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to_dataset keeps the last row per dim tuple instead of raising on duplicates

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Bewertung

Schwierigkeit
3/5
Geschätzter Aufwand
1-2 Tage
Anfängerfreundlichkeit
58/100
Issue-Typ
Bug
Klarheit
Klar beschrieben
Aktivitätsstatus
Ruhig
Tech-Stack
python, sql
Bereich
data

Rechercherichtung

Start at to_dataset in the result class and read its docstring's Raises section, which promises a ValueError on duplicate dim tuples. Run the repro in the issue (3 time steps, lat/lon grid, SELECT without time) to confirm 12 rows collapse to 4. Done when that case raises ValueError naming the duplicate dims, and a test covers it. The issue leaves open whether the check should always run or only when dims were inferred, so confirm that with maintainers first.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Beschreibung

When a query projects a dimension away, the rows that dimension separated collapse onto the same dim tuple. to_dataset builds a grid from them anyway, keeping whichever row comes last, and returns it without a warning. The docstring says it raises instead:

Raises:
    ValueError: ``dims`` cannot be inferred, names a missing
        column, or the result has duplicate dim tuples;

Repro on 0.3.3 and 0.4.0rc1:

import numpy as np
import pandas as pd
import xarray as xr

from xarray_sql import XarrayContext

temp = np.arange(12.0).reshape(3, 2, 2)
ds = xr.Dataset(
    {"temperature": (["time", "lat", "lon"], temp)},
    coords={
        "time": pd.date_range("2020-01-01", periods=3),
        "lat": [10.0, 20.0],
        "lon": [100.0, 110.0],
    },
)

ctx = XarrayContext()
ctx.from_dataset("temperature", ds, chunks={"time": 3, "lat": 2, "lon": 2})
result = ctx.sql("SELECT lat, lon, temperature FROM temperature")

print(len(result.to_pandas()))  # 12 rows, 4 unique (lat, lon)
print(result.to_dataset(dims=["lat", "lon"])["temperature"].values)

12
[[ 8.  9.]
 [10. 11.]]

That is temp[-1], the last time step. Two thirds of the input is gone and nothing says so. Inferring the dims (dims=None) gives the same result.

The value is also arbitrary rather than merely lossy: it depends on the order rows happen to arrive in, so the same query can return different numbers across partitionings.

Raising the documented ValueError seems right to me, since the alternative is picking an aggregation the caller did not ask for. Detecting it costs a scan, so making it conditional (or checking only when dims was inferred) may be worth it.

I hit this from Lumen, where a projecting SQL transform drops a dim before the source grids the result. Happy to send a PR if you want it fixed this way.

Vorherrschende Sprache
Python
Sterne
142
Forks
19
Ø Merge
1 T. 9 Std.
Gemergte PRs (30 T.)
9

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  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

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