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Adds support to TimestampNTZ type in Delta
Previously this type was not supported in Spark and Spark 3.3 added support for this
To prevent(gate) older writers/readers from reading to this column we need a protocol(feature) bump that does the gating
* This PR creates a new TableFeature TimestampNTZ feature that is a ReaderWriter feature
* This is how to feature is automatically enabled
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Scenario | Previously | With this change
-- | -- | --
User creates a new table with timestamp NTZ column | AnalysisException saying type not supported | Protocol upgraded to feature vector protocol and TimestampNTZ Feature automatically enabled and DDL successful
User adds a new column of type TimestampNTZ on legacy protocol version | AnalysisException saying type not supported | User DDL completes successful.(Protocol also upgraded automatically)
User adds a new column of type TimestampNTZ on table with TimestampNTZFeature enabled on the table | AnalysisException saying type not supported | User DDL completes successful.
Closes #1626
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129 changes: 129 additions & 0 deletions
129
core/src/test/scala/org/apache/spark/sql/delta/DeltaTimestampNTZSuite.scala
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| /* | ||
| * Copyright (2021) The Delta Lake Project Authors. | ||
| * | ||
| * Licensed 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. | ||
| */ | ||
|
|
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| package org.apache.spark.sql.delta | ||
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| import java.sql.Timestamp | ||
| import java.time.LocalDateTime | ||
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| import org.apache.spark.sql.delta.actions.Protocol | ||
| import org.apache.spark.sql.delta.test.DeltaSQLCommandTest | ||
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| import org.apache.spark.SparkThrowable | ||
| import org.apache.spark.sql.{QueryTest, Row} | ||
| import org.apache.spark.sql.catalyst.TableIdentifier | ||
| import org.apache.spark.sql.test.SharedSparkSession | ||
| import org.apache.spark.sql.types.StructType | ||
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| class DeltaTimestampNTZSuite extends QueryTest | ||
| with SharedSparkSession with DeltaSQLCommandTest { | ||
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| private def getProtocolForTable(table: String): Protocol = { | ||
| val deltaLog = DeltaLog.forTable(spark, TableIdentifier(table)) | ||
| deltaLog.unsafeVolatileSnapshot.protocol | ||
| } | ||
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| test("create a new table with TIMESTAMP_NTZ, higher protocol and feature should be picked.") { | ||
| withTable("tbl") { | ||
| sql("CREATE TABLE tbl(c1 STRING, c2 TIMESTAMP, c3 TIMESTAMP_NTZ) USING DELTA") | ||
| sql( | ||
| """INSERT INTO tbl VALUES | ||
| |('foo','2022-01-02 03:04:05.123456','2022-01-02 03:04:05.123456')""".stripMargin) | ||
| assert(spark.table("tbl").head == Row( | ||
| "foo", | ||
| new Timestamp(2022 - 1900, 0, 2, 3, 4, 5, 123456000), | ||
| LocalDateTime.of(2022, 1, 2, 3, 4, 5, 123456000))) | ||
| assert(getProtocolForTable("tbl") == | ||
| TimestampNTZTableFeature.minProtocolVersion.withFeature(TimestampNTZTableFeature) | ||
| ) | ||
| } | ||
| } | ||
|
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| test("creating a table without TIMESTAMP_NTZ should use the usual minimum protocol") { | ||
| withTable("tbl") { | ||
| sql("CREATE TABLE tbl(c1 STRING, c2 TIMESTAMP, c3 TIMESTAMP) USING DELTA") | ||
| assert(getProtocolForTable("tbl") == Protocol(1, 2)) | ||
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| val deltaLog = DeltaLog.forTable(spark, TableIdentifier("tbl")) | ||
| assert( | ||
| !deltaLog.unsafeVolatileSnapshot.protocol.isFeatureSupported(TimestampNTZTableFeature), | ||
| s"Table tbl contains TimestampNTZFeature descriptor when its not supposed to" | ||
| ) | ||
| } | ||
| } | ||
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| test("add a new column using TIMESTAMP_NTZ should upgrade to the correct protocol versions") { | ||
| withTable("tbl") { | ||
| sql("CREATE TABLE tbl(c1 STRING, c2 TIMESTAMP) USING delta") | ||
| assert(getProtocolForTable("tbl") == Protocol(1, 2)) | ||
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| // Should throw error | ||
| val e = intercept[SparkThrowable] { | ||
| sql("ALTER TABLE tbl ADD COLUMN c3 TIMESTAMP_NTZ") | ||
| } | ||
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| // add table feature | ||
| sql(s"ALTER TABLE tbl " + | ||
| s"SET TBLPROPERTIES('delta.feature.timestampNtz' = 'supported')") | ||
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| sql("ALTER TABLE tbl ADD COLUMN c3 TIMESTAMP_NTZ") | ||
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| sql( | ||
| """INSERT INTO tbl VALUES | ||
| |('foo','2022-01-02 03:04:05.123456','2022-01-02 03:04:05.123456')""".stripMargin) | ||
| assert(spark.table("tbl").head == Row( | ||
| "foo", | ||
| new Timestamp(2022 - 1900, 0, 2, 3, 4, 5, 123456000), | ||
| LocalDateTime.of(2022, 1, 2, 3, 4, 5, 123456000))) | ||
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| assert(getProtocolForTable("tbl") == | ||
| TimestampNTZTableFeature.minProtocolVersion | ||
| .withFeature(TimestampNTZTableFeature) | ||
| .withFeature(InvariantsTableFeature) | ||
| .withFeature(AppendOnlyTableFeature) | ||
| ) | ||
| } | ||
| } | ||
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| test("use TIMESTAMP_NTZ in a partition column") { | ||
| withTable("delta_test") { | ||
| sql( | ||
| """CREATE TABLE delta_test(c1 STRING, c2 TIMESTAMP, c3 TIMESTAMP_NTZ) | ||
| |USING delta | ||
| |PARTITIONED BY (c3)""".stripMargin) | ||
| sql( | ||
| """INSERT INTO delta_test VALUES | ||
| |('foo','2022-01-02 03:04:05.123456','2022-01-02 03:04:05.123456')""".stripMargin) | ||
| assert(spark.table("delta_test").head == Row( | ||
| "foo", | ||
| new Timestamp(2022 - 1900, 0, 2, 3, 4, 5, 123456000), | ||
| LocalDateTime.of(2022, 1, 2, 3, 4, 5, 123456000))) | ||
| assert(getProtocolForTable("delta_test") == | ||
| TimestampNTZTableFeature.minProtocolVersion.withFeature(TimestampNTZTableFeature) | ||
| ) | ||
| } | ||
| } | ||
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| test("min/max stats collection should not apply on TIMESTAMP_NTZ") { | ||
| withTable("delta_test") { | ||
| sql("CREATE TABLE delta_test(c1 STRING, c2 TIMESTAMP, c3 TIMESTAMP_NTZ) USING delta") | ||
| val statsSchema = DeltaLog.forTable(spark, TableIdentifier("delta_test")).snapshot.statsSchema | ||
| assert(statsSchema("minValues").dataType == StructType.fromDDL("c1 STRING, c2 TIMESTAMP")) | ||
| assert(statsSchema("maxValues").dataType == StructType.fromDDL("c1 STRING, c2 TIMESTAMP")) | ||
| } | ||
| } | ||
| } |
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