【Spark篇】---SparkSQL中自定义UDF和UDAF，开窗函数的应用

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; @Override
public 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; @Override
public 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();

val conf = new SparkConf()
conf.setMaster("local").setAppName("udf")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc);
val rdd = sc.makeRDD(Array("zhansan","lisi","wangwu"))
val rowRDD = rdd.map { x => {
RowFactory.create(x)
} }
val schema = DataTypes.createStructType(Array(StructField("name",StringType,true)))
val df = sqlContext.createDataFrame(rowRDD, schema)
df.registerTempTable("user")
//sqlContext.udf.register("StrLen",(s : String)=>{s.length()})
//sqlContext.sql("select name ,StrLen(name) as length from user").show
sqlContext.udf.register("StrLen",(s : String,i:Int)=>{s.length()+i})
sqlContext.sql("select name ,StrLen(name,10) as length from user").show
sc.stop()

package com.spark.sparksql.udf_udaf;import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.expressions.MutableAggregationBuffer;
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
/**
* UDAF 用户自定义聚合函数
* @author root
*
*/
public class UDAF {
public static void main(String[] args) {
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("zhangsan","lisi","wangwu","zhangsan","zhangsan","lisi"));
JavaRDD<Row> rowRDD = parallelize.map(new Function<String, Row>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public 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; /**
* 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
*/
@Override
public void initialize(MutableAggregationBuffer buffer) {
buffer.update(0, 0);
} /**
* 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
*/
@Override
public 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) : 大聚合的时候 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
* 也可以是一个节点里面的多个executor合并
*/
@Override
public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
buffer1.update(0, buffer1.getInt(0) + buffer2.getInt(0));
}
/**
* 在进行聚合操作的时候所要处理的数据的结果的类型
*/
@Override
public StructType bufferSchema() {
return DataTypes.createStructType(Arrays.asList(DataTypes.createStructField("bffer111", DataTypes.IntegerType, true)));
}
/**
* 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
*/
@Override
public Object evaluate(Row row) {
return row.getInt(0);
}
/**
* 指定UDAF函数计算后返回的结果类型
*/
@Override
public DataType dataType() {
return DataTypes.IntegerType;
}
/**
* 指定输入字段的字段及类型
*/
@Override
public StructType inputSchema() {
return DataTypes.createStructType(Arrays.asList(DataTypes.createStructField("nameeee", DataTypes.StringType, true)));
}
/**
* 确保一致性 一般用true,用以标记针对给定的一组输入，UDAF是否总是生成相同的结果。
*/
@Override
public boolean deterministic() {
return true;
} }); sqlContext.sql("select name ,StringCount(name) as strCount from user group by name").show(); sc.stop();
}
}

package com.spark.sparksql.windowfun;import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.hive.HiveContext;/**是hive的函数，必须在集群中运行。
* row_number()开窗函数：
* 主要是按照某个字段分组，然后取另一字段的前几个的值，相当于 分组取topN
* row_number() over (partition by xxx order by xxx desc) xxx
* 注意：
* 如果SQL语句里面使用到了开窗函数，那么这个SQL语句必须使用HiveContext来执行，HiveContext默认情况下在本地无法创建
* @author root
*
*/
public class RowNumberWindowFun {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setAppName("windowfun");
JavaSparkContext sc = new JavaSparkContext(conf);
HiveContext hiveContext = new HiveContext(sc);
hiveContext.sql("use spark");
hiveContext.sql("drop table if exists sales");
hiveContext.sql("create table if not exists sales (riqi string,leibie string,jine Int) "
+ "row format delimited fields terminated by '\t'");
hiveContext.sql("load data local inpath '/root/test/sales' into table sales");
/**
* 开窗函数格式：
* 【 row_number() over (partition by XXX order by XXX) as rank】//起个别名
* 注意：rank 从1开始
*/
/**
* 以类别分组，按每种类别金额降序排序，显示 【日期，种类，金额】 结果，如：
*
* 1 A 100
* 2 B 200
* 3 A 300
* 4 B 400
* 5 A 500
* 6 B 600
* 排序后：
* 5 A 500 --rank 1
* 3 A 300 --rank 2
* 1 A 100 --rank 3
* 6 B 600 --rank 1
* 4 B 400 --rank 2
* 2 B 200 --rank 3
*
*/
DataFrame result = hiveContext.sql("select riqi,leibie,jine "
+ "from ("
+ "select riqi,leibie,jine,"
+ "row_number() over (partition by leibie order by jine desc) rank "
+ "from sales) t "
+ "where t.rank<=3");
result.show(100);
/**
* 将结果保存到hive表sales_result
*/
result.write().mode(SaveMode.Overwrite).saveAsTable("sales_result");
sc.stop();
}
}

val conf = new SparkConf()
conf.setAppName("windowfun")
val sc = new SparkContext(conf)
val hiveContext = new HiveContext(sc)
hiveContext.sql("use spark");
hiveContext.sql("drop table if exists sales");
hiveContext.sql("create table if not exists sales (riqi string,leibie string,jine Int) "
+ "row format delimited fields terminated by '\t'");
hiveContext.sql("load data local inpath '/root/test/sales' into table sales");
/**
* 开窗函数格式：
* 【 rou_number() over (partitin by XXX order by XXX) 】
*/
val result = hiveContext.sql("select riqi,leibie,jine "
+ "from ("
+ "select riqi,leibie,jine,"
+ "row_number() over (partition by leibie order by jine desc) rank "
+ "from sales) t "
+ "where t.rank<=3");
result.show();
sc.stop()