spark3-extensions/src/test/java/org/apache/iceberg/spark/extensions/SparkRowLevelOperationsTestBase.java

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 * 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
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 * Unless required by applicable law or agreed to in writing,
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package org.apache.iceberg.spark.extensions;

import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.Random;
import java.util.concurrent.ThreadLocalRandom;
import java.util.stream.Collectors;
import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
import org.apache.iceberg.spark.SparkCatalog;
import org.apache.iceberg.spark.SparkSessionCatalog;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.junit.Assert;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;

import static org.apache.iceberg.TableProperties.DEFAULT_FILE_FORMAT;
import static org.apache.iceberg.TableProperties.PARQUET_VECTORIZATION_ENABLED;

@RunWith(Parameterized.class)
public abstract class SparkRowLevelOperationsTestBase extends SparkExtensionsTestBase {

  private static final Random RANDOM = ThreadLocalRandom.current();

  protected final String fileFormat;
  protected final boolean vectorized;

  public SparkRowLevelOperationsTestBase(String catalogName, String implementation,
                                         Map<String, String> config, String fileFormat,
                                         boolean vectorized) {
    super(catalogName, implementation, config);
    this.fileFormat = fileFormat;
    this.vectorized = vectorized;
  }

  @Parameters(name = "catalogName = {0}, implementation = {1}, config = {2}, format = {3}, vectorized = {4}")
  public static Object[][] parameters() {
    return new Object[][] {
        { "testhive", SparkCatalog.class.getName(),
            ImmutableMap.of(
                "type", "hive",
                "default-namespace", "default"
            ),
            "orc",
            true
        },
        { "testhadoop", SparkCatalog.class.getName(),
            ImmutableMap.of(
                "type", "hadoop"
            ),
            "parquet",
            RANDOM.nextBoolean()
        },
        { "spark_catalog", SparkSessionCatalog.class.getName(),
            ImmutableMap.of(
                "type", "hive",
                "default-namespace", "default",
                "clients", "1",
                "parquet-enabled", "false",
                "cache-enabled", "false" // Spark will delete tables using v1, leaving the cache out of sync
            ),
            "avro",
            false
        }
    };
  }

  protected abstract Map<String, String> extraTableProperties();

  protected void initTable() {
    sql("ALTER TABLE %s SET TBLPROPERTIES('%s' '%s')", tableName, DEFAULT_FILE_FORMAT, fileFormat);

    switch (fileFormat) {
      case "parquet":
        sql("ALTER TABLE %s SET TBLPROPERTIES('%s' '%b')", tableName, PARQUET_VECTORIZATION_ENABLED, vectorized);
        break;
      case "orc":
        Assert.assertTrue(vectorized);
        break;
      case "avro":
        Assert.assertFalse(vectorized);
        break;
    }

    Map<String, String> props = extraTableProperties();
    props.forEach((prop, value) -> {
      sql("ALTER TABLE %s SET TBLPROPERTIES('%s' '%s')", tableName, prop, value);
    });
  }

  protected void createAndInitTable(String schema) {
    sql("CREATE TABLE %s (%s) USING iceberg", tableName, schema);
    initTable();
  }

  protected void createOrReplaceView(String name, String jsonData) {
    createOrReplaceView(name, null, jsonData);
  }

  protected void createOrReplaceView(String name, String schema, String jsonData) {
    List<String> jsonRows = Arrays.stream(jsonData.split("\n"))
        .filter(str -> str.trim().length() > 0)
        .collect(Collectors.toList());
    Dataset<String> jsonDS = spark.createDataset(jsonRows, Encoders.STRING());

    if (schema != null) {
      spark.read().schema(schema).json(jsonDS).createOrReplaceTempView(name);
    } else {
      spark.read().json(jsonDS).createOrReplaceTempView(name);
    }
  }
}