Multi-engine backends: pushdown DuckDB adapter, engine-neutral Arrow dataset, caching helpers #227
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| """Benchmark: DuckDB adapter v1 (stream) vs v2 (pushdown) vs ceilings. | ||
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Collaborator
There was a problem hiding this comment. Are these versions references to something documented in this branch, or to a claude code conversation context?
Author
There was a problem hiding this comment. This is from a claude code conversation context from a previous attempt indeed. I'll remove all references to v1 vs v2 |
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| Usage: .venv-duckdb/bin/python bench_v2.py | ||
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Collaborator
There was a problem hiding this comment. Should this benchmark be long-lived? Would it be better served to be converted to a performance test of some sort? |
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| """ | ||
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| import time | ||
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| import duckdb | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pyarrow.dataset as pads | ||
| import xarray as xr | ||
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| import xarray_sql as xql | ||
| from xarray_sql.backends.duckdb import XarrayArrowStream | ||
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| np.random.seed(0) | ||
| N_TIME, N_LAT, N_LON = 1000, 100, 100 # 10M rows | ||
| ds = xr.Dataset( | ||
| { | ||
| "temperature": ( | ||
| ["time", "lat", "lon"], | ||
| np.random.rand(N_TIME, N_LAT, N_LON), | ||
| ), | ||
| "humidity": ( | ||
| ["time", "lat", "lon"], | ||
| np.random.rand(N_TIME, N_LAT, N_LON), | ||
| ), | ||
| }, | ||
| coords={ | ||
| "time": pd.date_range("2020-01-01", periods=N_TIME, freq="h"), | ||
| "lat": np.linspace(-90, 90, N_LAT), | ||
| "lon": np.linspace(-180, 180, N_LON), | ||
| }, | ||
| ).chunk({"time": 50}) # 20 partitions | ||
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| con = duckdb.connect() | ||
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| QUERIES = { | ||
| "full AVG scan": "SELECT AVG(temperature) FROM {t}", | ||
| "1pct time filter": ( | ||
| "SELECT AVG(temperature) FROM {t} WHERE time < '2020-01-01 10:00:00'" | ||
| ), | ||
| "bbox filter": ( | ||
| "SELECT AVG(temperature) FROM {t} " | ||
| "WHERE lat BETWEEN 0 AND 10 AND lon BETWEEN 0 AND 20" | ||
| ), | ||
| "projection (1 of 2 vars)": "SELECT AVG(humidity) FROM {t}", | ||
| "count only": "SELECT COUNT(*) FROM {t}", | ||
| } | ||
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| def bench(table, label, n=3): | ||
| print(f"\n== {label} ==") | ||
| for qname, q in QUERIES.items(): | ||
| sql = q.format(t=table) | ||
| times = [] | ||
| for _ in range(n): | ||
| t0 = time.perf_counter() | ||
| r = con.sql(sql).fetchall() | ||
| times.append(time.perf_counter() - t0) | ||
| print(f" {qname:28s} {min(times):8.3f}s -> {r[0][0]:.6g}") | ||
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| # v1: re-scannable stream (registered via the stream wrapper explicitly) | ||
| con.register("t_v1", XarrayArrowStream(ds)) | ||
| bench("t_v1", "v1 stream (no pushdown)") | ||
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| # v2: default register() — pushdown path (once implemented) | ||
| xql.register(con, "t_v2", ds) | ||
| bench("t_v2", "v2 register() [pushdown]") | ||
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| # ceiling: materialized pa.Table via pyarrow.dataset | ||
| table = xql.read_xarray(ds).read_all() | ||
| con.register("t_ceiling", pads.dataset(table)) | ||
| bench("t_ceiling", "ceiling: in-memory pyarrow.dataset") | ||
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| # reference: DataFusion engine on the same dataset | ||
| ctx = xql.XarrayContext() | ||
| xql.register(ctx, "t_df", ds) | ||
| print("\n== DataFusion reference ==") | ||
| for qname, q in QUERIES.items(): | ||
| sql = q.format(t="t_df") | ||
| times = [] | ||
| for _ in range(3): | ||
| t0 = time.perf_counter() | ||
| r = ctx.sql(sql).to_pandas() | ||
| times.append(time.perf_counter() - t0) | ||
| print(f" {qname:28s} {min(times):8.3f}s -> {float(r.iloc[0, 0]):.6g}") | ||
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