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Spark 3.4: Avoid local sort for MERGE cardinality check (apache#7558)
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227
...park-extensions/src/jmh/java/org/apache/iceberg/spark/MergeCardinalityCheckBenchmark.java
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| /* | ||
| * 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; | ||
|
|
||
| import static org.apache.spark.sql.functions.current_date; | ||
| import static org.apache.spark.sql.functions.date_add; | ||
| import static org.apache.spark.sql.functions.expr; | ||
|
|
||
| import java.util.UUID; | ||
| import org.apache.hadoop.conf.Configuration; | ||
| import org.apache.iceberg.DistributionMode; | ||
| import org.apache.iceberg.RowLevelOperationMode; | ||
| import org.apache.iceberg.Table; | ||
| import org.apache.iceberg.TableProperties; | ||
| import org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions; | ||
| import org.apache.spark.sql.Dataset; | ||
| import org.apache.spark.sql.Row; | ||
| import org.apache.spark.sql.SparkSession; | ||
| import org.apache.spark.sql.catalyst.analysis.NoSuchTableException; | ||
| import org.apache.spark.sql.catalyst.parser.ParseException; | ||
| import org.apache.spark.sql.internal.SQLConf; | ||
| import org.apache.spark.sql.types.StructType; | ||
| import org.openjdk.jmh.annotations.Benchmark; | ||
| import org.openjdk.jmh.annotations.BenchmarkMode; | ||
| import org.openjdk.jmh.annotations.Fork; | ||
| import org.openjdk.jmh.annotations.Measurement; | ||
| import org.openjdk.jmh.annotations.Mode; | ||
| import org.openjdk.jmh.annotations.Scope; | ||
| import org.openjdk.jmh.annotations.Setup; | ||
| import org.openjdk.jmh.annotations.State; | ||
| import org.openjdk.jmh.annotations.TearDown; | ||
| import org.openjdk.jmh.annotations.Threads; | ||
| import org.openjdk.jmh.annotations.Warmup; | ||
|
|
||
| /** | ||
| * A benchmark that evaluates the performance of the cardinality check in MERGE operations. | ||
| * | ||
| * <p>To run this benchmark for spark-3.4: <code> | ||
| * ./gradlew -DsparkVersions=3.4 :iceberg-spark:iceberg-spark-extensions-3.4_2.12:jmh | ||
| * -PjmhIncludeRegex=MergeCardinalityCheckBenchmark | ||
| * -PjmhOutputPath=benchmark/iceberg-merge-cardinality-check-benchmark.txt | ||
| * </code> | ||
| */ | ||
| @Fork(1) | ||
| @State(Scope.Benchmark) | ||
| @Warmup(iterations = 3) | ||
| @Measurement(iterations = 5) | ||
| @BenchmarkMode(Mode.SingleShotTime) | ||
| public class MergeCardinalityCheckBenchmark { | ||
|
|
||
| private static final String TABLE_NAME = "test_table"; | ||
| private static final int NUM_FILES = 5; | ||
| private static final int NUM_ROWS_PER_FILE = 1_000_000; | ||
| private static final int NUM_UNMATCHED_RECORDS_PER_MERGE = 100_000; | ||
|
|
||
| private final Configuration hadoopConf = new Configuration(); | ||
| private SparkSession spark; | ||
| private long originalSnapshotId; | ||
|
|
||
| @Setup | ||
| public void setupBenchmark() throws NoSuchTableException, ParseException { | ||
| setupSpark(); | ||
| initTable(); | ||
| appendData(); | ||
|
|
||
| Table table = Spark3Util.loadIcebergTable(spark, TABLE_NAME); | ||
| this.originalSnapshotId = table.currentSnapshot().snapshotId(); | ||
| } | ||
|
|
||
| @TearDown | ||
| public void tearDownBenchmark() { | ||
| tearDownSpark(); | ||
| dropTable(); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void copyOnWriteMergeCardinalityCheck10PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.COPY_ON_WRITE, 0.1); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void copyOnWriteMergeCardinalityCheck30PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.COPY_ON_WRITE, 0.3); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void copyOnWriteMergeCardinalityCheck90PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.COPY_ON_WRITE, 0.9); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void mergeOnReadMergeCardinalityCheck10PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.MERGE_ON_READ, 0.1); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void mergeOnReadMergeCardinalityCheck30PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.MERGE_ON_READ, 0.3); | ||
| } | ||
|
|
||
| @Benchmark | ||
| @Threads(1) | ||
| public void mergeOnReadMergeCardinalityCheck90PercentUpdates() { | ||
| runBenchmark(RowLevelOperationMode.MERGE_ON_READ, 0.9); | ||
| } | ||
|
|
||
| private void runBenchmark(RowLevelOperationMode mode, double updatePercentage) { | ||
| sql( | ||
| "ALTER TABLE %s SET TBLPROPERTIES ('%s' '%s')", | ||
| TABLE_NAME, TableProperties.MERGE_MODE, mode.modeName()); | ||
|
|
||
| Dataset<Long> insertDataDF = spark.range(-NUM_UNMATCHED_RECORDS_PER_MERGE, 0, 1); | ||
| Dataset<Long> updateDataDF = spark.range((long) (updatePercentage * NUM_ROWS_PER_FILE)); | ||
| Dataset<Long> sourceDF = updateDataDF.union(insertDataDF); | ||
| sourceDF.createOrReplaceTempView("source"); | ||
|
|
||
| sql( | ||
| "MERGE INTO %s t USING source s " | ||
| + "ON t.id = s.id " | ||
| + "WHEN MATCHED THEN " | ||
| + " UPDATE SET stringCol = 'invalid' " | ||
| + "WHEN NOT MATCHED THEN " | ||
| + " INSERT (id, intCol, floatCol, doubleCol, decimalCol, dateCol, timestampCol, stringCol) " | ||
| + " VALUES (s.id, null, null, null, null, null, null, 'new')", | ||
| TABLE_NAME); | ||
|
|
||
| sql( | ||
| "CALL system.rollback_to_snapshot(table => '%s', snapshot_id => %dL)", | ||
| TABLE_NAME, originalSnapshotId); | ||
| } | ||
|
|
||
| private void setupSpark() { | ||
| this.spark = | ||
| SparkSession.builder() | ||
| .config("spark.ui.enabled", false) | ||
| .config("spark.sql.extensions", IcebergSparkSessionExtensions.class.getName()) | ||
| .config("spark.sql.catalog.spark_catalog", SparkSessionCatalog.class.getName()) | ||
| .config("spark.sql.catalog.spark_catalog.type", "hadoop") | ||
| .config("spark.sql.catalog.spark_catalog.warehouse", newWarehouseDir()) | ||
| .config(SQLConf.DYNAMIC_PARTITION_PRUNING_ENABLED().key(), "false") | ||
| .config(SQLConf.ADAPTIVE_EXECUTION_ENABLED().key(), "false") | ||
| .config(SQLConf.SHUFFLE_PARTITIONS().key(), "2") | ||
| .master("local") | ||
| .getOrCreate(); | ||
| } | ||
|
|
||
| private void tearDownSpark() { | ||
| spark.stop(); | ||
| } | ||
|
|
||
| private void initTable() { | ||
| sql( | ||
| "CREATE TABLE %s ( " | ||
| + " id LONG, intCol INT, floatCol FLOAT, doubleCol DOUBLE, " | ||
| + " decimalCol DECIMAL(20, 5), dateCol DATE, timestampCol TIMESTAMP, " | ||
| + " stringCol STRING)" | ||
| + "USING iceberg " | ||
| + "TBLPROPERTIES (" | ||
| + " '%s' '%s'," | ||
| + " '%s' '%d'," | ||
| + " '%s' '%d')", | ||
| TABLE_NAME, | ||
| TableProperties.MERGE_DISTRIBUTION_MODE, | ||
| DistributionMode.NONE.modeName(), | ||
| TableProperties.SPLIT_OPEN_FILE_COST, | ||
| Integer.MAX_VALUE, | ||
| TableProperties.FORMAT_VERSION, | ||
| 2); | ||
|
|
||
| sql("ALTER TABLE %s WRITE ORDERED BY id", TABLE_NAME); | ||
| } | ||
|
|
||
| private void dropTable() { | ||
| sql("DROP TABLE IF EXISTS %s PURGE", TABLE_NAME); | ||
| } | ||
|
|
||
| private void appendData() throws NoSuchTableException { | ||
| for (int fileNum = 1; fileNum <= NUM_FILES; fileNum++) { | ||
| Dataset<Row> inputDF = | ||
| spark | ||
| .range(NUM_ROWS_PER_FILE) | ||
| .withColumn("intCol", expr("CAST(id AS INT)")) | ||
| .withColumn("floatCol", expr("CAST(id AS FLOAT)")) | ||
| .withColumn("doubleCol", expr("CAST(id AS DOUBLE)")) | ||
| .withColumn("decimalCol", expr("CAST(id AS DECIMAL(20, 5))")) | ||
| .withColumn("dateCol", date_add(current_date(), fileNum)) | ||
| .withColumn("timestampCol", expr("TO_TIMESTAMP(dateCol)")) | ||
| .withColumn("stringCol", expr("CAST(dateCol AS STRING)")); | ||
| appendAsFile(inputDF); | ||
| } | ||
| } | ||
|
|
||
| private void appendAsFile(Dataset<Row> df) throws NoSuchTableException { | ||
| // ensure the schema is precise (including nullability) | ||
| StructType sparkSchema = spark.table(TABLE_NAME).schema(); | ||
| spark.createDataFrame(df.rdd(), sparkSchema).coalesce(1).writeTo(TABLE_NAME).append(); | ||
| } | ||
|
|
||
| private String newWarehouseDir() { | ||
| return hadoopConf.get("hadoop.tmp.dir") + UUID.randomUUID(); | ||
| } | ||
|
|
||
| private void sql(String query, Object... args) { | ||
| spark.sql(String.format(query, args)); | ||
| } | ||
| } |
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