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95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Tests for Spark dialect support in sqlglot.
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Verifies that a spark:// SQLAlchemy connection resolves to SparkEngineSpec,
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which uses the sqlglot Spark dialect and preserves Spark SQL functions like
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BOOL_OR (instead of rewriting them to LOGICAL_OR as the Hive dialect does).
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"""
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import pytest
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from sqlalchemy.engine.url import make_url
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from superset.db_engine_specs import get_engine_spec
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from superset.db_engine_specs.spark import SparkEngineSpec
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from superset.sql.parse import LimitMethod, SQLScript, SQLStatement
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def test_spark_url_resolves_to_spark_engine_spec() -> None:
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"""A spark:// SQLAlchemy URI should resolve to SparkEngineSpec."""
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url = make_url("spark://localhost:10009/default")
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backend = url.get_backend_name()
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engine_spec = get_engine_spec(backend)
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assert engine_spec is SparkEngineSpec
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def test_spark_engine_spec_engine_attribute() -> None:
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"""SparkEngineSpec.engine should be 'spark', not inherited 'hive'."""
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assert SparkEngineSpec.engine == "spark"
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@pytest.mark.parametrize(
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("sql", "expected"),
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[
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(
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"SELECT BOOL_OR(col) FROM my_table",
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"SELECT\n BOOL_OR(col)\nFROM my_table",
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),
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(
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"SELECT BOOL_OR('test_value' IN ('test', 'test_value'))",
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"SELECT\n BOOL_OR('test_value' IN ('test', 'test_value'))",
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),
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],
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)
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def test_spark_preserves_bool_or(sql: str, expected: str) -> None:
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"""BOOL_OR should be preserved when using the Spark engine.
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The Hive dialect rewrites BOOL_OR to LOGICAL_OR via sqlglot, but Spark SQL
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supports BOOL_OR natively so it must remain unchanged.
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"""
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script = SQLScript(sql, SparkEngineSpec.engine)
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result = script.statements[0].format()
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assert result == expected
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def test_spark_preserves_bool_or_with_limit() -> None:
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"""BOOL_OR should be preserved after applying a LIMIT (the SQLLab flow).
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In SQLLab, Superset parses the user's SQL, applies a LIMIT, and regenerates
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the SQL using the engine's sqlglot dialect. This test replicates that full
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flow for a spark:// connection.
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"""
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sql = "SELECT BOOL_OR('test_value' IN ('test', 'test_value'))"
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statement = SQLStatement(sql, SparkEngineSpec.engine)
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statement.set_limit_value(1001, LimitMethod.FORCE_LIMIT)
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result = statement.format()
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expected = "SELECT\n BOOL_OR('test_value' IN ('test', 'test_value'))\nLIMIT 1001"
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assert result == expected
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def test_hive_rewrites_bool_or_to_logical_or() -> None:
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"""Contrast: the Hive dialect rewrites BOOL_OR to LOGICAL_OR."""
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sql = "SELECT BOOL_OR('test_value' IN ('test', 'test_value'))"
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statement = SQLStatement(sql, "hive")
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statement.set_limit_value(1001, LimitMethod.FORCE_LIMIT)
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result = statement.format()
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assert "LOGICAL_OR" in result
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assert "BOOL_OR" not in result
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