io.trino.tests.product.iceberg.TestIcebergSparkCompatibility Maven / Gradle / Ivy
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/*
* 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.
*/
package io.trino.tests.product.iceberg;
import com.google.common.collect.ImmutableList;
import com.google.common.collect.ImmutableMap;
import com.google.common.collect.Streams;
import com.google.inject.Inject;
import io.airlift.concurrent.MoreFutures;
import io.trino.plugin.hive.metastore.thrift.ThriftMetastoreClient;
import io.trino.tempto.AfterMethodWithContext;
import io.trino.tempto.BeforeMethodWithContext;
import io.trino.tempto.ProductTest;
import io.trino.tempto.hadoop.hdfs.HdfsClient;
import io.trino.tempto.query.QueryExecutionException;
import io.trino.tempto.query.QueryExecutor;
import io.trino.tempto.query.QueryResult;
import io.trino.tests.product.hive.Engine;
import io.trino.tests.product.hive.TestHiveMetastoreClientFactory;
import org.apache.thrift.TException;
import org.testng.SkipException;
import org.testng.annotations.DataProvider;
import org.testng.annotations.Test;
import java.math.BigDecimal;
import java.net.URI;
import java.sql.Date;
import java.sql.Timestamp;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.CyclicBarrier;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.function.Function;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
import java.util.stream.Stream;
import static com.google.common.collect.ImmutableList.toImmutableList;
import static com.google.common.collect.Iterables.getOnlyElement;
import static io.trino.tempto.assertions.QueryAssert.Row;
import static io.trino.tempto.assertions.QueryAssert.Row.row;
import static io.trino.tempto.assertions.QueryAssert.assertQueryFailure;
import static io.trino.testing.DataProviders.cartesianProduct;
import static io.trino.testing.DataProviders.toDataProvider;
import static io.trino.testing.TestingNames.randomNameSuffix;
import static io.trino.tests.product.TestGroups.ICEBERG;
import static io.trino.tests.product.TestGroups.ICEBERG_JDBC;
import static io.trino.tests.product.TestGroups.ICEBERG_NESSIE;
import static io.trino.tests.product.TestGroups.ICEBERG_REST;
import static io.trino.tests.product.TestGroups.PROFILE_SPECIFIC_TESTS;
import static io.trino.tests.product.iceberg.TestIcebergSparkCompatibility.CreateMode.CREATE_TABLE_AND_INSERT;
import static io.trino.tests.product.iceberg.TestIcebergSparkCompatibility.CreateMode.CREATE_TABLE_AS_SELECT;
import static io.trino.tests.product.iceberg.TestIcebergSparkCompatibility.CreateMode.CREATE_TABLE_WITH_NO_DATA_AND_INSERT;
import static io.trino.tests.product.iceberg.util.IcebergTestUtils.getLatestMetadataFilename;
import static io.trino.tests.product.iceberg.util.IcebergTestUtils.getTableLocation;
import static io.trino.tests.product.iceberg.util.IcebergTestUtils.stripNamenodeURI;
import static io.trino.tests.product.utils.QueryExecutors.onHive;
import static io.trino.tests.product.utils.QueryExecutors.onSpark;
import static io.trino.tests.product.utils.QueryExecutors.onTrino;
import static java.lang.String.format;
import static java.util.Arrays.asList;
import static java.util.Locale.ENGLISH;
import static java.util.concurrent.TimeUnit.SECONDS;
import static java.util.stream.Collectors.toUnmodifiableSet;
import static org.assertj.core.api.Assertions.assertThat;
import static org.assertj.core.api.Assertions.assertThatThrownBy;
/**
* Tests compatibility between Iceberg connector and Spark Iceberg.
*/
public class TestIcebergSparkCompatibility
extends ProductTest
{
@Inject
private HdfsClient hdfsClient;
@Inject
private TestHiveMetastoreClientFactory testHiveMetastoreClientFactory;
private ThriftMetastoreClient metastoreClient;
@BeforeMethodWithContext
public void setup()
throws TException
{
metastoreClient = testHiveMetastoreClientFactory.createMetastoreClient();
// Create 'default' schema if it doesn't exist because JDBC catalog doesn't have such schema
onTrino().executeQuery("CREATE SCHEMA IF NOT EXISTS iceberg.default WITH (location = 'hdfs://hadoop-master:9000/user/hive/warehouse/default')");
}
@AfterMethodWithContext
public void tearDown()
{
metastoreClient.close();
metastoreClient = null;
}
// see spark-defaults.conf
private static final String SPARK_CATALOG = "iceberg_test";
private static final String TRINO_CATALOG = "iceberg";
private static final String TEST_SCHEMA_NAME = "default";
@BeforeMethodWithContext
public void setUp()
{
// we create default schema so that we can re-use the same test for the Iceberg REST catalog (since Iceberg itself doesn't support a default schema)
onTrino().executeQuery(format("CREATE SCHEMA IF NOT EXISTS %s.%s", TRINO_CATALOG, TEST_SCHEMA_NAME));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadingSparkData(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_trino_reading_primitive_types_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (" +
" _string STRING" +
", _bigint BIGINT" +
", _integer INTEGER" +
", _real REAL" +
", _double DOUBLE" +
", _short_decimal decimal(8,2)" +
", _long_decimal decimal(38,19)" +
", _boolean BOOLEAN" +
", _timestamp TIMESTAMP" +
", _date DATE" +
", _binary BINARY" +
") USING ICEBERG " +
"TBLPROPERTIES ('write.format.default'='%s', 'format-version' = %s)",
sparkTableName,
storageFormat,
specVersion));
// Validate queries on an empty table created by Spark
assertThat(onTrino().executeQuery(format("SELECT * FROM %s", trinoTableName("\"" + baseTableName + "$snapshots\"")))).hasNoRows();
assertThat(onTrino().executeQuery(format("SELECT * FROM %s", trinoTableName))).hasNoRows();
assertThat(onTrino().executeQuery(format("SELECT * FROM %s WHERE _integer > 0", trinoTableName))).hasNoRows();
onSpark().executeQuery(format(
"INSERT INTO %s VALUES (" +
"'a_string'" +
", 1000000000000000" +
", 1000000000" +
", 10000000.123" +
", 100000000000.123" +
", CAST('123456.78' AS decimal(8,2))" +
", CAST('1234567890123456789.0123456789012345678' AS decimal(38,19))" +
", true" +
", TIMESTAMP '2020-06-28 14:16:00.456'" +
", DATE '1950-06-28'" +
", X'000102f0feff'" +
")",
sparkTableName));
Row row = row(
"a_string",
1000000000000000L,
1000000000,
10000000.123F,
100000000000.123,
new BigDecimal("123456.78"),
new BigDecimal("1234567890123456789.0123456789012345678"),
true,
Timestamp.valueOf("2020-06-28 14:16:00.456"),
Date.valueOf("1950-06-28"),
new byte[] {00, 01, 02, -16, -2, -1});
assertThat(onSpark().executeQuery(
"SELECT " +
" _string" +
", _bigint" +
", _integer" +
", _real" +
", _double" +
", _short_decimal" +
", _long_decimal" +
", _boolean" +
", _timestamp" +
", _date" +
", _binary" +
" FROM " + sparkTableName))
.containsOnly(row);
assertThat(onTrino().executeQuery(
"SELECT " +
" _string" +
", _bigint" +
", _integer" +
", _real" +
", _double" +
", _short_decimal" +
", _long_decimal" +
", _boolean" +
", CAST(_timestamp AS TIMESTAMP)" + // TODO test the value without a CAST from timestamp with time zone to timestamp
", _date" +
", _binary" +
" FROM " + trinoTableName))
.containsOnly(row);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE}, dataProvider = "testSparkReadingTrinoDataDataProvider")
public void testSparkReadingTrinoData(StorageFormat storageFormat, CreateMode createMode)
{
String baseTableName = toLowerCase("test_spark_reading_primitive_types_" + storageFormat + "_" + createMode);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
String namedValues = "SELECT " +
" VARCHAR 'a_string' _string " +
", 1000000000000000 _bigint " +
", 1000000000 _integer " +
", REAL '10000000.123' _real " +
", DOUBLE '100000000000.123' _double " +
", DECIMAL '123456.78' _short_decimal " +
", DECIMAL '1234567890123456789.0123456789012345678' _long_decimal " +
", true _boolean " +
", TIMESTAMP '2020-06-28 14:16:00.123456' _timestamp " +
", TIMESTAMP '2021-08-03 08:32:21.123456 Europe/Warsaw' _timestamptz " +
", DATE '1950-06-28' _date " +
", X'000102f0feff' _binary " +
", UUID '406caec7-68b9-4778-81b2-a12ece70c8b1' _uuid " +
//", TIME '01:23:45.123456' _time " +
"";
switch (createMode) {
case CREATE_TABLE_AND_INSERT:
onTrino().executeQuery(format(
"CREATE TABLE %s (" +
" _string VARCHAR" +
", _bigint BIGINT" +
", _integer INTEGER" +
", _real REAL" +
", _double DOUBLE" +
", _short_decimal decimal(8,2)" +
", _long_decimal decimal(38,19)" +
", _boolean BOOLEAN" +
", _timestamp TIMESTAMP" +
", _timestamptz timestamp(6) with time zone" +
", _date DATE" +
", _binary VARBINARY" +
", _uuid UUID" +
//", _time time(6)" + -- per https://iceberg.apache.org/spark-writes/ Iceberg's time is currently not supported with Spark
") WITH (format = '%s')",
trinoTableName,
storageFormat));
onTrino().executeQuery(format("INSERT INTO %s %s", trinoTableName, namedValues));
break;
case CREATE_TABLE_AS_SELECT:
onTrino().executeQuery(format("CREATE TABLE %s AS %s", trinoTableName, namedValues));
break;
case CREATE_TABLE_WITH_NO_DATA_AND_INSERT:
onTrino().executeQuery(format("CREATE TABLE %s AS %s WITH NO DATA", trinoTableName, namedValues));
onTrino().executeQuery(format("INSERT INTO %s %s", trinoTableName, namedValues));
break;
default:
throw new UnsupportedOperationException("Unsupported create mode: " + createMode);
}
Row row = row(
"a_string",
1000000000000000L,
1000000000,
10000000.123F,
100000000000.123,
new BigDecimal("123456.78"),
new BigDecimal("1234567890123456789.0123456789012345678"),
true,
"2020-06-28 14:16:00.123456",
"2021-08-03 06:32:21.123456 UTC", // Iceberg's timestamptz stores point in time, without zone
"1950-06-28",
new byte[] {00, 01, 02, -16, -2, -1},
"406caec7-68b9-4778-81b2-a12ece70c8b1"
// "01:23:45.123456"
/**/);
assertThat(onTrino().executeQuery(
"SELECT " +
" _string" +
", _bigint" +
", _integer" +
", _real" +
", _double" +
", _short_decimal" +
", _long_decimal" +
", _boolean" +
", CAST(_timestamp AS varchar)" +
", CAST(_timestamptz AS varchar)" +
", CAST(_date AS varchar)" +
", _binary" +
", _uuid" +
//", CAST(_time AS varchar)" +
" FROM " + trinoTableName))
.containsOnly(row);
assertThat(onSpark().executeQuery(
"SELECT " +
" _string" +
", _bigint" +
", _integer" +
", _real" +
", _double" +
", _short_decimal" +
", _long_decimal" +
", _boolean" +
", CAST(_timestamp AS string)" +
", CAST(_timestamptz AS string) || ' UTC'" + // Iceberg timestamptz is mapped to Spark timestamp and gets represented without time zone
", CAST(_date AS string)" +
", _binary" +
", _uuid" +
// ", CAST(_time AS string)" +
" FROM " + sparkTableName))
.containsOnly(row);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@DataProvider
public Object[][] testSparkReadingTrinoDataDataProvider()
{
return Stream.of(storageFormats())
.map(array -> getOnlyElement(asList(array)))
.flatMap(storageFormat -> Stream.of(
new Object[] {storageFormat, CREATE_TABLE_AND_INSERT},
new Object[] {storageFormat, CREATE_TABLE_AS_SELECT},
new Object[] {storageFormat, CREATE_TABLE_WITH_NO_DATA_AND_INSERT}))
.toArray(Object[][]::new);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE}, dataProvider = "specVersions")
public void testSparkCreatesTrinoDrops(int specVersion)
{
String baseTableName = "test_spark_creates_trino_drops";
onSpark().executeQuery(format("CREATE TABLE %s (_string STRING, _bigint BIGINT) USING ICEBERG TBLPROPERTIES('format-version' = %s)", sparkTableName(baseTableName), specVersion));
onTrino().executeQuery("DROP TABLE " + trinoTableName(baseTableName));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE})
public void testTrinoCreatesSparkDrops()
{
String baseTableName = "test_trino_creates_spark_drops";
onTrino().executeQuery(format("CREATE TABLE %s (_string VARCHAR, _bigint BIGINT)", trinoTableName(baseTableName)));
onSpark().executeQuery("DROP TABLE " + sparkTableName(baseTableName));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadsTrinoPartitionedTable(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reads_trino_partitioned_table_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format("CREATE TABLE %s (_string VARCHAR, _varbinary VARBINARY, _bigint BIGINT) WITH (partitioning = ARRAY['_string', '_varbinary'], format = '%s')", trinoTableName, storageFormat));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', X'0ff102f0feff', 1001), ('b', X'0ff102f0fefe', 1002), ('c', X'0ff102fdfeff', 1003)", trinoTableName));
Row row1 = row("b", new byte[] {15, -15, 2, -16, -2, -2}, 1002);
String selectByString = "SELECT * FROM %s WHERE _string = 'b'";
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row1);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row1);
Row row2 = row("a", new byte[] {15, -15, 2, -16, -2, -1}, 1001);
String selectByVarbinary = "SELECT * FROM %s WHERE _varbinary = X'0ff102f0feff'";
assertThat(onTrino().executeQuery(format(selectByVarbinary, trinoTableName)))
.containsOnly(row2);
assertThat(onSpark().executeQuery(format(selectByVarbinary, sparkTableName)))
.containsOnly(row2);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadsSparkPartitionedTable(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_trino_reads_spark_partitioned_table_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (_string STRING, _varbinary BINARY, _bigint BIGINT) USING ICEBERG PARTITIONED BY (_string, _varbinary) TBLPROPERTIES ('write.format.default'='%s', 'format-version' = %s)",
sparkTableName,
storageFormat,
specVersion));
onSpark().executeQuery(format("INSERT INTO %s VALUES ('a', X'0ff102f0feff', 1001), ('b', X'0ff102f0fefe', 1002), ('c', X'0ff102fdfeff', 1003)", sparkTableName));
Row row1 = row("a", new byte[] {15, -15, 2, -16, -2, -1}, 1001);
String select = "SELECT * FROM %s WHERE _string = 'a'";
assertThat(onSpark().executeQuery(format(select, sparkTableName)))
.containsOnly(row1);
assertThat(onTrino().executeQuery(format(select, trinoTableName)))
.containsOnly(row1);
Row row2 = row("c", new byte[] {15, -15, 2, -3, -2, -1}, 1003);
String selectByVarbinary = "SELECT * FROM %s WHERE _varbinary = X'0ff102fdfeff'";
assertThat(onTrino().executeQuery(format(selectByVarbinary, trinoTableName)))
.containsOnly(row2);
assertThat(onSpark().executeQuery(format(selectByVarbinary, sparkTableName)))
.containsOnly(row2);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, dataProvider = "storageFormats")
public void testSparkReadsTrinoNestedPartitionedTable(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reads_trino_nested_partitioned_table_" + storageFormat + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (_string VARCHAR, _bigint BIGINT, _struct ROW(_field INT, _another_field VARCHAR))" +
" WITH (partitioning = ARRAY['\"_struct._field\"'], format = '%s')",
trinoTableName,
storageFormat));
onTrino().executeQuery(format(
"INSERT INTO %s VALUES" +
" ('update', 1001, ROW(1, 'x'))," +
" ('b', 1002, ROW(2, 'y'))," +
" ('c', 1003, ROW(3, 'z'))",
trinoTableName));
onTrino().executeQuery("UPDATE " + trinoTableName + " SET _string = 'a' WHERE _struct._field = 1");
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE _struct._another_field = 'y'");
onTrino().executeQuery("ALTER TABLE " + trinoTableName + " EXECUTE OPTIMIZE");
assertQueryFailure(() -> onTrino().executeQuery("ALTER TABLE " + trinoTableName + " DROP COLUMN _struct._field"))
.hasMessageContaining("Cannot drop partition field: _struct._field");
List expectedRows = ImmutableList.of(
row("a", 1001, 1, "x"),
row("c", 1003, 3, "z"));
String select = "SELECT _string, _bigint, _struct._field, _struct._another_field FROM %s" +
" WHERE _struct._field = 1 OR _struct._another_field = 'z'";
assertThat(onTrino().executeQuery(format(select, trinoTableName)))
.containsOnly(expectedRows);
assertThat(onSpark().executeQuery(format(select, sparkTableName)))
.containsOnly(expectedRows);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, dataProvider = "storageFormats")
public void testTrinoReadsSparkNestedPartitionedTable(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_trino_reads_spark_nested_partitioned_table_" + storageFormat + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (_string STRING, _varbinary BINARY, _bigint BIGINT, _struct STRUCT<_field:INT, _another_field:STRING>)" +
" USING ICEBERG PARTITIONED BY (_struct._field) TBLPROPERTIES ('write.format.default'='%s', 'format-version' = 2)",
sparkTableName,
storageFormat));
onSpark().executeQuery(format(
"INSERT INTO %s VALUES" +
" ('update', X'0ff102f0feff', 1001, named_struct('_field', 1, '_another_field', 'x'))," +
" ('b', X'0ff102f0fefe', 1002, named_struct('_field', 2, '_another_field', 'y'))," +
" ('c', X'0ff102fdfeff', 1003, named_struct('_field', 3, '_another_field', 'z'))",
sparkTableName));
onSpark().executeQuery("UPDATE " + sparkTableName + " SET _string = 'a' WHERE _struct._field = 1");
assertThatThrownBy(() -> onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE _struct._another_field = 'y'"))
.hasMessageContaining("Cannot filter by nested column: 6: _another_field: optional string");
assertQueryFailure(() -> onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP COLUMN _struct._field"))
.hasMessageContaining("Cannot find source column for partition field: 1000: _struct._field: identity(5)");
Row[] expectedRows = new Row[] {
row("a", new byte[] {15, -15, 2, -16, -2, -1}, 1001, 1, "x"),
row("c", new byte[] {15, -15, 2, -3, -2, -1}, 1003, 3, "z")
};
String select = "SELECT _string, _varbinary, _bigint, _struct._field, _struct._another_field FROM %s" +
" WHERE _struct._field = 1 OR _struct._another_field = 'z'";
assertThat(onTrino().executeQuery(format(select, trinoTableName)))
.containsOnly(expectedRows);
assertThat(onSpark().executeQuery(format(select, sparkTableName)))
.containsOnly(expectedRows);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, dataProvider = "storageFormats")
public void testSparkReadsTrinoNestedPartitionedTableWithOneFieldStruct(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reads_trino_nested_partitioned_table_with_one_field_struct_" + storageFormat + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (_string VARCHAR, _bigint BIGINT, _struct ROW(_field BIGINT))" +
" WITH (partitioning = ARRAY['\"_struct._field\"'], format = '%s')",
trinoTableName,
storageFormat));
onTrino().executeQuery(format(
"INSERT INTO %s VALUES" +
" ('a', 1001, ROW(1))," +
" ('b', 1002, ROW(2))," +
" ('c', 1003, ROW(3))",
trinoTableName));
Row expectedRow = row("a", 1001, 1);
String select = "SELECT _string, _bigint, _struct._field FROM %s WHERE _string = 'a'";
assertThat(onTrino().executeQuery(format(select, trinoTableName)))
.containsOnly(expectedRow);
if (storageFormat == StorageFormat.ORC) {
// Open iceberg issue https://github.com/apache/iceberg/issues/3139 to read ORC table with nested partition column
assertThatThrownBy(() -> onSpark().executeQuery(format(select, sparkTableName)))
.hasMessageContaining("java.lang.IndexOutOfBoundsException: Index 2 out of bounds for length 2");
}
else {
assertThat(onSpark().executeQuery(format(select, sparkTableName)))
.containsOnly(expectedRow);
}
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, dataProvider = "storageFormats")
public void testTrinoReadsSparkNestedPartitionedTableWithOneFieldStruct(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_trino_reads_spark_nested_partitioned_table_with_one_field_struct_" + storageFormat + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (_string STRING, _bigint BIGINT, _struct STRUCT<_field:STRING>)" +
" USING ICEBERG PARTITIONED BY (_struct._field) TBLPROPERTIES ('write.format.default'='%s', 'format-version' = 2)",
sparkTableName,
storageFormat));
onSpark().executeQuery(format(
"INSERT INTO %s VALUES" +
" ('a', 1001, named_struct('_field', 'field1'))," +
" ('b', 1002, named_struct('_field', 'field2'))," +
" ('c', 1003, named_struct('_field', 'field3'))",
sparkTableName));
Row expectedRow = row("a", 1001, "field1");
String selectNested = "SELECT _string, _bigint, _struct._field FROM %s WHERE _struct._field = 'field1'";
assertThat(onTrino().executeQuery(format(selectNested, trinoTableName)))
.containsOnly(expectedRow);
if (storageFormat == StorageFormat.ORC) {
// Open iceberg issue https://github.com/apache/iceberg/issues/3139 to read ORC table with nested partition column
assertThatThrownBy(() -> onSpark().executeQuery(format(selectNested, sparkTableName)))
.hasMessageContaining("java.lang.IndexOutOfBoundsException: Index 2 out of bounds for length 2");
}
else {
assertThat(onSpark().executeQuery(format(selectNested, sparkTableName)))
.containsOnly(expectedRow);
}
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testTrinoPartitionedByRealWithNaN(StorageFormat storageFormat)
{
testTrinoPartitionedByNaN("REAL", storageFormat, Float.NaN);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testTrinoPartitionedByDoubleWithNaN(StorageFormat storageFormat)
{
testTrinoPartitionedByNaN("DOUBLE", storageFormat, Double.NaN);
}
private void testTrinoPartitionedByNaN(String typeName, StorageFormat storageFormat, Object expectedValue)
{
String baseTableName = "test_trino_partitioned_by_nan_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("CREATE TABLE " + trinoTableName + " " +
"WITH (format = '" + storageFormat + "', partitioning = ARRAY['col'])" +
"AS SELECT " + typeName + " 'NaN' AS col");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(row(expectedValue));
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(row(expectedValue));
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkPartitionedByRealWithNaN(StorageFormat storageFormat)
{
testSparkPartitionedByNaN("FLOAT", storageFormat, Float.NaN);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkPartitionedByDoubleWithNaN(StorageFormat storageFormat)
{
testSparkPartitionedByNaN("DOUBLE", storageFormat, Double.NaN);
}
private void testSparkPartitionedByNaN(String typeName, StorageFormat storageFormat, Object expectedValue)
{
String baseTableName = "test_spark_partitioned_by_nan_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " " +
"PARTITIONED BY (col) " +
"TBLPROPERTIES ('write.format.default' = '" + storageFormat + "')" +
"AS SELECT CAST('NaN' AS " + typeName + ") AS col");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(row(expectedValue));
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(row(expectedValue));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadingCompositeSparkData(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_trino_reading_spark_composites_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery(format("" +
"CREATE TABLE %s (" +
" doc_id string,\n" +
" info MAP,\n" +
" pets ARRAY,\n" +
" user_info STRUCT)" +
" USING ICEBERG" +
" TBLPROPERTIES ('write.format.default'='%s', 'format-version' = %s)",
sparkTableName, storageFormat, specVersion));
onSpark().executeQuery(format(
"INSERT INTO TABLE %s SELECT 'Doc213', map('age', 28, 'children', 3), array('Dog', 'Cat', 'Pig'), \n" +
"named_struct('name', 'Santa', 'surname', 'Claus','age', 1000,'gender', 'MALE')",
sparkTableName));
assertThat(onTrino().executeQuery("SELECT doc_id, info['age'], pets[2], user_info.surname FROM " + trinoTableName))
.containsOnly(row("Doc213", 28, "Cat", "Claus"));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadingCompositeTrinoData(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reading_trino_composites_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (" +
" doc_id VARCHAR,\n" +
" info MAP(VARCHAR, INTEGER),\n" +
" pets ARRAY(VARCHAR),\n" +
" user_info ROW(name VARCHAR, surname VARCHAR, age INTEGER, gender VARCHAR)) " +
" WITH (format = '%s')",
trinoTableName,
storageFormat));
onTrino().executeQuery(format(
"INSERT INTO %s VALUES('Doc213', MAP(ARRAY['age', 'children'], ARRAY[28, 3]), ARRAY['Dog', 'Cat', 'Pig'], ROW('Santa', 'Claus', 1000, 'MALE'))",
trinoTableName));
assertThat(onSpark().executeQuery("SELECT doc_id, info['age'], pets[1], user_info.surname FROM " + sparkTableName))
.containsOnly(row("Doc213", 28, "Cat", "Claus"));
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadingSparkIcebergTablePropertiesData(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_trino_reading_spark_iceberg_table_properties_" + storageFormat);
String propertiesTableName = "\"" + baseTableName + "$properties\"";
String sparkTableName = sparkTableName(baseTableName);
String trinoPropertiesTableName = trinoTableName(propertiesTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (\n" +
" doc_id STRING)\n" +
" USING ICEBERG TBLPROPERTIES (" +
" 'write.format.default'='%s'," +
" 'format-version' = %s," +
" 'custom.table-property' = 'my_custom_value')",
sparkTableName,
storageFormat.toString(),
specVersion));
assertThat(onTrino().executeQuery("SELECT key, value FROM " + trinoPropertiesTableName))
// Use contains method because the result may contain format-specific properties
.contains(
row("custom.table-property", "my_custom_value"),
row("write.format.default", storageFormat.name()),
row("owner", "hive"));
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadingNestedSparkData(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_trino_reading_nested_spark_data_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (\n" +
" doc_id STRING\n" +
", nested_map MAP>>\n" +
", nested_array ARRAY>>>\n" +
", nested_struct STRUCT>>>>)\n" +
" USING ICEBERG TBLPROPERTIES ('write.format.default'='%s', 'format-version' = %s)",
sparkTableName,
storageFormat,
specVersion));
onSpark().executeQuery(format(
"INSERT INTO TABLE %s SELECT" +
" 'Doc213'" +
", map('s1', array(named_struct('sname', 'ASName1', 'snumber', 201), named_struct('sname', 'ASName2', 'snumber', 202)))" +
", array(map('m1', array(named_struct('mname', 'MAS1Name1', 'mnumber', 301), named_struct('mname', 'MAS1Name2', 'mnumber', 302)))" +
" ,map('m2', array(named_struct('mname', 'MAS2Name1', 'mnumber', 401), named_struct('mname', 'MAS2Name2', 'mnumber', 402))))" +
", named_struct('name', 'S1'," +
" 'complicated', array(map('m1', array(named_struct('mname', 'SAMA1Name1', 'mnumber', 301), named_struct('mname', 'SAMA1Name2', 'mnumber', 302)))" +
" ,map('m2', array(named_struct('mname', 'SAMA2Name1', 'mnumber', 401), named_struct('mname', 'SAMA2Name2', 'mnumber', 402)))))",
sparkTableName));
Row row = row("Doc213", "ASName2", 201, "MAS2Name1", 302, "SAMA1Name1", 402);
assertThat(onSpark().executeQuery(
"SELECT" +
" doc_id" +
", nested_map['s1'][1].sname" +
", nested_map['s1'][0].snumber" +
", nested_array[1]['m2'][0].mname" +
", nested_array[0]['m1'][1].mnumber" +
", nested_struct.complicated[0]['m1'][0].mname" +
", nested_struct.complicated[1]['m2'][1].mnumber" +
" FROM " + sparkTableName))
.containsOnly(row);
assertThat(onTrino().executeQuery("SELECT" +
" doc_id" +
", nested_map['s1'][2].sname" +
", nested_map['s1'][1].snumber" +
", nested_array[2]['m2'][1].mname" +
", nested_array[1]['m1'][2].mnumber" +
", nested_struct.complicated[1]['m1'][1].mname" +
", nested_struct.complicated[2]['m2'][2].mnumber" +
" FROM " + trinoTableName))
.containsOnly(row);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadingNestedTrinoData(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reading_nested_trino_data_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (\n" +
" doc_id VARCHAR\n" +
", nested_map MAP(VARCHAR, ARRAY(ROW(sname VARCHAR, snumber INT)))\n" +
", nested_array ARRAY(MAP(VARCHAR, ARRAY(ROW(mname VARCHAR, mnumber INT))))\n" +
", nested_struct ROW(name VARCHAR, complicated ARRAY(MAP(VARCHAR, ARRAY(ROW(mname VARCHAR, mnumber INT))))))" +
" WITH (format = '%s')",
trinoTableName,
storageFormat));
onTrino().executeQuery(format(
"INSERT INTO %s SELECT" +
" 'Doc213'" +
", map(array['s1'], array[array[row('ASName1', 201), row('ASName2', 202)]])" +
", array[map(array['m1'], array[array[row('MAS1Name1', 301), row('MAS1Name2', 302)]])" +
" ,map(array['m2'], array[array[row('MAS2Name1', 401), row('MAS2Name2', 402)]])]" +
", row('S1'" +
" ,array[map(array['m1'], array[array[row('SAMA1Name1', 301), row('SAMA1Name2', 302)]])" +
" ,map(array['m2'], array[array[row('SAMA2Name1', 401), row('SAMA2Name2', 402)]])])",
trinoTableName));
Row row = row("Doc213", "ASName2", 201, "MAS2Name1", 302, "SAMA1Name1", 402);
assertThat(onTrino().executeQuery(
"SELECT" +
" doc_id" +
", nested_map['s1'][2].sname" +
", nested_map['s1'][1].snumber" +
", nested_array[2]['m2'][1].mname" +
", nested_array[1]['m1'][2].mnumber" +
", nested_struct.complicated[1]['m1'][1].mname" +
", nested_struct.complicated[2]['m2'][2].mnumber" +
" FROM " + trinoTableName))
.containsOnly(row);
QueryResult sparkResult = onSpark().executeQuery(
"SELECT" +
" doc_id" +
", nested_map['s1'][1].sname" +
", nested_map['s1'][0].snumber" +
", nested_array[1]['m2'][0].mname" +
", nested_array[0]['m1'][1].mnumber" +
", nested_struct.complicated[0]['m1'][0].mname" +
", nested_struct.complicated[1]['m2'][1].mnumber" +
" FROM " + sparkTableName);
assertThat(sparkResult).containsOnly(row);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testIdBasedFieldMapping(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_schema_evolution_for_nested_fields_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (" +
"remove_col BIGINT, " +
"rename_col BIGINT, " +
"keep_col BIGINT, " +
"drop_and_add_col BIGINT, " +
"CaseSensitiveCol BIGINT, " +
"a_struct STRUCT, " +
"a_partition BIGINT) "
+ " USING ICEBERG"
+ " PARTITIONED BY (a_partition)"
+ " TBLPROPERTIES ('write.format.default' = '%s', 'format-version' = %s)",
sparkTableName,
storageFormat,
specVersion));
onSpark().executeQuery(format(
"INSERT INTO TABLE %s SELECT " +
"1, " + // remove_col
"2, " + // rename_col
"3, " + // keep_col
"4, " + // drop_and_add_col,
"5, " + // CaseSensitiveCol,
" named_struct('removed', 10, 'rename', 11, 'keep', 12, 'drop_and_add', 13, 'CaseSensitive', 14), " // a_struct
+ "1001", // a_partition
sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s DROP COLUMN remove_col", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s RENAME COLUMN rename_col TO quite_renamed_col", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s DROP COLUMN drop_and_add_col", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s ADD COLUMN drop_and_add_col BIGINT", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s ADD COLUMN add_col BIGINT", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s DROP COLUMN a_struct.removed", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s RENAME COLUMN a_struct.rename TO renamed", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s DROP COLUMN a_struct.drop_and_add", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s ADD COLUMN a_struct.drop_and_add BIGINT", sparkTableName));
onSpark().executeQuery(format("ALTER TABLE %s ADD COLUMN a_struct.added BIGINT", sparkTableName));
assertThat(onTrino().executeQuery("DESCRIBE " + trinoTableName).project(1, 2))
.containsOnly(
row("quite_renamed_col", "bigint"),
row("keep_col", "bigint"),
row("drop_and_add_col", "bigint"),
row("add_col", "bigint"),
row("casesensitivecol", "bigint"),
row("a_struct", "row(renamed bigint, keep bigint, CaseSensitive bigint, drop_and_add bigint, added bigint)"),
row("a_partition", "bigint"));
assertThat(onTrino().executeQuery(format("SELECT quite_renamed_col, keep_col, drop_and_add_col, add_col, casesensitivecol, a_struct, a_partition FROM %s", trinoTableName)))
.containsOnly(row(
2L, // quite_renamed_col
3L, // keep_col
null, // drop_and_add_col; dropping and re-adding changes id
null, // add_col
5L, // CaseSensitiveCol
rowBuilder()
// Rename does not change id
.addField("renamed", 11L)
.addField("keep", 12L)
.addField("CaseSensitive", 14L)
// Dropping and re-adding changes id
.addField("drop_and_add", null)
.addField("added", null)
.build(),
1001L));
// make sure predicates are also ID based
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE drop_and_add_col IS NULL", trinoTableName))).containsOnly(row(3L));
// smoke test for dereference
assertThat(onTrino().executeQuery(format("SELECT a_struct.renamed FROM %s", trinoTableName))).containsOnly(row(11L));
assertThat(onTrino().executeQuery(format("SELECT a_struct.keep FROM %s", trinoTableName))).containsOnly(row(12L));
assertThat(onTrino().executeQuery(format("SELECT a_struct.casesensitive FROM %s", trinoTableName))).containsOnly(row(14L));
assertThat(onTrino().executeQuery(format("SELECT a_struct.drop_and_add FROM %s", trinoTableName))).containsOnly(row((Object) null));
assertThat(onTrino().executeQuery(format("SELECT a_struct.added FROM %s", trinoTableName))).containsOnly(row((Object) null));
// smoke test for dereference in a predicate
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE a_struct.renamed = 11", trinoTableName))).containsOnly(row(3L));
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE a_struct.keep = 12", trinoTableName))).containsOnly(row(3L));
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE a_struct.casesensitive = 14", trinoTableName))).containsOnly(row(3L));
// make sure predicates are also ID based
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE a_struct.drop_and_add IS NULL", trinoTableName))).containsOnly(row(3L));
assertThat(onTrino().executeQuery(format("SELECT keep_col FROM %s WHERE a_struct.added IS NULL", trinoTableName))).containsOnly(row(3L));
onSpark().executeQuery(format(
"INSERT INTO TABLE %s SELECT " +
"12, " + // quite_renamed_col
"13, " + // keep_col
"15, " + // CaseSensitiveCol,
"named_struct('renamed', 111, 'keep', 112, 'CaseSensitive', 113, 'drop_and_add', 114, 'added', 115), " + // a_struct
"1001, " + // a_partition,
"14, " + // drop_and_add_col
"15", // add_col
sparkTableName));
assertThat(onTrino().executeQuery("SELECT DISTINCT a_struct.renamed, a_struct.added, a_struct.keep FROM " + trinoTableName)).containsOnly(
row(11L, null, 12L),
row(111L, 115L, 112L));
assertThat(onTrino().executeQuery("SELECT DISTINCT a_struct.renamed, a_struct.keep FROM " + trinoTableName + " WHERE a_struct.added IS NULL")).containsOnly(
row(11L, 12L));
assertThat(onTrino().executeQuery("SELECT a_struct FROM " + trinoTableName + " WHERE a_struct.added IS NOT NULL")).containsOnly(
row(rowBuilder()
.addField("renamed", 111L)
.addField("keep", 112L)
.addField("CaseSensitive", 113L)
.addField("drop_and_add", 114L)
.addField("added", 115L)
.build()));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testReadAfterPartitionEvolution(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_read_after_partition_evolution_" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (" +
"int_col BIGINT, " +
"struct_col STRUCT, " +
"timestamp_col TIMESTAMP) "
+ " USING ICEBERG"
+ " TBLPROPERTIES ('write.format.default' = '%s', 'format-version' = %s)",
sparkTableName,
storageFormat,
specVersion));
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, named_struct('field_one', 1, 'field_two', 1), TIMESTAMP '2021-06-28 14:16:00.456')");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD bucket(3, int_col)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (2, named_struct('field_one', 2, 'field_two', 2), TIMESTAMP '2022-06-28 14:16:00.456')");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD struct_col.field_one");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (3, named_struct('field_one', 3, 'field_two', 3), TIMESTAMP '2023-06-28 14:16:00.456')");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD struct_col.field_one");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD struct_col.field_two");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (4, named_struct('field_one', 4, 'field_two', 4), TIMESTAMP '2024-06-28 14:16:00.456')");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD bucket(3, int_col)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD struct_col.field_two");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD days(timestamp_col)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (5, named_struct('field_one', 5, 'field_two', 5), TIMESTAMP '2025-06-28 14:16:00.456')");
// The Iceberg documentation states it is not necessary to drop a day transform partition field in order to add an hourly one
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD hours(timestamp_col)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (6, named_struct('field_one', 6, 'field_two', 6), TIMESTAMP '2026-06-28 14:16:00.456')");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD int_col");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (7, named_struct('field_one', 7, 'field_two', 7), TIMESTAMP '2027-06-28 14:16:00.456')");
Function buildStructColValue = intValue -> rowBuilder()
.addField("field_one", intValue)
.addField("field_two", intValue)
.build();
assertThat(onTrino().executeQuery("SELECT int_col, struct_col, CAST(timestamp_col AS TIMESTAMP) FROM " + trinoTableName))
.containsOnly(
row(1, buildStructColValue.apply(1), Timestamp.valueOf("2021-06-28 14:16:00.456")),
row(2, buildStructColValue.apply(2), Timestamp.valueOf("2022-06-28 14:16:00.456")),
row(3, buildStructColValue.apply(3), Timestamp.valueOf("2023-06-28 14:16:00.456")),
row(4, buildStructColValue.apply(4), Timestamp.valueOf("2024-06-28 14:16:00.456")),
row(5, buildStructColValue.apply(5), Timestamp.valueOf("2025-06-28 14:16:00.456")),
row(6, buildStructColValue.apply(6), Timestamp.valueOf("2026-06-28 14:16:00.456")),
row(7, buildStructColValue.apply(7), Timestamp.valueOf("2027-06-28 14:16:00.456")));
assertThat(onTrino().executeQuery("SELECT struct_col.field_two FROM " + trinoTableName))
.containsOnly(row(1), row(2), row(3), row(4), row(5), row(6), row(7));
assertThat(onTrino().executeQuery("SELECT CAST(timestamp_col AS TIMESTAMP) FROM " + trinoTableName + " WHERE struct_col.field_two = 2"))
.containsOnly(row(Timestamp.valueOf("2022-06-28 14:16:00.456")));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE int_col = 2"))
.containsOnly(row(1));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE int_col % 2 = 0"))
.containsOnly(row(3));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE struct_col.field_one = 2"))
.containsOnly(row(1));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE struct_col.field_one % 2 = 0"))
.containsOnly(row(3));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE year(timestamp_col) = 2022"))
.containsOnly(row(1));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE year(timestamp_col) % 2 = 0"))
.containsOnly(row(3));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "specVersions")
public void testTrinoShowingSparkCreatedTables(int specVersion)
{
String sparkTable = "test_table_listing_for_spark";
String trinoTable = "test_table_listing_for_trino";
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTable);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTable);
onSpark().executeQuery(format("CREATE TABLE %s (_integer INTEGER ) USING ICEBERG TBLPROPERTIES('format-version' = %s)", sparkTableName(sparkTable), specVersion));
onTrino().executeQuery(format("CREATE TABLE %s (_integer INTEGER )", trinoTableName(trinoTable)));
assertThat(onTrino().executeQuery(format("SHOW TABLES FROM %s.%s LIKE '%s'", TRINO_CATALOG, TEST_SCHEMA_NAME, "test_table_listing_for_%")))
.containsOnly(row(sparkTable), row(trinoTable));
onSpark().executeQuery("DROP TABLE " + sparkTableName(sparkTable));
onTrino().executeQuery("DROP TABLE " + trinoTableName(trinoTable));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "specVersions")
public void testCreateAndDropTableWithSameLocationWorksOnSpark(int specVersion)
{
String dataPath = "hdfs://hadoop-master:9000/user/hive/warehouse/test_create_table_same_location/obj-data";
String tableSameLocation1 = "test_same_location_spark_1_" + randomNameSuffix();
String tableSameLocation2 = "test_same_location_spark_2_" + randomNameSuffix();
onSpark().executeQuery(format("CREATE TABLE %s (_integer INTEGER ) USING ICEBERG LOCATION '%s' TBLPROPERTIES('format-version' = %s)",
sparkTableName(tableSameLocation1), dataPath, specVersion));
onSpark().executeQuery(format("CREATE TABLE %s (_integer INTEGER ) USING ICEBERG LOCATION '%s' TBLPROPERTIES('format-version' = %s)",
sparkTableName(tableSameLocation2), dataPath, specVersion));
onSpark().executeQuery(format("DROP TABLE IF EXISTS %s", sparkTableName(tableSameLocation1)));
assertThat(onTrino().executeQuery(format("SELECT * FROM %s", trinoTableName(tableSameLocation2)))).hasNoRows();
onSpark().executeQuery(format("DROP TABLE %s", sparkTableName(tableSameLocation2)));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "specVersions")
public void testCreateAndDropTableWithSameLocationFailsOnTrino(int specVersion)
{
String dataPath = "hdfs://hadoop-master:9000/user/hive/warehouse/test_create_table_same_location/obj-data";
String tableSameLocation1 = "test_same_location_trino_1_" + randomNameSuffix();
String tableSameLocation2 = "test_same_location_trino_2_" + randomNameSuffix();
onSpark().executeQuery(format("CREATE TABLE %s (_integer INTEGER ) USING ICEBERG LOCATION '%s' TBLPROPERTIES('format-version' = %s)",
sparkTableName(tableSameLocation1), dataPath, specVersion));
onSpark().executeQuery(format("CREATE TABLE %s (_integer INTEGER ) USING ICEBERG LOCATION '%s' TBLPROPERTIES('format-version' = %s)",
sparkTableName(tableSameLocation2), dataPath, specVersion));
onTrino().executeQuery(format("DROP TABLE %s", trinoTableName(tableSameLocation1)));
assertQueryFailure(() -> onTrino().executeQuery(format("SELECT * FROM %s", trinoTableName(tableSameLocation2))))
.hasMessageMatching(".*Metadata not found in metadata location for table default." + tableSameLocation2);
onTrino().executeQuery(format("DROP TABLE %s", trinoTableName(tableSameLocation2)));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE})
public void testTrinoWritingDataAfterSpark()
{
String baseTableName = toLowerCase("test_trino_write_after_spark");
String sparkTableName = sparkTableName(baseTableName);
String trinoTableName = trinoTableName(baseTableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " (a INT) USING ICEBERG");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES 1");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES 2");
List expected = ImmutableList.of(row(1), row(2));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, ICEBERG_JDBC, PROFILE_SPECIFIC_TESTS, ICEBERG_NESSIE}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoWritingDataWithObjectStorageLocationProvider(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_object_storage_location_provider_" + storageFormat);
String sparkTableName = sparkTableName(baseTableName);
String trinoTableName = trinoTableName(baseTableName);
String dataPath = "hdfs://hadoop-master:9000/user/hive/warehouse/test_object_storage_location_provider/obj-data";
onSpark().executeQuery(format("CREATE TABLE %s (_string STRING, _bigint BIGINT) USING ICEBERG TBLPROPERTIES (" +
"'write.object-storage.enabled'=true," +
"'write.object-storage.path'='%s'," +
"'write.format.default' = '%s'," +
"'format-version' = %s)",
sparkTableName, dataPath, storageFormat, specVersion));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a_string', 1000000000000000)", trinoTableName));
Row result = row("a_string", 1000000000000000L);
assertThat(onSpark().executeQuery(format("SELECT _string, _bigint FROM %s", sparkTableName))).containsOnly(result);
assertThat(onTrino().executeQuery(format("SELECT _string, _bigint FROM %s", trinoTableName))).containsOnly(result);
QueryResult queryResult = onTrino().executeQuery(format("SELECT file_path FROM %s", trinoTableName("\"" + baseTableName + "$files\"")));
assertThat(queryResult).hasRowsCount(1).hasColumnsCount(1);
assertThat(((String) queryResult.getOnlyValue())).contains(dataPath);
// TODO: support path override in Iceberg table creation: https://github.com/trinodb/trino/issues/8861
assertQueryFailure(() -> onTrino().executeQuery("DROP TABLE " + trinoTableName))
.hasMessageContaining("contains Iceberg path override properties and cannot be dropped from Trino");
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, ICEBERG_JDBC, PROFILE_SPECIFIC_TESTS, ICEBERG_NESSIE}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoWritingDataWithWriterDataPathSet(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_writer_data_path_" + storageFormat);
String sparkTableName = sparkTableName(baseTableName);
String trinoTableName = trinoTableName(baseTableName);
String dataPath = "hdfs://hadoop-master:9000/user/hive/warehouse/test_writer_data_path_/obj-data";
onSpark().executeQuery(format("CREATE TABLE %s (_string STRING, _bigint BIGINT) USING ICEBERG TBLPROPERTIES (" +
"'write.data.path'='%s'," +
"'write.format.default' = '%s'," +
"'format-version' = %s)",
sparkTableName, dataPath, storageFormat, specVersion));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a_string', 1000000000000000)", trinoTableName));
Row result = row("a_string", 1000000000000000L);
assertThat(onSpark().executeQuery(format("SELECT _string, _bigint FROM %s", sparkTableName))).containsOnly(result);
assertThat(onTrino().executeQuery(format("SELECT _string, _bigint FROM %s", trinoTableName))).containsOnly(result);
QueryResult queryResult = onTrino().executeQuery(format("SELECT file_path FROM %s", trinoTableName("\"" + baseTableName + "$files\"")));
assertThat(queryResult).hasRowsCount(1).hasColumnsCount(1);
assertThat(((String) queryResult.getOnlyValue())).contains(dataPath);
assertQueryFailure(() -> onTrino().executeQuery("DROP TABLE " + trinoTableName))
.hasMessageContaining("contains Iceberg path override properties and cannot be dropped from Trino");
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
private static final List SPECIAL_CHARACTER_VALUES = ImmutableList.of(
"with-hyphen",
"with.dot",
"with:colon",
"with/slash",
"with\\\\backslashes",
"with\\backslash",
"with=equal",
"with?question",
"with!exclamation",
"with%percent",
"with%%percents",
"with$dollar",
"with#hash",
"with*star",
"with=equals",
"with\"quote",
"with'apostrophe",
"with space",
" with space prefix",
"with space suffix ",
"with€euro",
"with non-ascii ąęłóść Θ Φ Δ",
"with👨🏭combining character",
" 👨🏭",
"👨🏭 ");
private static final String TRINO_INSERTED_PARTITION_VALUES =
Streams.mapWithIndex(SPECIAL_CHARACTER_VALUES.stream(), (value, index) -> format("(%d, '%s')", index, escapeTrinoString(value)))
.collect(Collectors.joining(", "));
private static final String SPARK_INSERTED_PARTITION_VALUES =
Streams.mapWithIndex(SPECIAL_CHARACTER_VALUES.stream(), (value, index) -> format("(%d, '%s')", index, escapeSparkString(value)))
.collect(Collectors.joining(", "));
private static final List EXPECTED_PARTITION_VALUES =
Streams.mapWithIndex(SPECIAL_CHARACTER_VALUES.stream(), (value, index) -> row((int) index, value))
.collect(toImmutableList());
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE})
public void testStringPartitioningWithSpecialCharactersCtasInTrino()
{
String baseTableName = "test_string_partitioning_with_special_chars_ctas_in_trino";
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (id, part_col) " +
"WITH (partitioning = ARRAY['part_col']) " +
"AS VALUES %s",
trinoTableName,
TRINO_INSERTED_PARTITION_VALUES));
assertSelectsOnSpecialCharacters(trinoTableName, sparkTableName);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE})
public void testStringPartitioningWithSpecialCharactersInsertInTrino()
{
String baseTableName = "test_string_partitioning_with_special_chars_ctas_in_trino";
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (id BIGINT, part_col VARCHAR) WITH (partitioning = ARRAY['part_col'])",
trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES %s", trinoTableName, TRINO_INSERTED_PARTITION_VALUES));
assertSelectsOnSpecialCharacters(trinoTableName, sparkTableName);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC, ICEBERG_NESSIE})
public void testStringPartitioningWithSpecialCharactersInsertInSpark()
{
String baseTableName = "test_string_partitioning_with_special_chars_ctas_in_spark";
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format(
"CREATE TABLE %s (id BIGINT, part_col VARCHAR) WITH (partitioning = ARRAY['part_col'])",
trinoTableName));
onSpark().executeQuery(format("INSERT INTO %s VALUES %s", sparkTableName, SPARK_INSERTED_PARTITION_VALUES));
assertSelectsOnSpecialCharacters(trinoTableName, sparkTableName);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC})
public void testPartitionedByNonLowercaseColumn()
{
String baseTableName = "test_partitioned_by_non_lowercase_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " USING ICEBERG PARTITIONED BY (`PART`) TBLPROPERTIES ('format-version'='2') AS SELECT 1 AS data, 2 AS `PART`");
try {
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).contains(row(1, 2));
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (3, 4)");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).contains(row(1, 2), row(3, 4));
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE data = 3");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).contains(row(1, 2));
onTrino().executeQuery("UPDATE " + trinoTableName + " SET part = 20");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).contains(row(1, 20));
onTrino().executeQuery("MERGE INTO " + trinoTableName + " USING (SELECT 1 a) input ON true WHEN MATCHED THEN DELETE");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).hasNoRows();
}
finally {
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testPartitioningWithMixedCaseColumnInTrino()
{
String baseTableName = "test_partitioning_with_mixed_case_column_in_spark";
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (id INTEGER, `mIxEd_COL` STRING) USING ICEBERG",
sparkTableName));
assertQueryFailure(() -> onTrino().executeQuery("ALTER TABLE " + trinoTableName + " SET PROPERTIES partitioning = ARRAY['mIxEd_COL']"))
.hasMessageContaining("Unable to parse partitioning value");
onTrino().executeQuery("ALTER TABLE " + trinoTableName + " SET PROPERTIES partitioning = ARRAY['\"mIxEd_COL\"']");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (1, 'trino')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (2, 'spark')");
List expected = ImmutableList.of(row(1, "trino"), row(2, "spark"));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).contains(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).contains(expected);
assertThat((String) onTrino().executeQuery("SHOW CREATE TABLE " + trinoTableName).getOnlyValue())
.contains("partitioning = ARRAY['\"mIxEd_COL\"']");
assertThat((String) onSpark().executeQuery("SHOW CREATE TABLE " + sparkTableName).getOnlyValue())
.contains("PARTITIONED BY (mIxEd_COL)");
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testInsertReadingFromParquetTableWithNestedRowFieldNotPresentInDataFile()
{
// regression test for https://github.com/trinodb/trino/issues/9264
String sourceTableNameBase = "test_nested_missing_row_field_source";
String trinoSourceTableName = trinoTableName(sourceTableNameBase);
String sparkSourceTableName = sparkTableName(sourceTableNameBase);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoSourceTableName);
onTrino().executeQuery(
"CREATE TABLE " + trinoSourceTableName + " WITH (format = 'PARQUET') AS " +
" SELECT CAST(" +
" ROW(1, ROW(2, 3)) AS " +
" ROW(foo BIGINT, a_sub_struct ROW(x BIGINT, y BIGINT)) " +
") AS a_struct");
onSpark().executeQuery("ALTER TABLE " + sparkSourceTableName + " ADD COLUMN a_struct.a_sub_struct_2 STRUCT");
onTrino().executeQuery(
"INSERT INTO " + trinoSourceTableName +
" SELECT CAST(" +
" ROW(1, ROW(2, 3), ROW(4)) AS " +
" ROW(foo BIGINT,\n" +
" a_sub_struct ROW(x BIGINT, y BIGINT), " +
" a_sub_struct_2 ROW(z BIGINT)" +
" )" +
") AS a_struct");
String trinoTargetTableName = trinoTableName("test_nested_missing_row_field_target");
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTargetTableName);
onTrino().executeQuery("CREATE TABLE " + trinoTargetTableName + " WITH (format = 'PARQUET') AS SELECT * FROM " + trinoSourceTableName);
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTargetTableName))
.containsOnly(
row(
rowBuilder()
.addField("foo", 1L)
.addField(
"a_sub_struct",
rowBuilder()
.addField("x", 2L)
.addField("y", 3L)
.build())
.addField(
"a_sub_struct_2",
null)
.build()),
row(
rowBuilder()
.addField("foo", 1L)
.addField(
"a_sub_struct",
rowBuilder()
.addField("x", 2L)
.addField("y", 3L)
.build())
.addField(
"a_sub_struct_2",
rowBuilder()
.addField("z", 4L)
.build())
.build()));
}
private void assertSelectsOnSpecialCharacters(String trinoTableName, String sparkTableName)
{
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(EXPECTED_PARTITION_VALUES);
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(EXPECTED_PARTITION_VALUES);
for (String value : SPECIAL_CHARACTER_VALUES) {
String trinoValue = escapeTrinoString(value);
String sparkValue = escapeSparkString(value);
// Ensure Trino written metadata is readable from Spark and vice versa
assertThat(onSpark().executeQuery("SELECT count(*) FROM " + sparkTableName + " WHERE part_col = '" + sparkValue + "'"))
.withFailMessage("Spark query with predicate containing '" + value + "' contained no matches, expected one")
.containsOnly(row(1));
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE part_col = '" + trinoValue + "'"))
.withFailMessage("Trino query with predicate containing '" + value + "' contained no matches, expected one")
.containsOnly(row(1));
}
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testTrinoReadsSparkSortOrder()
{
String sourceTableNameBase = "test_insert_into_sorted_table_" + randomNameSuffix();
String trinoTableName = trinoTableName(sourceTableNameBase);
String sparkTableName = sparkTableName(sourceTableNameBase);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " (a INT, b INT, c INT) USING ICEBERG");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " WRITE ORDERED BY b, c DESC NULLS LAST");
assertThat((String) onTrino().executeQuery("SHOW CREATE TABLE " + trinoTableName).getOnlyValue())
.contains("sorted_by = ARRAY['b ASC NULLS FIRST','c DESC NULLS LAST']");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (3, 2, 1), (1, 2, 3), (NULL, NULL, NULL)");
assertThat(onSpark().executeQuery("SELECT _pos, a, b, c FROM " + sparkTableName))
.contains(
row(0, null, null, null),
row(1, 1, 2, 3),
row(2, 3, 2, 1));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testTrinoIgnoresUnsupportedSparkSortOrder()
{
String sourceTableNameBase = "test_insert_into_sorted_table_" + randomNameSuffix();
String trinoTableName = trinoTableName(sourceTableNameBase);
String sparkTableName = sparkTableName(sourceTableNameBase);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " (a INT, b INT, c INT) USING ICEBERG");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " WRITE ORDERED BY truncate(b, 3), a NULLS LAST");
assertThat((String) onTrino().executeQuery("SHOW CREATE TABLE " + trinoTableName).getOnlyValue())
.doesNotContain("sorted_by");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (3, 2333, 1), (NULL, NULL, NULL), (1, 2222, 3)");
assertThat(onSpark().executeQuery("SELECT _pos, a, b, c FROM " + sparkTableName))
.contains(
row(0, 1, 2222, 3),
row(1, 3, 2333, 1),
row(2, null, null, null));
}
/**
* @see TestIcebergInsert#testIcebergConcurrentInsert()
*/
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS, ICEBERG_REST, ICEBERG_JDBC}, timeOut = 60_000)
public void testTrinoSparkConcurrentInsert()
throws Exception
{
int insertsPerEngine = 7;
String baseTableName = "trino_spark_insert_concurrent_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("CREATE TABLE " + trinoTableName + "(e varchar, a bigint)");
ExecutorService executor = Executors.newFixedThreadPool(2);
try {
CyclicBarrier barrier = new CyclicBarrier(2);
QueryExecutor onTrino = onTrino();
QueryExecutor onSpark = onSpark();
List allInserted = executor.invokeAll(
Stream.of(Engine.TRINO, Engine.SPARK)
.map(engine -> (Callable>) () -> {
List inserted = new ArrayList<>();
for (int i = 0; i < insertsPerEngine; i++) {
barrier.await(20, SECONDS);
String engineName = engine.name().toLowerCase(ENGLISH);
long value = i;
switch (engine) {
case TRINO:
try {
onTrino.executeQuery(format("INSERT INTO %s VALUES ('%s', %d)", trinoTableName, engineName, value));
}
catch (QueryExecutionException queryExecutionException) {
// failed to insert
continue; // next loop iteration
}
break;
case SPARK:
onSpark.executeQuery(format("INSERT INTO %s VALUES ('%s', %d)", sparkTableName, engineName, value));
break;
default:
throw new UnsupportedOperationException("Unexpected engine: " + engine);
}
inserted.add(row(engineName, value));
}
return inserted;
})
.collect(toImmutableList())).stream()
.map(MoreFutures::getDone)
.flatMap(List::stream)
.collect(toImmutableList());
// At least one INSERT per round should succeed
assertThat(allInserted).hasSizeBetween(insertsPerEngine, insertsPerEngine * 2);
// All Spark inserts should succeed (and not be obliterated)
assertThat(onTrino().executeQuery("SELECT count(*) FROM " + trinoTableName + " WHERE e = 'spark'"))
.containsOnly(row(insertsPerEngine));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName))
.containsOnly(allInserted);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
finally {
executor.shutdownNow();
}
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsAndCompressionCodecs")
public void testTrinoReadingSparkCompressedData(StorageFormat storageFormat, String compressionCodec)
{
String baseTableName = toLowerCase("test_spark_compression" +
"_" + storageFormat +
"_" + compressionCodec +
"_" + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
List rows = IntStream.range(0, 555)
.mapToObj(i -> row("a" + i, i))
.collect(toImmutableList());
switch (storageFormat) {
case PARQUET:
onSpark().executeQuery("SET spark.sql.parquet.compression.codec = " + compressionCodec);
break;
case ORC:
if ("GZIP".equals(compressionCodec)) {
onSpark().executeQuery("SET spark.sql.orc.compression.codec = zlib");
}
else {
onSpark().executeQuery("SET spark.sql.orc.compression.codec = " + compressionCodec);
}
break;
case AVRO:
if ("NONE".equals(compressionCodec)) {
onSpark().executeQuery("SET spark.sql.avro.compression.codec = uncompressed");
}
else if ("SNAPPY".equals(compressionCodec)) {
onSpark().executeQuery("SET spark.sql.avro.compression.codec = snappy");
}
else if ("ZSTD".equals(compressionCodec)) {
onSpark().executeQuery("SET spark.sql.avro.compression.codec = zstandard");
}
else {
assertQueryFailure(() -> onSpark().executeQuery("SET spark.sql.avro.compression.codec = " + compressionCodec))
.hasMessageContaining("The value of spark.sql.avro.compression.codec should be one of bzip2, deflate, uncompressed, xz, snappy, zstandard");
throw new SkipException("Unsupported compression codec");
}
break;
default:
throw new UnsupportedOperationException("Unsupported storage format: " + storageFormat);
}
onSpark().executeQuery(
"CREATE TABLE " + sparkTableName + " (a string, b bigint) " +
"USING ICEBERG TBLPROPERTIES ('write.format.default' = '" + storageFormat + "')");
onSpark().executeQuery(
"INSERT INTO " + sparkTableName + " VALUES " +
rows.stream()
.map(row -> format("('%s', %s)", row.getValues().get(0), row.getValues().get(1)))
.collect(Collectors.joining(", ")));
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName))
.containsOnly(rows);
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName))
.containsOnly(rows);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsAndCompressionCodecs")
public void testSparkReadingTrinoCompressedData(StorageFormat storageFormat, String compressionCodec)
{
String baseTableName = toLowerCase("test_trino_compression" +
"_" + storageFormat +
"_" + compressionCodec +
"_" + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("SET SESSION iceberg.compression_codec = '" + compressionCodec + "'");
String createTable = "CREATE TABLE " + trinoTableName + " WITH (format = '" + storageFormat + "') AS TABLE tpch.tiny.nation";
if (storageFormat == StorageFormat.PARQUET && "LZ4".equals(compressionCodec)) {
// TODO (https://github.com/trinodb/trino/issues/9142) LZ4 is not supported with native Parquet writer
assertQueryFailure(() -> onTrino().executeQuery(createTable))
.hasMessageMatching("\\QQuery failed (#\\E\\S+\\Q): Compression codec LZ4 not supported for Parquet");
return;
}
if (storageFormat == StorageFormat.AVRO && compressionCodec.equals("LZ4")) {
assertQueryFailure(() -> onTrino().executeQuery(createTable))
.hasMessageMatching("\\QQuery failed (#\\E\\S+\\Q): Compression codec LZ4 not supported for Avro");
return;
}
onTrino().executeQuery(createTable);
List expected = onTrino().executeQuery("TABLE tpch.tiny.nation").rows().stream()
.map(row -> row(row.toArray()))
.collect(toImmutableList());
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName))
.containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName))
.containsOnly(expected);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void verifyCompressionCodecsDataProvider()
{
assertThat(onTrino().executeQuery("SHOW SESSION LIKE 'iceberg.compression_codec'"))
.containsOnly(row(
"iceberg.compression_codec",
"ZSTD",
"ZSTD",
"varchar",
"Compression codec to use when writing files. Possible values: " + compressionCodecs()));
}
@DataProvider
public Object[][] storageFormatsAndCompressionCodecs()
{
List compressionCodecs = compressionCodecs();
return Stream.of(StorageFormat.values())
.flatMap(storageFormat -> compressionCodecs.stream()
.map(compressionCodec -> new Object[] {storageFormat, compressionCodec}))
.toArray(Object[][]::new);
}
private List compressionCodecs()
{
return List.of(
"NONE",
"SNAPPY",
"LZ4",
"ZSTD",
"GZIP");
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testTrinoReadingMigratedNestedData(StorageFormat storageFormat)
{
String baseTableName = "test_trino_reading_migrated_nested_data_" + randomNameSuffix();
String defaultCatalogTableName = sparkDefaultCatalogTableName(baseTableName);
String sparkTableDefinition = "" +
"CREATE TABLE %s (\n" +
" doc_id STRING\n" +
", nested_map MAP>>\n" +
", nested_array ARRAY>>>\n" +
", nested_struct STRUCT>)\n" +
" USING %s";
onSpark().executeQuery(format(sparkTableDefinition, defaultCatalogTableName, storageFormat.name().toLowerCase(ENGLISH)));
String insert = "" +
"INSERT INTO TABLE %s SELECT" +
" 'Doc213'" +
", map('s1', array(named_struct('sName', 'ASName1', 'sNumber', 201), named_struct('sName', 'ASName2', 'sNumber', 202)))" +
", array(map('m1', array(named_struct('mName', 'MAS1Name1', 'mNumber', 301), named_struct('mName', 'MAS1Name2', 'mNumber', 302)))" +
" ,map('m2', array(named_struct('mName', 'MAS2Name1', 'mNumber', 401), named_struct('mName', 'MAS2Name2', 'mNumber', 402))))" +
", named_struct('id', 1, 'name', 'P. Sherman', 'address', named_struct('street_number', 42, 'street_name', 'Wallaby Way'))";
onSpark().executeQuery(format(insert, defaultCatalogTableName));
try {
onSpark().executeQuery(format("CALL system.migrate('%s')", defaultCatalogTableName));
}
catch (QueryExecutionException e) {
if (e.getMessage().contains("Cannot use catalog spark_catalog: not a ProcedureCatalog")) {
throw new SkipException("This catalog doesn't support calling system.migrate procedure");
}
}
String sparkTableName = sparkTableName(baseTableName);
Row row = row("Doc213", "ASName2", 201, "MAS2Name1", 302, "P. Sherman", 42, "Wallaby Way");
String sparkSelect = "SELECT" +
" doc_id" +
", nested_map['s1'][1].sName" +
", nested_map['s1'][0].sNumber" +
", nested_array[1]['m2'][0].mName" +
", nested_array[0]['m1'][1].mNumber" +
", nested_struct.name" +
", nested_struct.address.street_number" +
", nested_struct.address.street_name" +
" FROM ";
QueryResult sparkResult = onSpark().executeQuery(sparkSelect + sparkTableName);
// The Spark behavior when the default name mapping does not exist is not consistent
assertThat(sparkResult).containsOnly(row);
String trinoSelect = "SELECT" +
" doc_id" +
", nested_map['s1'][2].sName" +
", nested_map['s1'][1].sNumber" +
", nested_array[2]['m2'][1].mName" +
", nested_array[1]['m1'][2].mNumber" +
", nested_struct.name" +
", nested_struct.address.street_number" +
", nested_struct.address.street_name" +
" FROM ";
String trinoTableName = trinoTableName(baseTableName);
QueryResult trinoResult = onTrino().executeQuery(trinoSelect + trinoTableName);
assertThat(trinoResult).containsOnly(row);
// After removing the name mapping, columns from migrated files should be null since they are missing the Iceberg Field IDs
onSpark().executeQuery(format("ALTER TABLE %s UNSET TBLPROPERTIES ('schema.name-mapping.default')", sparkTableName));
assertThat(onTrino().executeQuery(trinoSelect + trinoTableName)).containsOnly(row(null, null, null, null, null, null, null, null));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(row(null, null, null, null));
assertThat(onTrino().executeQuery("SELECT nested_struct.address.street_number, nested_struct.address.street_name FROM " + trinoTableName)).containsOnly(row(null, null));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testMigratedDataWithAlteredSchema(StorageFormat storageFormat)
{
String baseTableName = "test_migrated_data_with_altered_schema_" + randomNameSuffix();
String defaultCatalogTableName = sparkDefaultCatalogTableName(baseTableName);
String sparkTableDefinition = "" +
"CREATE TABLE %s (\n" +
" doc_id STRING\n" +
", nested_struct STRUCT>)\n" +
" USING %s";
onSpark().executeQuery(format(sparkTableDefinition, defaultCatalogTableName, storageFormat));
String insert = "" +
"INSERT INTO TABLE %s SELECT" +
" 'Doc213'" +
", named_struct('id', 1, 'name', 'P. Sherman', 'address', named_struct('a', 42, 'b', 'Wallaby Way'))";
onSpark().executeQuery(format(insert, defaultCatalogTableName));
try {
onSpark().executeQuery(format("CALL system.migrate('%s')", defaultCatalogTableName));
}
catch (QueryExecutionException e) {
if (e.getMessage().contains("Cannot use catalog spark_catalog: not a ProcedureCatalog")) {
throw new SkipException("This catalog doesn't support calling system.migrate procedure");
}
}
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " RENAME COLUMN nested_struct TO nested_struct_moved");
String select = "SELECT" +
" nested_struct_moved.name" +
", nested_struct_moved.address.a" +
", nested_struct_moved.address.b" +
" FROM ";
Row row = row("P. Sherman", 42, "Wallaby Way");
QueryResult sparkResult = onSpark().executeQuery(select + sparkTableName);
assertThat(sparkResult).containsOnly(ImmutableList.of(row));
String trinoTableName = trinoTableName(baseTableName);
assertThat(onTrino().executeQuery(select + trinoTableName)).containsOnly(ImmutableList.of(row));
// After removing the name mapping, columns from migrated files should be null since they are missing the Iceberg Field IDs
onSpark().executeQuery(format("ALTER TABLE %s UNSET TBLPROPERTIES ('schema.name-mapping.default')", sparkTableName));
assertThat(onTrino().executeQuery(select + trinoTableName)).containsOnly(row(null, null, null));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testMigratedDataWithPartialNameMapping(StorageFormat storageFormat)
{
String baseTableName = "test_migrated_data_with_partial_name_mapping_" + randomNameSuffix();
String defaultCatalogTableName = sparkDefaultCatalogTableName(baseTableName);
String sparkTableDefinition = "CREATE TABLE %s (a INT, b INT) USING " + storageFormat.name().toLowerCase(ENGLISH);
onSpark().executeQuery(format(sparkTableDefinition, defaultCatalogTableName));
String insert = "INSERT INTO TABLE %s SELECT 1, 2";
onSpark().executeQuery(format(insert, defaultCatalogTableName));
try {
onSpark().executeQuery(format("CALL system.migrate('%s')", defaultCatalogTableName));
}
catch (QueryExecutionException e) {
if (e.getMessage().contains("Cannot use catalog spark_catalog: not a ProcedureCatalog")) {
throw new SkipException("This catalog doesn't support calling system.migrate procedure");
}
}
String sparkTableName = sparkTableName(baseTableName);
String trinoTableName = trinoTableName(baseTableName);
// Test missing entry for column 'b'
onSpark().executeQuery(format(
"ALTER TABLE %s SET TBLPROPERTIES ('schema.name-mapping.default'='[{\"field-id\": 1, \"names\": [\"a\"]}, {\"field-id\": 2, \"names\": [\"c\"]} ]')",
sparkTableName));
assertThat(onTrino().executeQuery("SELECT a, b FROM " + trinoTableName))
.containsOnly(row(1, null));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}) // Run only with ICEBERG because Spark returns the different stats based on the catalog
public void testPartialStats()
{
String tableName = "test_partial_stats_" + randomNameSuffix();
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(col0 INT, col1 INT, col2 STRING, col3 BINARY)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 2, 'col2Value0', X'000102f0feff')");
assertThat(onTrino().executeQuery("SHOW STATS FOR " + trinoTableName))
.containsOnly(
row("col0", null, null, 0.0, null, "1", "1"),
row("col1", null, null, 0.0, null, "2", "2"),
row("col2", 124.0, null, 0.0, null, null, null),
row("col3", 58.0, null, 0.0, null, null, null),
row(null, null, null, null, 1.0, null, null));
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " SET TBLPROPERTIES (write.metadata.metrics.column.col1='none')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (3, 4, 'col2Value1', X'000102f0feee')");
assertThat(onTrino().executeQuery("SHOW STATS FOR " + trinoTableName))
.containsOnly(
row("col0", null, null, 0.0, null, "1", "3"),
row("col1", null, null, null, null, null, null),
row("col2", 248.0, null, 0.0, null, null, null),
row("col3", 117.0, null, 0.0, null, null, null),
row(null, null, null, null, 2.0, null, null));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testStatsAfterAddingPartitionField()
{
String tableName = "test_stats_after_adding_partition_field_" + randomNameSuffix();
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(col0 INT, col1 INT)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 2)");
assertThat(onTrino().executeQuery("SHOW STATS FOR " + trinoTableName))
.containsOnly(row("col0", null, null, 0.0, null, "1", "1"), row("col1", null, null, 0.0, null, "2", "2"), row(null, null, null, null, 1.0, null, null));
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD col1");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (3, 4)");
assertThat(onTrino().executeQuery("SHOW STATS FOR " + trinoTableName))
.containsOnly(row("col0", null, null, 0.0, null, "1", "3"), row("col1", null, null, 0.0, null, "2", "4"), row(null, null, null, null, 2.0, null, null));
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD col1");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD bucket(3, col1)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (5, 6)");
assertThat(onTrino().executeQuery("SHOW STATS FOR " + trinoTableName))
.containsOnly(row("col0", null, null, 0.0, null, "1", "5"), row("col1", null, null, 0.0, null, "2", "6"), row(null, null, null, null, 3.0, null, null));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "tableFormatWithDeleteFormat")
public void testTrinoReadsSparkRowLevelDeletes(StorageFormat tableStorageFormat, StorageFormat deleteFileStorageFormat)
{
String tableName = toLowerCase(format("test_trino_reads_spark_row_level_deletes_%s_%s_%s", tableStorageFormat.name(), deleteFileStorageFormat.name(), randomNameSuffix()));
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(a INT, b INT) " +
"USING ICEBERG PARTITIONED BY (b) " +
"TBLPROPERTIES ('format-version'='2', 'write.delete.mode'='merge-on-read'," +
"'write.format.default'='" + tableStorageFormat.name() + "'," +
"'write.delete.format.default'='" + deleteFileStorageFormat.name() + "')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 2), (2, 2), (3, 2), (11, 12), (12, 12), (13, 12)");
// Spark inserts may create multiple files. rewrite_data_files ensures it is compacted to one file so a row level delete occurs.
onSpark().executeQuery("CALL " + SPARK_CATALOG + ".system.rewrite_data_files(table=>'" + TEST_SCHEMA_NAME + "." + tableName + "', options => map('min-input-files','1'))");
// Delete one row in a file
onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE a = 13");
// Delete an entire partition
onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE b = 2");
List expected = ImmutableList.of(row(11, 12), row(12, 12));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
// Delete to a file that already has deleted rows
onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE a = 12");
expected = ImmutableList.of(row(11, 12));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "tableFormatWithDeleteFormat")
public void testTrinoReadsSparkRowLevelDeletesWithRowTypes(StorageFormat tableStorageFormat, StorageFormat deleteFileStorageFormat)
{
String tableName = toLowerCase(format("test_trino_reads_spark_row_level_deletes_row_types_%s_%s_%s", tableStorageFormat.name(), deleteFileStorageFormat.name(), randomNameSuffix()));
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(part_key INT, int_t INT, row_t STRUCT) " +
"USING ICEBERG PARTITIONED BY (part_key) " +
"TBLPROPERTIES ('format-version'='2', 'write.delete.mode'='merge-on-read'," +
"'write.format.default'='" + tableStorageFormat.name() + "'," +
"'write.delete.format.default'='" + deleteFileStorageFormat.name() + "')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES " +
"(1, 1, named_struct('a', 1, 'b', 2)), (1, 2, named_struct('a', 3, 'b', 4)), (1, 3, named_struct('a', 5, 'b', 6)), (2, 4, named_struct('a', 1, 'b',2))");
// Spark inserts may create multiple files. rewrite_data_files ensures it is compacted to one file so a row level delete occurs.
onSpark().executeQuery("CALL " + SPARK_CATALOG + ".system.rewrite_data_files(table=>'" + TEST_SCHEMA_NAME + "." + tableName + "', options => map('min-input-files','1'))");
onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE int_t = 2");
List expected = ImmutableList.of(row(1, 2), row(1, 6), row(2, 2));
assertThat(onTrino().executeQuery("SELECT part_key, row_t.b FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT part_key, row_t.b FROM " + sparkTableName)).containsOnly(expected);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadsTrinoRowLevelDeletes(StorageFormat storageFormat)
{
String tableName = toLowerCase(format("test_spark_reads_trino_row_level_deletes_%s_%s", storageFormat.name(), randomNameSuffix()));
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onTrino().executeQuery("CREATE TABLE " + trinoTableName + "(a INT, b INT) WITH(partitioning = ARRAY['b'], format_version = 2, format = '" + storageFormat.name() + "')");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (1, 2), (2, 2), (3, 2), (11, 12), (12, 12), (13, 12)");
// Delete one row in a file
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE a = 13");
// Delete an entire partition
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE b = 2");
List expected = ImmutableList.of(row(11, 12), row(12, 12));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
// Delete to a file that already has deleted rows
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE a = 12");
expected = ImmutableList.of(row(11, 12));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadsTrinoRowLevelDeletesWithRowTypes(StorageFormat storageFormat)
{
String tableName = toLowerCase(format("test_spark_reads_trino_row_level_deletes_row_types_%s_%s", storageFormat.name(), randomNameSuffix()));
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onTrino().executeQuery("CREATE TABLE " + trinoTableName + "(part_key INT, int_t INT, row_t ROW(a INT, b INT)) " +
"WITH(partitioning = ARRAY['part_key'], format_version = 2, format = '" + storageFormat.name() + "') ");
onTrino().executeQuery("INSERT INTO " + trinoTableName + " VALUES (1, 1, row(1, 2)), (1, 2, row(3, 4)), (1, 3, row(5, 6)), (2, 4, row(1, 2))");
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE int_t = 2");
List expected = ImmutableList.of(row(1, 2), row(1, 6), row(2, 2));
assertThat(onTrino().executeQuery("SELECT part_key, row_t.b FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT part_key, row_t.b FROM " + sparkTableName)).containsOnly(expected);
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testDeleteAfterPartitionEvolution(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_delete_after_partition_evolution_" + storageFormat + randomNameSuffix());
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery("DROP TABLE IF EXISTS " + sparkTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (" +
"col0 BIGINT, " +
"col1 BIGINT, " +
"col2 BIGINT) "
+ " USING ICEBERG"
+ " TBLPROPERTIES ('write.format.default' = '%s', 'format-version' = 2, 'write.delete.mode' = 'merge-on-read')",
sparkTableName,
storageFormat));
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 11, 21)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD bucket(3, col0)");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (2, 12, 22)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD col1");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (3, 13, 23)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD col1");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD col2");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (4, 14, 24)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD bucket(3, col0)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD col2");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD col0");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (5, 15, 25)");
List expected = new ArrayList<>();
expected.add(row(1, 11, 21));
expected.add(row(2, 12, 22));
expected.add(row(3, 13, 23));
expected.add(row(4, 14, 24));
expected.add(row(5, 15, 25));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
for (int columnValue = 1; columnValue <= 5; columnValue++) {
onTrino().executeQuery("DELETE FROM " + trinoTableName + " WHERE col0 = " + columnValue);
// Rows are in order so removing the first one always matches columnValue
expected.remove(0);
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
}
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testMissingMetrics()
{
String tableName = "test_missing_metrics_" + randomNameSuffix();
String sparkTableName = sparkTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + " (name STRING, country STRING) USING ICEBERG " +
"PARTITIONED BY (country) TBLPROPERTIES ('write.metadata.metrics.default'='none')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES ('Christoph', 'AT'), (NULL, 'RO')");
assertThat(onTrino().executeQuery(format("SELECT count(*) FROM %s.%s.\"%s$partitions\" WHERE data IS NOT NULL", TRINO_CATALOG, TEST_SCHEMA_NAME, tableName)))
.containsOnly(row(0));
onSpark().executeQuery("DROP TABLE " + sparkTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testOptimizeOnV2IcebergTable()
{
String tableName = format("test_optimize_on_v2_iceberg_table_%s", randomNameSuffix());
String sparkTableName = sparkTableName(tableName);
String trinoTableName = trinoTableName(tableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(a INT, b INT) " +
"USING ICEBERG PARTITIONED BY (b) " +
"TBLPROPERTIES ('format-version'='2', 'write.delete.mode'='merge-on-read')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 2), (2, 2), (3, 2), (11, 12), (12, 12), (13, 12)");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE OPTIMIZE", trinoTableName));
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName))
.containsOnly(row(1, 2), row(2, 2), row(3, 2), row(11, 12), row(12, 12), row(13, 12));
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testAlterTableExecuteProceduresOnEmptyTable()
{
String baseTableName = "test_alter_table_execute_procedures_on_empty_table_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onSpark().executeQuery(format(
"CREATE TABLE %s (" +
" _string STRING" +
", _bigint BIGINT" +
", _integer INTEGER" +
") USING ICEBERG",
sparkTableName));
onTrino().executeQuery("ALTER TABLE " + trinoTableName + " EXECUTE optimize");
onTrino().executeQuery("ALTER TABLE " + trinoTableName + " EXECUTE expire_snapshots(retention_threshold => '7d')");
onTrino().executeQuery("ALTER TABLE " + trinoTableName + " EXECUTE remove_orphan_files(retention_threshold => '7d')");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).hasNoRows();
}
private static String escapeSparkString(String value)
{
return value.replace("\\", "\\\\").replace("'", "\\'");
}
private static String escapeTrinoString(String value)
{
return value.replace("'", "''");
}
private static String sparkTableName(String tableName)
{
return format("%s.%s.%s", SPARK_CATALOG, TEST_SCHEMA_NAME, tableName);
}
private static String sparkDefaultCatalogTableName(String tableName)
{
return format("%s.%s", TEST_SCHEMA_NAME, tableName);
}
private static String trinoTableName(String tableName)
{
return format("%s.%s.%s", TRINO_CATALOG, TEST_SCHEMA_NAME, tableName);
}
private io.trino.jdbc.Row.Builder rowBuilder()
{
return io.trino.jdbc.Row.builder();
}
@DataProvider
public static Object[][] specVersions()
{
return new Object[][] {{1}, {2}};
}
@DataProvider
public static Object[][] storageFormats()
{
return Stream.of(StorageFormat.values())
.map(storageFormat -> new Object[] {storageFormat})
.toArray(Object[][]::new);
}
// Provides each supported table formats paired with each delete file format.
@DataProvider
public static Object[][] tableFormatWithDeleteFormat()
{
return Stream.of(StorageFormat.values())
.flatMap(tableStorageFormat -> Arrays.stream(StorageFormat.values())
.map(deleteFileStorageFormat -> new Object[] {tableStorageFormat, deleteFileStorageFormat}))
.toArray(Object[][]::new);
}
@DataProvider
public static Object[][] storageFormatsWithSpecVersion()
{
List storageFormats = Stream.of(StorageFormat.values())
.collect(toImmutableList());
List specVersions = ImmutableList.of(1, 2);
return storageFormats.stream()
.flatMap(storageFormat -> specVersions.stream().map(specVersion -> new Object[] {storageFormat, specVersion}))
.toArray(Object[][]::new);
}
public enum StorageFormat
{
PARQUET,
ORC,
AVRO,
/**/;
}
public enum CreateMode
{
CREATE_TABLE_AND_INSERT,
CREATE_TABLE_AS_SELECT,
CREATE_TABLE_WITH_NO_DATA_AND_INSERT
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormats")
public void testSparkReadsTrinoTableAfterCleaningUp(StorageFormat storageFormat)
{
String baseTableName = toLowerCase("test_spark_reads_trino_partitioned_table_after_expiring_snapshots" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format("CREATE TABLE %s (_string VARCHAR, _bigint BIGINT) WITH (partitioning = ARRAY['_string'], format = '%s')", trinoTableName, storageFormat));
// separate inserts give us snapshot per insert
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1001)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1002)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1003)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1004)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1005)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1006)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1007)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1008)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1009)", trinoTableName));
onTrino().executeQuery(format("DELETE FROM %s WHERE _string = '%s'", trinoTableName, 'b'));
int initialNumberOfMetadataFiles = calculateMetadataFilesForPartitionedTable(baseTableName);
onTrino().executeQuery("SET SESSION iceberg.expire_snapshots_min_retention = '0s'");
onTrino().executeQuery("SET SESSION iceberg.remove_orphan_files_min_retention = '0s'");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE EXPIRE_SNAPSHOTS (retention_threshold => '0s')", trinoTableName));
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE REMOVE_ORPHAN_FILES (retention_threshold => '0s')", trinoTableName));
int updatedNumberOfMetadataFiles = calculateMetadataFilesForPartitionedTable(baseTableName);
assertThat(updatedNumberOfMetadataFiles).isLessThan(initialNumberOfMetadataFiles);
Row row = row(3006);
String selectByString = "SELECT SUM(_bigint) FROM %s WHERE _string = 'a'";
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testSparkReadsTrinoTableAfterOptimizeAndCleaningUp(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_spark_reads_trino_partitioned_table_after_expiring_snapshots_after_optimize" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format("CREATE TABLE %s (_string VARCHAR, _bigint BIGINT) WITH (partitioning = ARRAY['_string'], format = '%s', format_version = %s)", trinoTableName, storageFormat, specVersion));
// separate inserts give us snapshot per insert
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1001)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1002)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1003)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1004)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1005)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('b', 1006)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1007)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1008)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('c', 1009)", trinoTableName));
onTrino().executeQuery(format("DELETE FROM %s WHERE _string = '%s'", trinoTableName, 'b'));
int initialNumberOfFiles = onTrino().executeQuery(format("SELECT * FROM iceberg.default.\"%s$files\"", baseTableName)).getRowsCount();
int initialNumberOfMetadataFiles = calculateMetadataFilesForPartitionedTable(baseTableName);
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE OPTIMIZE", trinoTableName));
onTrino().executeQuery("SET SESSION iceberg.expire_snapshots_min_retention = '0s'");
onTrino().executeQuery("SET SESSION iceberg.remove_orphan_files_min_retention = '0s'");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE EXPIRE_SNAPSHOTS (retention_threshold => '0s')", trinoTableName));
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE REMOVE_ORPHAN_FILES (retention_threshold => '0s')", trinoTableName));
int updatedNumberOfFiles = onTrino().executeQuery(format("SELECT * FROM iceberg.default.\"%s$files\"", baseTableName)).getRowsCount();
assertThat(updatedNumberOfFiles).isLessThan(initialNumberOfFiles);
int updatedNumberOfMetadataFiles = calculateMetadataFilesForPartitionedTable(baseTableName);
assertThat(updatedNumberOfMetadataFiles).isLessThan(initialNumberOfMetadataFiles);
Row row = row(3006);
String selectByString = "SELECT SUM(_bigint) FROM %s WHERE _string = 'a'";
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "storageFormatsWithSpecVersion")
public void testTrinoReadsTrinoTableWithSparkDeletesAfterOptimizeAndCleanUp(StorageFormat storageFormat, int specVersion)
{
String baseTableName = toLowerCase("test_spark_reads_trino_partitioned_table_with_deletes_after_expiring_snapshots_after_optimize" + storageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onTrino().executeQuery(format("CREATE TABLE %s (_string VARCHAR, _bigint BIGINT) WITH (partitioning = ARRAY['_string'], format = '%s', format_version = %s)", trinoTableName, storageFormat, specVersion));
// separate inserts give us snapshot per insert
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1001)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1002)", trinoTableName));
onSpark().executeQuery(format("DELETE FROM %s WHERE _bigint = 1002", sparkTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1003)", trinoTableName));
onTrino().executeQuery(format("INSERT INTO %s VALUES ('a', 1004)", trinoTableName));
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE OPTIMIZE", trinoTableName));
onTrino().executeQuery("SET SESSION iceberg.expire_snapshots_min_retention = '0s'");
onTrino().executeQuery("SET SESSION iceberg.remove_orphan_files_min_retention = '0s'");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE EXPIRE_SNAPSHOTS (retention_threshold => '0s')", trinoTableName));
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE REMOVE_ORPHAN_FILES (retention_threshold => '0s')", trinoTableName));
Row row = row(3008);
String selectByString = "SELECT SUM(_bigint) FROM %s WHERE _string = 'a'";
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row);
onTrino().executeQuery("DROP TABLE " + trinoTableName);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS}, dataProvider = "tableFormatWithDeleteFormat")
public void testCleaningUpIcebergTableWithRowLevelDeletes(StorageFormat tableStorageFormat, StorageFormat deleteFileStorageFormat)
{
String baseTableName = toLowerCase("test_cleaning_up_iceberg_table_fails_for_table_v2" + tableStorageFormat);
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(part_key INT, int_t INT, row_t STRUCT) " +
"USING ICEBERG PARTITIONED BY (part_key) " +
"TBLPROPERTIES ('format-version'='2', 'write.delete.mode'='merge-on-read'," +
"'write.format.default'='" + tableStorageFormat.name() + "'," +
"'write.delete.format.default'='" + deleteFileStorageFormat.name() + "')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES " +
"(1, 1, named_struct('a', 1, 'b', 2)), (1, 2, named_struct('a', 3, 'b', 4)), (1, 3, named_struct('a', 5, 'b', 6)), (2, 4, named_struct('a', 1, 'b', 2)), (2, 2, named_struct('a', 2, 'b', 3))");
// Spark inserts may create multiple files. rewrite_data_files ensures it is compacted to one file so a row level delete occurs.
onSpark().executeQuery("CALL " + SPARK_CATALOG + ".system.rewrite_data_files(table=>'" + TEST_SCHEMA_NAME + "." + baseTableName + "', options => map('min-input-files','1'))");
onSpark().executeQuery("DELETE FROM " + sparkTableName + " WHERE int_t = 2");
Row row = row(4);
String selectByString = "SELECT SUM(int_t) FROM %s WHERE part_key = 1";
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row);
onTrino().executeQuery("SET SESSION iceberg.expire_snapshots_min_retention = '0s'");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE EXPIRE_SNAPSHOTS (retention_threshold => '0s')", trinoTableName));
onTrino().executeQuery("SET SESSION iceberg.remove_orphan_files_min_retention = '0s'");
onTrino().executeQuery(format("ALTER TABLE %s EXECUTE REMOVE_ORPHAN_FILES (retention_threshold => '0s')", trinoTableName));
assertThat(onTrino().executeQuery(format(selectByString, trinoTableName)))
.containsOnly(row);
assertThat(onSpark().executeQuery(format(selectByString, sparkTableName)))
.containsOnly(row);
}
@Test(groups = {ICEBERG, PROFILE_SPECIFIC_TESTS})
public void testUpdateAfterSchemaEvolution()
{
String baseTableName = "test_update_after_schema_evolution_" + randomNameSuffix();
String trinoTableName = trinoTableName(baseTableName);
String sparkTableName = sparkTableName(baseTableName);
onTrino().executeQuery("DROP TABLE IF EXISTS " + trinoTableName);
onSpark().executeQuery("CREATE TABLE " + sparkTableName + "(part_key INT, a INT, b INT, c INT) " +
"USING ICEBERG PARTITIONED BY (part_key) " +
"TBLPROPERTIES ('format-version'='2', 'write.delete.mode'='merge-on-read')");
onSpark().executeQuery("INSERT INTO " + sparkTableName + " VALUES (1, 2, 3, 4), (11, 12, 13, 14)");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP PARTITION FIELD part_key");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD PARTITION FIELD a");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP COLUMN b");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " DROP COLUMN c");
onSpark().executeQuery("ALTER TABLE " + sparkTableName + " ADD COLUMN c INT");
List expected = ImmutableList.of(row(1, 2, null), row(11, 12, null));
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
// Because of the DROP/ADD on column c these two should be no-op updates
onTrino().executeQuery("UPDATE " + trinoTableName + " SET c = c + 1");
onTrino().executeQuery("UPDATE " + trinoTableName + " SET a = a + 1 WHERE c = 4");
assertThat(onTrino().executeQuery("SELECT * FROM " + trinoTableName)).containsOnly(expected);
assertThat(onSpark().executeQuery("SELECT * FROM " + sparkTableName)).containsOnly(expected);
// Check the new data files are using the updated partition scheme
List