feat: support read_csv for backends with no native support

ibis/backends/__init__.py

@@ -4,6 +4,7 @@
 import collections.abc
 import contextlib
 import functools
+import glob
 import importlib.metadata
 import keyword
 import re
@@ -1269,6 +1270,117 @@
             f"{cls.name} backend has not implemented `has_operation` API"
         )
 
+    @util.experimental
+    def read_csv(
+        self, path: str | Path, table_name: str | None = None, **kwargs: Any
+    ) -> ir.Table:
+        """Register a CSV file as a table in the current backend.
+
+        This function reads a CSV file and registers it as a table in the current
+        backend. Note that for Impala and Trino backends, the performance may be suboptimal.
+
+        Parameters
+        ----------
+        path
+            The data source. A string or Path to the CSV file.
+        table_name
+            An optional name to use for the created table. This defaults to
+            a sequentially generated name.
+        **kwargs
+            Additional keyword arguments passed to the backend loading function.
+            Common options are skip_rows, column_names, delimiter, and include_columns.
+            More details could be found:
+            https://arrow.apache.org/docs/python/generated/pyarrow.csv.ReadOptions.html
+            https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html
+            https://arrow.apache.org/docs/python/generated/pyarrow.csv.ConvertOptions.html
+
+        Returns
+        -------
+        ir.Table
+            The just-registered table
+
+        Examples
+        --------
+        Connect to a SQLite database:
+
+        >>> con = ibis.sqlite.connect()
+
+        Read a single csv file:
+
+        >>> table = con.read_csv("path/to/file.csv")
+
+        Read all csv files in a directory:
+
+        >>> table = con.read_parquet("path/to/csv_directory/*")
+
+        Read all csv files with a glob pattern:
+
+        >>> table = con.read_csv("path/to/csv_directory/test_*.csv")
+
+        Read csv file from s3:
+
+        >>> table = con.read_csv("s3://bucket/path/to/file.csv")
+
+        Read csv file with custom pyarrow options:
+
+        >>> table = con.read_csv(
+        ...     "path/to/file.csv", delimiter=",", include_columns=["col1", "col3"]
+        ... )
+        """
+        pa = self._import_pyarrow()
+        import pyarrow.csv as pcsv
+        from pyarrow import fs
+
+        read_options_args = {}
+        parse_options_args = {}
+        convert_options_args = {}
+        memory_pool = None
+
+        for key, value in kwargs.items():
+            if hasattr(pcsv.ReadOptions, key):
+                read_options_args[key] = value
+            elif hasattr(pcsv.ParseOptions, key):
+                parse_options_args[key] = value
+            elif hasattr(pcsv.ConvertOptions, key):
+                convert_options_args[key] = value
+            elif key == "memory_pool":
+                memory_pool = value
+            else:
+                raise ValueError(f"Invalid args: {key!r}")
+
+        read_options = pcsv.ReadOptions(**read_options_args)
+        parse_options = pcsv.ParseOptions(**parse_options_args)
+        convert_options = pcsv.ConvertOptions(**convert_options_args)
+        if not memory_pool:
+            memory_pool = pa.default_memory_pool()
+
+        path = str(path)
+        file_system, path = fs.FileSystem.from_uri(path)
+
+        if isinstance(file_system, fs.LocalFileSystem):
+            paths = glob.glob(path)
+            if not paths:
+                raise FileNotFoundError(f"No files found at {path!r}")
+        else:
+            paths = [path]
+
+        pyarrow_tables = []
+        for path in paths:
+            with file_system.open_input_file(path) as f:
+                pyarrow_table = pcsv.read_csv(
+                    f,
+                    read_options=read_options,
+                    parse_options=parse_options,
+                    convert_options=convert_options,
+                    memory_pool=memory_pool,
+                )
+                pyarrow_tables.append(pyarrow_table)
+
+        pyarrow_table = pa.concat_tables(pyarrow_tables)
+        table_name = table_name or util.gen_name("read_csv")
+        self.create_table(table_name, pyarrow_table)
+        return self.table(table_name)
+
     def _transpile_sql(self, query: str, *, dialect: str | None = None) -> str:
         # only transpile if dialect was passed
         if dialect is None:

ibis/backends/tests/test_register.py

@@ -12,7 +12,11 @@
 
 import ibis
 from ibis.backends.conftest import TEST_TABLES
-from ibis.backends.tests.errors import PySparkAnalysisException
+from ibis.backends.tests.errors import (
+    MySQLOperationalError,
+    PyODBCProgrammingError,
+    PySparkAnalysisException,
+)
 from ibis.conftest import IS_SPARK_REMOTE
 
 if TYPE_CHECKING:
@@ -21,9 +25,10 @@
     import pyarrow as pa
 
 pytestmark = [
-    pytest.mark.notimpl(["druid", "exasol", "oracle"]),
     pytest.mark.notyet(
-        ["pyspark"], condition=IS_SPARK_REMOTE, raises=PySparkAnalysisException
+        ["pyspark"],
+        condition=IS_SPARK_REMOTE,
+        raises=PySparkAnalysisException,
     ),
 ]
 
@@ -103,6 +108,7 @@ def gzip_csv(data_dir, tmp_path):
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_register_csv(con, data_dir, fname, in_table_name, out_table_name):
     with pushd(data_dir / "csv"):
         with pytest.warns(FutureWarning, match="v9.1"):
@@ -114,7 +120,7 @@ def test_register_csv(con, data_dir, fname, in_table_name, out_table_name):
 
 
 # TODO: rewrite or delete test when register api is removed
-@pytest.mark.notimpl(["datafusion"])
+@pytest.mark.notimpl(["datafusion", "druid", "exasol", "oracle"])
 @pytest.mark.notyet(
     [
         "bigquery",
@@ -154,6 +160,7 @@ def test_register_csv_gz(con, data_dir, gzip_csv):
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_register_with_dotted_name(con, data_dir, tmp_path):
     basename = "foo.bar.baz/diamonds.csv"
     f = tmp_path.joinpath(basename)
@@ -211,6 +218,7 @@ def read_table(path: Path) -> Iterator[tuple[str, pa.Table]]:
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_register_parquet(
     con, tmp_path, data_dir, fname, in_table_name, out_table_name
 ):
@@ -249,6 +257,7 @@ def test_register_parquet(
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_register_iterator_parquet(
     con,
     tmp_path,
@@ -277,7 +286,7 @@ def test_register_iterator_parquet(
 # TODO: remove entirely when `register` is removed
 # This same functionality is implemented across all backends
 # via `create_table` and tested in `test_client.py`
-@pytest.mark.notimpl(["datafusion"])
+@pytest.mark.notimpl(["datafusion", "druid", "exasol", "oracle"])
 @pytest.mark.notyet(
     [
         "bigquery",
@@ -311,7 +320,7 @@ def test_register_pandas(con):
 # TODO: remove entirely when `register` is removed
 # This same functionality is implemented across all backends
 # via `create_table` and tested in `test_client.py`
-@pytest.mark.notimpl(["datafusion", "polars"])
+@pytest.mark.notimpl(["datafusion", "polars", "druid", "exasol", "oracle"])
 @pytest.mark.notyet(
     [
         "bigquery",
@@ -352,6 +361,7 @@ def test_register_pyarrow_tables(con):
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_csv_reregister_schema(con, tmp_path):
     foo = tmp_path.joinpath("foo.csv")
     with foo.open("w", newline="") as csvfile:
@@ -380,10 +390,13 @@ def test_csv_reregister_schema(con, tmp_path):
         "bigquery",
         "clickhouse",
         "datafusion",
+        "druid",
+        "exasol",
         "flink",
         "impala",
         "mysql",
         "mssql",
+        "oracle",
         "polars",
         "postgres",
         "risingwave",
@@ -417,6 +430,7 @@ def test_register_garbage(con, monkeypatch):
 @pytest.mark.notyet(
     ["flink", "impala", "mssql", "mysql", "postgres", "risingwave", "sqlite", "trino"]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_read_parquet(con, tmp_path, data_dir, fname, in_table_name):
     pq = pytest.importorskip("pyarrow.parquet")
 
@@ -457,6 +471,7 @@ def ft_data(data_dir):
         "trino",
     ]
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_read_parquet_glob(con, tmp_path, ft_data):
     pq = pytest.importorskip("pyarrow.parquet")
 
@@ -473,18 +488,10 @@ def test_read_parquet_glob(con, tmp_path, ft_data):
     assert table.count().execute() == nrows * ntables
 
 
-@pytest.mark.notyet(
-    [
-        "flink",
-        "impala",
-        "mssql",
-        "mysql",
-        "postgres",
-        "risingwave",
-        "sqlite",
-        "trino",
-    ]
-)
+@pytest.mark.notyet(["flink"])
+@pytest.mark.notimpl(["druid"])
+@pytest.mark.notimpl(["mssql"], raises=PyODBCProgrammingError)
+@pytest.mark.notimpl(["mysql"], raises=MySQLOperationalError)
 def test_read_csv_glob(con, tmp_path, ft_data):
     pc = pytest.importorskip("pyarrow.csv")
 
@@ -519,6 +526,7 @@ def test_read_csv_glob(con, tmp_path, ft_data):
     raises=ValueError,
     reason="read_json() missing required argument: 'schema'",
 )
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_read_json_glob(con, tmp_path, ft_data):
     nrows = len(ft_data)
     ntables = 2
@@ -562,14 +570,26 @@ def num_diamonds(data_dir):
     "in_table_name",
     [param(None, id="default"), param("fancy_stones", id="file_name")],
 )
-@pytest.mark.notyet(
-    ["flink", "impala", "mssql", "mysql", "postgres", "risingwave", "sqlite", "trino"]
-)
+@pytest.mark.notyet(["flink"])
+@pytest.mark.notimpl(["druid", "exasol", "oracle"])
 def test_read_csv(con, data_dir, in_table_name, num_diamonds):
+    if con.name in ("trino", "impala"):
+        # TODO: remove after trino and impala have efficient insertion
+        pytest.skip(
+            "Both Impala and Trino lack efficient data insertion methods from Python."
+        )
     fname = "diamonds.csv"
     with pushd(data_dir / "csv"):
-        if con.name == "pyspark":
-            # pyspark doesn't respect CWD
+        if con.name in (
+            "pyspark",
+            "sqlite",
+            "mysql",
+            "postgres",
+            "risingwave",
+            "mssql",
+        ):
+            # pyspark backend doesn't respect CWD
+            # backends using pyarrow implementation need absolute path
             fname = str(Path(fname).absolute())
         table = con.read_csv(fname, table_name=in_table_name)
 
@@ -594,3 +614,73 @@ def test_read_csv(con, data_dir, in_table_name, num_diamonds):
         }
     )
     assert table.count().execute() == num_diamonds
+
+
+@pytest.mark.parametrize(
+    ("skip_rows", "new_column_names", "delimiter", "include_columns"),
+    [
+        param(True, True, False, False, id="skip_rows_with_column_names"),
+        param(False, False, False, True, id="include_columns"),
+        param(False, False, True, False, id="delimiter"),
+    ],
+)
+@pytest.mark.notyet(["flink"])
+@pytest.mark.notimpl(["druid"])
+@pytest.mark.never(
+    [
+        "duckdb",
+        "polars",
+        "bigquery",
+        "clickhouse",
+        "datafusion",
+        "snowflake",
+        "pyspark",
+    ],
+    reason="backend implements its own read_csv",
+)
+@pytest.mark.notimpl(["mssql"], raises=PyODBCProgrammingError)
+@pytest.mark.notimpl(["mysql"], raises=MySQLOperationalError)
+def test_read_csv_pyarrow_options(
+    con, tmp_path, ft_data, skip_rows, new_column_names, delimiter, include_columns
+):
+    pc = pytest.importorskip("pyarrow.csv")
+
+    if con.name in (
+        "duckdb",
+        "polars",
+        "bigquery",
+        "clickhouse",
+        "datafusion",
+        "snowflake",
+        "pyspark",
+    ):
+        pytest.skip(f"{con.name} implements its own `read_parquet`")
+
+    column_names = ft_data.column_names
+    num_rows = ft_data.num_rows
+    fname = "tmp.csv"
+    pc.write_csv(ft_data, tmp_path / fname)
+
+    options = {}
+    if skip_rows:
+        options["skip_rows"] = 2
+        num_rows = num_rows - options["skip_rows"] + 1
+    if new_column_names:
+        column_names = [f"col_{i}" for i in range(ft_data.num_columns)]
+        options["column_names"] = column_names
+    if delimiter:
+        new_delimiter = "*"
+        options["delimiter"] = new_delimiter
+        pc.write_csv(
+            ft_data, tmp_path / fname, pc.WriteOptions(delimiter=new_delimiter)
+        )
+    if include_columns:
+        # try to include all types here
+        # pick the first 12 columns
+        column_names = column_names[:12]
+        options["include_columns"] = column_names
+
+    table = con.read_csv(tmp_path / fname, **options)
+
+    assert set(table.columns) == set(column_names)
+    assert table.count().execute() == num_rows