需求：消费Kafka数据，进行数据清洗及维表关联补齐字段，结果数据写入Kafka。
import java.time.Duration
import com.sm.function.udf._
import com.sm.common.conf.PropManager
import com.sm.constants.Constants
import com.sm.utils.FlinkUtils
import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.common.time.Time
import org.apache.flink.streaming.api.environment.{CheckpointConfig, ExecutionCheckpointingOptions}
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.{CheckpointingMode, TimeCharacteristic}
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
import org.apache.log4j.Level
import org.slf4j.LoggerFactory
/**
* flink sql 维表关联
* 消费kafka, MySQL维表关联, 结果写入kafka
*
* create by LiuJinHe 2020/10/22
*/
object LoginEtlKafka2Kafka {
private var logger: org.slf4j.Logger = _
def main(args: Array[String]): Unit = {
logger = LoggerFactory.getLogger(this.getClass.getSimpleName)
org.apache.log4j.Logger.getLogger("org.apache.hadoop").setLevel(Level.WARN)
org.apache.log4j.Logger.getLogger("org.apache").setLevel(Level.INFO)
// 初始化 stream 环境
// 本地测试，需要 flink-runtime-web 依赖
// val env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI()
val env = StreamExecutionEnvironment.getExecutionEnvironment
// 本地测试线程 1
// env.setParallelism(1)
// val params = ParameterTool.fromArgs(args)
// env.getConfig.setGlobalJobParameters(params)
// 失败重启,固定间隔,每隔3秒重启1次,总尝试重启10次
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(10, 3))
// 事件处理的时间，由系统时间决定
env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime)
// 创建 streamTable 环境
val tableEnv: StreamTableEnvironment = StreamTableEnvironment.create(env, FlinkUtils.getSettings)
// tableConfig.setIdleStateRetentionTime(Time.hours(12), Time.hours(24))
// checkpoint 设置
val tableConfig = tableEnv.getConfig.getConfiguration
tableConfig.set(ExecutionCheckpointingOptions.CHECKPOINTING_MODE, CheckpointingMode.EXACTLY_ONCE)
// checkpoint的超时时间周期，1 分钟做一次checkpoint, 每次checkpoint 完成后 sink 才会执行
tableConfig.set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL, Duration.ofSeconds(60))
// checkpoint的超时时间, 检查点一分钟内没有完成将被丢弃
tableConfig.set(ExecutionCheckpointingOptions.CHECKPOINTING_TIMEOUT, Duration.ofSeconds(60))
// checkpoint 最小间隔，两个检查点之间至少间隔 30 秒
tableConfig.set(ExecutionCheckpointingOptions.MIN_PAUSE_BETWEEN_CHECKPOINTS, Duration.ofSeconds(30))
// 同一时间只允许进行一个检查点
tableConfig.set(ExecutionCheckpointingOptions.MAX_CONCURRENT_CHECKPOINTS, Integer.valueOf(1))
// 手动cancel时是否保留checkpoint
tableConfig.set(ExecutionCheckpointingOptions.EXTERNALIZED_CHECKPOINT,
CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
// 设置状态的最小空闲时间和最大的空闲时间
tableEnv.getConfig.setIdleStateRetentionTime(Time.hours(12), Time.hours(24))
tableConfig.setString("table.exec.mini-batch.enabled", "true")
tableConfig.setString("table.exec.mini-batch.allow-latency", "2s")
tableConfig.setString("table.exec.mini-batch.size", "5000")
tableConfig.setString("table.optimizer.distinct-agg.split.enabled", "true")
// 测试
// val odsLoginTopic = "ods_login"
// val bootstrapServers = "192.168.100.39:9092"
// val groupId = "flink-test-group"
// val startMode = "group-offsets"
// val dwdSdkUserLogin = "dwd_sdk_user_login"
/**
* 加载配置
*/
val odsUserLoginTopic = PropManager.getProp(Constants.ODS_USER_LOGIN_TOPIC)
val dwdUserLoginTopic = PropManager.getProp(Constants.DWD_USER_LOGIN_TOPIC)
val bootstrapServers = PropManager.getProp(Constants.BOOTSTRAP_SERVERS)
val ConsumerGroupId = PropManager.getProp(Constants.CONSUMER_GROUP_ID)
val ProducerGroupId = PropManager.getProp(Constants.PRODUCER_GROUP_ID)
val startMode = PropManager.getProp(Constants.STARTUP_MODE)
/**
* kafka source table
*/
tableEnv.executeSql("drop table if exists login_kafka_Source")
val kafkaSourceDDL =
s"""
|create table $odsUserLoginTopic (
| _game_id int,
| _login_name string,
| _time bigint,
| _time_server bigint,
| _device_key string,
| _os string,
| _imei string,
| ts as to_timestamp(from_unixtime(_time / 1000, 'yyyy-MM-dd HH:mm:ss')),
| proc_time as proctime()
|) with (
| 'connector' = 'kafka',
| 'topic' = '$odsUserLoginTopic',
| 'properties.bootstrap.servers' = '$bootstrapServers',
| 'properties.group.id' = '$ConsumerGroupId',
| 'scan.startup.mode' = '$startMode',
| 'format' = 'json',
| 'json.fail-on-missing-field' = 'false',
| 'json.ignore-parse-errors' = 'true'
|)
""".stripMargin
tableEnv.executeSql(kafkaSourceDDL)
/**
* dwd
*/
tableEnv.executeSql(s"drop table if exists $dwdUserLoginTopic")
val kafkaSinkDDL =
s"""
|create table $dwdUserLoginTopic (
| cp_game_id int,
| game_id int,
| login_account string,
| `time` string,
| time_server string,
| device_key string,
| os string,
| primary key (`time`, package_id, core_account) not enforced
|) with (
| 'connector' = 'kafka',
| 'topic' = '$dwdUserLoginTopic',
| 'properties.bootstrap.servers' = '$bootstrapServers',
| 'properties.group.id' = '$ProducerGroupId',
| 'sink.partitioner' = 'round-robin',
| 'format' = 'json',
| 'json.fail-on-missing-field' = 'false',
| 'json.ignore-parse-errors' = 'true'
|)
""".stripMargin
tableEnv.executeSql(kafkaSinkDDL)
/**
* DIM
*/
val dimSourceDDL =
"""
|create table dim_mysql_games (
| ID bigint,
| CP_GAME_ID int
|) with (
| 'connector' = 'jdbc',
| 'url' = 'jdbc:mysql://cdh-slave04:3306/game_platform?useSSL=false&useUnicode=true&characterEncoding=UTF-8&characterSetResults=UTF-8&zeroDateTimeBehavior=CONVERT_TO_NULL&serverTimezone=UTC',
| 'username' = 'root',
| 'password' = '123456',
| 'table-name' = 'games',
| 'driver' = 'com.mysql.cj.jdbc.Driver',
| 'scan.fetch-size' = '200',
| 'lookup.cache.max-rows' = '2000',
| 'lookup.cache.ttl' = '300s'
|)
""".stripMargin
tableEnv.executeSql(dimSourceDDL)
/**
* 注册自定义函数
*/
tableEnv.createTemporarySystemFunction("ParseDeviceCode", classOf[ParseDeviceCode])
tableEnv.createTemporarySystemFunction("ParseDeviceKey", classOf[ParseDeviceKey])
tableEnv.createTemporarySystemFunction("ParseOsVersion", classOf[ParseOsVersion])
tableEnv.createTemporarySystemFunction("ParseDeviceId", classOf[ParseDeviceId])
tableEnv.createTemporarySystemFunction("ParseMd5DeviceId", classOf[ParseMd5DeviceId])
/**
* temporal join
*/
val queryDDL =
s"""
|insert into $dwdUserLoginTopic
|select
| dim.CP_GAME_ID as cp_game_id,
| _game_id as game_id,
| coalesce(_login_name, '') as login_account,
| from_unixtime(_time / 1000, 'yyyy-MM-dd HH:mm:ss') as `time`,
| from_unixtime(_time_server / 1000, 'yyyy-MM-dd HH:mm:ss') as time_server,
| ParseDeviceKey(_device_key, _os, _imei) as device_key,
| _os as os
|from $odsUserLoginTopic as login
| left join dim_mysql_games for system_time as of login.proc_time as dim
| on dim.ID = login._game_id
""".stripMargin
tableEnv.executeSql(queryDDL)
}
}
配置加载
默认加载resources下 application.properties
import com.typesafe.config.{Config, ConfigFactory}
/**
* create by LiuJinHe 2020/3/11
*/
object PropManager {
final val config: Config = ConfigFactory.load
def getProp(key: String): String = config.getString(key).trim
}
自定义udf
import com.mysql.cj.util.StringUtils
import com.sm.common.utils.MD5Utils
import org.apache.flink.table.functions.ScalarFunction
/**
* 自定义UDF, 解析 Device key
*
* create by LiuJinHe 2020/9/22
*/
class ParseDeviceKey extends ScalarFunction {
def eval（deviceKey: String, os: String, imei: String): String = {
if (!StringUtils.isNullOrEmpty(deviceKey)) {
deviceKey.toLowerCase
} else if (os == "1") {
""
} else {
MD5Utils.MD5Encode(imei).toLowerCase
}
}
}
pom
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<scala.version>2.11.12</scala.version>
<scala.binary.version>2.11</scala.binary.version>
<flink.version>1.11.2</flink.version>
<hive.version>2.1.1-cdh6.2.0</hive.version>
<hadoop.version>3.0.0-cdh6.2.0</hadoop.version>
</properties>
<repositories>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>
<dependencies>
<dependency>
<groupId>com.sm.common</groupId>
<artifactId>common</artifactId>
<version>1.0-SNAPSHOT</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<!-- Flink Dependency -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-runtime-web_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-core</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-scala_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-common</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-scala-bridge_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
<version>${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>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-json</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>com.alibaba.ververica</groupId>
<artifactId>flink-sql-connector-mysql-cdc</artifactId>
<version>1.1.0</version>
<scope>provided</scope>
</dependency>
<!-- other Dependency-->
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.16</version>
<!-- <scope>provided</scope>-->
</dependency>
</dependencies>
<build>
<plugins>
<!-- 该插件用于将Scala代码编译成class文件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>4.2.0</version>
<executions>
<execution>
<goals>
<!--声明绑定到maven的compile阶段-->
<goal>compile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.0.0</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
提交到集群，$FLINK_HOME/lib 下放入以下jar，重启集群，打包时全部 <scope>provided</scope>
flink-connector-jdbc_2.11-1.11.2.jar
flink-sql-connector-kafka_2.11-1.11.2.jar
flink-sql-connector-mysql-cdc-1.1.0.jar # mysql cdc,包含mysql-connector-java
