Add a simple test suite for doing downstream tests

.github/workflows/downstream.yml

@@ -0,0 +1,45 @@
+name: CI
+
+on:
+  push:
+    branches: master
+  pull_request:
+    branches: master
+
+jobs:
+  checks:
+    name: Downstream tests (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ["ubuntu-latest", "macos-latest", "windows-latest"]
+        python-version: ["3.7", "3.8"]
+        pyarrow-version: [">=1,<2", ">=2,<3"]
+
+    - uses: conda-incubator/setup-miniconda@v1
+      with:
+        auto-update-conda: true
+        python-version: ${{ matrix.python-version }}
+        mamba-version: "*"
+
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Install conda dependencies
+      run: >
+        mamba create -n test-downstreams
+          -c conda-forge
+          'python=${{ matrix.python-version }}'
+          'pyarrow=${{ matrix.pyarrow-version }}'
+          pip botocore aiobotocore moto pytest fastparquet
+
+    - name: Install portions from source
+      run: |
+        conda activate test-downstreams
+        pip install git+https://github.com/intake/filesystem_spec/ --no-deps
+
+    - name: Run test suite
+      run: |
+        conda activate test-downstreams
+        pytest -vrsx downstream_tests

downstream_tests/test_dask_dataframe.py

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+import pytest
+from pytest import fixture
+import pandas as pd
+import numpy as np
+import time
+import pyarrow as pa
+import dask.dataframe as dd
+import s3fs
+import moto.server
+import sys
+
+
+@fixture(scope="session")
+def partitioned_dataset() -> dict:
+    rows = 500000
+    cds_df = pd.DataFrame(
+        {
+            "id": range(rows),
+            "part_key": np.random.choice(["A", "B", "C", "D"], rows),
+            "timestamp": np.random.randint(1051638817, 1551638817, rows),
+            "int_value": np.random.randint(0, 60000, rows),
+        }
+    )
+    return {"dataframe": cds_df, "partitioning_column": "part_key"}
+
+
+def free_port():
+    import socketserver
+
+    with socketserver.TCPServer(("localhost", 0), None) as s:
+        free_port = s.server_address[1]
+        return free_port
+
+
+@fixture(scope="session")
+def moto_server():
+    import subprocess
+
+    port = free_port()
+    process = subprocess.Popen([
+        sys.executable,
+        moto.server.__file__,
+        '--port', str(port),
+        '--host', 'localhost',
+        's3'
+    ])
+
+    s3fs_kwargs = dict(
+        client_kwargs={"endpoint_url": f'http://localhost:{port}'},
+    )
+
+    start = time.time()
+    while True:
+        try:
+            fs = s3fs.S3FileSystem(skip_instance_cache=True, **s3fs_kwargs)
+            fs.ls("/")
+        except:
+            if time.time() - start > 30:
+                raise TimeoutError("Could not get a working moto server in time")
+        time.sleep(0.1)
+
+        break
+
+    yield s3fs_kwargs
+
+    process.terminate()
+
+
+@fixture(scope="session")
+def moto_s3fs(moto_server):
+    return s3fs.S3FileSystem(**moto_server)
+
+
+@fixture(scope="session")
+def s3_bucket(moto_server):
+    test_bucket_name = 'test'
+    from botocore.session import Session
+    # NB: we use the sync botocore client for setup
+    session = Session()
+    client = session.create_client('s3', **moto_server['client_kwargs'])
+    client.create_bucket(Bucket=test_bucket_name, ACL='public-read')
+    return test_bucket_name
+
+
+@fixture(scope="session")
+def partitioned_parquet_path(partitioned_dataset, moto_s3fs, s3_bucket):
+    cds_df = partitioned_dataset["dataframe"]
+    table = pa.Table.from_pandas(cds_df, preserve_index=False)
+    path = s3_bucket + "/partitioned/dataset"
+    import pyarrow.parquet
+
+    pyarrow.parquet.write_to_dataset(
+        table,
+        path,
+        filesystem=moto_s3fs,
+        partition_cols=[
+            partitioned_dataset["partitioning_column"]
+        ],  # new parameter included
+    )
+
+    # storage_options = dict(use_listings_cache=False)
+    # storage_options.update(docker_aws_s3.s3fs_kwargs)
+    #
+    # import dask.dataframe
+    #
+    # ddf = dask.dataframe.read_parquet(
+    #     f"s3://{path}", storage_options=storage_options, gather_statistics=False
+    # )
+    # all_rows = ddf.compute()
+    # assert "name" in all_rows.columns
+    return path
+
+
+@pytest.fixture(scope='session', params=[
+    pytest.param("pyarrow"),
+    pytest.param("fastparquet"),
+])
+def parquet_engine(request):
+    return request.param
+
+
+@pytest.fixture(scope='session', params=[
+    pytest.param(False, id='gather_statistics=F'),
+    pytest.param(True, id='gather_statistics=T'),
+])
+def gather_statistics(request):
+    return request.param
+
+
+def test_partitioned_read(partitioned_dataset, partitioned_parquet_path, moto_server, parquet_engine, gather_statistics):
+    """The directory based reading is quite finicky"""
+    storage_options = moto_server.copy()
+    ddf = dd.read_parquet(
+        f"s3://{partitioned_parquet_path}",
+        storage_options=storage_options,
+        gather_statistics=gather_statistics,
+        engine=parquet_engine
+    )
+
+    assert 'part_key' in ddf.columns
+    actual = ddf.compute().sort_values('id')
+
+    assert actual == partitioned_dataset["dataframe"]
+
+
+def test_non_partitioned_read(partitioned_dataset, partitioned_parquet_path, moto_server, parquet_engine, gather_statistics):
+    """The directory based reading is quite finicky"""
+    storage_options = moto_server.copy()
+    ddf = dd.read_parquet(
+        f"s3://{partitioned_parquet_path}/part_key=A",
+        storage_options=storage_options,
+        gather_statistics=gather_statistics,
+        engine=parquet_engine
+    )
+
+    if parquet_engine == 'pyarrow':
+        assert 'part_key' in ddf.columns
+    actual = ddf.compute().sort_values('id')
+    expected = partitioned_dataset["dataframe"]
+    expected = expected.loc[expected.part_key == "A"]
+
+    assert actual == expected