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Perf: update open_era5.py to use read_xarray_table with filter pushdown and lazy loading

Abierto
  • ¿Algo no está bien?
#139 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Nadie ha tomado este issue todavía.

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
52/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
google-cloud, python
Área
data-engineering, performance

Línea de trabajo

Start with perf_tests/open_era5.py and compare its existing read_xarray call with the proposed read_xarray_table flow. Add measurements for registration, a time filtered query, and a full scan, then run the benchmark with GCP authentication available and check that the output reports timings for each workload.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Context

perf_tests/open_era5.py currently uses the older read_xarray API, which returns an eager pa.RecordBatchReader. It also only prints the schema, making it impossible to benchmark real query performance.

Current state

# open_era5.py
era5_ds = xr.open_zarr("gs://gcp-public-data-arco-era5/...", chunks={"time": 240, "level": 1})
era5_wind_df = xql.read_xarray(
    era5_ds[["u_component_of_wind", "v_component_of_wind"]]
)
print(era5_wind_df.schema)

Issues:

  1. Uses read_xarray instead of read_xarray_table — loses filter pushdown and lazy partition loading
  2. Only inspects schema, never runs a query — no way to measure actual query performance
  3. Does not demonstrate the partition pruning benefit for time-range queries

Proposed update

# open_era5.py
import time
from datafusion import SessionContext
import xarray as xr
import xarray_sql as xql

era5_ds = xr.open_zarr(
    "gs://gcp-public-data-arco-era5/ar/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2",
    chunks={"time": 240, "level": 1},
)
wind_ds = era5_ds[["u_component_of_wind", "v_component_of_wind"]]

# read_xarray_table: lazy, supports filter pushdown
table = xql.read_xarray_table(wind_ds, chunks={"time": 240})

ctx = SessionContext()
ctx.register_table("era5_wind", table)

# Filter pushdown: should prune most partitions
t0 = time.perf_counter()
result = ctx.sql(
    "SELECT AVG(u_component_of_wind), AVG(v_component_of_wind) "
    "FROM era5_wind WHERE time > '2020-01-01'"
).to_arrow_table()
print(f"Filter query: {time.perf_counter() - t0:.2f}s  {result}")

Goals

  • Serve as a real-world benchmark for ERA5 (the primary large-scale use case)
  • Show filter pushdown pruning thousands of partitions
  • Compare registration time, filter query time, and full-scan time
  • Can be run with py-spy to generate flame graphs once GCP auth is available
Lenguaje dominante
Python
Estrellas
142
Forks
19
Merge medio
1 d 9 h
PR fusionados (30 d)
9

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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