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Spark 2.4.0编程指南--Spark SQL UDF和UDAF ![]()
更多资源视频<iframe width="800" height="500" src="//player.bilibili.com/player.html?aid=38193405&cid=67137841&page=4" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe> 文档前置条件- 已安装好java(选用的是java 1.8.0_191)
- 已安装好scala(选用的是scala 2.11.121)
- 已安装好hadoop(选用的是Hadoop 3.1.1)
- 已安装好spark(选用的是spark 2.4.0)
技能标签UDF用户定义函数（User-defined functions, UDFs）是大多数 SQL 环境的关键特性，用于扩展系统的内置功能。 UDF允许开发人员通过抽象其低级语言实现来在更高级语言（如SQL）中启用新功能。 Apache Spark 也不例外，并且提供了用于将 UDF 与 Spark SQL工作流集成的各种选项。 - 用户定义函数（User-defined functions, UDFs）
- UDF对表中的单行进行转换，以便为每行生成单个对应的输出值
##示例 BaseSparkSession/** * 得到SparkSession * 首先 extends BaseSparkSession * 本地: val spark = sparkSession(true) * 集群: val spark = sparkSession() */class BaseSparkSession { var appName = "sparkSession" var master = "spark://standalone.com:7077" //本地模式:local standalone:spark://master:7077 def sparkSession(): SparkSession = { val spark = SparkSession.builder .master(master) .appName(appName) .config("spark.eventLog.enabled","true") .config("spark.history.fs.logDirectory","hdfs://standalone.com:9000/spark/log/historyEventLog") .config("spark.eventLog.dir","hdfs://standalone.com:9000/spark/log/historyEventLog") .getOrCreate() spark.sparkContext.addJar("/opt/n_001_workspaces/bigdata/spark-scala-maven-2.4.0/target/spark-scala-maven-2.4.0-1.0-SNAPSHOT.jar") //import spark.implicits._ spark } def sparkSession(isLocal:Boolean = false): SparkSession = { if(isLocal){ master = "local" val spark = SparkSession.builder .master(master) .appName(appName) .getOrCreate() //spark.sparkContext.addJar("/opt/n_001_workspaces/bigdata/spark-scala-maven-2.4.0/target/spark-scala-maven-2.4.0-1.0-SNAPSHOT.jar") //import spark.implicits._ spark }else{ val spark = SparkSession.builder .master(master) .appName(appName) .config("spark.eventLog.enabled","true") .config("spark.history.fs.logDirectory","hdfs://standalone.com:9000/spark/log/historyEventLog") .config("spark.eventLog.dir","hdfs://standalone.com:9000/spark/log/historyEventLog") .getOrCreate() // spark.sparkContext.addJar("/opt/n_001_workspaces/bigdata/spark-scala-maven-2.4.0/target/spark-scala-maven-2.4.0-1.0-SNAPSHOT.jar") //import spark.implicits._ spark } } /** * 得到当前工程的路径 * @return */ def getProjectPath:String=System.getProperty("user.dir")}UDF (统计字段长度)- 对数据集中，每行数据的特定字段，计算字符长度
- 通过 spark.sql 直接在字段查询处调用函数名称
/** * 自定义匿名函数 * 功能: 得到某列数据长度的函数 */object Run extends BaseSparkSession{ def main(args: Array[String]): Unit = { val spark = sparkSession(true) val ds = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employees.json") ds.show()// +-------+------+// | name|salary|// +-------+------+// |Michael| 3000|// | Andy| 4500|// | Justin| 3500|// | Berta| 4000|// +-------+------+ spark.udf.register("strLength",(str: String) => str.length()) ds.createOrReplaceTempView("employees") spark.sql("select name,salary,strLength(name) as name_Length from employees").show()// +-------+------+-----------+// | name|salary|name_Length|// +-------+------+-----------+// |Michael| 3000| 7|// | Andy| 4500| 4|// | Justin| 3500| 6|// | Berta| 4000| 5|// +-------+------+-----------+ spark.stop() }}UDF (字段转成大写)- 对数据集中，每行数据的特定字段，计算字符长度
- 通过 dataSet.withColumn 调用column
- Column通过udf函数转换
import com.opensource.bigdata.spark.standalone.base.BaseSparkSession/** * 自定义匿名函数 * 功能: 得到某列数据长度的函数 */object Run extends BaseSparkSession{ def main(args: Array[String]): Unit = { val spark = sparkSession(true) val ds = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employees.json") ds.show()// +-------+------+// | name|salary|// +-------+------+// |Michael| 3000|// | Andy| 4500|// | Justin| 3500|// | Berta| 4000|// +-------+------+ import org.apache.spark.sql.functions._ val strUpper = udf((str: String) => str.toUpperCase()) import spark.implicits._ ds.withColumn("toUpperCase", strUpper($"name")).show// +-------+------+-----------+// | name|salary|toUpperCase|// +-------+------+-----------+// |Michael| 3000| MICHAEL|// | Andy| 4500| ANDY|// | Justin| 3500| JUSTIN|// | Berta| 4000| BERTA|// +-------+------+-----------+ spark.stop() }}UDAF- UDAF（user-defined aggregate function, 用户定义的聚合函数
- 同时处理多行，并且返回一个结果，通常结合使用 GROUP BY 语句（例如 COUNT 或 SUM）
countpackage com.opensource.bigdata.spark.sql.n_08_spark_udaf.n_01_spark_udaf_countimport com.opensource.bigdata.spark.standalone.base.BaseSparkSessionimport org.apache.spark.sql.Rowimport org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}import org.apache.spark.sql.types._/** * ).initialize()方法,初使使，即没数据时的值 * ).update() 方法把每一行的数据进行计算，放到缓冲对象中 * ).merge() 把每个分区，缓冲对象进行合并 * ).evaluate()计算结果表达式，把缓冲对象中的数据进行最终计算 */object Run2 extends BaseSparkSession{ object CustomerCount extends UserDefinedAggregateFunction{ //聚合函数的输入参数数据类型 def inputSchema: StructType = { StructType(StructField("inputColumn",StringType) :: Nil) } //中间缓存的数据类型 def bufferSchema: StructType = { StructType(StructField("sum",LongType) :: Nil) } //最终输出结果的数据类型 def dataType: DataType = LongType def deterministic: Boolean = true //初始值，要是DataSet没有数据，就返回该值 def initialize(buffer: MutableAggregationBuffer): Unit = { buffer(0) = 0L } /** * * @param buffer 相当于把当前分区的，每行数据都需要进行计算，计算的结果保存到buffer中 * @param input */ def update(buffer: MutableAggregationBuffer, input: Row): Unit ={ if(!input.isNullAt(0)){ buffer(0) = buffer.getLong(0) + 1 } } /** * 相当于把每个分区的数据进行汇总 * @param buffer1 分区一的数据 * @param buffer2 分区二的数据 */ def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit={ buffer1(0) = buffer1.getLong(0) +buffer2.getLong(0) // salary } //计算最终的结果 def evaluate(buffer: Row): Long = buffer.getLong(0) } def main(args: Array[String]): Unit = { val spark = sparkSession(true) spark.udf.register("customerCount",CustomerCount) val df = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employees.json") df.createOrReplaceTempView("employees") val sqlDF = spark.sql("select customerCount(name) as average_salary from employees ") df.show()// +-------+------+// | name|salary|// +-------+------+// |Michael| 3000|// | Andy| 4500|// | Justin| 3500|// | Berta| 4000|// +-------+------+ sqlDF.show()// +--------------+// |average_salary|// +--------------+// | 4.0|// +--------------+ spark.stop() }}maxpackage com.opensource.bigdata.spark.sql.n_08_spark_udaf.n_03_spark_udaf_sumimport com.opensource.bigdata.spark.standalone.base.BaseSparkSessionimport org.apache.spark.sql.Rowimport org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}import org.apache.spark.sql.types._/** * ).initialize()方法,初使使，即没数据时的值 * ).update() 方法把每一行的数据进行计算，放到缓冲对象中 * ).merge() 把每个分区，缓冲对象进行合并 * ).evaluate()计算结果表达式，把缓冲对象中的数据进行最终计算 */object Run extends BaseSparkSession{ object CustomerSum extends UserDefinedAggregateFunction{ //聚合函数的输入参数数据类型 def inputSchema: StructType = { StructType(StructField("inputColumn",LongType) :: Nil) } //中间缓存的数据类型 def bufferSchema: StructType = { StructType(StructField("sum",LongType) :: StructField("count",LongType) :: Nil) } //最终输出结果的数据类型 def dataType: DataType = LongType def deterministic: Boolean = true //初始值，要是DataSet没有数据，就返回该值 def initialize(buffer: MutableAggregationBuffer): Unit = { buffer(0) = 0L } /** * * @param buffer 相当于把当前分区的，每行数据都需要进行计算，计算的结果保存到buffer中 * @param input */ def update(buffer: MutableAggregationBuffer, input: Row): Unit ={ if(!input.isNullAt(0)){ buffer(0) = buffer.getLong(0) + input.getLong(0) } } /** * 相当于把每个分区的数据进行汇总 * @param buffer1 分区一的数据 * @param buffer2 分区二的数据 */ def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit={ buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0) } //计算最终的结果 def evaluate(buffer: Row): Long = buffer.getLong(0) } def main(args: Array[String]): Unit = { val spark = sparkSession(true) spark.udf.register("customerSum",CustomerSum) val df = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employees.json") df.createOrReplaceTempView("employees") val sqlDF = spark.sql("select customerSum(salary) as average_salary from employees ") df.show// +-------+------+// | name|salary|// +-------+------+// |Michael| 3000|// | Andy| 4500|// | Justin| 3500|// | Berta| 4000|// +-------+------+ sqlDF.show()// +--------------+// |average_salary|// +--------------+// | 15000|// +--------------+ spark.stop() }}averagepackage com.opensource.bigdata.spark.sql.n_08_spark_udaf.n_04_spark_udaf_averageimport com.opensource.bigdata.spark.standalone.base.BaseSparkSessionimport org.apache.spark.sql.Rowimport org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}import org.apache.spark.sql.types._object Run extends BaseSparkSession{ object MyAverage extends UserDefinedAggregateFunction{ //聚合函数的输入参数数据类型 def inputSchema: StructType = { StructType(StructField("inputColumn",LongType) :: Nil) } //中间缓存的数据类型 def bufferSchema: StructType = { StructType(StructField("sum",LongType) :: StructField("count",LongType) :: Nil) } //最终输出结果的数据类型 def dataType: DataType = DoubleType def deterministic: Boolean = true //初始值，要是DataSet没有数据，就返回该值 def initialize(buffer: MutableAggregationBuffer): Unit = { buffer(0) = 0L buffer(1) = 0L } /** * * @param buffer 相当于把当前分区的，每行数据都需要进行计算，计算的结果保存到buffer中 * @param input */ def update(buffer: MutableAggregationBuffer, input: Row): Unit ={ if(!input.isNullAt(0)){ buffer(0) = buffer.getLong(0) + input.getLong(0) // salary buffer(1) = buffer.getLong(1) + 1 // count } } /** * 相当于把每个分区的数据进行汇总 * @param buffer1 分区一的数据 * @param buffer2 分区二的数据 */ def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit={ buffer1(0) = buffer1.getLong(0) +buffer2.getLong(0) // salary buffer1(1) = buffer1.getLong(1) +buffer2.getLong(1) // count } //计算最终的结果 def evaluate(buffer: Row): Double = buffer.getLong(0).toDouble / buffer.getLong(1) } def main(args: Array[String]): Unit = { val spark = sparkSession(true) spark.udf.register("MyAverage",MyAverage) val df = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employees.json") df.createOrReplaceTempView("employees") val sqlDF = spark.sql("select MyAverage(salary) as average_salary from employees ") sqlDF.show() spark.stop() }}group by max- 按性别分组统计收入最高是多少
- 即统计男，女，各收入最高是多少
package com.opensource.bigdata.spark.sql.n_08_spark_udaf.n_05_spark_udaf_groupby_maximport com.opensource.bigdata.spark.standalone.base.BaseSparkSessionimport org.apache.spark.sql.Rowimport org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}import org.apache.spark.sql.types._/** * ).initialize()方法,初使使，即没数据时的值 * ).update() 方法把每一行的数据进行计算，放到缓冲对象中 * ).merge() 把每个分区，缓冲对象进行合并 * ).evaluate()计算结果表达式，把缓冲对象中的数据进行最终计算 */object Run extends BaseSparkSession{ object CustomerMax extends UserDefinedAggregateFunction{ //聚合函数的输入参数数据类型 def inputSchema: StructType = { StructType(StructField("inputColumn",LongType) :: Nil) } //中间缓存的数据类型 def bufferSchema: StructType = { StructType(StructField("sum",LongType) :: StructField("count",LongType) :: Nil) } //最终输出结果的数据类型 def dataType: DataType = LongType def deterministic: Boolean = true //初始值，要是DataSet没有数据，就返回该值 def initialize(buffer: MutableAggregationBuffer): Unit = { buffer(0) = 0L } /** * * @param buffer 相当于把当前分区的，每行数据都需要进行计算，计算的结果保存到buffer中 * @param input */ def update(buffer: MutableAggregationBuffer, input: Row): Unit ={ if(!input.isNullAt(0)){ if(input.getLong(0) > buffer.getLong(0)){ buffer(0) = input.getLong(0) } } } /** * 相当于把每个分区的数据进行汇总 * @param buffer1 分区一的数据 * @param buffer2 分区二的数据 */ def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit={ if( buffer2.getLong(0) > buffer1.getLong(0)) buffer1(0) = buffer2.getLong(0) } //计算最终的结果 def evaluate(buffer: Row): Long = buffer.getLong(0) } def main(args: Array[String]): Unit = { val spark = sparkSession(true) spark.udf.register("customerMax",CustomerMax) val df = spark.read.json("hdfs://standalone.com:9000/home/liuwen/data/employeesCN.json") df.createOrReplaceTempView("employees") val sqlDF = spark.sql("select gender,customerMax(salary) as average_salary from employees group by gender ") df.show// +------+----+------+// |gender|name|salary|// +------+----+------+// | 男|小王| 30000|// | 女|小丽| 50000|// | 男|小军| 80000|// | 女|小李| 90000|// +------+----+------+ sqlDF.show()// +------+--------------+// |gender|average_salary|// +------+--------------+// | 男| 80000|// | 女| 90000|// +------+--------------+ spark.stop() }}其它支持- Spark SQL 支持集成现有 Hive 中的 UDF ,UDAF 和 UDTF 的（Java或Scala）实现。
- UDTFs（user-defined table functions, 用户定义的表函数）可以返回多列和多行 end
转自 开源中国
地址 https://my.oschina.net/u/723009/blog/2989933
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