issue_comments: 1164064469

Thanks for the tips, I was investigating inline_array=True and still no luck. The graph seems OK though. I can attach it if you want but I think zarr is not the culprit.

Here is why :

In the first case, where we build the array from scratch, the ones array is simple. Dask seems to understand that it does not have to make many copies of it. So when replacing ones with random data, we observe the same behavior as opening the dataset from a ZARR store (high memory usage on a worker) :

```python import dask.array as da import numpy as np import xarray as xr

ds = xr.Dataset( dict( anom_u=(["time", "face", "j", "i"], da.random.random((10311, 1, 987, 1920), chunks=(10, 1, 987, 1920))), anom_v=(["time", "face", "j", "i"], da.random.random((10311, 1, 987, 1920), chunks=(10, 1, 987, 1920))), ) )

ds["anom_uu_mean"] = (["face", "j", "i"], np.mean(ds.anom_u.data2, axis=0)) ds["anom_vv_mean"] = (["face", "j", "i"], np.mean(ds.anom_v.data2, axis=0)) ds["anom_uv_mean"] = (["face", "j", "i"], np.mean(ds.anom_u.data * ds.anom_v.data, axis=0))

ds[["anom_uu_mean", "anom_vv_mean", "anom_uv_mean"]].compute() ```

I think the question now is why Dask must load so many data when doing my operation :

If we take the computation graph (I've put the non optimized version), my understanding is that we could do the following : - Load the first chunk of anom_u - Load the second chunk of anom_v - Do the multiplication anom_uanom_v, anom_u*, anom_v ** 2 - Do the mean-chunk task - Unload all the previous tasks - Redo the same and combine the mean-chunks tasks

For information, one chunk is about 1.4G, so I expect see peaks of 5*1.4 = 7G in memory (plus what's needed to store the mean_chunk), but I instead got 15G+ in my worker, most of it taken by the random-samples

Is my understanding of distributed mean wrong ? Why are the random-sample not flushed ?