Spark---DataFrame存储、Spark UDF函数、UDAF函数-编程知识

四、DataFrame存储+Spark UDF函数

1、储存DataFrame

1）、将DataFrame存储为parquet文件

2）、将DataFrame存储到JDBC数据库

3）、将DataFrame存储到Hive表

2、UDF：用户自定义函数

可以自定义类实现UDFX接口

java：

SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("udf");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<String> parallelize = sc.parallelize(Arrays.asList("zhansan","lisi","wangwu"));
JavaRDD<Row> rowRDD = parallelize.map(new Function<String, Row>() {/*** */private static final long serialVersionUID = 1L;@Overridepublic Row call(String s) throws Exception {
return RowFactory.create(s);}
});List<StructField> fields = new ArrayList<StructField>();
fields.add(DataTypes.createStructField("name", DataTypes.StringType,true));StructType schema = DataTypes.createStructType(fields);
DataFrame df = sqlContext.createDataFrame(rowRDD,schema);
df.registerTempTable("user");/*** 根据UDF函数参数的个数来决定是实现哪一个UDF  UDF1，UDF2。。。。UDF1xxx*/
sqlContext.udf().register("StrLen", new UDF1<String,Integer>() {/*** */private static final long serialVersionUID = 1L;@Overridepublic Integer call(String t1) throws Exception {return t1.length();}
}, DataTypes.IntegerType);
sqlContext.sql("select name ,StrLen(name) as length from user").show();//sqlContext.udf().register("StrLen",new UDF2<String, Integer, Integer>() {
//
//	/**
//	 * 
//	 */
//	private static final long serialVersionUID = 1L;
//
//	@Override
//	public Integer call(String t1, Integer t2) throws Exception {
//return t1.length()+t2;
//	}
//} ,DataTypes.IntegerType );
//sqlContext.sql("select name ,StrLen(name,10) as length from user").show();sc.stop();

scala：

1.val spark = SparkSession.builder().master("local").appName("UDF").getOrCreate()
2.val nameList: List[String] = List[String]("zhangsan", "lisi", "wangwu", "zhaoliu", "tianqi")
3.import spark.implicits._
4.val nameDF: DataFrame = nameList.toDF("name")
5.nameDF.createOrReplaceTempView("students")
6.nameDF.show()
7.
8.spark.udf.register("STRLEN",(name:String)=>{
9.name.length
10.})
11.spark.sql("select name ,STRLEN(name) as length from students order by length desc").show(100)

五、UDAF函数

1、UDAF：用户自定义聚合函数

1）、实现UDAF函数如果要自定义类要继承

UserDefinedAggregateFunction类

2）、UDAF原理图

java：

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("udaf");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<String> parallelize = sc.parallelize(Arrays.asList("zhansan","lisi","wangwu","zhangsan","zhangsan","lisi"));
JavaRDD<Row> rowRDD = parallelize.map(new Function<String, Row>() {/*** */private static final long serialVersionUID = 1L;@Overridepublic Row call(String s) throws Exception {return RowFactory.create(s);}
});List<StructField> fields = new ArrayList<StructField>();
fields.add(DataTypes.createStructField("name", DataTypes.StringType, true));
StructType schema = DataTypes.createStructType(fields);
DataFrame df = sqlContext.createDataFrame(rowRDD, schema);
df.registerTempTable("user");
/*** 注册一个UDAF函数,实现统计相同值得个数* 注意：这里可以自定义一个类继承UserDefinedAggregateFunction类也是可以的*/
sqlContext.udf().register("StringCount", new UserDefinedAggregateFunction() {/*** */private static final long serialVersionUID = 1L;/*** 更新 可以认为一个一个地将组内的字段值传递进来 实现拼接的逻辑* buffer.getInt(0)获取的是上一次聚合后的值* 相当于map端的combiner，combiner就是对每一个map task的处理结果进行一次小聚合 * 大聚和发生在reduce端.* 这里即是:在进行聚合的时候，每当有新的值进来，对分组后的聚合如何进行计算*/@Overridepublic void update(MutableAggregationBuffer buffer, Row arg1) {buffer.update(0, buffer.getInt(0)+1);}/*** 合并 update操作，可能是针对一个分组内的部分数据，在某个节点上发生的 但是可能一个分组内的数据，会分布在多个节点上处理* 此时就要用merge操作，将各个节点上分布式拼接好的串，合并起来* buffer1.getInt(0) : 大聚和的时候 上一次聚合后的值       * buffer2.getInt(0) : 这次计算传入进来的update的结果* 这里即是：最后在分布式节点完成后需要进行全局级别的Merge操作*/@Overridepublic void merge(MutableAggregationBuffer buffer1, Row buffer2) {buffer1.update(0, buffer1.getInt(0) + buffer2.getInt(0));}/*** 指定输入字段的字段及类型*/@Overridepublic StructType inputSchema() {return DataTypes.createStructType(Arrays.asList(DataTypes.createStructField("name", DataTypes.StringType, true)));}/*** 初始化一个内部的自己定义的值,在Aggregate之前每组数据的初始化结果*/@Overridepublic void initialize(MutableAggregationBuffer buffer) {buffer.update(0, 0);}/*** 最后返回一个和DataType的类型要一致的类型，返回UDAF最后的计算结果*/@Overridepublic Object evaluate(Row row) {return row.getInt(0);}@Overridepublic boolean deterministic() {//设置为truereturn true;}/*** 指定UDAF函数计算后返回的结果类型*/@Overridepublic DataType dataType() {return DataTypes.IntegerType;}/*** 在进行聚合操作的时候所要处理的数据的结果的类型*/@Overridepublic StructType bufferSchema() {return DataTypes.createStructType(Arrays.asList(DataTypes.createStructField("bf", DataTypes.IntegerType, true)));}});sqlContext.sql("select name ,StringCount(name) from user group by name").show();sc.stop();

scala:

1.class MyCount extends UserDefinedAggregateFunction{
2.  //输入数据的类型
3.  override def inputSchema: StructType =    StructType(List[StructField](StructField("xx",StringType,true)))
4.
5.  //在聚合过程中处理的数据类型
6.  override def bufferSchema: StructType =   StructType(List[StructField](StructField("xx",IntegerType,true)))
7.
8.  //最终返回值的类型，与evaluate返回的值保持一致
9.  override def dataType: DataType = IntegerType
10.
11.  //多次运行数据是否一致
12.  override def deterministic: Boolean = true
13.
14.  //每个分区中每组key 对应的初始值
15.  override def initialize(buffer: MutableAggregationBuffer): Unit = buffer.update(0,0)
16.
17.  //每个分区中，每个分组内进行聚合操作
18.  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
19.    buffer.update(0,buffer.getInt(0) + 1)
20.  }
21.
22.  //不同的分区中相同的key的数据进行聚合
23.  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
24.    buffer1.update(0,buffer1.getInt(0)+buffer2.getInt(0))
25.  }
26.
27.  //聚合之后，每个分组最终返回的值,类型要和dataType 一致
28.  override def evaluate(buffer: Row): Any = buffer.getInt(0)
29.}
30.
31.object Test {
32.  def main(args: Array[String]): Unit = {
33.    val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
34.    val list = List[String]("zhangsan","lisi","wangwu","zhangsan","lisi","zhangsan")
35.
36.    import session.implicits._
37.    val frame = list.toDF("name")
38.    frame.createTempView("mytable")
39.
40.    session.udf.register("MyCount",new MyCount())
41.
42.    val result = session.sql("select name,MyCount(name) from mytable group by name")
43.    result.show()
44.
45.  }
46.}
47.