所以,我想的東西,不知道這是最有效的選擇,但我現在沒有看到任何其他。
SparkConf sf = new SparkConf().setAppName("name").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(sf);
SQLContext sqlCon = new SQLContext(sc);
Map map = new HashMap<String, Map<String, String>>();
map.put("test1", putMap);
HashMap putMap = new HashMap<String, String>();
putMap.put("1", "test");
List<Tuple2<String, HashMap>> list = new ArrayList<Tuple2<String, HashMap>>();
Set<String> allKeys = map.keySet();
for (String key : allKeys) {
list.add(new Tuple2<String, HashMap>(key, (HashMap) map.get(key)));
};
JavaRDD<Tuple2<String, HashMap>> rdd = sc.parallelize(list);
System.out.println(rdd.first());
List<StructField> fields = new ArrayList<>();
StructField field1 = DataTypes.createStructField("String", DataTypes.StringType, true);
StructField field2 = DataTypes.createStructField("Map",
DataTypes.createMapType(DataTypes.StringType, DataTypes.StringType), true);
fields.add(field1);
fields.add(field2);
StructType struct = DataTypes.createStructType(fields);
JavaRDD<Row> rowRDD = rdd.map(new Function<Tuple2<String, HashMap>, Row>() {
@Override
public Row call(Tuple2<String, HashMap> arg0) throws Exception {
return RowFactory.create(arg0._1, arg0._2);
}
});
DataFrame df = sqlCon.createDataFrame(rowRDD, struct);
df.show();
在這種情況下,我假定Dataframe中的Map是Type(String,String)。希望這可以幫助!
編輯:顯然你可以刪除所有的打印。我爲了可視化目的做了這個!