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From cloud-fan <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-14346] SHOW CREATE TABLE for data sourc...
Date Wed, 11 May 2016 14:38:52 GMT
Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12781#discussion_r62860157
  
    --- Diff: sql/hive/src/test/scala/org/apache/spark/sql/hive/ShowCreateTableSuite.scala
---
    @@ -0,0 +1,171 @@
    +/*
    + * 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.hive
    +
    +import org.apache.spark.sql.QueryTest
    +import org.apache.spark.sql.catalyst.TableIdentifier
    +import org.apache.spark.sql.catalyst.catalog.CatalogTable
    +import org.apache.spark.sql.hive.test.TestHiveSingleton
    +import org.apache.spark.sql.test.SQLTestUtils
    +import org.apache.spark.util.Utils
    +
    +class ShowCreateTableSuite extends QueryTest with SQLTestUtils with TestHiveSingleton
{
    +  import testImplicits._
    +
    +  test("data source table with user specified schema") {
    +    withTable("ddl_test1") {
    +      val jsonFilePath = Utils.getSparkClassLoader.getResource("sample.json").getFile
    +
    +      sql(
    +        s"""CREATE TABLE ddl_test1 (
    +           |  a STRING,
    +           |  b STRING,
    +           |  `extra col` ARRAY<INT>,
    +           |  `<another>` STRUCT<x: INT, y: ARRAY<BOOLEAN>>
    +           |)
    +           |USING json
    +           |OPTIONS (
    +           | PATH '$jsonFilePath'
    +           |)
    +         """.stripMargin
    +      )
    +
    +      checkCreateTable("ddl_test1")
    +    }
    +  }
    +
    +  test("data source table CTAS") {
    +    withTable("ddl_test2") {
    +      sql(
    +        s"""CREATE TABLE ddl_test2
    +           |USING json
    +           |AS SELECT 1 AS a, "foo" AS b
    +         """.stripMargin
    +      )
    +
    +      checkCreateTable("ddl_test2")
    +    }
    +  }
    +
    +  test("partitioned data source table") {
    +    withTable("ddl_test3") {
    +      sql(
    +        s"""CREATE TABLE ddl_test3
    +           |USING json
    +           |PARTITIONED BY (b)
    +           |AS SELECT 1 AS a, "foo" AS b
    +         """.stripMargin
    +      )
    +
    +      checkCreateTable("ddl_test3")
    +    }
    +  }
    +
    +  test("bucketed data source table") {
    +    withTable("ddl_test3") {
    +      sql(
    +        s"""CREATE TABLE ddl_test3
    +           |USING json
    +           |CLUSTERED BY (a) SORTED BY (b) INTO 2 BUCKETS
    +           |AS SELECT 1 AS a, "foo" AS b
    +         """.stripMargin
    +      )
    +
    +      checkCreateTable("ddl_test3")
    +    }
    +  }
    +
    +  test("partitioned bucketed data source table") {
    +    withTable("ddl_test4") {
    +      sql(
    +        s"""CREATE TABLE ddl_test4
    +           |USING json
    +           |PARTITIONED BY (c)
    +           |CLUSTERED BY (a) SORTED BY (b) INTO 2 BUCKETS
    +           |AS SELECT 1 AS a, "foo" AS b, 2.5 AS c
    +         """.stripMargin
    +      )
    +
    +      checkCreateTable("ddl_test4")
    +    }
    +  }
    +
    +  test("data source table using Dataset API") {
    +    withTable("ddl_test5") {
    +      spark
    +        .range(3)
    +        .select('id as 'a, 'id as 'b, 'id as 'c, 'id as 'd, 'id as 'e)
    +        .write
    +        .mode("overwrite")
    +        .partitionBy("a", "b")
    +        .bucketBy(2, "c", "d")
    +        .saveAsTable("ddl_test5")
    +
    +      checkCreateTable(TableIdentifier("ddl_test5", Some("default")))
    +    }
    +  }
    +
    +  private def checkCreateTable(table: String): Unit = {
    +    checkCreateTable(TableIdentifier(table, Some("default")))
    +  }
    +
    +  private def checkCreateTable(table: TableIdentifier): Unit = {
    +    val db = table.database.getOrElse("default")
    +    val expected = spark.externalCatalog.getTable(db, table.table)
    +    val shownDDL = sql(s"SHOW CREATE TABLE ${table.quotedString}").head().getString(0)
    +    sql(s"DROP TABLE ${table.quotedString}")
    +
    +    withTable(table.table) {
    +      val newDDL = shownDDL.replaceFirst(table.table, table.table)
    +      sql(newDDL)
    +      val actual = spark.externalCatalog.getTable(db, table.table)
    +      checkCatalogTables(expected, actual)
    +    }
    +  }
    +
    +  private def checkCatalogTables(expected: CatalogTable, actual: CatalogTable): Unit
= {
    +    def normalize(table: CatalogTable): CatalogTable = {
    +      val nondeterministicProps = Set(
    +        "CreateTime",
    +        "transient_lastDdlTime",
    +        "grantTime",
    +        "lastUpdateTime",
    +        "last_modified_by",
    +        "last_modified_time",
    +        "Owner:",
    +        "COLUMN_STATS_ACCURATE",
    +        // The following are hive specific schema parameters which we do not need to
match exactly.
    +        "numFiles",
    +        "numRows",
    +        "rawDataSize",
    +        "totalSize",
    +        "totalNumberFiles",
    +        "maxFileSize",
    +        "minFileSize"
    +      )
    +
    +      table.copy(
    +        identifier = expected.identifier,
    --- End diff --
    
    looks like `expected` and `actual` will always have same identifier? We remove the table
and create a new one with same name in `checkCreateTable`.


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