Add example to create a virtual dataset using lithops

examples/virtualizarr-with-lithops/Dockerfile_virtualizarr

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+# Python 3.11
+FROM python:3.11-slim-buster
+
+
+RUN apt-get update \
+    # Install aws-lambda-cpp build dependencies
+    && apt-get install -y \
+      g++ \
+      make \
+      cmake \
+      unzip \
+    # cleanup package lists, they are not used anymore in this image
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-cache search linux-headers-generic
+
+ARG FUNCTION_DIR="/function"
+
+# Copy function code
+RUN mkdir -p ${FUNCTION_DIR}
+
+# Update pip
+# NB botocore/boto3 are pinned due to https://github.com/boto/boto3/issues/3648
+#    using versions from https://github.com/aio-libs/aiobotocore/blob/72b8dd5d7d4ef2f1a49a0ae0c37b47e5280e2070/setup.py
+#    due to s3fs dependency
+RUN pip install --upgrade --ignore-installed pip wheel six setuptools \
+    && pip install --upgrade --no-cache-dir --ignore-installed \
+        awslambdaric \
+        botocore==1.29.76 \
+        boto3==1.26.76 \
+        redis \
+        httplib2 \
+        requests \
+        numpy \
+        scipy \
+        pandas \
+        pika \
+        kafka-python \
+        cloudpickle \
+        ps-mem \
+        tblib
+
+# Set working directory to function root directory
+WORKDIR ${FUNCTION_DIR}
+
+# Add Lithops
+COPY lithops_lambda.zip ${FUNCTION_DIR}
+RUN unzip lithops_lambda.zip \
+    && rm lithops_lambda.zip \
+    && mkdir handler \
+    && touch handler/__init__.py \
+    && mv entry_point.py handler/
+
+# Put your dependencies here, using RUN pip install... or RUN apt install...
+
+COPY requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+ENTRYPOINT [ "/usr/local/bin/python", "-m", "awslambdaric" ]
+CMD [ "handler.entry_point.lambda_handler" ]

examples/virtualizarr-with-lithops/README.md

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+# Generate a virtual zarr dataset using lithops
+
+This example walks through how to create a virtual dataset from a collection of
+netCDF files on s3 using lithops to open each file in parallel then concatenate
+them into a single virtual dataset.
+
+## Credits
+Inspired by Pythia's cookbook: https://projectpythia.org/kerchunk-cookbook
+by norlandrhagen.
+
+Please, contribute improvements.
+
+
+
+1. Set up a Python environment
+```bash
+conda create --name virtualizarr-lithops -y python=3.11
+conda activate virtualizarr-lithops
+pip install -r requirements.txt
+```
+
+2. Configure compute and storage backends for [lithops](https://lithops-cloud.github.io/docs/source/configuration.html).
+The configuration in `lithops.yaml` uses AWS Lambda for [compute](https://lithops-cloud.github.io/docs/source/compute_config/aws_lambda.html) and AWS S3 for [storage](https://lithops-cloud.github.io/docs/source/storage_config/aws_s3.html).
+To use those backends, simply edit `lithops.yaml` with your `bucket` and `execution_role`.
+
+1. Build a runtime image for Cubed
+```bash
+export LITHOPS_CONFIG_FILE=$(pwd)/lithops.yaml
+lithops runtime build -b aws_lambda -f Dockerfile_virtualizarr virtualizarr-runtime
+```
+
+1. Run the script
+```bash
+python virtualizarr-with-lithops.py
+```
+
+## Cleaning up
+To rebuild the Lithops image, delete the existing one by running
+```bash
+lithops runtime delete -b aws_lambda -d virtualizarr-runtime
+```

examples/virtualizarr-with-lithops/lithops.yaml

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+lithops:
+    backend: aws_lambda
+    storage: aws_s3
+
+aws:
+    region: us-west-2
+
+aws_lambda:
+    execution_role: arn:aws:iam::807615458658:role/lambdaLithopsExecutionRole
+    runtime: virtualizarr-runtime
+    runtime_memory: 2000
+
+aws_s3:
+    bucket: arn:aws:s3:::cubed-thodson-temp

examples/virtualizarr-with-lithops/requirements.txt

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+boto
+cftime
+h5py
+kerchunk
+lithops
+s3fs
+virtualizarr
+xarray

examples/virtualizarr-with-lithops/virtualizarr-with-lithops.py

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+# Use lithops to create a virtual dataset from a collection of necdf files on s3.
+#
+# Inspired by Pythia's cookbook: https://projectpythia.org/kerchunk-cookbook
+# by norlandrhagen.
+#
+# Please, contribute improvements.
+
+import fsspec
+import lithops
+import xarray as xr
+
+from virtualizarr import open_virtual_dataset
+
+# to demonstrate this workflow, we will use a collection of netcdf files from the WRF-SE-AK-AR5 project.
+fs_read = fsspec.filesystem("s3", anon=True, skip_instance_cache=True)
+files_paths = fs_read.glob("s3://wrf-se-ak-ar5/ccsm/rcp85/daily/2060/*")
+file_pattern = sorted(["s3://" + f for f in files_paths])
+
+# optionally, truncate file_pattern while debugging
+# file_pattern = file_pattern[:4]
+
+print(f"{len(file_pattern)} file paths were retrieved.")
+
+
+def map_references(fil):
+    """Map function to open virtual datasets."""
+    vds = open_virtual_dataset(
+        fil,
+        indexes={},
+        loadable_variables=["Time"],
+        cftime_variables=["Time"],
+    )
+    return vds
+
+
+def reduce_references(results):
+    """Reduce to concat virtual datasets."""
+    combined_vds = xr.combine_nested(
+        results,
+        concat_dim=["Time"],
+        coords="minimal",
+        compat="override",
+    )
+    return combined_vds
+
+
+fexec = lithops.FunctionExecutor(config_file="lithops.yaml")
+
+futures = fexec.map_reduce(
+    map_references,
+    file_pattern,
+    reduce_references,
+    spawn_reducer=100,
+)
+
+ds = futures.get_result()
+
+# write out the virtual dataset to a kerchunk json
+ds.virtualize.to_kerchunk("combined.json", format="json")