private static void runParquetSchemaMergingExample(SparkSession spark) {
// $example on:schema_merging$
List<Square> squares = new ArrayList<>();
for (int value = 1; value <= 5; value++) {
Square square = new Square();
square.setValue(value);
square.setSquare(value * value);
squares.add(square);
}
// Create a simple DataFrame, store into a partition directory
Dataset<Row> squaresDF = spark.createDataFrame(squares, Square.class);
squaresDF.write().parquet("data/test_table/key=1");
List<Cube> cubes = new ArrayList<>();
for (int value = 6; value <= 10; value++) {
Cube cube = new Cube();
cube.setValue(value);
cube.setCube(value * value * value);
cubes.add(cube);
}
// Create another DataFrame in a new partition directory,
// adding a new column and dropping an existing column
Dataset<Row> cubesDF = spark.createDataFrame(cubes, Cube.class);
cubesDF.write().parquet("data/test_table/key=2");
// Read the partitioned table
Dataset<Row> mergedDF = spark.read().option("mergeSchema", true).parquet("data/test_table");
mergedDF.printSchema();
// The final schema consists of all 3 columns in the Parquet files together
// with the partitioning column appeared in the partition directory paths
// root
// |-- value: int (nullable = true)
// |-- square: int (nullable = true)
// |-- cube: int (nullable = true)
// |-- key: int (nullable = true)
// $example off:schema_merging$
}
private static void runJsonDatasetExample(SparkSession spark) {
// $example on:json_dataset$
// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");
// The inferred schema can be visualized using the printSchema() method
people.printSchema();
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
people.createOrReplaceTempView("people");
// SQL statements can be run by using the sql methods provided by spark
Dataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
namesDF.show();
// +------+
// | name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// an RDD[String] storing one JSON object per string.
List<String> jsonData =
Arrays.asList("{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
JavaRDD<String> anotherPeopleRDD =
new JavaSparkContext(spark.sparkContext()).parallelize(jsonData);
Dataset anotherPeople = spark.read().json(anotherPeopleRDD);
anotherPeople.show();
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
// $example off:json_dataset$
}
