Best practice to store and load data-columns of equal-length from GCS (data not on a regular grid)

ofk123 · July 28, 2023, 1:19pm

Hi!

I thought to ask on here, if am I missing out on even faster ways to load data.
From several zarr-stores that each store 5 data-columns of equal length, I want to load 4 of them into dask-worker-memory (columnlength ~2e6, and size of each columns is 17MB) and use them in embarrasingly parallel computations. The zarr-store with data and metadata is saved to GCS, and I start a cluster of workers using the GC-jupyter-hub-deployment, in the same way illustrated in this notebook.

So far I load data in ~1 sec using either xr.open_zarr(mapper, consolidated=True, chunks='auto') or zarr.open_consolidated(mapper) as shown further down in the notebook. In other words, very approximately 17MB*4=68MB/sec.

Can I do better in the way I create the dataset and the zarr store?
When I want to load these 4 columns, what would a more optimal, or conventional, loading-function look like?

Would be happy to try out any advice.

Edit: From reading answers in a similar looking topic (but with different dataset-structure) I guess I could try putting the 4 columns in 1 variable instead of 4. But perhaps there exist a way around that?

Best, Ola

github.com

ofk123/dataloading-example/blob/main/pangeo_loading_data_into_workermemory.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from numpy import pi, sin, cos, arccos, clip, deg2rad\n",
    "import numpy.ma as ma\n",
    "from datetime import datetime\n",
    "import dask\n",
    "import time\n",
    "import zarr"
   ]
  },
  {
   "cell_type": "code",

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ofk123 · August 28, 2023, 12:39am

I client.scatter data to workers beforehand instead.

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Best practice to store and load data-columns of equal-length from GCS (data not on a regular grid)

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