content: /*
   * 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.spark.sql.hive;
  
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.List;
  
  import org.junit.After;
  import org.junit.Assert;
  import org.junit.Before;
  import org.junit.Test;
  
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.sql.*;
  import org.apache.spark.sql.expressions.Window;
  import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
  import static org.apache.spark.sql.functions.*;
  import org.apache.spark.sql.hive.test.TestHive$;
  import org.apache.spark.sql.hive.aggregate.MyDoubleSum;
  
  public class JavaDataFrameSuite {
    private transient JavaSparkContext sc;
    private transient SQLContext hc;
  
    Dataset<Row> df;
  
    private static void checkAnswer(Dataset<Row> actual, List<Row> expected) {
      String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
      if (errorMessage != null) {
        Assert.fail(errorMessage);
      }
    }
  
    @Before
    public void setUp() throws IOException {
      hc = TestHive$.MODULE$;
      sc = new JavaSparkContext(hc.sparkContext());
  
      List<String> jsonObjects = new ArrayList<>(10);
      for (int i = 0; i < 10; i++) {
        jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}");
      }
      df = hc.read().json(sc.parallelize(jsonObjects));
      df.createOrReplaceTempView("window_table");
    }
  
    @After
    public void tearDown() throws IOException {
      // Clean up tables.
      if (hc != null) {
        hc.sql("DROP TABLE IF EXISTS window_table");
      }
    }
  
    @Test
    public void saveTableAndQueryIt() {
      checkAnswer(
        df.select(avg("key").over(
          Window.partitionBy("value").orderBy("key").rowsBetween(-1, 1))),
        hc.sql("SELECT avg(key) " +
          "OVER (PARTITION BY value " +
          "      ORDER BY key " +
          "      ROWS BETWEEN 1 preceding and 1 following) " +
          "FROM window_table").collectAsList());
    }
  
    @Test
    public void testUDAF() {
      Dataset<Row> df = hc.range(0, 100).union(hc.range(0, 100)).select(col("id").as("value"));
      UserDefinedAggregateFunction udaf = new MyDoubleSum();
      UserDefinedAggregateFunction registeredUDAF = hc.udf().register("mydoublesum", udaf);
      // Create Columns for the UDAF. For now, callUDF does not take an argument to specific if
      // we want to use distinct aggregation.
      Dataset<Row> aggregatedDF =
        df.groupBy()
          .agg(
            udaf.distinct(col("value")),
            udaf.apply(col("value")),
            registeredUDAF.apply(col("value")),
            callUDF("mydoublesum", col("value")));
  
      List<Row> expectedResult = new ArrayList<>();
      expectedResult.add(RowFactory.create(4950.0, 9900.0, 9900.0, 9900.0));
      checkAnswer(
        aggregatedDF,
        expectedResult);
    }
  }
  