Upgrade to Datafusion 43

Cargo.toml

@@ -37,9 +37,10 @@ substrait = ["dep:datafusion-substrait"]
 tokio = { version = "1.39", features = ["macros", "rt", "rt-multi-thread", "sync"] }
 pyo3 = { version = "0.22", features = ["extension-module", "abi3", "abi3-py38"] }
 arrow = { version = "53", features = ["pyarrow"] }
-datafusion = { version = "42.0.0", features = ["pyarrow", "avro", "unicode_expressions"] }
-datafusion-substrait = { version = "42.0.0", optional = true }
-datafusion-proto = { version = "42.0.0" }
+datafusion = { version = "43.0.0", features = ["pyarrow", "avro", "unicode_expressions"] }
+datafusion-substrait = { version = "43.0.0", optional = true }
+datafusion-proto = { version = "43.0.0" }
+datafusion-functions-window-common = { version = "43.0.0" }
 prost = "0.13" # keep in line with `datafusion-substrait`
 uuid = { version = "1.11", features = ["v4"] }
 mimalloc = { version = "0.1", optional = true, default-features = false, features = ["local_dynamic_tls"] }
@@ -58,4 +59,4 @@ crate-type = ["cdylib", "rlib"]
 
 [profile.release]
 lto = true
-codegen-units = 1
+codegen-units = 1

examples/tpch/_tests.py

@@ -25,7 +25,7 @@
 def df_selection(col_name, col_type):
     if col_type == pa.float64() or isinstance(col_type, pa.Decimal128Type):
         return F.round(col(col_name), lit(2)).alias(col_name)
-    elif col_type == pa.string():
+    elif col_type == pa.string() or col_type == pa.string_view():
         return F.trim(col(col_name)).alias(col_name)
     else:
         return col(col_name)
@@ -43,7 +43,7 @@ def load_schema(col_name, col_type):
 def expected_selection(col_name, col_type):
     if col_type == pa.int64() or col_type == pa.int32():
         return F.trim(col(col_name)).cast(col_type).alias(col_name)
-    elif col_type == pa.string():
+    elif col_type == pa.string() or col_type == pa.string_view():
         return F.trim(col(col_name)).alias(col_name)
     else:
         return col(col_name)

python/datafusion/expr.py

@@ -51,7 +51,6 @@
 Column = expr_internal.Column
 CreateMemoryTable = expr_internal.CreateMemoryTable
 CreateView = expr_internal.CreateView
-CrossJoin = expr_internal.CrossJoin
 Distinct = expr_internal.Distinct
 DropTable = expr_internal.DropTable
 EmptyRelation = expr_internal.EmptyRelation
@@ -140,7 +139,6 @@
     "Join",
     "JoinType",
     "JoinConstraint",
-    "CrossJoin",
     "Union",
     "Unnest",
     "UnnestExpr",
@@ -376,6 +374,8 @@ def literal(value: Any) -> Expr:
 
         ``value`` must be a valid PyArrow scalar value or easily castable to one.
         """
+        if isinstance(value, str):
+            value = pa.scalar(value, type=pa.string_view())
         if not isinstance(value, pa.Scalar):
             value = pa.scalar(value)
         return Expr(expr_internal.Expr.literal(value))

python/datafusion/functions.py

@@ -295,7 +295,7 @@ def decode(input: Expr, encoding: Expr) -> Expr:
 
 def array_to_string(expr: Expr, delimiter: Expr) -> Expr:
     """Converts each element to its text representation."""
-    return Expr(f.array_to_string(expr.expr, delimiter.expr))
+    return Expr(f.array_to_string(expr.expr, delimiter.expr.cast(pa.string())))
 
 
 def array_join(expr: Expr, delimiter: Expr) -> Expr:
@@ -1065,7 +1065,10 @@ def struct(*args: Expr) -> Expr:
 
 def named_struct(name_pairs: list[tuple[str, Expr]]) -> Expr:
     """Returns a struct with the given names and arguments pairs."""
-    name_pair_exprs = [[Expr.literal(pair[0]), pair[1]] for pair in name_pairs]
+    name_pair_exprs = [
+        [Expr.literal(pa.scalar(pair[0], type=pa.string())), pair[1]]
+        for pair in name_pairs
+    ]
 
     # flatten
     name_pairs = [x.expr for xs in name_pair_exprs for x in xs]
@@ -1422,7 +1425,9 @@ def array_sort(array: Expr, descending: bool = False, null_first: bool = False)
     nulls_first = "NULLS FIRST" if null_first else "NULLS LAST"
     return Expr(
         f.array_sort(
-            array.expr, Expr.literal(desc).expr, Expr.literal(nulls_first).expr
+            array.expr,
+            Expr.literal(pa.scalar(desc, type=pa.string())).expr,
+            Expr.literal(pa.scalar(nulls_first, type=pa.string())).expr,
         )
     )
 

python/datafusion/udf.py

@@ -229,6 +229,7 @@ def udaf(
         which this UDAF is used. The following examples are all valid.
 
         .. code-block:: python
+
             import pyarrow as pa
             import pyarrow.compute as pc
 

python/tests/test_expr.py

@@ -85,14 +85,18 @@ def test_limit(test_ctx):
 
     plan = plan.to_variant()
     assert isinstance(plan, Limit)
-    assert plan.skip() == 0
+    # TODO: Upstream now has expressions for skip and fetch
+    # REF: https://github.com/apache/datafusion/pull/12836
+    # assert plan.skip() == 0
 
     df = test_ctx.sql("select c1 from test LIMIT 10 OFFSET 5")
     plan = df.logical_plan()
 
     plan = plan.to_variant()
     assert isinstance(plan, Limit)
-    assert plan.skip() == 5
+    # TODO: Upstream now has expressions for skip and fetch
+    # REF: https://github.com/apache/datafusion/pull/12836
+    # assert plan.skip() == 5
 
 
 def test_aggregate_query(test_ctx):
@@ -126,7 +130,10 @@ def test_relational_expr(test_ctx):
     ctx = SessionContext()
 
     batch = pa.RecordBatch.from_arrays(
-        [pa.array([1, 2, 3]), pa.array(["alpha", "beta", "gamma"])],
+        [
+            pa.array([1, 2, 3]),
+            pa.array(["alpha", "beta", "gamma"], type=pa.string_view()),
+        ],
         names=["a", "b"],
     )
     df = ctx.create_dataframe([[batch]], name="batch_array")
@@ -141,7 +148,8 @@ def test_relational_expr(test_ctx):
     assert df.filter(col("b") == "beta").count() == 1
     assert df.filter(col("b") != "beta").count() == 2
 
-    assert df.filter(col("a") == "beta").count() == 0
+    with pytest.raises(Exception):
+        df.filter(col("a") == "beta").count()
 
 
 def test_expr_to_variant():

python/tests/test_functions.py

@@ -34,9 +34,9 @@ def df():
     # create a RecordBatch and a new DataFrame from it
     batch = pa.RecordBatch.from_arrays(
         [
-            pa.array(["Hello", "World", "!"]),
+            pa.array(["Hello", "World", "!"], type=pa.string_view()),
             pa.array([4, 5, 6]),
-            pa.array(["hello ", " world ", " !"]),
+            pa.array(["hello ", " world ", " !"], type=pa.string_view()),
             pa.array(
                 [
                     datetime(2022, 12, 31),
@@ -88,16 +88,18 @@ def test_literal(df):
     assert len(result) == 1
     result = result[0]
     assert result.column(0) == pa.array([1] * 3)
-    assert result.column(1) == pa.array(["1"] * 3)
-    assert result.column(2) == pa.array(["OK"] * 3)
+    assert result.column(1) == pa.array(["1"] * 3, type=pa.string_view())
+    assert result.column(2) == pa.array(["OK"] * 3, type=pa.string_view())
     assert result.column(3) == pa.array([3.14] * 3)
     assert result.column(4) == pa.array([True] * 3)
     assert result.column(5) == pa.array([b"hello world"] * 3)
 
 
 def test_lit_arith(df):
     """Test literals with arithmetic operations"""
-    df = df.select(literal(1) + column("b"), f.concat(column("a"), literal("!")))
+    df = df.select(
+        literal(1) + column("b"), f.concat(column("a").cast(pa.string()), literal("!"))
+    )
     result = df.collect()
     assert len(result) == 1
     result = result[0]
@@ -578,21 +580,33 @@ def test_array_function_obj_tests(stmt, py_expr):
             f.ascii(column("a")),
             pa.array([72, 87, 33], type=pa.int32()),
         ),  # H = 72; W = 87; ! = 33
-        (f.bit_length(column("a")), pa.array([40, 40, 8], type=pa.int32())),
-        (f.btrim(literal(" World ")), pa.array(["World", "World", "World"])),
+        (
+            f.bit_length(column("a").cast(pa.string())),
+            pa.array([40, 40, 8], type=pa.int32()),
+        ),
+        (
+            f.btrim(literal(" World ")),
+            pa.array(["World", "World", "World"], type=pa.string_view()),
+        ),
         (f.character_length(column("a")), pa.array([5, 5, 1], type=pa.int32())),
         (f.chr(literal(68)), pa.array(["D", "D", "D"])),
         (
             f.concat_ws("-", column("a"), literal("test")),
             pa.array(["Hello-test", "World-test", "!-test"]),
         ),
-        (f.concat(column("a"), literal("?")), pa.array(["Hello?", "World?", "!?"])),
+        (
+            f.concat(column("a").cast(pa.string()), literal("?")),
+            pa.array(["Hello?", "World?", "!?"]),
+        ),
         (f.initcap(column("c")), pa.array(["Hello ", " World ", " !"])),
         (f.left(column("a"), literal(3)), pa.array(["Hel", "Wor", "!"])),
         (f.length(column("c")), pa.array([6, 7, 2], type=pa.int32())),
         (f.lower(column("a")), pa.array(["hello", "world", "!"])),
         (f.lpad(column("a"), literal(7)), pa.array(["  Hello", "  World", "      !"])),
-        (f.ltrim(column("c")), pa.array(["hello ", "world ", "!"])),
+        (
+            f.ltrim(column("c")),
+            pa.array(["hello ", "world ", "!"], type=pa.string_view()),
+        ),
         (
             f.md5(column("a")),
             pa.array(
@@ -618,19 +632,25 @@ def test_array_function_obj_tests(stmt, py_expr):
             f.rpad(column("a"), literal(8)),
             pa.array(["Hello   ", "World   ", "!       "]),
         ),
-        (f.rtrim(column("c")), pa.array(["hello", " world", " !"])),
+        (
+            f.rtrim(column("c")),
+            pa.array(["hello", " world", " !"], type=pa.string_view()),
+        ),
         (
             f.split_part(column("a"), literal("l"), literal(1)),
             pa.array(["He", "Wor", "!"]),
         ),
         (f.starts_with(column("a"), literal("Wor")), pa.array([False, True, False])),
         (f.strpos(column("a"), literal("o")), pa.array([5, 2, 0], type=pa.int32())),
-        (f.substr(column("a"), literal(3)), pa.array(["llo", "rld", ""])),
+        (
+            f.substr(column("a"), literal(3)),
+            pa.array(["llo", "rld", ""], type=pa.string_view()),
+        ),
         (
             f.translate(column("a"), literal("or"), literal("ld")),
             pa.array(["Helll", "Wldld", "!"]),
         ),
-        (f.trim(column("c")), pa.array(["hello", "world", "!"])),
+        (f.trim(column("c")), pa.array(["hello", "world", "!"], type=pa.string_view())),
         (f.upper(column("c")), pa.array(["HELLO ", " WORLD ", " !"])),
         (f.ends_with(column("a"), literal("llo")), pa.array([True, False, False])),
         (
@@ -772,9 +792,9 @@ def test_temporal_functions(df):
         f.date_trunc(literal("month"), column("d")),
         f.datetrunc(literal("day"), column("d")),
         f.date_bin(
-            literal("15 minutes"),
+            literal("15 minutes").cast(pa.string()),
             column("d"),
-            literal("2001-01-01 00:02:30"),
+            literal("2001-01-01 00:02:30").cast(pa.string()),
         ),
         f.from_unixtime(literal(1673383974)),
         f.to_timestamp(literal("2023-09-07 05:06:14.523952")),
@@ -836,8 +856,8 @@ def test_case(df):
     result = df.collect()
     result = result[0]
     assert result.column(0) == pa.array([10, 8, 8])
-    assert result.column(1) == pa.array(["Hola", "Mundo", "!!"])
-    assert result.column(2) == pa.array(["Hola", "Mundo", None])
+    assert result.column(1) == pa.array(["Hola", "Mundo", "!!"], type=pa.string_view())
+    assert result.column(2) == pa.array(["Hola", "Mundo", None], type=pa.string_view())
 
 
 def test_when_with_no_base(df):
@@ -855,8 +875,10 @@ def test_when_with_no_base(df):
     result = df.collect()
     result = result[0]
     assert result.column(0) == pa.array([4, 5, 6])
-    assert result.column(1) == pa.array(["too small", "just right", "too big"])
-    assert result.column(2) == pa.array(["Hello", None, None])
+    assert result.column(1) == pa.array(
+        ["too small", "just right", "too big"], type=pa.string_view()
+    )
+    assert result.column(2) == pa.array(["Hello", None, None], type=pa.string_view())
 
 
 def test_regr_funcs_sql(df):
@@ -999,8 +1021,13 @@ def test_regr_funcs_df(func, expected):
 
 def test_binary_string_functions(df):
     df = df.select(
-        f.encode(column("a"), literal("base64")),
-        f.decode(f.encode(column("a"), literal("base64")), literal("base64")),
+        f.encode(column("a").cast(pa.string()), literal("base64").cast(pa.string())),
+        f.decode(
+            f.encode(
+                column("a").cast(pa.string()), literal("base64").cast(pa.string())
+            ),
+            literal("base64").cast(pa.string()),
+        ),
     )
     result = df.collect()
     assert len(result) == 1

python/tests/test_imports.py

@@ -46,7 +46,6 @@
     Join,
     JoinType,
     JoinConstraint,
-    CrossJoin,
     Union,
     Like,
     ILike,
@@ -129,7 +128,6 @@ def test_class_module_is_datafusion():
         Join,
         JoinType,
         JoinConstraint,
-        CrossJoin,
         Union,
         Like,
         ILike,

python/tests/test_sql.py

@@ -468,6 +468,13 @@ def test_simple_select(ctx, tmp_path, arr):
     batches = ctx.sql("SELECT a AS tt FROM t").collect()
     result = batches[0].column(0)
 
+    # In DF 43.0.0 we now default to having BinaryView and StringView
+    # so the array that is saved to the parquet is slightly different
+    # than the array read. Convert to values for comparison.
+    if isinstance(result, pa.BinaryViewArray) or isinstance(result, pa.StringViewArray):
+        arr = arr.tolist()
+        result = result.tolist()
+
     np.testing.assert_equal(result, arr)
 
 

src/context.rs

@@ -287,7 +287,7 @@ impl PySessionContext {
         } else {
             RuntimeConfig::default()
         };
-        let runtime = Arc::new(RuntimeEnv::new(runtime_config)?);
+        let runtime = Arc::new(RuntimeEnv::try_new(runtime_config)?);
         let session_state = SessionStateBuilder::new()
             .with_config(config)
             .with_runtime_env(runtime)

src/dataframe.rs

@@ -409,7 +409,9 @@ impl PyDataFrame {
 
     #[pyo3(signature = (column, preserve_nulls=true))]
     fn unnest_column(&self, column: &str, preserve_nulls: bool) -> PyResult<Self> {
-        let unnest_options = UnnestOptions { preserve_nulls };
+        // TODO: expose RecursionUnnestOptions
+        // REF: https://github.com/apache/datafusion/pull/11577
+        let unnest_options = UnnestOptions::default().with_preserve_nulls(preserve_nulls);
         let df = self
             .df
             .as_ref()
@@ -420,7 +422,9 @@ impl PyDataFrame {
 
     #[pyo3(signature = (columns, preserve_nulls=true))]
     fn unnest_columns(&self, columns: Vec<String>, preserve_nulls: bool) -> PyResult<Self> {
-        let unnest_options = UnnestOptions { preserve_nulls };
+        // TODO: expose RecursionUnnestOptions
+        // REF: https://github.com/apache/datafusion/pull/11577
+        let unnest_options = UnnestOptions::default().with_preserve_nulls(preserve_nulls);
         let cols = columns.iter().map(|s| s.as_ref()).collect::<Vec<&str>>();
         let df = self
             .df

src/expr.rs

@@ -65,7 +65,6 @@ pub mod column;
 pub mod conditional_expr;
 pub mod create_memory_table;
 pub mod create_view;
-pub mod cross_join;
 pub mod distinct;
 pub mod drop_table;
 pub mod empty_relation;
@@ -775,7 +774,6 @@ pub(crate) fn init_module(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<join::PyJoin>()?;
     m.add_class::<join::PyJoinType>()?;
     m.add_class::<join::PyJoinConstraint>()?;
-    m.add_class::<cross_join::PyCrossJoin>()?;
     m.add_class::<union::PyUnion>()?;
     m.add_class::<unnest::PyUnnest>()?;
     m.add_class::<unnest_expr::PyUnnestExpr>()?;