Query Xarray with SQL
pip install xarray-sqlThis is an experiment to provide a SQL interface for raster data.
import xarray as xr
import xarray_sql as qr
ds = xr.tutorial.open_dataset('air_temperature')
# The same as a dask-sql Context; i.e. an Apache DataFusion Context.
c = qr.Context(ds)
c.create_table('air', ds, chunks=dict(time=24))
df = c.sql('''
SELECT
"lat", "lon", AVG("air") as air_total
FROM
"air"
GROUP BY
"lat", "lon"
''')
# A table of the average temperature for each location across time.
df.compute()
# Alternatively, you can just create the DataFrame from the Dataset:
df = qr.read_xarray(ds)
df.head()Succinctly, we "pivot" Xarray Datasets to treat them like tables so we can run SQL queries against them.
A few reasons:
- Even though SQL is the lingua franca of data, scientific datasets are often inaccessible to non-scientists (SQL users).
- Joining tabular data with raster data is common yet difficult. It could be easy.
- There are many cloud-native, Xarray-openable datasets, from Google Earth Engine to Pangeo Forge. Wouldn’t it be great if these were also SQL-accessible? How can the bridge be built with minimal effort?
This is a light-weight way to prove the value of the interface.
The larger goal is to explore the hypothesis that Pangeo is a scientific database. Here, xarray-sql can be thought of as a missing DB front end.
All chunks in an Xarray Dataset are transformed into a Dask DataFrame via
from_map() and to_dataframe(). For SQL support, we just use dask-sql.
That's it!
Underneath Xarray, Dask, and Pandas, there are NumPy arrays. These are paged in
chunks and represented contiguously in memory. It is only a matter of metadata
that breaks them up into ndarrays. to_dataframe()
just changes this metadata (via a ravel()/reshape()), back into a column
amenable to a DataFrame.
There is added overhead from duplicating dimensions as columns, which we see as worth the convenience of DataFrames.
Dask doesn't support
MultiIndexs (dask/dask#1493). If
it did, I suspect performance for many types of queries would greatly improve.
Further, while this does play well with dask-geopandas (for geospatial query
support), certain types of operations don't quite match standard geopandas.
Spatial joins come to mind as a killer feature, but only inner joins are
supported (geopandas/dask-geopandas#72)
.
I have a few ideas so far. One approach involves applying operations directly on
Xarray Datasets. This approach is being pursued
here, as xql.
Deeper still: I was thinking we could make a virtual filesystem for parquet that would internally map to Zarr. Raster-backed virtual parquet would open up integrations to numerous tools like dask, pyarrow, duckdb, and BigQuery. More thoughts on this in #4.
I want to give a special thanks to the following folks and institutions:
- Pramod Gupta and the Anthromet Team at Google Research for the problem formation and design inspiration.
- Jake Wall and AI2/Ecoscope for compute resources and key use cases.
- Charles Stern, Stephan Hoyer, Alexander Kmoch, Wei Ji, and Qiusheng Wu for the early review and discussion of this project.
Copyright 2024 Alexander Merose
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