Create simple storage writer

components/write_to_file/Dockerfile

@@ -0,0 +1,28 @@
+FROM --platform=linux/amd64 python:3.10-slim as base
+
+# System dependencies
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install git -y
+
+# Install requirements
+COPY requirements.txt /
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# Install Fondant
+# This is split from other requirements to leverage caching
+ARG FONDANT_VERSION=main
+RUN pip3 install fondant[component,aws,azure,gcp]@git+https://github.com/ml6team/fondant@${FONDANT_VERSION}
+
+# Set the working directory to the component folder
+WORKDIR /component
+COPY src/ src/
+
+FROM base as test
+COPY tests/ tests/
+RUN pip3 install --no-cache-dir -r tests/requirements.txt
+RUN python -m pytest tests
+
+FROM base
+WORKDIR /component/src
+ENTRYPOINT ["fondant", "execute", "main"]

components/write_to_file/README.md

@@ -0,0 +1,75 @@
+# Write to file
+
+<a id="write_to_file#description"></a>
+## Description
+A Fondant component to write a dataset to file on a local machine or to a cloud storage bucket. The dataset can be written as csv or parquet.
+
+<a id="write_to_file#inputs_outputs"></a>
+## Inputs / outputs 
+
+<a id="write_to_file#consumes"></a>
+### Consumes 
+
+**This component can consume additional fields**
+- <field_name>: <dataset_field_name>
+This defines a mapping to update the fields consumed by the operation as defined in the component spec.
+The keys are the names of the fields to be received by the component, while the values are 
+the name of the field to map from the input dataset
+
+See the usage example below on how to define a field name for additional fields.
+
+
+
+
+<a id="write_to_file#produces"></a>  
+### Produces 
+
+
+**This component does not produce data.**
+
+<a id="write_to_file#arguments"></a>
+## Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| path | str | Path to store the dataset, whether it's a local path or a cloud storage bucket,  must be specified. A separate filename will be generated for each partition. If you are using the local runner and export the data to a local directory,  ensure that you mount the path to the directory using the `--extra-volumes` argument. | / |
+| format | str | Format for storing the dataframe can be either `csv` or `parquet`. As default  `parquet` is used. The CSV files contain the column as a header and use a comma as a delimiter. | parquet |
+
+<a id="write_to_file#usage"></a>
+## Usage 
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import Pipeline
+
+
+pipeline = Pipeline(...)
+
+dataset = pipeline.read(...)
+
+dataset = dataset.apply(...)
+
+dataset.write(
+    "write_to_file",
+    arguments={
+        # Add arguments
+        # "path": ,
+        # "format": "parquet",
+    },
+    consumes={
+         <field_name>: <dataset_field_name>,
+         ..., # Add fields
+     },
+)
+```
+
+<a id="write_to_file#testing"></a>
+## Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```

components/write_to_file/fondant_component.yaml

@@ -0,0 +1,26 @@
+name: Write to file
+description: >-
+  A Fondant component to write a dataset to file on a local machine or to a cloud storage bucket.
+  The dataset can be written as csv or parquet.
+image: 'fndnt/write_to_file:dev'
+tags:
+  - Data writing
+
+consumes:
+  additionalProperties: true
+
+args:
+  path:
+    description: >- 
+      Path to store the dataset, whether it's a local path or a cloud storage bucket, 
+      must be specified. A separate filename will be generated for each partition.
+      If you are using the local runner and export the data to a local directory, 
+      ensure that you mount the path to the directory using the `--extra-volumes` argument.
+    type: str
+  format:
+    description: >- 
+      Format for storing the dataframe can be either `csv` or `parquet`. As default 
+      `parquet` is used.
+      The CSV files contain the column as a header and use a comma as a delimiter.
+    type: str
+    default: parquet

components/write_to_file/src/main.py

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+import dask.dataframe as dd
+from fondant.component import DaskWriteComponent
+
+
+class WriteToFile(DaskWriteComponent):
+    def __init__(self, *, path: str, format: str):
+        """Initialize the write to file component."""
+        self.path = path
+        self.format = format
+
+    def write(self, dataframe: dd.DataFrame) -> None:
+        """
+        Writes the data from the given Dask DataFrame to a file either locally or
+        to a remote storage bucket.
+
+        Args:
+            dataframe (dd.DataFrame): The Dask DataFrame containing the data to be written.
+        """
+        if self.format.lower() == "csv":
+            self.path = self.path + "/export-*.csv"
+            dataframe.to_csv(self.path)
+        elif self.format.lower() == "parquet":
+            dataframe.to_parquet(self.path)
+        else:
+            msg = (
+                f"Not supported file format {self.format}. Writing to file is only "
+                f"supported for `csv` and `parquet`."
+            )
+            raise ValueError(msg)

components/write_to_file/tests/pytest.ini

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+[pytest]
+pythonpath = ../src

components/write_to_file/tests/requirements.txt

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+pytest==7.4.2

components/write_to_file/tests/write_to_file_test.py

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+import tempfile
+
+import dask.dataframe as dd
+import pandas as pd
+
+from src.main import WriteToFile
+
+
+def test_write_to_csv():
+    """Test case for write to file component."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        entries = 10
+
+        dask_dataframe = dd.DataFrame.from_dict(
+            {
+                "text": [
+                    "Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo",
+                ]
+                * entries,
+            },
+            npartitions=1,
+        )
+
+        component = WriteToFile(
+            path=tmpdir,
+            format="csv",
+        )
+
+        component.write(dask_dataframe)
+
+        df = pd.read_csv(tmpdir + "/export-0.csv")
+        assert len(df) == entries
+
+
+def test_write_to_parquet():
+    """Test case for write to file component."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        entries = 10
+
+        dask_dataframe = dd.DataFrame.from_dict(
+            {
+                "text": [
+                    "Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo",
+                ]
+                * entries,
+            },
+            npartitions=1,
+        )
+
+        component = WriteToFile(
+            path=tmpdir,
+            format="parquet",
+        )
+
+        component.write(dask_dataframe)
+
+        ddf = dd.read_parquet(tmpdir)
+        assert len(ddf) == entries

examples/sample_pipeline/README.md

@@ -7,7 +7,10 @@ returns the received dataframe.
 The pipeline can be executed with the Fondant cli:
 
 ```bash
-fondant run local pipeline.py
+fondant run local pipeline.py --extra-volumes ./data:/data
 ```
 
+> We are mounting the data folder using `--extra-volumes` to ensure that the component 
+> can load the sample data and write to the folder.
+
 The automated integration test will use the `run.sh` script. 

examples/sample_pipeline/pipeline.py

@@ -57,8 +57,10 @@ def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
         return dataframe
 
 
-_ = dataset.apply(
+dataset = dataset.apply(
     ref=CalculateChunkLength,
     produces={"chunk_length": pa.int32()},
     arguments={"arg_x": "value_x"},
 )
+
+dataset.write(ref="write_to_file", arguments={"path": "/data/export"})