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Spark SystemFunctions are not pushed down during JOIN #9233
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222 changes: 222 additions & 0 deletions
222
...rk-extensions/src/test/java/org/apache/iceberg/spark/extensions/TestSPJWithBucketing.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,222 @@ | ||
| /* | ||
| * 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.extensions; | ||
|
|
||
| import java.util.Map; | ||
| import java.util.concurrent.TimeoutException; | ||
| import java.util.concurrent.atomic.AtomicInteger; | ||
| import java.util.function.Consumer; | ||
| import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap; | ||
| import org.apache.iceberg.spark.SparkCatalogConfig; | ||
| import org.apache.iceberg.spark.SparkSQLProperties; | ||
| import org.apache.spark.scheduler.SparkListener; | ||
| import org.apache.spark.scheduler.SparkListenerTaskEnd; | ||
| import org.apache.spark.sql.Dataset; | ||
| import org.apache.spark.sql.Row; | ||
| import org.apache.spark.sql.SparkSession; | ||
| import org.apache.spark.sql.internal.SQLConf; | ||
| import org.assertj.core.api.Assertions; | ||
| import org.junit.After; | ||
| import org.junit.Before; | ||
| import org.junit.Test; | ||
| import org.junit.runners.Parameterized; | ||
|
|
||
| public class TestSPJWithBucketing extends SparkExtensionsTestBase { | ||
|
|
||
| @Test | ||
| public void testMergeSPJwithCondition() { | ||
| testWithCondition( | ||
| " AND (" | ||
| + "(t.year_month='202306' AND t.day='01' AND testhive.system.bucket(4, t.id) = 0) OR\n" | ||
| + "(t.year_month='202306' AND t.day='01' AND testhive.system.bucket(4, t.id) = 1) OR\n" | ||
| + "(t.year_month='202306' AND t.day='02' AND testhive.system.bucket(4, t.id) = 0) OR\n" | ||
| + "(t.year_month='202307' AND t.day='01' AND testhive.system.bucket(4, t.id) = 3)\n" | ||
| + ")"); | ||
| } | ||
|
|
||
| @Test | ||
| public void testMergeSPJwithoutCondition() { | ||
| testWithCondition(""); | ||
| } | ||
|
|
||
| private void testWithCondition(String condition) { | ||
| createPartitionedTable(spark, targetTableName); | ||
| insertRecords(spark, targetTableName); | ||
| createPartitionedTable(spark, sourceTableName); | ||
| insertRecordsToUpdate(spark, sourceTableName); | ||
| int tasks = | ||
| executeAndCountTasks( | ||
| spark, | ||
| (s) -> | ||
| withSQLConf( | ||
| ENABLED_SPJ_SQL_CONF, | ||
| () -> | ||
| sql( | ||
| s, | ||
| "MERGE INTO %s t USING (SELECT * FROM %s) s \n" | ||
| + "ON t.id = s.id AND t.year_month = s.year_month AND t.day = s.day\n" | ||
| + "%s\n" | ||
| + "WHEN MATCHED THEN UPDATE SET\n" | ||
| + " t.data = s.data\n" | ||
| + "WHEN NOT MATCHED THEN INSERT *", | ||
| targetTableName, | ||
| sourceTableName, | ||
| condition))); | ||
| long affectedPartitions = | ||
| sql( | ||
| spark, | ||
| "SELECT DISTINCT(t.year_month, t.day, testhive.system.bucket(4, t.id)) FROM %s t WHERE 1=1 %s", | ||
| targetTableName, | ||
| condition) | ||
| .count(); | ||
| int shufflePartitions = Integer.parseInt(spark.conf().get("spark.sql.shuffle.partitions")); | ||
| Assertions.assertThat(tasks).isEqualTo(affectedPartitions * 2 + shufflePartitions); | ||
| } | ||
|
|
||
| private final String sourceTableName; | ||
| private final String targetTableName; | ||
|
|
||
| public TestSPJWithBucketing( | ||
| String catalogName, String implementation, Map<String, String> config) { | ||
| super(catalogName, implementation, config); | ||
| sourceTableName = tablePrefix() + ".source"; | ||
| targetTableName = tablePrefix() + ".target"; | ||
| } | ||
|
|
||
| @Parameterized.Parameters(name = "catalogName = {0}, implementation = {1}, config = {2}") | ||
| public static Object[][] parameters() { | ||
| return new Object[][] { | ||
| { | ||
| SparkCatalogConfig.HIVE.catalogName(), | ||
| SparkCatalogConfig.HIVE.implementation(), | ||
| SparkCatalogConfig.HIVE.properties(), | ||
| }, | ||
| }; | ||
| } | ||
|
|
||
| private static Dataset<Row> sql(SparkSession sparkSession, String sqlFormat, Object... args) { | ||
| return sparkSession.sql(String.format(sqlFormat, args)); | ||
| } | ||
|
|
||
| private static void createTable(SparkSession spark, String tableName, String partitionCol) { | ||
| sql( | ||
| spark, | ||
| "CREATE TABLE %s (id STRING, year_month STRING, day STRING, data STRING) USING iceberg PARTITIONED BY (%s)", | ||
| tableName, | ||
| partitionCol); | ||
| sql( | ||
| spark, | ||
| "ALTER TABLE %s SET TBLPROPERTIES ('write.merge.distribution-mode'='none')", | ||
| tableName); | ||
| } | ||
|
|
||
| private static void insertRecords(SparkSession sparkSession, String tableName) { | ||
| sql( | ||
| spark, | ||
| "INSERT INTO TABLE %s VALUES %s", | ||
| tableName, | ||
| String.join( | ||
| ", ", | ||
| "('3', '202306', '01', 'data-0')", // 202306/01/0 | ||
| "('9', '202306', '01', 'data-0')", // 202306/01/1 | ||
| "('11', '202306', '01', 'data-0')", // 202306/01/2 | ||
| "('0', '202306', '01', 'data-0')", // 202306/01/3 | ||
| "('3', '202306', '02', 'data-0')", // 202306/02/0 | ||
| "('9', '202306', '02', 'data-0')", // 202306/02/1 | ||
| "('0', '202307', '01', 'data-0')" // 202307/01/3 | ||
| )); | ||
| } | ||
|
|
||
| private static void insertRecordsToUpdate(SparkSession sparkSession, String tableName) { | ||
| sql( | ||
| spark, | ||
| "INSERT INTO TABLE %s VALUES %s", | ||
| tableName, | ||
| String.join( | ||
| ", ", | ||
| "('3', '202306', '01', 'data-1')", // 202306/01/0 | ||
| "('9', '202306', '01', 'data-1')", // 202306/01/1 | ||
| "('3', '202306', '02', 'data-1')", // 202306/02/0 | ||
| "('0', '202307', '01', 'data-1')" // 202307/01/3 | ||
| )); | ||
| } | ||
|
|
||
| private static void createPartitionedTable(SparkSession spark, String tableName) { | ||
| createTable(spark, tableName, "year_month, day, bucket(4, id)"); | ||
| } | ||
|
|
||
| @Before | ||
| public void before() { | ||
| sql("USE %s", catalogName); | ||
| } | ||
|
|
||
| @After | ||
| public void removeTables() { | ||
| sql("DROP TABLE IF EXISTS %s", tablePrefix() + ".source"); | ||
| sql("DROP TABLE IF EXISTS %s", tablePrefix() + ".target"); | ||
| } | ||
|
|
||
| private String tablePrefix() { | ||
| return (catalogName.equals("spark_catalog") ? "" : catalogName + ".") + "default"; | ||
| } | ||
|
|
||
| private static final Map<String, String> ENABLED_SPJ_SQL_CONF = | ||
| ImmutableMap.of( | ||
| SQLConf.V2_BUCKETING_ENABLED().key(), | ||
| "true", | ||
| SQLConf.V2_BUCKETING_PUSH_PART_VALUES_ENABLED().key(), | ||
| "true", | ||
| SQLConf.REQUIRE_ALL_CLUSTER_KEYS_FOR_CO_PARTITION().key(), | ||
| "false", | ||
| SQLConf.ADAPTIVE_EXECUTION_ENABLED().key(), | ||
| "false", | ||
| SQLConf.AUTO_BROADCASTJOIN_THRESHOLD().key(), | ||
| "-1", | ||
| SparkSQLProperties.PRESERVE_DATA_GROUPING, | ||
| "true"); | ||
|
|
||
| public static int executeAndCountTasks(SparkSession spark, Consumer<SparkSession> f) { | ||
|
|
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| CountTaskListener listener = new CountTaskListener(); | ||
| spark.sparkContext().addSparkListener(listener); | ||
|
|
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| f.accept(spark); | ||
|
|
||
| try { | ||
| spark.sparkContext().listenerBus().waitUntilEmpty(); | ||
| } catch (TimeoutException e) { | ||
| throw new RuntimeException("Timeout while waiting for processing events", e); | ||
| } | ||
|
|
||
| return listener.getTaskCount(); | ||
| } | ||
|
|
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| private static class CountTaskListener extends SparkListener { | ||
| private final AtomicInteger tasks = new AtomicInteger(0); | ||
|
|
||
| @Override | ||
| public void onTaskEnd(SparkListenerTaskEnd taskEnd) { | ||
| tasks.incrementAndGet(); | ||
| } | ||
|
|
||
| public int getTaskCount() { | ||
| return tasks.get(); | ||
| } | ||
| } | ||
| } |
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Here the join condition can be pushed to the leaf node by the Spark optimizer, right? I think this can not cover the COW/MOR cases. COW/MOR needs to do some special handling here. I plan to do it, however, I've been quite busy lately.
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I discovered the bug working with a MERGE statement and actually this works both with CoW and MoR, I have it running on my cluster like that, and it's correctly pruning all the partitions