TEST-#6830: Use local s3 server instead of public s3 buckets

modin/conftest.py

@@ -695,10 +695,26 @@ def s3_resource(s3_base):
         raise RuntimeError("Could not create bucket")
 
     s3fs.S3FileSystem.clear_instance_cache()
-    yield conn
 
     s3 = s3fs.S3FileSystem(client_kwargs={"endpoint_url": s3_base})
 
+    test_s3_files = [
+        ("modin-bugs/multiple_csv/", "modin/pandas/test/data/multiple_csv/"),
+        (
+            "modin-bugs/test_data_dir.parquet/",
+            "modin/pandas/test/data/test_data_dir.parquet/",
+        ),
+        ("modin-bugs/test_data.parquet", "modin/pandas/test/data/test_data.parquet"),
+        ("modin-bugs/test_data.json", "modin/pandas/test/data/test_data.json"),
+        ("modin-bugs/test_data.fwf", "modin/pandas/test/data/test_data.fwf"),
+        ("modin-bugs/test_data.feather", "modin/pandas/test/data/test_data.feather"),
+        ("modin-bugs/issue5159.parquet/", "modin/pandas/test/data/issue5159.parquet/"),
+    ]
+    for s3_key, file_name in test_s3_files:
+        s3.put(file_name, f"{bucket}/{s3_key}", recursive=s3_key.endswith("/"))
+
+    yield conn
+
     s3.rm(bucket, recursive=True)
     for _ in range(20):
         # We want to wait until the deletion finishes.

modin/core/io/column_stores/feather_dispatcher.py

@@ -75,5 +75,9 @@ def _read(cls, path, columns=None, **kwargs):
             # Filtering out the columns that describe the frame's index
             columns = [col for col in reader.schema.names if col not in index_cols]
         return cls.build_query_compiler(
-            path, columns, use_threads=False, dtype_backend=kwargs["dtype_backend"]
+            path,
+            columns,
+            use_threads=False,
+            storage_options=kwargs["storage_options"],
+            dtype_backend=kwargs["dtype_backend"],
         )

modin/core/io/text/json_dispatcher.py

@@ -47,7 +47,9 @@ def _read(cls, path_or_buf, **kwargs):
         path_or_buf = stringify_path(path_or_buf)
         path_or_buf = cls.get_path_or_buffer(path_or_buf)
         if isinstance(path_or_buf, str):
-            if not cls.file_exists(path_or_buf):
+            if not cls.file_exists(
+                path_or_buf, storage_options=kwargs.get("storage_options")
+            ):
                 return cls.single_worker_read(
                     path_or_buf, reason=cls._file_not_found_msg(path_or_buf), **kwargs
                 )
@@ -60,12 +62,21 @@ def _read(cls, path_or_buf, **kwargs):
             return cls.single_worker_read(
                 path_or_buf, reason="`lines` argument not supported", **kwargs
             )
-        with OpenFile(path_or_buf, "rb") as f:
+        with OpenFile(
+            path_or_buf,
+            "rb",
+            **(kwargs.get("storage_options", None) or {}),
+        ) as f:
             columns = pandas.read_json(BytesIO(b"" + f.readline()), lines=True).columns
         kwargs["columns"] = columns
         empty_pd_df = pandas.DataFrame(columns=columns)
 
-        with OpenFile(path_or_buf, "rb", kwargs.get("compression", "infer")) as f:
+        with OpenFile(
+            path_or_buf,
+            "rb",
+            kwargs.get("compression", "infer"),
+            **(kwargs.get("storage_options", None) or {}),
+        ) as f:
             column_widths, num_splits = cls._define_metadata(empty_pd_df, columns)
             args = {"fname": path_or_buf, "num_splits": num_splits, **kwargs}
             splits, _ = cls.partitioned_file(

modin/experimental/core/io/text/csv_glob_dispatcher.py

@@ -68,7 +68,9 @@ def _read(cls, filepath_or_buffer, **kwargs):
                     reason=cls._file_not_found_msg(filepath_or_buffer),
                     **kwargs,
                 )
-            filepath_or_buffer = cls.get_path(filepath_or_buffer)
+            filepath_or_buffer = cls.get_path(
+                filepath_or_buffer, kwargs.get("storage_options")
+            )
         elif not cls.pathlib_or_pypath(filepath_or_buffer):
             return cls.single_worker_read(
                 filepath_or_buffer,
@@ -314,14 +316,16 @@ def file_exists(cls, file_path: str, storage_options=None) -> bool:
         return exists or len(fs.glob(file_path)) > 0
 
     @classmethod
-    def get_path(cls, file_path: str) -> list:
+    def get_path(cls, file_path: str, storage_options=None) -> list:
         """
         Return the path of the file(s).
 
         Parameters
         ----------
         file_path : str
             String representing a path.
+        storage_options : dict, optional
+            Keyword from `read_*` functions.
 
         Returns
         -------
@@ -363,11 +367,17 @@ def get_file_path(fs_handle) -> List[str]:
             fs_addresses = [fs_handle.unstrip_protocol(path) for path in file_paths]
             return fs_addresses
 
-        fs, _ = fsspec.core.url_to_fs(file_path)
+        if storage_options is not None:
+            new_storage_options = dict(storage_options)
+            new_storage_options.pop("anon", None)
+        else:
+            new_storage_options = {}
+
+        fs, _ = fsspec.core.url_to_fs(file_path, **new_storage_options)
         try:
             return get_file_path(fs)
         except credential_error_type:
-            fs, _ = fsspec.core.url_to_fs(file_path, anon=True)
+            fs, _ = fsspec.core.url_to_fs(file_path, anon=True, **new_storage_options)
         return get_file_path(fs)
 
     @classmethod

modin/experimental/pandas/test/test_io_exp.py

@@ -180,11 +180,15 @@ def test_read_single_csv_with_parse_dates(self, parse_dates):
 @pytest.mark.parametrize(
     "path",
     [
-        "s3://modin-datasets/testing/multiple_csv/test_data*.csv",
+        "s3://modin-test/modin-bugs/multiple_csv/test_data*.csv",
         "gs://modin-testing/testing/multiple_csv/test_data*.csv",
     ],
 )
-def test_read_multiple_csv_cloud_store(path):
+def test_read_multiple_csv_cloud_store(path, s3_resource, s3_storage_options):
+    storage_options_new = {"anon": True}
+    if path.startswith("s3"):
+        storage_options_new = s3_storage_options
+
     def _pandas_read_csv_glob(path, storage_options):
         pandas_dfs = [
             pandas.read_csv(
@@ -200,7 +204,7 @@ def _pandas_read_csv_glob(path, storage_options):
         lambda module, **kwargs: pd.read_csv_glob(path, **kwargs).reset_index(drop=True)
         if hasattr(module, "read_csv_glob")
         else _pandas_read_csv_glob(path, **kwargs),
-        storage_options={"anon": True},
+        storage_options=storage_options_new,
     )
 
 
@@ -212,17 +216,19 @@ def _pandas_read_csv_glob(path, storage_options):
     reason=f"{Engine.get()} does not have experimental API",
 )
 @pytest.mark.parametrize(
-    "storage_options",
-    [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}, None],
+    "storage_options_extra",
+    [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}],
 )
-def test_read_multiple_csv_s3_storage_opts(storage_options):
-    path = "s3://modin-datasets/testing/multiple_csv/"
+def test_read_multiple_csv_s3_storage_opts(
+    s3_resource, s3_storage_options, storage_options_extra
+):
+    s3_path = "s3://modin-test/modin-bugs/multiple_csv/"
 
     def _pandas_read_csv_glob(path, storage_options):
         pandas_df = pandas.concat(
             [
                 pandas.read_csv(
-                    f"{path}test_data{i}.csv",
+                    f"{s3_path}test_data{i}.csv",
                     storage_options=storage_options,
                 )
                 for i in range(2)
@@ -233,10 +239,10 @@ def _pandas_read_csv_glob(path, storage_options):
     eval_general(
         pd,
         pandas,
-        lambda module, **kwargs: pd.read_csv_glob(path, **kwargs)
+        lambda module, **kwargs: pd.read_csv_glob(s3_path, **kwargs)
         if hasattr(module, "read_csv_glob")
-        else _pandas_read_csv_glob(path, **kwargs),
-        storage_options=storage_options,
+        else _pandas_read_csv_glob(s3_path, **kwargs),
+        storage_options=s3_storage_options | storage_options_extra,
     )
 
 

modin/pandas/test/data/multiple_csv/test_data0.csv

@@ -0,0 +1,5 @@
+a,b,c
+0,True,x
+1,False,y
+2,True,z
+3,False,w

modin/pandas/test/data/multiple_csv/test_data1.csv

@@ -0,0 +1,5 @@
+a,b,c
+4,True,m
+5,False,n
+6,True,t
+7,True,l

modin/pandas/test/data/test_data.json

@@ -0,0 +1,6 @@
+{"Duration":60,"Pulse":110,"Maxpulse":130,"Calories":409}
+{"Duration":60,"Pulse":117,"Maxpulse":145,"Calories":479}
+{"Duration":60,"Pulse":103,"Maxpulse":135,"Calories":340}
+{"Duration":45,"Pulse":109,"Maxpulse":175,"Calories":282}
+{"Duration":45,"Pulse":117,"Maxpulse":148,"Calories":406}
+{"Duration":60,"Pulse":102,"Maxpulse":127,"Calories":300}

modin/pandas/test/test_io.py

@@ -1939,26 +1939,29 @@ def test_read_parquet_hdfs(self, engine):
         "path_type",
         ["object", "directory", "url"],
     )
-    def test_read_parquet_s3(self, path_type, engine):
-        dataset_url = "s3://modin-datasets/testing/test_data.parquet"
+    def test_read_parquet_s3(self, s3_resource, path_type, engine, s3_storage_options):
+        s3_path = "s3://modin-test/modin-bugs/test_data.parquet"
         if path_type == "object":
             import s3fs
 
-            fs = s3fs.S3FileSystem(anon=True)
-            with fs.open(dataset_url, "rb") as file_obj:
+            fs = s3fs.S3FileSystem(
+                endpoint_url=s3_storage_options["client_kwargs"]["endpoint_url"]
+            )
+            with fs.open(s3_path, "rb") as file_obj:
                 eval_io("read_parquet", path=file_obj, engine=engine)
         elif path_type == "directory":
+            s3_path = "s3://modin-test/modin-bugs/test_data_dir.parquet"
             eval_io(
                 "read_parquet",
-                path="s3://modin-datasets/test_data_dir.parquet",
-                storage_options={"anon": True},
+                path=s3_path,
+                storage_options=s3_storage_options,
                 engine=engine,
             )
         else:
             eval_io(
                 "read_parquet",
-                path=dataset_url,
-                storage_options={"anon": True},
+                path=s3_path,
+                storage_options=s3_storage_options,
                 engine=engine,
             )
 
@@ -2078,15 +2081,16 @@ def test_read_parquet_5767(self, tmp_path, engine):
         # both Modin and pandas read column "b" as a category
         df_equals(test_df, read_df.astype("int64"))
 
-    def test_read_parquet_s3_with_column_partitioning(self, engine):
-        # This test case comes from
+    def test_read_parquet_s3_with_column_partitioning(
+        self, s3_resource, engine, s3_storage_options
+    ):
         # https://github.com/modin-project/modin/issues/4636
-        dataset_url = "s3://modin-datasets/modin-bugs/modin_bug_5159_parquet/df.parquet"
+        s3_path = "s3://modin-test/modin-bugs/issue5159.parquet"
         eval_io(
             fn_name="read_parquet",
-            path=dataset_url,
+            path=s3_path,
             engine=engine,
-            storage_options={"anon": True},
+            storage_options=s3_storage_options,
         )
 
 
@@ -2134,16 +2138,17 @@ def comparator(df1, df2):
         )
 
     @pytest.mark.parametrize(
-        "storage_options",
-        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}, None],
+        "storage_options_extra",
+        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}],
     )
-    def test_read_json_s3(self, storage_options):
+    def test_read_json_s3(self, s3_resource, s3_storage_options, storage_options_extra):
+        s3_path = "s3://modin-test/modin-bugs/test_data.json"
         eval_io(
             fn_name="read_json",
-            path_or_buf="s3://modin-datasets/testing/test_data.json",
+            path_or_buf=s3_path,
             lines=True,
             orient="records",
-            storage_options=storage_options,
+            storage_options=s3_storage_options | storage_options_extra,
         )
 
     def test_read_json_categories(self):
@@ -2929,14 +2934,15 @@ def test_read_fwf_empty_frame(self, make_fwf_file):
         df_equals(modin_df, pandas_df)
 
     @pytest.mark.parametrize(
-        "storage_options",
-        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}, None],
+        "storage_options_extra",
+        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}],
     )
-    def test_read_fwf_s3(self, storage_options):
+    def test_read_fwf_s3(self, s3_resource, s3_storage_options, storage_options_extra):
+        s3_path = "s3://modin-test/modin-bugs/test_data.fwf"
         eval_io(
             fn_name="read_fwf",
-            filepath_or_buffer="s3://modin-datasets/testing/test_data.fwf",
-            storage_options=storage_options,
+            filepath_or_buffer=s3_path,
+            storage_options=s3_storage_options | storage_options_extra,
         )
 
 
@@ -3027,14 +3033,17 @@ def comparator(df1, df2):
         )
 
     @pytest.mark.parametrize(
-        "storage_options",
-        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}, None],
+        "storage_options_extra",
+        [{"anon": False}, {"anon": True}, {"key": "123", "secret": "123"}],
     )
-    def test_read_feather_s3(self, storage_options):
+    def test_read_feather_s3(
+        self, s3_resource, s3_storage_options, storage_options_extra
+    ):
+        s3_path = "s3://modin-test/modin-bugs/test_data.feather"
         eval_io(
             fn_name="read_feather",
-            path="s3://modin-datasets/testing/test_data.feather",
-            storage_options=storage_options,
+            path=s3_path,
+            storage_options=s3_storage_options | storage_options_extra,
         )
 
     def test_read_feather_path_object(self, make_feather_file):

modin/test/test_headers.py

@@ -44,6 +44,8 @@ def test_line_endings():
         if any(i in subdir for i in [".git", ".idea", "__pycache__"]):
             continue
         for file in files:
+            if file.endswith(".parquet"):
+                continue
             filepath = os.path.join(subdir, file)
             with open(filepath, "rb+") as f:
                 file_contents = f.read()