Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/20208#discussion_r162781286 --- Diff: sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SchemaEvolutionTest.scala --- @@ -0,0 +1,436 @@ +/* + * 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. + */ + +package org.apache.spark.sql.execution.datasources + +import java.io.File + +import org.apache.spark.sql.{QueryTest, Row} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils} + +/** + * Schema can evolve in several ways and the followings are supported in file-based data sources. + * + * 1. Add a column + * 2. Remove a column + * 3. Change a column position + * 4. Change a column type + * + * Here, we consider safe evolution without data loss. For example, data type evolution should be + * from small types to larger types like `int`-to-`long`, not vice versa. + * + * So far, file-based data sources have schema evolution coverages like the followings. + * + * | File Format | Coverage | Note | + * | ------------ | ------------ | ------------------------------------------------------ | + * | TEXT | N/A | Schema consists of a single string column. | + * | CSV | 1, 2, 4 | | + * | JSON | 1, 2, 3, 4 | | + * | ORC | 1, 2, 3, 4 | Native vectorized ORC reader has the widest coverage. | + * | PARQUET | 1, 2, 3 | | + * + * This aims to provide an explicit test coverage for schema evolution on file-based data sources. + * Since a file format has its own coverage of schema evolution, we need a test suite + * for each file-based data source with corresponding supported test case traits. + * + * The following is a hierarchy of test traits. + * + * SchemaEvolutionTest + * -> AddColumnEvolutionTest + * -> RemoveColumnEvolutionTest + * -> ChangePositionEvolutionTest + * -> BooleanTypeEvolutionTest + * -> IntegralTypeEvolutionTest + * -> ToDoubleTypeEvolutionTest + * -> ToDecimalTypeEvolutionTest + */ + +trait SchemaEvolutionTest extends QueryTest with SQLTestUtils with SharedSQLContext { + val format: String + val options: Map[String, String] = Map.empty[String, String] +} + +/** + * Add column. + * This test suite assumes that the missing column should be `null`. + */ +trait AddColumnEvolutionTest extends SchemaEvolutionTest { + import testImplicits._ + + test("append column at the end") { + withTempDir { dir => + val path = dir.getCanonicalPath + + val df1 = Seq("a", "b").toDF("col1") + val df2 = df1.withColumn("col2", lit("x")) + val df3 = df2.withColumn("col3", lit("y")) + + val dir1 = s"$path${File.separator}part=one" + val dir2 = s"$path${File.separator}part=two" + val dir3 = s"$path${File.separator}part=three" + + df1.write.mode("overwrite").format(format).options(options).save(dir1) + df2.write.mode("overwrite").format(format).options(options).save(dir2) + df3.write.mode("overwrite").format(format).options(options).save(dir3) + + val df = spark.read + .schema(df3.schema) + .format(format) + .options(options) + .load(path) + + checkAnswer(df, Seq( + Row("a", null, null, "one"), + Row("b", null, null, "one"), + Row("a", "x", null, "two"), + Row("b", "x", null, "two"), + Row("a", "x", "y", "three"), + Row("b", "x", "y", "three"))) + } + } +} + +/** + * Remove column. + * This test suite is identical with AddColumnEvolutionTest, + * but this test suite ensures that the schema and result are truncated to the given schema. + */ +trait RemoveColumnEvolutionTest extends SchemaEvolutionTest { + import testImplicits._ + + test("remove column at the end") { + withTempDir { dir => + val path = dir.getCanonicalPath + + val df1 = Seq(("1", "a"), ("2", "b")).toDF("col1", "col2") + val df2 = df1.withColumn("col3", lit("y")) + + val dir1 = s"$path${File.separator}part=two" + val dir2 = s"$path${File.separator}part=three" + + df1.write.mode("overwrite").format(format).options(options).save(dir1) + df2.write.mode("overwrite").format(format).options(options).save(dir2) + + val df = spark.read + .schema(df1.schema) + .format(format) + .options(options) + .load(path) + + checkAnswer(df, Seq( + Row("1", "a", "two"), + Row("2", "b", "two"), + Row("1", "a", "three"), + Row("2", "b", "three"))) + } + } +} + +/** + * Change column positions. + * This suite assumes that all data set have the same number of columns. + */ +trait ChangePositionEvolutionTest extends SchemaEvolutionTest { + import testImplicits._ + + test("change column position") { + withTempDir { dir => + // val path = dir.getCanonicalPath + val path = "/tmp/change" + + val df1 = Seq(("1", "a"), ("2", "b"), ("3", "c")).toDF("col1", "col2") + val df2 = Seq(("d", "4"), ("e", "5"), ("f", "6")).toDF("col2", "col1") + val unionDF = df1.unionByName(df2) + + val dir1 = s"$path${File.separator}part=one" + val dir2 = s"$path${File.separator}part=two" + + df1.write.mode("overwrite").format(format).options(options).save(dir1) + df2.write.mode("overwrite").format(format).options(options).save(dir2) + + val df = spark.read + .schema(unionDF.schema) + .format(format) + .options(options) + .load(path) + .select("col1", "col2") + + checkAnswer(df, unionDF) + } + } +} + +trait BooleanTypeEvolutionTest extends SchemaEvolutionTest { + import testImplicits._ + + test("boolean to byte/short/int/long") { + withTempDir { dir => + val path = dir.getCanonicalPath + + val values = (1 to 10).map(_ % 2) + val booleanDF = (1 to 10).map(_ % 2 == 1).toDF("col1") + val byteDF = values.map(_.toByte).toDF("col1") + val shortDF = values.map(_.toShort).toDF("col1") + val intDF = values.toDF("col1") + val longDF = values.map(_.toLong).toDF("col1") + + booleanDF.write.mode("overwrite").format(format).options(options).save(path) + + val df1 = spark.read + .schema("col1 byte") + .format(format) + .options(options) + .load(path) + checkAnswer(df1, byteDF) + + val df2 = spark.read + .schema("col1 short") + .format(format) + .options(options) + .load(path) + checkAnswer(df2, shortDF) + + val df3 = spark.read + .schema("col1 int") + .format(format) + .options(options) + .load(path) + checkAnswer(df3, intDF) + + val df4 = spark.read + .schema("col1 long") + .format(format) + .options(options) + .load(path) + checkAnswer(df4, longDF) + } + } +} + +trait IntegralTypeEvolutionTest extends SchemaEvolutionTest { + + import testImplicits._ + + test("change column type from `byte` to `short/int/long`") { --- End diff -- nit: `` `byte` `` -> `byte` or the opposite for consistency with the same instances.
--- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org