Test Spark 4.0

.github/workflows/data/core/matrix.yml

@@ -22,4 +22,4 @@ latest: &latest
 matrix:
   small: [*max]
   full: [*min, *max]
-  nightly: [*min, *max, *latest]
+  nightly: [*min, *latest]

onetl/_util/scala.py

@@ -11,4 +11,6 @@ def get_default_scala_version(spark_version: Version) -> Version:
     """
     if spark_version.major < 3:
         return Version("2.11")
-    return Version("2.12")
+    if spark_version.major < 4:
+        return Version("2.12")
+    return Version("2.13")

onetl/connection/db_connection/jdbc_mixin/connection.py

@@ -431,10 +431,11 @@ def _execute_on_driver(
         statement_args = self._get_statement_args()
         jdbc_statement = self._build_statement(statement, statement_type, jdbc_connection, statement_args)
 
-        return self._execute_statement(jdbc_statement, statement, options, callback, read_only)
+        return self._execute_statement(jdbc_connection, jdbc_statement, statement, options, callback, read_only)
 
     def _execute_statement(
         self,
+        jdbc_connection,
         jdbc_statement,
         statement: str,
         options: JDBCFetchOptions | JDBCExecuteOptions,
@@ -472,7 +473,7 @@ def _execute_statement(
             else:
                 jdbc_statement.executeUpdate(statement)
 
-            return callback(jdbc_statement)
+            return callback(jdbc_connection, jdbc_statement)
 
     @staticmethod
     def _build_statement(
@@ -501,11 +502,11 @@ def _build_statement(
 
         return jdbc_connection.createStatement(*statement_args)
 
-    def _statement_to_dataframe(self, jdbc_statement) -> DataFrame:
+    def _statement_to_dataframe(self, jdbc_connection, jdbc_statement) -> DataFrame:
         result_set = jdbc_statement.getResultSet()
-        return self._resultset_to_dataframe(result_set)
+        return self._resultset_to_dataframe(jdbc_connection, result_set)
 
-    def _statement_to_optional_dataframe(self, jdbc_statement) -> DataFrame | None:
+    def _statement_to_optional_dataframe(self, jdbc_connection, jdbc_statement) -> DataFrame | None:
         """
         Returns ``org.apache.spark.sql.DataFrame`` or ``None``, if ResultSet is does not contain any columns.
 
@@ -522,9 +523,9 @@ def _statement_to_optional_dataframe(self, jdbc_statement) -> DataFrame | None:
         if not result_column_count:
             return None
 
-        return self._resultset_to_dataframe(result_set)
+        return self._resultset_to_dataframe(jdbc_connection, result_set)
 
-    def _resultset_to_dataframe(self, result_set) -> DataFrame:
+    def _resultset_to_dataframe(self, jdbc_connection, result_set) -> DataFrame:
         """
         Converts ``java.sql.ResultSet`` to ``org.apache.spark.sql.DataFrame`` using Spark's internal methods.
 
@@ -545,13 +546,25 @@ def _resultset_to_dataframe(self, result_set) -> DataFrame:
 
         java_converters = self.spark._jvm.scala.collection.JavaConverters  # type: ignore
 
-        if get_spark_version(self.spark) >= Version("3.4"):
+        if get_spark_version(self.spark) >= Version("4.0"):
+            result_schema = jdbc_utils.getSchema(
+                jdbc_connection,
+                result_set,
+                jdbc_dialect,
+                False,  # noqa: WPS425
+                False,  # noqa: WPS425
+            )
+        elif get_spark_version(self.spark) >= Version("3.4"):
             # https://github.com/apache/spark/commit/2349175e1b81b0a61e1ed90c2d051c01cf78de9b
             result_schema = jdbc_utils.getSchema(result_set, jdbc_dialect, False, False)  # noqa: WPS425
         else:
             result_schema = jdbc_utils.getSchema(result_set, jdbc_dialect, False)  # noqa: WPS425
 
-        result_iterator = jdbc_utils.resultSetToRows(result_set, result_schema)
+        if get_spark_version(self.spark) >= Version("4.0"):
+            result_iterator = jdbc_utils.resultSetToRows(result_set, result_schema, jdbc_dialect)
+        else:
+            result_iterator = jdbc_utils.resultSetToRows(result_set, result_schema)
+
         result_list = java_converters.seqAsJavaListConverter(result_iterator.toSeq()).asJava()
         jdf = self.spark._jsparkSession.createDataFrame(result_list, result_schema)  # type: ignore
 

onetl/connection/db_connection/kafka/connection.py

@@ -432,8 +432,13 @@ def get_packages(
             raise ValueError(f"Spark version must be at least 2.4, got {spark_ver}")
 
         scala_ver = Version(scala_version).min_digits(2) if scala_version else get_default_scala_version(spark_ver)
+
+        if spark_ver.major < 4:
+            version = spark_ver.format("{0}.{1}.{2}")
+        else:
+            version = "4.0.0-preview1"
         return [
-            f"org.apache.spark:spark-sql-kafka-0-10_{scala_ver.format('{0}.{1}')}:{spark_ver.format('{0}.{1}.{2}')}",
+            f"org.apache.spark:spark-sql-kafka-0-10_{scala_ver.format('{0}.{1}')}:{version}",
         ]
 
     def __enter__(self):

onetl/connection/file_df_connection/spark_s3/connection.py

@@ -246,9 +246,14 @@ def get_packages(
             # https://issues.apache.org/jira/browse/SPARK-23977
             raise ValueError(f"Spark version must be at least 3.x, got {spark_ver}")
 
+        if spark_ver.major < 4:
+            version = spark_ver.format("{0}.{1}.{2}")
+        else:
+            version = "4.0.0-preview1"
+
         scala_ver = Version(scala_version).min_digits(2) if scala_version else get_default_scala_version(spark_ver)
         # https://mvnrepository.com/artifact/org.apache.spark/spark-hadoop-cloud
-        return [f"org.apache.spark:spark-hadoop-cloud_{scala_ver.format('{0}.{1}')}:{spark_ver.format('{0}.{1}.{2}')}"]
+        return [f"org.apache.spark:spark-hadoop-cloud_{scala_ver.format('{0}.{1}')}:{version}"]
 
     @slot
     def path_from_string(self, path: os.PathLike | str) -> RemotePath:

onetl/file/format/avro.py

@@ -163,7 +163,12 @@ def get_packages(
         if scala_ver < Version("2.11"):
             raise ValueError(f"Scala version should be at least 2.11, got {scala_ver.format('{0}.{1}')}")
 
-        return [f"org.apache.spark:spark-avro_{scala_ver.format('{0}.{1}')}:{spark_ver.format('{0}.{1}.{2}')}"]
+        if spark_ver.major < 4:
+            version = spark_ver.format("{0}.{1}.{2}")
+        else:
+            version = "4.0.0-preview1"
+
+        return [f"org.apache.spark:spark-avro_{scala_ver.format('{0}.{1}')}:{version}"]
 
     @slot
     def check_if_supported(self, spark: SparkSession) -> None:

onetl/file/format/xml.py

@@ -193,6 +193,9 @@ def get_packages(  # noqa: WPS231
             )
 
         """
+        spark_ver = Version(spark_version)
+        if spark_ver.major >= 4:
+            return []
 
         if package_version:
             version = Version(package_version).min_digits(3)
@@ -202,7 +205,6 @@ def get_packages(  # noqa: WPS231
         else:
             version = Version("0.18.0").min_digits(3)
 
-        spark_ver = Version(spark_version)
         scala_ver = Version(scala_version).min_digits(2) if scala_version else get_default_scala_version(spark_ver)
 
         # Ensure compatibility with Spark and Scala versions

requirements/tests/spark-latest.txt

@@ -1,5 +1,5 @@
 numpy>=1.16
 pandas>=1.0
 pyarrow>=1.0
-pyspark
+pyspark==4.0.0.dev1
 sqlalchemy

tests/fixtures/spark.py

@@ -103,7 +103,7 @@ def maven_packages(request):
             # There is no MongoDB connector for Spark less than 3.2
             packages.extend(MongoDB.get_packages(spark_version=str(pyspark_version)))
 
-        if "excel" in markers:
+        if "excel" in markers and pyspark_version < Version("4.0"):
             # There is no Excel files support for Spark less than 3.2
             packages.extend(Excel.get_packages(spark_version=str(pyspark_version)))