return person;
}
});
// Apply a schema to an RDD of Java Beans and register it as a table.
JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
schemaPeople.registerTempTable("people");
// SQL can be run over RDDs that have been registered as tables.
JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
@Override
public String call(Row row) {
return "Name: " + row.getString(0);
}
}).collect();
for (String name: teenagerNames) {
System.out.println(name);
}
System.out.println("=== Data source: Parquet File ===");
// JavaSchemaRDDs can be saved as parquet files, maintaining the schema information.
schemaPeople.saveAsParquetFile("people.parquet");
// Read in the parquet file created above.
// Parquet files are self-describing so the schema is preserved.
// The result of loading a parquet file is also a JavaSchemaRDD.
JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
//Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerTempTable("parquetFile");
JavaSchemaRDD teenagers2 =
sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
teenagerNames = teenagers2.map(new Function<Row, String>() {
@Override
public String call(Row row) {
return "Name: " + row.getString(0);
}
}).collect();
for (String name: teenagerNames) {
System.out.println(name);
}
System.out.println("=== Data source: JSON Dataset ===");
// A JSON dataset is pointed by path.
// The path can be either a single text file or a directory storing text files.
String path = "examples/src/main/resources/people.json";
// Create a JavaSchemaRDD from the file(s) pointed by path
JavaSchemaRDD peopleFromJsonFile = sqlCtx.jsonFile(path);
// Because the schema of a JSON dataset is automatically inferred, to write queries,
// it is better to take a look at what is the schema.
peopleFromJsonFile.printSchema();
// The schema of people is ...
// root
// |-- age: IntegerType
// |-- name: StringType
// Register this JavaSchemaRDD as a table.
peopleFromJsonFile.registerTempTable("people");
// SQL statements can be run by using the sql methods provided by sqlCtx.
JavaSchemaRDD teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
// The results of SQL queries are JavaSchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
teenagerNames = teenagers3.map(new Function<Row, String>() {
@Override
public String call(Row row) { return "Name: " + row.getString(0); }
}).collect();
for (String name: teenagerNames) {
System.out.println(name);
}
// Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by
// a RDD[String] storing one JSON object per string.
List<String> jsonData = Arrays.asList(
"{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData);
JavaSchemaRDD peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD);
// Take a look at the schema of this new JavaSchemaRDD.
peopleFromJsonRDD.printSchema();
// The schema of anotherPeople is ...
// root
// |-- address: StructType
// | |-- city: StringType
// | |-- state: StringType
// |-- name: StringType
peopleFromJsonRDD.registerTempTable("people2");
JavaSchemaRDD peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
List<String> nameAndCity = peopleWithCity.map(new Function<Row, String>() {
@Override
public String call(Row row) {
return "Name: " + row.getString(0) + ", City: " + row.getString(1);
}
}).collect();