rdblue commented on code in PR #5035: URL: https://github.com/apache/iceberg/pull/5035#discussion_r910189031
########## spark/v3.2/spark/src/test/java/org/apache/iceberg/spark/source/TestSparkReaderWithBloomFilter.java: ########## @@ -0,0 +1,321 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.iceberg.spark.source; + +import java.io.Closeable; +import java.io.IOException; +import java.math.BigDecimal; +import java.time.LocalDate; +import java.util.List; +import java.util.Locale; +import java.util.Map; +import org.apache.hadoop.hive.conf.HiveConf; +import org.apache.iceberg.BaseTable; +import org.apache.iceberg.CatalogUtil; +import org.apache.iceberg.DataFile; +import org.apache.iceberg.DataFiles; +import org.apache.iceberg.FileFormat; +import org.apache.iceberg.Files; +import org.apache.iceberg.Schema; +import org.apache.iceberg.StructLike; +import org.apache.iceberg.Table; +import org.apache.iceberg.TableMetadata; +import org.apache.iceberg.TableOperations; +import org.apache.iceberg.TableProperties; +import org.apache.iceberg.TestHelpers.Row; +import org.apache.iceberg.catalog.Namespace; +import org.apache.iceberg.catalog.TableIdentifier; +import org.apache.iceberg.data.GenericAppenderFactory; +import org.apache.iceberg.data.GenericRecord; +import org.apache.iceberg.data.Record; +import org.apache.iceberg.exceptions.AlreadyExistsException; +import org.apache.iceberg.hive.HiveCatalog; +import org.apache.iceberg.hive.TestHiveMetastore; +import org.apache.iceberg.io.FileAppender; +import org.apache.iceberg.io.OutputFile; +import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.SparkValueConverter; +import org.apache.iceberg.types.Types; +import org.apache.iceberg.util.PropertyUtil; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.SparkSession; +import org.junit.After; +import org.junit.AfterClass; +import org.junit.Assert; +import org.junit.Before; +import org.junit.BeforeClass; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import static org.apache.hadoop.hive.conf.HiveConf.ConfVars.METASTOREURIS; +import static org.apache.iceberg.TableProperties.DEFAULT_FILE_FORMAT; +import static org.apache.iceberg.TableProperties.DEFAULT_FILE_FORMAT_DEFAULT; +import static org.apache.iceberg.TableProperties.PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX; +import static org.apache.iceberg.TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES; +import static org.apache.iceberg.TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES_DEFAULT; + +@RunWith(Parameterized.class) +public class TestSparkReaderWithBloomFilter { + + protected String tableName = null; + protected Table table = null; + protected List<Record> records = null; + protected DataFile dataFile = null; + + private static TestHiveMetastore metastore = null; + protected static SparkSession spark = null; + protected static HiveCatalog catalog = null; + protected final boolean vectorized; + protected final boolean useBloomFilter; + + public TestSparkReaderWithBloomFilter(boolean vectorized, boolean useBloomFilter) { + this.vectorized = vectorized; + this.useBloomFilter = useBloomFilter; + } + + // Schema passed to create tables + public static final Schema SCHEMA = new Schema( + Types.NestedField.required(1, "id", Types.IntegerType.get()), + Types.NestedField.required(2, "id_long", Types.LongType.get()), + Types.NestedField.required(3, "id_double", Types.DoubleType.get()), + Types.NestedField.required(4, "id_float", Types.FloatType.get()), + Types.NestedField.required(5, "id_string", Types.StringType.get()), + Types.NestedField.optional(6, "id_boolean", Types.BooleanType.get()), + Types.NestedField.optional(7, "id_date", Types.DateType.get()), + Types.NestedField.optional(8, "id_int_decimal", Types.DecimalType.of(8, 2)), + Types.NestedField.optional(9, "id_long_decimal", Types.DecimalType.of(14, 2)), + Types.NestedField.optional(10, "id_fixed_decimal", Types.DecimalType.of(31, 2)) + ); + + private static final int INT_MIN_VALUE = 30; + private static final int INT_MAX_VALUE = 329; + private static final int INT_VALUE_COUNT = INT_MAX_VALUE - INT_MIN_VALUE + 1; + private static final long LONG_BASE = 1000L; + private static final double DOUBLE_BASE = 10000D; + private static final float FLOAT_BASE = 100000F; + private static final String BINARY_PREFIX = "BINARY测试_"; + + @Rule + public TemporaryFolder temp = new TemporaryFolder(); + + @Before + public void writeTestDataFile() throws IOException { + this.tableName = "test"; + createTable(tableName, SCHEMA); + this.records = Lists.newArrayList(); + + // records all use IDs that are in bucket id_bucket=0 + GenericRecord record = GenericRecord.create(table.schema()); + + for (int i = 0; i < INT_VALUE_COUNT; i += 1) { + records.add(record.copy(ImmutableMap.of( + "id", INT_MIN_VALUE + i, + "id_long", LONG_BASE + INT_MIN_VALUE + i, + "id_double", DOUBLE_BASE + INT_MIN_VALUE + i, + "id_float", FLOAT_BASE + INT_MIN_VALUE + i, + "id_string", BINARY_PREFIX + (INT_MIN_VALUE + i), + "id_boolean", (i % 2 == 0) ? true : false, + "id_date", LocalDate.parse("2021-09-05"), + "id_int_decimal", new BigDecimal(String.valueOf(77.77)), + "id_long_decimal", new BigDecimal(String.valueOf(88.88)), + "id_fixed_decimal", new BigDecimal(String.valueOf(99.99))))); + } + + this.dataFile = writeDataFile(Files.localOutput(temp.newFile()), Row.of(0), records); + + table.newAppend() + .appendFile(dataFile) + .commit(); + } + + @After + public void cleanup() throws IOException { + dropTable("test"); + } + + @Parameterized.Parameters(name = "vectorized = {0}, useBloomFilter = {1}") + public static Object[][] parameters() { + return new Object[][] { + {false, false}, {true, false}, {false, true}, {true, true} + }; + } + + @BeforeClass + public static void startMetastoreAndSpark() { + metastore = new TestHiveMetastore(); + metastore.start(); + HiveConf hiveConf = metastore.hiveConf(); + + spark = SparkSession.builder() + .master("local[2]") + .config("spark.hadoop." + METASTOREURIS.varname, hiveConf.get(METASTOREURIS.varname)) + .enableHiveSupport() + .getOrCreate(); + + catalog = (HiveCatalog) + CatalogUtil.loadCatalog(HiveCatalog.class.getName(), "hive", ImmutableMap.of(), hiveConf); + + try { + catalog.createNamespace(Namespace.of("default")); + } catch (AlreadyExistsException ignored) { + // the default namespace already exists. ignore the create error + } + } + + @AfterClass + public static void stopMetastoreAndSpark() throws Exception { + catalog = null; + metastore.stop(); + metastore = null; + spark.stop(); + spark = null; + } + + protected void createTable(String name, Schema schema) { + table = catalog.createTable(TableIdentifier.of("default", name), schema); + TableOperations ops = ((BaseTable) table).operations(); + TableMetadata meta = ops.current(); + ops.commit(meta, meta.upgradeToFormatVersion(2)); + + if (useBloomFilter) { + table.updateProperties() + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_long", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_double", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_float", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_string", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_boolean", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_date", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_int_decimal", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_long_decimal", "true") + .set(PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX + "id_fixed_decimal", "true") + .commit(); + } + + table.updateProperties() + .set(TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES, "100") // to have multiple row groups + .commit(); + if (vectorized) { + table.updateProperties() + .set(TableProperties.PARQUET_VECTORIZATION_ENABLED, "true") + .set(TableProperties.PARQUET_BATCH_SIZE, "4") + .commit(); + } + } + + protected void dropTable(String name) { + catalog.dropTable(TableIdentifier.of("default", name)); + } + + private DataFile writeDataFile(OutputFile out, StructLike partition, List<Record> rows) Review Comment: Why not use the `Parquet` utils to write the file? -- This is an automated message from the Apache Git Service. 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