我有一个使用其他应用程序罐子的应用程序。我的名为com_Streams_H2O_ML.java的应用程序使用HazelcastInsance。
package com.escomled.machinelearning.ml;
import static com.com.machinelearning.ml.PropertiesGenerator.getProperty;
import java.io.File;
import java.util.Properties;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.springframework.stereotype.Repository;
import com.com.blackboard.Blackboard;
import com.com.machinelearning.ml.Escomled_Streams_H2O_ML;
import com.com.machinelearning.startmap.ContextHolder;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IMap;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.exception.PredictException;
import hex.genmodel.easy.prediction.RegressionModelPrediction;
/**
*
*
* Creates a new Kafka Streams application for prediction of watts The
* application uses the GBM model "comPOJO" (built with H2O.ai and python
* application) to infer messages sent to Kafka topic "comInputTopic". The
* outcome of model inference is sent to Kafka topic "comOutputTopic".
*
*/
public class com_Streams_H2O_ML {
// Name of the generated H2O model
private static String modelClassName = getProperty("pojoFullName");
// Prediction Value
private static double watts = 0;
public static boolean isNumeric(String str) {
return str.matches("-?\\d+(\\.\\d+)?"); // match a number with optional '-' and decimal.
}
public static void main(final String[] args) throws Exception {
System.out.println(new File(".").getAbsolutePath());
// Create H2O object (see gbm_pojo_test.java)
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
EasyPredictModelWrapper model = new EasyPredictModelWrapper(
new EasyPredictModelWrapper.Config().setModel(rawModel).setConvertUnknownCategoricalLevelsToNa(false));
HazelcastInstance instance = ((Blackboard) ContextHolder.getContext().getBean("blackboard")).getHazelcastInstance();
IMap<String, String> dimmingMap = instance.getMap("dim_board_map");
// Configure Kafka Streams Application
final String bootstrapServers = args.length > 0 ? args[0] : getProperty("bootstrapServers");
final Properties streamsConfiguration = new Properties();
// Give the Streams application a unique name. The name must be unique
// in the Kafka cluster
// against which the application is run.
streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "Escomled_Streams_ML");
// Where to find Kafka broker(s).
streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
// Specify default (de)serializers for record keys and for record
// values.
streamsConfiguration.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
streamsConfiguration.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
// For illustrative purposes we disable record caches
streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
// In the subsequent lines we define the processing topology of the
// Streams application.
final KStreamBuilder builder = new KStreamBuilder();
// Construct a `KStream` from the input topic "EscomledInputTopic", where
// message values
// represent lines of text (for the sake of this example, we ignore
// whatever may be stored
// in the message keys).
String inputTopic = getProperty("inputTopic");
final KStream<String, String> predictionInput = builder.stream(inputTopic);
// Stream Processor (in this case 'foreach' to add custom logic, i.e. apply the
// analytic model)
predictionInput.foreach((key, value) -> {
String[] values = value.split(",");
System.out.println("Key " + key);
System.out.println("Value " + value);
if (value != null && !value.equals("") && isNumeric(value)) {
RowData row = new RowData();
row.put("Board Number", key);
row.put("Dimming", value);
RegressionModelPrediction p = null;
try {
p = model.predictRegression(row);
watts = p.value;
System.out.println("Prediction for " + key + "/" + value + "% :" + watts + "W");
} catch (PredictException e) {
e.printStackTrace();
}
dimmingMap.remove(key);
} else {
System.out.println("ENTER CORRECT VALUES");
}
});
// Transform message: Add prediction information
KStream<String, Object> transformedMessage = predictionInput.mapValues(value -> value + ", " + watts);
// Send prediction information to Output Topic
String outputTopic = getProperty("outputTopic");
transformedMessage.to(outputTopic);
// Start Kafka Streams Application to process new incoming messages from Input
// Topic
final KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration);
streams.cleanUp();
streams.start();
// Add shutdown hook to respond to SIGTERM and gracefully close Kafka
// Streams
Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
}
}
我的appContext.xml包含：
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hz="http://www.hazelcast.com/schema/spring"
xmlns:task="http://www.springframework.org/schema/task" xmlns:context="http://www.springframework.org/schema/context"
xmlns:int="http://www.springframework.org/schema/integration"
xmlns:util="http://www.springframework.org/schema/util"
xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
http://www.hazelcast.com/schema/spring http://www.hazelcast.com/schema/spring/hazelcast-spring-3.2.xsd
http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
http://www.springframework.org/schema/util http://www.springframework.org/schema/util/spring-util-2.5.xsd
http://www.springframework.org/schema/task http://www.springframework.org/schema/task/spring-task.xsd ">
<context:annotation-config />
<context:component-scan base-package="com.escomled" />
<context:component-scan base-package="com.escomled.*" />
<import resource="classpath*:config/blackBoard.xml" />
<task:annotation-driven />
<bean id="propertyConfigurer" class="org.springframework.beans.factory.config.PropertyPlaceholderConfigurer">
<property name="locations">
<list>
<value>file:///home/escomled/escomled_server/config/escomled.properties</value>
</list>
</property>
</bean>
<bean id="reportJdbcTemplate" class="org.springframework.jdbc.core.JdbcTemplate">
<property name="dataSource" ref="dataSource" />
</bean>
<bean id="blackboard" class="com.escomled.blackboard.impl.BlackboardImpl">
<property name="hazelcastInstance" ref="hazelcastClient" />
</bean>
</beans>
我还有一个名为escomled_common的应用程序，其中包含com.escomled.blackboard.impl软件包。在该程序包中，我使用名为blackboard的类来实现BlackboardImpl.java
BlackboardImpl.java内容
...@Component("blackboard")
public class BlackboardImpl implements Blackboard {
private HazelcastInstance hazelcastInstance;
@Override
public HazelcastInstance getHazelcastInstance() {
return hazelcastInstance;
}
public void setHazelcastInstance(HazelcastInstance hazelcastInstance) {
this.hazelcastInstance = hazelcastInstance;
}...
我遇到错误
线程“主”中的异常org.springframework.beans.factory.BeanCreationException：创建在类路径资源[appContext.xml]中定义的名称为'blackboard'的bean时出错：设置bean属性时无法解析对bean'instance'的引用'hazelcastInstance';嵌套的异常是org.springframework.beans.factory.NoSuchBeanDefinitionException：未定义名为'instance'的bean
有人帮助吗？