python/pyspark/sql/tests/test_nearest_by_join.py - master - githubmirror/spark

python/pyspark/sql/tests/test_nearest_by_join.py
263 строки9 KB
Zero Qu
[SPARK-57088][SQL] Allow non-deterministic ranking expression for EXACT NEAREST BY
27 май 2026, 08:47
27 май 2026, 08:472603f6a
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#


from pyspark.errors import AnalysisException
from pyspark.sql import Row
from pyspark.sql import functions as sf
from pyspark.testing import assertDataFrameEqual
from pyspark.testing.sqlutils import ReusedSQLTestCase


class NearestByJoinTestsMixin:
    """Mixin run against both classic (`ReusedSQLTestCase`) and Connect
    (`ReusedConnectTestCase`) to ensure parity between the two paths."""

    @property
    def users(self):
        return self.spark.createDataFrame([(1, 10.0), (2, 20.0), (3, 30.0)], ["user_id", "score"])

    @property
    def products(self):
        return self.spark.createDataFrame(
            [("A", 11.0), ("B", 22.0), ("C", 5.0)], ["product", "pscore"]
        )

    def test_inner_similarity_k1(self):
        users, products = self.users, self.products
        result = (
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=1,
                mode="approx",
                direction="similarity",
            )
            .select("user_id", "product")
            .orderBy("user_id")
        )
        assertDataFrameEqual(
            result,
            [Row(user_id=1, product="A"), Row(user_id=2, product="B"), Row(user_id=3, product="B")],
        )

    def test_inner_distance_k2(self):
        users, products = self.users, self.products
        result = (
            users.nearestByJoin(
                products,
                sf.abs(users.score - products.pscore),
                numResults=2,
                mode="approx",
                direction="distance",
            )
            .select("user_id", "product")
            .orderBy("user_id", "product")
        )
        assertDataFrameEqual(
            result,
            [
                Row(user_id=1, product="A"),
                Row(user_id=1, product="C"),
                Row(user_id=2, product="A"),
                Row(user_id=2, product="B"),
                Row(user_id=3, product="A"),
                Row(user_id=3, product="B"),
            ],
        )

    def test_left_outer_with_empty_right(self):
        users, products = self.users, self.products
        empty = products.filter(sf.lit(False))
        result = (
            users.nearestByJoin(
                empty,
                -sf.abs(users.score - empty.pscore),
                numResults=1,
                mode="exact",
                direction="similarity",
                joinType="leftouter",
            )
            .select("user_id", "product")
            .orderBy("user_id")
        )
        assertDataFrameEqual(
            result,
            [
                Row(user_id=1, product=None),
                Row(user_id=2, product=None),
                Row(user_id=3, product=None),
            ],
        )

    def test_select_star_schema_has_no_internal_columns(self):
        users, products = self.users, self.products
        result = users.nearestByJoin(
            products,
            -sf.abs(users.score - products.pscore),
            numResults=1,
            mode="exact",
            direction="similarity",
        )
        # No `__qid`, `__nearest_matches__`, or other rewrite-internal columns leak through.
        assert sorted(result.columns) == ["product", "pscore", "score", "user_id"]

    def test_invalid_num_results_low(self):
        users, products = self.users, self.products
        with self.assertRaises(AnalysisException) as pe:
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=0,
                mode="approx",
                direction="similarity",
            )
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.NUM_RESULTS_OUT_OF_RANGE",
            messageParameters={"numResults": "0", "min": "1", "max": "100000"},
        )

    def test_invalid_num_results_high(self):
        users, products = self.users, self.products
        with self.assertRaises(AnalysisException) as pe:
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=200000,
                mode="approx",
                direction="similarity",
            )
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.NUM_RESULTS_OUT_OF_RANGE",
            messageParameters={"numResults": "200000", "min": "1", "max": "100000"},
        )

    def test_invalid_join_type(self):
        users, products = self.users, self.products
        with self.assertRaises(AnalysisException) as pe:
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=1,
                mode="approx",
                direction="similarity",
                joinType="outer",
            )
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.UNSUPPORTED_JOIN_TYPE",
            messageParameters={"joinType": "outer", "supported": "'INNER', 'LEFT OUTER'"},
        )

    def test_invalid_mode(self):
        users, products = self.users, self.products
        with self.assertRaises(AnalysisException) as pe:
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=1,
                mode="bogus",
                direction="similarity",
            )
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.UNSUPPORTED_MODE",
            messageParameters={"mode": "bogus", "supported": "'approx', 'exact'"},
        )

    def test_invalid_direction(self):
        users, products = self.users, self.products
        with self.assertRaises(AnalysisException) as pe:
            users.nearestByJoin(
                products,
                -sf.abs(users.score - products.pscore),
                numResults=1,
                mode="approx",
                direction="elsewhere",
            )
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.UNSUPPORTED_DIRECTION",
            messageParameters={
                "direction": "elsewhere",
                "supported": "'distance', 'similarity'",
            },
        )

    def test_rejected_when_crossjoin_disabled(self):
        users, products = self.users, self.products
        with self.sql_conf({"spark.sql.crossJoin.enabled": "false"}):
            with self.assertRaises(AnalysisException) as pe:
                users.nearestByJoin(
                    products,
                    -sf.abs(users.score - products.pscore),
                    numResults=1,
                    mode="exact",
                    direction="similarity",
                ).collect()
            self.check_error(
                exception=pe.exception,
                errorClass="NEAREST_BY_JOIN.CROSS_JOIN_NOT_ENABLED",
                messageParameters={},
            )

    def test_exact_with_nondeterministic_ranking_accepted(self):
        users, products = self.users, self.products
        # Result rows are nondeterministic; only assert that each left row gets exactly one match.
        count = users.nearestByJoin(
            products,
            sf.rand(0) + products.pscore,
            numResults=1,
            mode="exact",
            direction="similarity",
        ).count()
        self.assertEqual(count, 3)

    def test_streaming_inputs_rejected(self):
        streaming_users = (
            self.spark.readStream.format("rate")
            .option("rowsPerSecond", 1)
            .load()
            .selectExpr("CAST(value AS INT) AS user_id", "CAST(value AS DOUBLE) AS score")
        )
        products = self.products
        with self.assertRaises(AnalysisException) as pe:
            # `.schema` forces analysis without starting the streaming query.
            _ = streaming_users.nearestByJoin(
                products,
                -sf.abs(streaming_users.score - products.pscore),
                numResults=1,
                mode="exact",
                direction="similarity",
            ).schema
        self.check_error(
            exception=pe.exception,
            errorClass="NEAREST_BY_JOIN.STREAMING_NOT_SUPPORTED",
            messageParameters={},
        )


class NearestByJoinTests(NearestByJoinTestsMixin, ReusedSQLTestCase):
    pass


if __name__ == "__main__":
    from pyspark.testing import main

    main()