UDF、UDAF、UDTF函数编写
一、UDF函数编写
1.步骤
1.继承UDF类
2.重写evalute方法
1、继承GenericUDF 2、实现initialize、evaluate、getDisplayString方法
2.案例
实现lower函数:
package com.xxx.udf; import org.apache.hadoop.hive.ql.exec.UDF; import org.apache.hadoop.io.Text; public class LowerUDF extends UDF { public Text evaluate(Text input){ if(null == input){ return null; } String inputValue = input.toString().trim() ; if(null == inputValue){ return null ; } return new Text(inputValue.toLowerCase()) ; } }
package com.xxx.udf; import org.apache.hadoop.hive.ql.exec.UDFArgumentException; import org.apache.hadoop.hive.ql.exec.UDFArgumentLengthException; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.udf.generic.GenericUDF; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory; import org.apache.hadoop.hive.serde2.objectinspector.primitive.StringObjectInspector; import org.apache.hadoop.io.Text; public class LowerUDF extends GenericUDF { StringObjectInspector str ; @Override public ObjectInspector initialize(ObjectInspector[] arguments) throws UDFArgumentException { //判断输入参数个数是否合法 if (arguments.length != 1) { throw new UDFArgumentLengthException("输入参数长度不合法,应该为一个参数"); } //判断输入参数类型是否合法 if (!(arguments[0] instanceof StringObjectInspector)) { throw new UDFArgumentException("输入非法参数，应为字符串类型"); } str=(StringObjectInspector)arguments[0]; //确定返回值类型 return PrimitiveObjectInspectorFactory.javaStringObjectInspector; } @Override public Object evaluate(DeferredObject[] arguments) throws HiveException { String input = str.getPrimitiveJavaObject(arguments[0].get()); return new Text(input.toLowerCase()); } @Override public String getDisplayString(String[] children) { return "方法的描述信息"; } }
3.打成jar包上传
mvn clean package
4.在hive中创建临时函数
add jar /home/xxx/yf/to_lower.jar; create temporary function to_lower as 'com.xxx.udf.LowerUDF'; select to_lower("DSJIFASD") from dual; drop temporary function comparestringbysplit;
二、UDAF函数编写
1.步骤
1、继承AbstractGenericUDAFResolver 2、继承GenericUDAFEvaluator 3、Evaluator需要实现 init、iterate、terminatePartial、merge、terminate这几个函数 init初始化 iterate函数处理读入的行数据 terminatePartial返回iterate处理的中间结果 merge合并上述处理结果 terminate返回最终值
2.案例
实现avg
package com.xxx.udf; import org.apache.hadoop.hive.ql.exec.UDAF; import org.apache.hadoop.hive.ql.exec.UDAFEvaluator; public class Avg extends UDAF { public static class AvgState { private long mCount; private double mSum; } public static class AvgEvaluator implements UDAFEvaluator { AvgState state; public AvgEvaluator() { super(); state = new AvgState(); init(); } /** * init函数类似于构造函数，用于UDAF的初始化 */ public void init() { state.mSum = 0; state.mCount = 0; } /** * iterate接收传入的参数，并进行内部的轮转。其返回类型为boolean * * @param o * @return */ public boolean iterate(Double o) { if (o != null) { state.mSum += o; state.mCount++; } return true; } /** * terminatePartial无参数，其为iterate函数遍历结束后，返回轮转数据， * terminatePartial类似于hadoop的Combiner * * @return */ public AvgState terminatePartial() { // combiner return state.mCount == 0 ? null : state; } /** * merge接收terminatePartial的返回结果，进行数据merge操作，其返回类型为boolean * * @param o * @return */ public boolean merge(AvgState avgState) { if (avgState != null) { state.mCount += avgState.mCount; state.mSum += avgState.mSum; } return true; } /** * terminate返回最终的聚集函数结果 * * @return */ public Double terminate() { return state.mCount == 0 ? null : Double.valueOf(state.mSum / state.mCount); } } }
实现sum
package com.xxx.udf; import org.apache.hadoop.hive.ql.exec.UDFArgumentException; import org.apache.hadoop.hive.ql.exec.UDFArgumentLengthException; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.parse.SemanticException; import org.apache.hadoop.hive.ql.udf.generic.AbstractGenericUDAFResolver; import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.primitive.DoubleObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorUtils; import org.apache.hadoop.hive.serde2.objectinspector.primitive.StringObjectInspector; import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; import org.apache.hadoop.io.DoubleWritable; import org.apache.hadoop.io.LongWritable; public class Test extends AbstractGenericUDAFResolver { /** * 获取处理逻辑类 * @param info * @return * @throws SemanticException */ @Override public GenericUDAFEvaluator getEvaluator(TypeInfo[] info) throws SemanticException { //判断输入参数是否合法,参数个数，参数类型 if (info.length != 1) { throw new UDFArgumentLengthException("输入参数个数非法，一个参数"); } return new GenericEvaluate(); } //处理逻辑类 public static class GenericEvaluate extends GenericUDAFEvaluator { private PrimitiveObjectInspector input; private DoubleWritable result ; //保存最终结果 private MyAggregationBuffer myAggregationBuffer; //自定义聚合列，保存临时结果 //自定义AggregationBuffer public static class MyAggregationBuffer implements AggregationBuffer { Double sum; } @Override //指定返回类型 public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException { super.init(m, parameters); result = new DoubleWritable(0); input = (PrimitiveObjectInspector) parameters[0]; // 指定返回结果类型 return PrimitiveObjectInspectorFactory.writableDoubleObjectInspector; } @Override //获得一个聚合的缓冲对象，每个map执行一次 public AggregationBuffer getNewAggregationBuffer() throws HiveException { MyAggregationBuffer myAggregationBuffer = new MyAggregationBuffer(); reset(myAggregationBuffer); // 重置聚合值 return myAggregationBuffer; } @Override public void reset(AggregationBuffer agg) throws HiveException { MyAggregationBuffer newAgg = (MyAggregationBuffer) agg; newAgg.sum = 0.0; } @Override // 传入参数值聚合 public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException { MyAggregationBuffer myAgg = (MyAggregationBuffer) agg; double inputNum = PrimitiveObjectInspectorUtils.getDouble(parameters[0], input); myAgg.sum += inputNum; } @Override // public Object terminatePartial(AggregationBuffer agg) throws HiveException { MyAggregationBuffer newAgg = (MyAggregationBuffer) agg; result.set(newAgg.sum); return result; } @Override // 合并 public void merge(AggregationBuffer agg, Object partial) throws HiveException { double inputNum = PrimitiveObjectInspectorUtils.getDouble(partial, input); MyAggregationBuffer newAgg = (MyAggregationBuffer) agg; newAgg.sum += inputNum; } @Override //输出最终结果 public Object terminate(AggregationBuffer agg) throws HiveException { MyAggregationBuffer aggregationBuffer = (MyAggregationBuffer) agg; result.set(aggregationBuffer.sum); return result; } } }
3.打包
mvn clean package
4.创建临时函数
add jar /home/xxx/yf/my_avg.jar; create temporary function my_avg as 'com.xxx.udf.UDTFExplode'; select my_avg() from dual; drop temporary function my_avg;
三、UDTF函数编写
1.步骤
1.继承GenericUDTF 2.重写initialize、process方法 initialize初始化校验参数是否正确、 process处理返回结果、 forward将结果返回
2.案例
将字符串按照元素索引分别输出，如：‘a,c,b’ -- > a,1 c,2 b,3
package com.suning.udf; import java.util.ArrayList; import org.apache.hadoop.hive.ql.exec.UDFArgumentException; import org.apache.hadoop.hive.ql.exec.UDFArgumentLengthException; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory; import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory; public class UDTFExplode extends GenericUDTF { @Override public void close() throws HiveException { // TODO Auto-generated method stub } @Override public StructObjectInspector initialize(ObjectInspector[] args) throws UDFArgumentException { if (args.length != 1) { throw new UDFArgumentLengthException("ExplodeMap takes only one argument"); } if (args[0].getCategory() != ObjectInspector.Category.PRIMITIVE) { throw new UDFArgumentException("ExplodeMap takes string as a parameter"); } ArrayList<String> fieldNames = new ArrayList<String>(); ArrayList<ObjectInspector> fieldOIs = new ArrayList<ObjectInspector>(); fieldNames.add("col1"); fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector); fieldNames.add("col2"); fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector); return ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs); } @Override public void process(Object[] args) throws HiveException { // TODO Auto-generated method stub String input = args[0].toString(); String[] test = input.split(","); for (int i = 0; i < test.length; i++) { try { String[] result = (test[i]+":"+String.valueOf(i+1)).split(":"); forward(result); } catch (Exception e) { continue; } } } }
3.打包
mvn clean package
4.创建临时函数
add jar /home/xxx/yf/str_index.jar; create temporary function str_index as 'com.xxx.udf.UDTFExplode'; select str_index("a,c,b") from dual; drop temporary function str_index;
pom.xml
<?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>spark-hive</groupId> <artifactId>spark-hive</artifactId> <version>1.0-SNAPSHOT</version> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <scala.version>2.11.8</scala.version> <spark.version>2.1.0.9</spark.version> <spark.artifactId.version>2.11</spark.artifactId.version> </properties> <dependencies> <dependency> <groupId>commons-logging</groupId> <artifactId>commons-logging</artifactId> <version>1.1.1</version> <type>jar</type> </dependency> <dependency> <groupId>org.apache.commons</groupId> <artifactId>commons-lang3</artifactId> <version>3.1</version> </dependency> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.6.2</version> </dependency> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.21</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>2.1.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.11</artifactId> <version>2.1.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming-kafka-0-8_2.11</artifactId> <version>2.1.0</version> </dependency> <dependency> <groupId>com.google.code.gson</groupId> <artifactId>gson</artifactId> <version>2.8.2</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>2.1.0</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>fastjson</artifactId> <version>1.2.29</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-hive_${spark.artifactId.version}</artifactId> <version>${spark.version}</version> <scope>provided</scope> </dependency> <!--flink dependency--> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.5.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.11</artifactId> <version>1.5.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-clients_2.11</artifactId> <version>1.5.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-wikiedits_2.11</artifactId> <version>1.5.0</version> </dependency> <!--hbase dependency--> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase</artifactId> <version>0.98.8-hadoop2</version> <type>pom</type> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-client</artifactId> <version>0.98.8-hadoop2</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-common</artifactId> <version>0.98.8-hadoop2</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-server</artifactId> <version>0.98.8-hadoop2</version> </dependency> </dependencies> <build> <plugins> <plugin> <artifactId>maven-assembly-plugin</artifactId> <configuration> <descriptorRefs> <descriptorRef>jar-with-dependencies</descriptorRef> </descriptorRefs> </configuration> </plugin> <plugin> <groupId>org.codehaus.mojo</groupId> <artifactId>build-helper-maven-plugin</artifactId> <version>1.8</version> <executions> <execution> <id>add-source</id> <phase>generate-sources</phase> <goals> <goal>add-source</goal> </goals> <configuration> <sources> <source>src/main/scala</source> <source>src/test/scala</source> </sources> </configuration> </execution> <execution> <id>add-test-source</id> <phase>generate-sources</phase> <goals> <goal>add-test-source</goal> </goals> <configuration> <sources> <source>src/test/scala</source> </sources> </configuration> </execution> </executions> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>2.3.2</version> <configuration> <source>1.7</source> <target>1.7</target> <encoding>${project.build.sourceEncoding}</encoding> </configuration> </plugin> <plugin> <groupId>org.scala-tools</groupId> <artifactId>maven-scala-plugin</artifactId> <executions> <execution> <goals> <goal>compile</goal> <goal>add-source</goal> <goal>testCompile</goal> </goals> </execution> </executions> <configuration> <scalaVersion>2.11.8</scalaVersion> <sourceDir>src/main/scala</sourceDir> <jvmArgs> <jvmArg>-Xms64m</jvmArg> <jvmArg>-Xmx1024m</jvmArg> </jvmArgs> </configuration> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-release-plugin</artifactId> <version>2.5.3</version> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <configuration> <skip>false</skip> </configuration> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <version>2.4.1</version> <executions> <execution> <phase>package</phase> <goals> <goal>shade</goal> </goals> <configuration> <filters> <filter> <artifact>*:*</artifact> <excludes> <exclude>META-INF/*.SF</exclude> org.apache.hive <exclude>META-INF/*.DSA</exclude> <exclude>META-INF/*.RSA</exclude> </excludes> </filter> </filters> <minimizeJar>false</minimizeJar> </configuration> </execution> </executions> </plugin> </plugins> <resources> <resource> <directory>src/main/resources</directory> <filtering>true</filtering> </resource> <resource> <directory>src/main/resources/${profiles.active}</directory> </resource> </resources> <!-- 修复 Plugin execution not covered by lifecycle configuration --> <pluginManagement> <plugins> <plugin> <groupId>org.eclipse.m2e</groupId> <artifactId>lifecycle-mapping</artifactId> <version>1.0.0</version> <configuration> <lifecycleMappingMetadata> <pluginExecutions> <pluginExecution> <pluginExecutionFilter> <groupId>org.codehaus.mojo</groupId> <artifactId>build-helper-maven-plugin</artifactId> <versionRange>[1.8,)</versionRange> <goals> <goal>add-source</goal> <goal>add-test-source</goal> </goals> </pluginExecutionFilter> <action> <ignore></ignore> </action> </pluginExecution> <pluginExecution> <pluginExecutionFilter> <groupId>org.scala-tools</groupId> <artifactId>maven-scala-plugin</artifactId> <versionRange>[1.8,)</versionRange> <goals> <goal>compile</goal> <goal>add-source</goal> <goal>testCompile</goal> </goals> </pluginExecutionFilter> <action> <ignore></ignore> </action> </pluginExecution> </pluginExecutions> </lifecycleMappingMetadata> </configuration> </plugin> </plugins> </pluginManagement> </build> </project>
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