Add example for xarray groupby reduction causing memory pressure

AB_environments/AB_baseline.conda.yaml

@@ -28,6 +28,7 @@ dependencies:
   - ipycytoscape ==1.3.3
   - click ==8.1.7
   - xarray ==2024.07.0
+  - flox ==0.9.9
   - zarr ==2.18.2
   - cftime ==1.6.4
   - msgpack-python

AB_environments/AB_sample.conda.yaml

@@ -34,6 +34,7 @@ dependencies:
   - ipycytoscape ==1.3.3
   - click ==8.1.7
   - xarray ==2024.07.0
+  - flox ==0.9.9
   - zarr ==2.18.2
   - cftime ==1.6.4
   - msgpack-python

ci/environment.yml

@@ -30,6 +30,7 @@ dependencies:
   - ipycytoscape ==1.3.3
   - click ==8.1.7
   - xarray ==2024.07.0
+  - flox ==0.9.9
   - zarr ==2.18.2
   - cftime ==1.6.4
   - msgpack-python

cluster_kwargs.yaml

@@ -33,6 +33,13 @@ spill_cluster:
   worker_disk_size: 64
   worker_vm_types: [m6i.large]  # 2CPU, 8GiB
 
+# For tests/benchmarks/test_xarray.py
+group_reduction_cluster:
+  n_workers: 20
+  worker_vm_types: [m6i.xlarge]  # 4CPU, 16GiB
+  spot_policy: spot_with_fallback
+  region: "us-east-1"  # Same region as dataset
+
 # For tests/workflows/test_embarrassingly_parallel.py
 embarrassingly_parallel:
   n_workers: 100

tests/benchmarks/test_array.py

@@ -37,10 +37,10 @@ def test_anom_mean(small_client, new_array):
         dims=["time", "x"],
         coords={"day": ("time", np.arange(data.shape[0]) % ngroups)},
     )
-
-    clim = arr.groupby("day").mean(dim="time")
-    anom = arr.groupby("day") - clim
-    anom_mean = anom.mean(dim="time")
+    with xarray.set_options(use_flox=False):
+        clim = arr.groupby("day").mean(dim="time")
+        anom = arr.groupby("day") - clim
+        anom_mean = anom.mean(dim="time")
 
     wait(anom_mean, small_client, 10 * 60)
 
@@ -136,7 +136,8 @@ def test_climatic_mean(small_client, new_array):
         coords={"init_date": np.arange(data.shape[1]) % 10},
     )
     # arr_clim = array.groupby("init_date.month").mean(dim="init_date")
-    arr_clim = array.groupby("init_date").mean(dim="init_date")
+    with xarray.set_options(use_flox=False):
+        arr_clim = array.groupby("init_date").mean(dim="init_date")
 
     wait(arr_clim, small_client, 15 * 60)
 

tests/benchmarks/test_xarray.py

@@ -0,0 +1,72 @@
+import uuid
+
+import fsspec
+import pytest
+from coiled import Cluster
+from distributed import Client
+from xarray.groupers import TimeResampler
+
+from tests.conftest import dump_cluster_kwargs
+from tests.utils_test import wait
+
+
+@pytest.fixture(scope="module")
+def group_reduction_cluster(dask_env_variables, cluster_kwargs, github_cluster_tags):
+    kwargs = dict(
+        name=f"xarray-group-reduction-{uuid.uuid4().hex[:8]}",
+        environ=dask_env_variables,
+        tags=github_cluster_tags,
+        **cluster_kwargs["group_reduction_cluster"],
+    )
+    dump_cluster_kwargs(kwargs, "group_reduction_cluster")
+    with Cluster(**kwargs) as cluster:
+        yield cluster
+
+
+@pytest.fixture
+def group_reduction_client(
+    group_reduction_cluster, cluster_kwargs, upload_cluster_dump, benchmark_all
+):
+    n_workers = cluster_kwargs["group_reduction_cluster"]["n_workers"]
+    with Client(group_reduction_cluster) as client:
+        group_reduction_cluster.scale(n_workers)
+        client.wait_for_workers(n_workers, timeout=600)
+        client.restart()
+        with upload_cluster_dump(client), benchmark_all(client):
+            yield client
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        pytest.param(
+            lambda x: x.groupby("time.month").mean(method="cohorts"), id="cohorts"
+        ),
+        pytest.param(
+            lambda x: x.groupby("time.month").mean(method="map-reduce"), id="map-reduce"
+        ),
+        pytest.param(
+            lambda x: x.chunk(time=TimeResampler("ME"))
+            .groupby("time.month")
+            .mean(method="cohorts"),
+            id="chunked-cohorts",
+        ),
+    ],
+)
+def test_xarray_groupby_reduction(group_reduction_client, func):
+    pytest.importorskip("xarray")
+    pytest.importorskip("flox")
+
+    import xarray as xr
+
+    ds = xr.open_zarr(
+        fsspec.get_mapper(
+            "s3://noaa-nwm-retrospective-2-1-zarr-pds/rtout.zarr", anon=True
+        ),
+        consolidated=True,
+    )
+    # slice dataset properly to keep runtime in check
+    subset = ds.zwattablrt.sel(time=slice("2001", "2002"))
+    subset = subset.isel(x=slice(0, 350 * 8), y=slice(0, 350 * 8))
+    result = func(subset)
+    wait(result, group_reduction_client, 10 * 60)