1.30.Flink SQL案例将Kafka数据写入hive
1.30.1.1.场景，环境，配置准备
1.30.1.2.案例代码
1.30.1.2.1.编写pom.xml文件
1.30.1.2.2.Maven工程resources下编写配置文件log4j2.properties
1.30.1.2.3.Maven工程resources下编写配置文件logback.xml
1.30.1.2.4.Maven工程resources下编写配置文件project-config-test.properties
1.30.1.2.5.编写com.xxxxx.log.utils.PropertiesUtils
1.30.1.2.6.编写com.xxxxx.log.flink.handler.ExceptionLogHandlerBySql
1.30.1.2.7.执行命令
1.30.Flink SQL案例将Kafka数据写入hive
1.30.1.1.场景，环境，配置准备
场景：通过Flink SQL的方式，将Kafka的数据实时写入到hive中。
(1)环境
hadoop 3.1.1.3.1.4-315
hive 3.1.0.3.1.4-315
flink 1.12.1前置准备：
将以下几个包添加到$FLINK_HOME/lib，其中hive-exec-3.1.0.3.1.4.0-315.jar和libfb303-0.9.3.jar从/usr/hdp/current/hive-client/lib中拷贝
1.30.1.2.案例代码
1.30.1.2.1.编写pom.xml文件
<?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>com.xxxxx.zczl</groupId>
<artifactId>flink-log-handler</artifactId>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<!--maven properties -->
<maven.test.skip>true</maven.test.skip>
<maven.javadoc.skip>true</maven.javadoc.skip>
<!-- compiler settings properties -->
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<slf4j.version>1.7.25</slf4j.version>
<fastjson.version>1.2.73</fastjson.version>
<joda-time.version>2.9.4</joda-time.version>
<flink.version>1.12.1</flink.version>
<scala.binary.version>2.11</scala.binary.version>
<hive.version>3.1.2</hive.version>
<hadoop.version>3.1.4</hadoop.version>
<!-- <hive.version>3.1.0.3.1.4.0-315</hive.version>
<hadoop.version>3.1.1.3.1.4.0-315</hadoop.version>-->
<!--<hadoop.version>3.3.0</hadoop.version>-->
<mysql.connector.java>8.0.22</mysql.connector.java>
<fileName>flink-log-handler</fileName>
<!--<mainClass>com.xxxxx.issue.flink.handler.IssueHandleFlinkHandlerByCustomRedisSink</mainClass>-->
</properties>
<version>1.0-SNAPSHOT</version>
<!--<distributionManagement>
<repository>
<id>releases</id>
<layout>default</layout>
<url>http://nexus.xxxxx.cn/nexus/content/repositories/releases/</url>
</repository>
<snapshotRepository>
<id>snapshots</id>
<name>snapshots</name>
<url>http://nexus.xxxxx.cn/nexus/content/repositories/snapshots/</url>
</snapshotRepository>
</distributionManagement>-->
<repositories>
<!-- <repository>
<id>releases</id>
<layout>default</layout>
<url>http://nexus.xxxxx.cn/nexus/content/repositories/releases/</url>
</repository>
<repository>
<id>snapshots</id>
<name>snapshots</name>
<url>http://nexus.xxxxx.cn/nexus/content/repositories/snapshots/</url>
<snapshots>
<enabled>true</enabled>
<updatePolicy>always</updatePolicy>
<checksumPolicy>warn</checksumPolicy>
</snapshots>
</repository>
<repository>
<id>xxxxx</id>
<name>xxxxx</name>
<url>http://nexus.xxxxx.cn/nexus/content/repositories/xxxxx/</url>
</repository>
<repository>
<id>public</id>
<name>public</name>
<url>http://nexus.xxxxx.cn/nexus/content/groups/public/</url>
</repository>-->
<!-- 新加 -->
<repository>
<id>cloudera</id>
<layout>default</layout>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
</repositories>
<!-- <repositories>
<!– Cloudera –>
<repository>
<id>cloudera-releases</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<!– Hortonworks –>
<repository>
<id>HDPReleases</id>
<name>HDP Releases</name>
<url>https://repo.hortonworks.com/content/repositories/releases/</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
<releases>
<enabled>true</enabled>
</releases>
</repository>
<repository>
<id>HortonworksJettyHadoop</id>
<name>HDP Jetty</name>
<url>https://repo.hortonworks.com/content/repositories/jetty-hadoop</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
<releases>
<enabled>true</enabled>
</releases>
</repository>
<!– MapR –>
<repository>
<id>mapr-releases</id>
<url>https://repository.mapr.com/maven/</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
<releases>
<enabled>true</enabled>
</releases>
</repository>
</repositories>-->
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<!-- flink以yarn模式启动，执行flink->sql->hive会用到flink-shaded-hadoop-2-uber包 -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-shaded-hadoop-2-uber</artifactId>
<version>2.7.5-9.0</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-sequence-file</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-java-bridge_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-json</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>${mysql.connector.java}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-hive_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-common</artifactId>
<version>${flink.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hive</groupId>
<artifactId>hive-jdbc</artifactId>
<version>${hive.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hive</groupId>
<artifactId>hive-metastore</artifactId>
<version>${hive.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hive</groupId>
<artifactId>hive-exec</artifactId>
<version>${hive.version}</version>
<!--<scope>compile</scope>-->
<scope>provided</scope>
</dependency>
<!-- <dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.9.1</version>
<scope>provided</scope>
</dependency>-->
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${joda-time.version}</version>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>${fastjson.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>${slf4j.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>
<finalName>${fileName}</finalName>
<plugins>
<!-- 编译插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.6.0</version>
<configuration>
<source>${maven.compiler.source}</source>
<target>${maven.compiler.target}</target>
<encoding>${project.build.sourceEncoding}</encoding>
<compilerVersion>${maven.compiler.source}</compilerVersion>
<showDeprecation>true</showDeprecation>
<showWarnings>true</showWarnings>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.12.4</version>
<configuration>
<skipTests>${maven.test.skip}</skipTests>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.rat</groupId>
<artifactId>apache-rat-plugin</artifactId>
<version>0.12</version>
<configuration>
<excludes>
<exclude>README.md</exclude>
</excludes>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-javadoc-plugin</artifactId>
<version>2.10.4</version>
<configuration>
<aggregate>true</aggregate>
<reportOutputDirectory>javadocs</reportOutputDirectory>
<locale>en</locale>
</configuration>
</plugin>
<!-- scala编译插件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.1.6</version>
<configuration>
<scalaCompatVersion>2.11</scalaCompatVersion>
<scalaVersion>2.11.12</scalaVersion>
<encoding>UTF-8</encoding>
</configuration>
<executions>
<execution>
<id>compile-scala</id>
<phase>compile</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>test-compile-scala</id>
<phase>test-compile</phase>
<goals>
<goal>add-source</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<!-- 打jar包插件(会包含所有依赖) -->
<plugin>
<!--<groupId>org.apache.maven.plugins</groupId>-->
<artifactId>maven-assembly-plugin</artifactId>
<!--<version>2.6</version>-->
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<!--<archive>
<manifest>
<mainClass>${mainClass}</mainClass>
</manifest>
</archive>-->
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>1.30.1.2.2.Maven工程resources下编写配置文件log4j2.properties
具体内容如下：
rootLogger.level = ERROR
rootLogger.appenderRef.console.ref = ConsoleAppender
appender.console.name = ConsoleAppender
appender.console.type = CONSOLE
appender.console.layout.type = PatternLayout
appender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n1.30.1.2.3.Maven工程resources下编写配置文件logback.xml
<configuration>
<appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
<encoder>
<pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{60} %X{sourceThread} - %msg%n</pattern>
</encoder>
</appender>
<appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">
<rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
<!-- Daily rollover -->
<fileNamePattern>log/generator.%d{yyyy-MM-dd}.log</fileNamePattern>
<!-- Keep 7 days' worth of history -->
<maxHistory>7</maxHistory>
</rollingPolicy>
<encoder>
<pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
</encoder>
</appender>
<root level="ERROR">
<appender-ref ref="FILE" />
<appender-ref ref="STDOUT" />
</root>
</configuration>1.30.1.2.4.Maven工程resources下编写配置文件project-config-test.properties
# 测试环境
####################################业务方kafka相关配置 start###########################################
kafka.bootstrap.servers=xxx.xxx.xxx.xxx:9094
# 消费者配置
kafka.consumer.group.id=logkit
kafka.consumer.enableAutoCommit=true
kafka.consumer.autoCommitInterval=1000
kafka.consumer.key.deserializer=org.apache.kafka.common.serialization.StringDeserializer
kafka.consumer.value.deserializer=org.apache.kafka.common.serialization.StringDeserializer
# 主题
kafka.exception.topic=lk_exception_log_statistics
kafka.log.topic=lk_log_info_statistics
####################################flink相关配置 start###########################################
# 间隔5s产生checkpoing
flink.checkpoint.interval=5000
# 确保检查点之间有至少1000 ms的间隔(可以把这个注释掉：提高checkpoint的写速度===todo===)
flink.checkpoint.minPauseBetweenCheckpoints=1000
# 检查点必须在1min内完成，或者被丢弃【checkpoint的超时时间】
flink.checkpoint.checkpointTimeout=60000
# 同一时间只允许进行一个检查点
flink.checkpoint.maxConcurrentCheckpoints=3
# 尝试重启次数
flink.fixedDelayRestart.times=3
# 每次尝试重启时之间的时间间隔
flink.fixedDelayRestart.interval=5
####################################source和sink
# kafka source读并发
flink.kafka.source.parallelism=1
# hive下沉的并发
flink.hive.sink.parallelism=1
#hive.conf=/usr/hdp/current/hive-client/conf
hive.conf=/usr/hdp/3.1.4.0-315/hive/conf/
hive.zhoushan.database=xxxx_1581.30.1.2.5.编写com.xxxxx.log.utils.PropertiesUtils
package com.xxxxx.log.utils;
import java.io.IOException;
import java.io.InputStream;
import java.util.Properties;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public final class PropertiesUtils {
private static Logger logger = LoggerFactory.getLogger(PropertiesUtils.class);
private static PropertiesUtils instance = null;
/** 间隔xxx秒产生checkpoing **/
private Integer flinkCheckpointsInterval = null;
/** 确保检查点之间有至少xxx ms的间隔 **/
private Integer flinkMinPauseBetweenCheckpoints = null;
/** 检查点必须在一分钟内完成，或者被丢弃【checkpoint的超时时间】 **/
private Integer flinkCheckpointTimeout = null;
/** 同一时间只允许进行一个检查点 **/
private Integer flinkMaxConcurrentCheckpoints = null;
/** 尝试重启次数 **/
private Integer flinkFixedDelayRestartTimes = null;
/** 每次尝试重启时之间的时间间隔 **/
private Integer flinkFixedDelayRestartInterval = null;
/** kafka source 的并行度 **/
private Integer kafkaSourceParallelism = null;
/** hive sink 的并行度 **/
private Integer hiveSinkParallelism = null;
/** kafka集群 **/
private String kafkServer = null;
/** 消费者组id **/
private String groupId = null;
private Boolean enableAutoCommit = null;
private Long autoCommitInterval = null;
private String keyDeserializer = null;
private String valueDeserializer = null;
private String exceptionTopic = null;
private String logTopic = null;
private String hiveConf = null;
private String database = null;
/**
* 静态代码块
*/
private PropertiesUtils() {
InputStream in = null;
try {
// 读取配置文件,通过类加载器的方式读取属性文件
in = PropertiesUtils.class.getClassLoader().getResourceAsStream("project-config-test.properties");
// in = PropertiesUtils.class.getClassLoader().getResourceAsStream("test-win10.properties");
// in = PropertiesUtils.class.getClassLoader().getResourceAsStream("test-linux.properties");
Properties prop = new Properties();
prop.load(in);
// flink配置
flinkCheckpointsInterval = Integer.parseInt(prop.getProperty("flink.checkpoint.interval").trim());
flinkMinPauseBetweenCheckpoints =
Integer.parseInt(prop.getProperty("flink.checkpoint.minPauseBetweenCheckpoints").trim());
flinkCheckpointTimeout = Integer.parseInt(prop.getProperty("flink.checkpoint.checkpointTimeout").trim());
flinkMaxConcurrentCheckpoints =
Integer.parseInt(prop.getProperty("flink.checkpoint.maxConcurrentCheckpoints").trim());
flinkFixedDelayRestartTimes = Integer.parseInt(prop.getProperty("flink.fixedDelayRestart.times").trim());
flinkFixedDelayRestartInterval =
Integer.parseInt(prop.getProperty("flink.fixedDelayRestart.interval").trim());
kafkaSourceParallelism = Integer.parseInt(prop.getProperty("flink.kafka.source.parallelism").trim());
hiveSinkParallelism = Integer.parseInt(prop.getProperty("flink.hive.sink.parallelism").trim());
// kafka配置
kafkServer = prop.getProperty("kafka.bootstrap.servers").trim();
groupId = prop.getProperty("kafka.consumer.group.id").trim();
enableAutoCommit = Boolean.valueOf(prop.getProperty("kafka.consumer.enableAutoCommit").trim());
autoCommitInterval = Long.valueOf(prop.getProperty("kafka.consumer.autoCommitInterval").trim());
keyDeserializer = prop.getProperty("kafka.consumer.key.deserializer").trim();
valueDeserializer = prop.getProperty("kafka.consumer.value.deserializer").trim();
exceptionTopic = prop.getProperty("kafka.exception.topic").trim();
logTopic = prop.getProperty("kafka.log.topic").trim();
hiveConf = prop.getProperty("hive.conf").trim();
database = prop.getProperty("hive.zhoushan.database").trim();
} catch (Exception e) {
throw new ExceptionInInitializerError(e);
} finally {
try {
if (in != null) {
in.close();
}
} catch (IOException e) {
e.printStackTrace();
logger.error("流关闭失败");
}
}
}
public static PropertiesUtils getInstance() {
if (instance == null) {
instance = new PropertiesUtils();
}
return instance;
}
public Integer getFlinkCheckpointsInterval() {
return flinkCheckpointsInterval;
}
public Integer getFlinkMinPauseBetweenCheckpoints() {
return flinkMinPauseBetweenCheckpoints;
}
public Integer getFlinkCheckpointTimeout() {
return flinkCheckpointTimeout;
}
public Integer getFlinkMaxConcurrentCheckpoints() {
return flinkMaxConcurrentCheckpoints;
}
public Integer getFlinkFixedDelayRestartTimes() {
return flinkFixedDelayRestartTimes;
}
public Integer getFlinkFixedDelayRestartInterval() {
return flinkFixedDelayRestartInterval;
}
public Integer getKafkaSourceParallelism() {
return kafkaSourceParallelism;
}
public Integer getHiveSinkParallelism() {
return hiveSinkParallelism;
}
public String getKafkServer() {
return kafkServer;
}
public String getGroupId() {
return groupId;
}
public Boolean getEnableAutoCommit() {
return enableAutoCommit;
}
public Long getAutoCommitInterval() {
return autoCommitInterval;
}
public String getKeyDeserializer() {
return keyDeserializer;
}
public String getValueDeserializer() {
return valueDeserializer;
}
public String getExceptionTopic() {
return exceptionTopic;
}
public String getLogTopic() {
return logTopic;
}
public String getHiveConf() {
return hiveConf;
}
public String getDatabase() {
return database;
}
}1.30.1.2.6.编写com.xxxxx.log.flink.handler.ExceptionLogHandlerBySql
具体内容是：
package com.xxxxx.log.flink.handler;
import java.util.concurrent.TimeUnit;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.xxxxx.log.utils.PropertiesUtils;
public class ExceptionLogHandlerBySql {
private static final Logger logger = LoggerFactory.getLogger(ExceptionLogHandlerBySql.class);
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
PropertiesUtils instance = PropertiesUtils.getInstance();
//重启策略之固定间隔 (Fixed delay)
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(instance.getFlinkFixedDelayRestartTimes(),
Time.of(instance.getFlinkFixedDelayRestartInterval(), TimeUnit.MINUTES)));
//设置间隔多长时间产生checkpoint
env.enableCheckpointing(instance.getFlinkCheckpointsInterval());
//设置模式为exactly-once （这是默认值）
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//确保检查点之间有至少500 ms的间隔【checkpoint最小间隔】
// env.getCheckpointConfig().setMinPauseBetweenCheckpoints(instance.getFlinkMinPauseBetweenCheckpoints());
//检查点必须在一分钟内完成，或者被丢弃【checkpoint的超时时间】
env.getCheckpointConfig().setCheckpointTimeout(instance.getFlinkCheckpointTimeout());
//同一时间只允许进行一个检查点
env.getCheckpointConfig().setMaxConcurrentCheckpoints(instance.getFlinkMaxConcurrentCheckpoints());
// 表示一旦Flink处理程序被cancel后，会保留Checkpoint数据，以便根据实际需要恢复到指定的Checkpoint【详细解释见备注】
env.getCheckpointConfig()
.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
env.setParallelism(1);
// flink table
EnvironmentSettings settings = EnvironmentSettings.newInstance().inStreamingMode().useBlinkPlanner().build();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);
// 构造 kafka source, 用 DEFAULT
tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
String sourceDrop = "drop table if exists kafka_exception";
String sourceTable = "CREATE TABLE kafka_exception ("
+ " serviceId STRING,"
+ " serverName STRING,"
+ " serverIp STRING,"
+ " title STRING,"
+ " operationPath STRING,"
+ " url STRING,"
+ " stack STRING,"
+ " exceptionName STRING,"
+ " exceptionInfo STRING,"
+ " operationUser STRING,"
+ " operationIp STRING,"
+ " orgId BIGINT,"
+ " methodClass STRING,"
+ " fileName STRING,"
+ " methodName STRING,"
+ " operationData STRING,"
+ " occurrenceTime BIGINT"
+ ") WITH ("
+ " 'connector' = 'kafka',"
+ " 'topic' = '" + instance.getExceptionTopic() + "',"
+ " 'properties.bootstrap.servers' = '" + instance.getKafkServer() + "',"
+ " 'properties.group.id' = '" + instance.getGroupId() + "',"
+ " 'scan.startup.mode' = 'earliest-offset',"
+ " 'format' = 'json',"
+ " 'json.fail-on-missing-field' = 'false',"
+ " 'json.ignore-parse-errors' = 'true'"
+ " )";
System.out.println("=================sourcesql打印开始========================");
tableEnv.executeSql(sourceDrop);
tableEnv.executeSql(sourceTable);
System.out.println(sourceTable);
System.out.println("=================sourcesql打印结束========================");
// 构造 hive catalog(这个可以任意编写)
String name = "mycatalog";
String defaultDatabase = instance.getDatabase();
String hiveConfDir = instance.getHiveConf();
HiveCatalog hive = new HiveCatalog(name, defaultDatabase, hiveConfDir);
tableEnv.registerCatalog(name, hive);
tableEnv.useCatalog(name);
// hive sink
tableEnv.getConfig().setSqlDialect(SqlDialect.HIVE);
tableEnv.useDatabase(defaultDatabase);
String sinkDrop = "drop table if exists hive_exception";
String sinkTable = "CREATE TABLE hive_exception ("
+ " service_id STRING,"
+ " server_name STRING,"
+ " server_ip STRING,"
+ " title STRING,"
+ " operation_path STRING,"
+ " url STRING,"
+ " stack STRING,"
+ " exception_name STRING,"
+ " exception_info STRING,"
+ " operation_user STRING,"
+ " operation_ip STRING,"
+ " org_id BIGINT,"
+ " method_class STRING,"
+ " file_name STRING,"
+ " method_name STRING,"
+ " operation_data STRING,"
+ " occurrence_time String"
+ " ) PARTITIONED BY (dt STRING) STORED AS parquet TBLPROPERTIES ("
+ " 'partition.time-extractor.timestamp-pattern'='$dt 00:00:00',"
+ " 'sink.partition-commit.trigger'='process-time',"
+ " 'sink.partition-commit.delay'='0 s',"
+ " 'sink.partition-commit.policy.kind'='metastore,success-file'"
+ ")";
System.out.println("=================sinksql打印开始========================");
tableEnv.executeSql(sinkDrop);
tableEnv.executeSql(sinkTable);
System.out.println(sinkTable);
System.out.println("=================sinksql打印结束========================");
String sql = "INSERT INTO TABLE hive_exception"
+ " SELECT serviceId, serverName, serverIp, title, operationPath, url, stack, exceptionName, exceptionInfo, operationUser, operationIp,"
+ " orgId, methodClass, fileName, methodName, operationData, from_unixtime(cast(occurrenceTime/1000 as bigint),'yyyy-MM-dd HH:mm:ss'), from_unixtime(cast(occurrenceTime/1000 as bigint),'yyyy-MM-dd')"
+ " FROM kafka_exception";
tableEnv.executeSql(sql);
}
}1.30.1.2.7.执行命令
第一种：standalone模式
$FLINK_HOME/bin/flink run \
-c com.xxxxx.log.flink.handler.ExceptionLogHandlerBySql \
/root/cf_temp/flink-log-handler.jar第二种：cluster-yarn
$FLINK_HOME/bin/flink run -d -m yarn-cluster \
-yqu real_time_processing_queue \
-p 1 -yjm 1024m -ytm 1024m -ynm ExceptionLogHandler \
-c com.xxxxx.log.flink.handler.ExceptionLogHandlerBySql \
/root/cf_temp/flink-log-handler.jar第三种：yarn-session(这个需要先提交到yarn获取对应的application_id，所以这个没测试)
$FLINK_HOME/bin/yarn-session.sh -d -nm yarnsession01 -n 2 -s 3 -jm 1024m -tm 2048m$FLINK_HOME/bin/flink run -d -yid application_1603447441975_0034 \
-c com.xxxxx.log.flink.handler.ExceptionLogHandlerBySql \
/root/cf_temp/flink-log-handler.jar ExceptionLogSession \Json格式：
{"serviceId":"test000","serverName":"xxx","serverIp":"xxx.xxx.xxx.xxx","title":"xxxx","operationPath":"/usr/currunt","url":"http://baidu.com","stack":"xxx","exceptionName":"xxxx","exceptionInfo":"xxxx","operationUser":"chenfeng","operationIp":"xxx.xxx.xxx.xxx","orgId":777777,"methodClass":"com.xxxxx.Test","fileName":"test.txt","methodName":"findname","operationData":"name=kk","occurrenceTime":"2021-05-12 09:23:20"}
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