += " GROUP BY " + ", ".join(group_cols)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#The group_by clause is built around line 219, and it's about to be patched.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# sed -n '215,230p' scubaduck/
server.py
where_parts.append(f"{f.column} {op} {val}")
if where_parts:
query += " WHERE " + " AND ".join(where_parts)
if group_cols:
query += " GROUP BY " + ", ".join(group_cols)
if params.order_by:
query += f" ORDER BY {params.order_by} {params.order_dir}"
if params.limit is not None:
query += f" LIMIT {params.limit}"
return query
def create_app(db_file: str | Path | None = None) -> Flask:
app = Flask(__name__, static_folder="static")
db_path = Path(db_file or Path(__file__).with_name("sample.csv")).resolve()
con = _load_database(db_path)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll simplify the logic while keeping order direction as ascending by default for timeseries.
> *** Begin Patch
> *** Update File: scubaduck/server.py
> @@
> if group_cols:
> query += " GROUP BY " + ", ".join(group_cols)
> - if params.order_by:
> - query += f" ORDER BY {params.order_by} {params.order_dir}"
> + if params.order_by:
> + query += f" ORDER BY {params.order_by} {params.order_dir}"
> + elif params.graph_type == "timeseries":
> + query += " ORDER BY bucket"
> if params.limit is not None:
> query += f" LIMIT {params.limit}"
> return query
> *** End Patch
> PATCH
Success. Updated the following files:
M scubaduck/server.py
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll run ruff format to check the code formatting.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# ruff format scubaduck/server
.py tests/test_server.py
2 files left unchanged
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll perform a ruff check now to review the code.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# ruff check scubaduck tests
All checks passed!(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# pyright0 errors, 0 warnings, 0 informations(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# pytest -q
bringing up nodes................................................................. [100%]
62 passed in 11.19s
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll examine the git diff to verify changes after all tests pass.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# git status --short
M scubaduck/server.py
M tests/test_server.py
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll retrieve the surrounding snippet lines related to the patch.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# nl -ba scubaduck/server.py |
sed -n '208,232p'
208 elif op == "!contains":
209 where_parts.append(f"{f.column} NOT ILIKE '%' || {val} || '%
'")
210 elif op == "empty":
211 where_parts.append(f"{f.column} = {val}")
212 elif op == "!empty":
213 where_parts.append(f"{f.column} != {val}")
214 else:
215 where_parts.append(f"{f.column} {op} {val}")
216 if where_parts:
217 query += " WHERE " + " AND ".join(where_parts)
218 if group_cols:
219 query += " GROUP BY " + ", ".join(group_cols)
220 if params.order_by:
221 query += f" ORDER BY {params.order_by} {params.order_dir}"
222 elif params.graph_type == "timeseries":
223 query += " ORDER BY bucket"
224 if params.limit is not None:
225 query += f" LIMIT {params.limit}"
226 return query
227
228
229 def create_app(db_file: str | Path | None = None) -> Flask:
230 app = Flask(__name__, static_folder="static")
231 db_path = Path(db_file or Path(__file__).with_name("sample.csv")).re
solve()
232 con = _load_database(db_path)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I’ll include a snippet for testing functionality here.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# nl -ba tests/test_server.py
| sed -n '356,386p'
356 "end": "2024-01-03 00:00:00",
357 "graph_type": "timeseries",
358 "limit": 100,
359 "group_by": ["user"],
360 "aggregate": "Count",
361 "columns": ["value"],
362 "x_axis": "timestamp",
363 "granularity": "1 day",
364 }
365 rv = client.post(
366 "/api/query", data=json.dumps(payload), content_type="applicatio
n/json"
367 )
368 data = rv.get_json()
369 assert rv.status_code == 200
370 assert len(data["rows"]) == 4
371
372
373 def test_timeseries_orders_by_xaxis() -> None:
374 app = server.app
375 client = app.test_client()
376 payload = {
377 "start": "2024-01-01 00:00:00",
378 "end": "2024-01-03 00:00:00",
379 "graph_type": "timeseries",
380 "limit": 100,
381 "columns": ["value"],
382 "x_axis": "timestamp",
383 "granularity": "1 day",
384 }
385 rv = client.post(
386 "/api/query", data=json.dumps(payload), content_type="applicatio
n/json"(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# nl -ba tests/test_server.py
| sed -n '386,414p'
386 "/api/query", data=json.dumps(payload), content_type="applicatio
n/json"
387 )
388 data = rv.get_json()
389 assert rv.status_code == 200
390 rows = data["rows"]
391 from dateutil import parser
392
393 timestamps = [parser.parse(r[0]).replace(tzinfo=None) for r in rows]
394 assert timestamps == sorted(timestamps)
395
396
397 def test_timeseries_string_column_error() -> None:
398 app = server.app
399 client = app.test_client()
400 payload = {
401 "start": "2024-01-01 00:00:00",
402 "end": "2024-01-03 00:00:00",
403 "graph_type": "timeseries",
404 "limit": 7,
405 "columns": ["timestamp", "event", "value", "user"],
406 "x_axis": "timestamp",
407 "granularity": "1 hour",
408 }
409 rv = client.post(
410 "/api/query", data=json.dumps(payload), content_type="applicatio
n/json"
411 )
412 data = rv.get_json()
413 assert rv.status_code == 400
414 assert "Aggregate" in data["error"]
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#Everything looks good, time to commit the changes now!
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# git add scubaduck/server.py
tests/test_server.py(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# git commit -m "feat(server):
sort timeseries queries" && git status --short
[work 46fedc2] feat(server): sort timeseries queries
2 files changed, 26 insertions(+)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I realized the output got truncated, so let's rerun 'git status' to check.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# git status --short
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll check the commit diff to verify my changes.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# git show --stat
commit 46fedc2d4a2209b682d52b745accae768b51b0c9 (HEAD -> work)
Author: Codex <codex@openai.com>
Date: Wed May 21 17:45:44 2025 +0000
feat(server): sort timeseries queries
scubaduck/server.py | 2 ++
tests/test_server.py | 24 ++++++++++++++++++++++++
2 files changed, 26 insertions(+)
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#I'll prepare the PR message and include citations for the patch lines, starting with server.py.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# nl -ba scubaduck/server.py |
sed -n '216,225p'
216 if where_parts:
217 query += " WHERE " + " AND ".join(where_parts)
218 if group_cols:
219 query += " GROUP BY " + ", ".join(group_cols)
220 if params.order_by:
221 query += f" ORDER BY {params.order_by} {params.order_dir}"
222 elif params.graph_type == "timeseries":
223 query += " ORDER BY bucket"
224 if params.limit is not None:
225 query += f" LIMIT {params.limit}"
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#Lines 220-223 have new code. I’ll also grab the test file lines.
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck# nl -ba tests/test_server.py
| sed -n '368,395p'
368 data = rv.get_json()
369 assert rv.status_code == 200
370 assert len(data["rows"]) == 4
371
372
373 def test_timeseries_orders_by_xaxis() -> None:
374 app = server.app
375 client = app.test_client()
376 payload = {
377 "start": "2024-01-01 00:00:00",
378 "end": "2024-01-03 00:00:00",
379 "graph_type": "timeseries",
380 "limit": 100,
381 "columns": ["value"],
382 "x_axis": "timestamp",
383 "granularity": "1 day",
384 }
385 rv = client.post(
386 "/api/query", data=json.dumps(payload), content_type="applicatio
n/json"
387 )
388 data = rv.get_json()
389 assert rv.status_code == 200
390 rows = data["rows"]
391 from dateutil import parser
392
393 timestamps = [parser.parse(r[0]).replace(tzinfo=None) for r in rows]
394 assert timestamps == sorted(timestamps)
395
(scubaduck) root@cd76d0add7bd:/workspace/scubaduck#It's time to write the PR body, link the lines, and prepare the commit.