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)
// $example off:schema_merging$
}
