refactor(api): refactor the implementation of windowing

ibis/backends/flink/tests/conftest.py

@@ -13,6 +13,25 @@
 if TYPE_CHECKING:
     from pyflink.table import StreamTableEnvironment
 
+TEST_TABLES["functional_alltypes"] = ibis.schema(
+    {
+        "id": "int32",
+        "bool_col": "boolean",
+        "tinyint_col": "int8",
+        "smallint_col": "int16",
+        "int_col": "int32",
+        "bigint_col": "int64",
+        "float_col": "float32",
+        "double_col": "float64",
+        "date_string_col": "string",
+        "string_col": "string",
+        "timestamp_col": "timestamp(3)",  # overriding the higher level fixture with precision because Flink's
+        # watermark must use a field of type TIMESTAMP(p) or TIMESTAMP_LTZ(p), where 'p' is from 0 to 3
+        "year": "int32",
+        "month": "int32",
+    }
+)
+
 
 def get_table_env(
     local_env: bool,
@@ -152,24 +171,7 @@ def awards_players_schema():
 
 @pytest.fixture
 def functional_alltypes_schema():
-    return ibis.schema(
-        {
-            "id": "int32",
-            "bool_col": "boolean",
-            "tinyint_col": "int8",
-            "smallint_col": "int16",
-            "int_col": "int32",
-            "bigint_col": "int64",
-            "float_col": "float32",
-            "double_col": "float64",
-            "date_string_col": "string",
-            "string_col": "string",
-            "timestamp_col": "timestamp(3)",  # overriding the higher level fixture with precision because Flink's
-            # watermark must use a field of type TIMESTAMP(p) or TIMESTAMP_LTZ(p), where 'p' is from 0 to 3
-            "year": "int32",
-            "month": "int32",
-        }
-    )
+    return TEST_TABLES["functional_alltypes"]
 
 
 @pytest.fixture
@@ -188,3 +190,33 @@ def generate_csv_configs(csv_file):
         }
 
     return generate_csv_configs
+
+
+@pytest.fixture(scope="session")
+def functional_alltypes_no_header(tmpdir_factory, data_dir):
+    file = tmpdir_factory.mktemp("data") / "functional_alltypes.csv"
+    with (
+        open(data_dir / "csv" / "functional_alltypes.csv") as reader,
+        open(str(file), mode="w") as writer,
+    ):
+        reader.readline()  # read the first line and discard it
+        for line in reader:
+            writer.write(line)
+    return file
+
+
+@pytest.fixture(scope="session", autouse=True)
+def functional_alltypes_with_watermark(con, functional_alltypes_no_header):
+    # create a streaming table with watermark for testing event-time based ops
+    t = con.create_table(
+        "functional_alltypes_with_watermark",
+        schema=TEST_TABLES["functional_alltypes"],
+        tbl_properties={
+            "connector": "filesystem",
+            "path": functional_alltypes_no_header,
+            "format": "csv",
+        },
+        watermark=ibis.watermark("timestamp_col", ibis.interval(seconds=10)),
+        temp=True,
+    )
+    return t

ibis/backends/flink/tests/test_compiler.py

@@ -1,13 +1,8 @@
 from __future__ import annotations
 
-from operator import methodcaller
-
 import pytest
 from pytest import param
 
-import ibis
-from ibis.common.deferred import _
-
 
 def test_sum(simple_table, assert_sql):
     expr = simple_table.a.sum()
@@ -103,48 +98,3 @@ def test_having(simple_table, assert_sql):
         .aggregate(simple_table.b.sum().name("b_sum"))
     )
     assert_sql(expr)
-
-
-@pytest.mark.parametrize(
-    "method",
-    [
-        methodcaller("tumble", window_size=ibis.interval(minutes=15)),
-        methodcaller(
-            "hop",
-            window_size=ibis.interval(minutes=15),
-            window_slide=ibis.interval(minutes=1),
-        ),
-        methodcaller(
-            "cumulate",
-            window_size=ibis.interval(minutes=1),
-            window_step=ibis.interval(seconds=10),
-        ),
-    ],
-    ids=["tumble", "hop", "cumulate"],
-)
-def test_windowing_tvf(simple_table, method, assert_sql):
-    expr = method(simple_table.window_by(time_col=simple_table.i))
-    assert_sql(expr)
-
-
-def test_window_aggregation(simple_table, assert_sql):
-    expr = (
-        simple_table.window_by(time_col=simple_table.i)
-        .tumble(window_size=ibis.interval(minutes=15))
-        .group_by(["window_start", "window_end", "g"])
-        .aggregate(mean=_.d.mean())
-    )
-    assert_sql(expr)
-
-
-def test_window_topn(simple_table, assert_sql):
-    expr = simple_table.window_by(time_col="i").tumble(
-        window_size=ibis.interval(seconds=600),
-    )["a", "b", "c", "d", "g", "window_start", "window_end"]
-    expr = expr.mutate(
-        rownum=ibis.row_number().over(
-            group_by=["window_start", "window_end"], order_by=ibis.desc("g")
-        )
-    )
-    expr = expr[expr.rownum <= 3]
-    assert_sql(expr)

ibis/backends/flink/tests/test_window.py

@@ -5,6 +5,7 @@
 from pytest import param
 
 import ibis
+from ibis import _
 from ibis.backends.tests.errors import Py4JJavaError
 
 
@@ -53,13 +54,37 @@ def test_window_invalid_start_end(con, window):
     con.execute(expr)
 
 
-def test_range_window(con, simple_table, assert_sql):
+def test_range_window(simple_table, assert_sql):
     expr = simple_table.f.sum().over(
         range=(-ibis.interval(minutes=500), 0), order_by=simple_table.f
     )
     assert_sql(expr)
 
 
-def test_rows_window(con, simple_table, assert_sql):
+def test_rows_window(simple_table, assert_sql):
     expr = simple_table.f.sum().over(rows=(-1000, 0), order_by=simple_table.f)
     assert_sql(expr)
+
+
+def test_tumble_window_by_grouped_agg(con):
+    t = con.table("functional_alltypes_with_watermark")
+    expr = (
+        t.window_by(time_col=t.timestamp_col)
+        .tumble(size=ibis.interval(seconds=30))
+        .agg(by=["string_col"], avg=_.float_col.mean())
+    )
+    result = expr.to_pandas()
+    assert list(result.columns) == ["window_start", "window_end", "string_col", "avg"]
+    assert result.shape == (610, 4)
+
+
+def test_tumble_window_by_ungrouped_agg(con):
+    t = con.table("functional_alltypes_with_watermark")
+    expr = (
+        t.window_by(time_col=t.timestamp_col)
+        .tumble(size=ibis.interval(seconds=30))
+        .agg(avg=_.float_col.mean())
+    )
+    result = expr.to_pandas()
+    assert list(result.columns) == ["window_start", "window_end", "avg"]
+    assert result.shape == (610, 3)