python/pyspark/sql/tests/typing/test_session.yml - master - githubmirror/spark

python/pyspark/sql/tests/typing/test_session.yml
100 строк5 KB
Tian Gao
[SPARK-55355][PYTHON] Upgrade mypy version to the latest
10 фев 2026, 02:29
10 фев 2026, 02:29b4b8165
#
# 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.
#

- case: createDataFrameStructsValid
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.types import StructType, StructField, StringType, IntegerType

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]
    schema = StructType([
        StructField("name", StringType(), True),
        StructField("age", IntegerType(), True)
    ])

    # Valid structs
    spark.createDataFrame(data)
    spark.createDataFrame(data, samplingRatio=0.1)
    spark.createDataFrame(data, ("name", "age"))
    spark.createDataFrame(data, schema)
    spark.createDataFrame(data, "name string, age integer")
    spark.createDataFrame([(1, ("foo", "bar"))], ("_1", "_2"))
    spark.createDataFrame(data, ("name", "age"), samplingRatio=0.1)


- case: createDataFrameScalarsValid
  main: |

    from pyspark.sql import SparkSession
    from pyspark.sql.types import StructType, StructField, StringType, IntegerType

    spark = SparkSession.builder.getOrCreate()

    # Scalars
    spark.createDataFrame([1, 2, 3], IntegerType())
    spark.createDataFrame(["foo", "bar"], "string")


- case: createDataFrameStructsInvalid
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.types import StructType, StructField, StringType, IntegerType

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]

    schema = StructType([
        StructField("name", StringType(), True),
        StructField("age", IntegerType(), True)
    ])

    # Invalid product should have StructType schema
    spark.createDataFrame(data, IntegerType())

    # This shouldn't type check, though is technically speaking valid
    # because  samplingRatio is ignored
    spark.createDataFrame(data, schema, samplingRatio=0.1)

  out: |
    main:14: error: Value of type variable "AtomicValue" of "createDataFrame" of "SparkSession" cannot be "tuple[str, int]"  [type-var]
    main:18: error: No overload variant of "createDataFrame" of "SparkSession" matches argument types "list[tuple[str, int]]", "StructType", "float"  [call-overload]
    main:18: note: Possible overload variants:
    main:18: note:     def [RowLike: (list[Any], tuple[Any, ...], Row)] createDataFrame(self, data: Iterable[RowLike], schema: list[str] | tuple[str, ...] = ..., samplingRatio: float | None = ...) -> DataFrame
    main:18: note:     def [RowLike: (list[Any], tuple[Any, ...], Row)] createDataFrame(self, data: RDD[RowLike], schema: list[str] | tuple[str, ...] = ..., samplingRatio: float | None = ...) -> DataFrame
    main:18: note:     def [RowLike: (list[Any], tuple[Any, ...], Row)] createDataFrame(self, data: Iterable[RowLike], schema: StructType | str, *, verifySchema: bool = ...) -> DataFrame
    main:18: note:     def [RowLike: (list[Any], tuple[Any, ...], Row)] createDataFrame(self, data: RDD[RowLike], schema: StructType | str, *, verifySchema: bool = ...) -> DataFrame
    main:18: note:     def [AtomicValue: (datetime, date, Decimal, bool, str, int, float)] createDataFrame(self, data: RDD[AtomicValue], schema: AtomicType | str, verifySchema: bool = ...) -> DataFrame
    main:18: note:     def [AtomicValue: (datetime, date, Decimal, bool, str, int, float)] createDataFrame(self, data: Iterable[AtomicValue], schema: AtomicType | str, verifySchema: bool = ...) -> DataFrame
    main:18: note:     def createDataFrame(self, data: DataFrame, samplingRatio: float | None = ...) -> DataFrame
    main:18: note:     def createDataFrame(self, data: Any, samplingRatio: float | None = ...) -> DataFrame
    main:18: note:     def createDataFrame(self, data: DataFrame, schema: StructType | str, verifySchema: bool = ...) -> DataFrame
    main:18: note:     def createDataFrame(self, data: Any, schema: StructType | str, verifySchema: bool = ...) -> DataFrame

- case: createDataFrameFromEmptyRdd
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.types import StructType

    spark = SparkSession.builder.getOrCreate()

    spark.createDataFrame(
        spark.sparkContext.emptyRDD(),
        schema=StructType(),
    )