FlinkSQL Kafka to Hive

╰半夏微凉° 2021-09-24 21:12 460阅读 0赞

基于CDH平台，Hive2.1.1-CDH6.2.0 ，写入Hive简单操作。

[Hive Streaming][]

import java.time.Duration
    
    import com.sm.common.conf.PropManager
    import com.sm.constants.Constants
    import com.sm.utils.FlinkUtils
    import org.apache.flink.streaming.api.{CheckpointingMode, TimeCharacteristic}
    import org.apache.flink.streaming.api.environment.{CheckpointConfig, ExecutionCheckpointingOptions}
    import org.apache.flink.streaming.api.scala._
    import org.apache.flink.table.api.SqlDialect
    import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
    import org.apache.flink.table.catalog.hive.HiveCatalog
    import org.apache.log4j.Level
    import org.slf4j.LoggerFactory
    
    /**
      * flink sql kafka to hive
      *
      * create by LiuJinHe 2020/10/22
      */
    object FlinkSqlKafka2Hive {
      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
    
        //    参数读取
        //    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))
        // 本地测试线程 1
        env.setParallelism(1)
    
        // 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)
    
        // 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
        tableConfig.set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL, Duration.ofSeconds(30))
        // 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))
    
        // 加载配置
        val catalog_name = PropManager.getProp(Constants.CATALOG_NAME)
        val database = PropManager.getProp(Constants.DEFAULT_DATABASE)
        val schemaDataBase = PropManager.getProp(Constants.SCHEMA_DATABASE)
    
        // 构造 hive catalog
        val hiveCatalog = new HiveCatalog(
          catalog_name,
          database,
          PropManager.getProp(Constants.HIVE_CONF_DIR)
        )
    
        tableEnv.registerCatalog(catalog_name, hiveCatalog)
        tableEnv.useCatalog(catalog_name)
    
        // 构造 kafka source, 用 DEFAULT
        tableEnv.getConfig.setSqlDialect(SqlDialect.DEFAULT)
        tableEnv.useDatabase(schemaDataBase)
    
        // 如果catalog中 kafka source 已建表，先删除
        tableEnv.executeSql("drop table if exists kafkaSourceTable")
        // kafka source table
        val kafkaSourceDDL =
          """
            |create table kafka_source_Table (
            | user_name string,
            | user_id bigint,
            | `time` bigint,
            | ts as to_timestamp(from_unixtime(`time` / 1000, 'yyyy-MM-dd HH:mm:ss')),
            | rt as proctime()
            |) with (
            | 'connector' = 'kafka',
            | 'topic' = 'flink_test',
            | 'properties.bootstrap.servers' = '192.168.100.39:9092',
            | 'properties.group.id' = 'flink-test-group',
            | 'format' = 'json',
            | 'scan.startup.mode' = 'group-offsets'
            |)
          """.stripMargin
        tableEnv.executeSql(kafkaSourceDDL)
    
        // hive catalog
        tableEnv.getConfig.setSqlDialect(SqlDialect.HIVE)
        tableEnv.useDatabase(database)
        // 删除表，已有 hive 表的情况下不用删除和建表
        // tableEnv.executeSql("drop table if exists test_hive_table")
        // hive sink table
        // 每次 checkpoint 完成后才会真正写入 Hive, 生成一个文件
        val hiveSinkDDL =
        """
          |create table hive_sink_table (
          | user_name string,
          | user_id bigint,
          | `time` bigint,
          | `date` string
          |)
          |partitioned by (`date` string)
          |row format delimited fields terminated by '\t'
          |stored as orc
          |location 'hdfs://BigdataCluster/user/hive/warehouse/test_data.db/test/test_hive_table'
          |tblproperties (
          | 'orc.compress'='SNAPPY',
          | 'partition.time-extractor.timestamp-pattern' = '$date 00:00:00',
          | 'sink.partition-commit.trigger' = 'process-time',
          | 'sink.partition-commit.policy.kind' = 'metastore,success-file'
          |)
        """.stripMargin
        tableEnv.executeSql(hiveSinkDDL)
        // 'sink.partition-commit.delay' = '10 s',
    
        // 写数据
        val insertSql =
          """
            |insert into hive_sink_table
            |select
            | user_name,
            | user_id,
            | `time`,
            | date_format(ts, 'yyyy-MM-dd') as `date`
            |from schema_data.kafka_source_Table 
          """.stripMargin
        tableEnv.executeSql(insertSql)
    
    
        // 测试打印输入数据, 1. 以 connector=print 打印; 2. 转成 dataStream.print
        //    val querySql =
        //      """
        //        |select
        //        | user_name,
        //        | user_id,
        //        | date_format(ts, 'yyyy-MM-dd') as `date`,
        //        | date_format(ts, 'HH'),
        //        | date_format(ts, 'mm')
        //        |from kafkaSourceTable
        //      """.stripMargin
        //    val table = tableEnv.sqlQuery(querySql)
        //    table.printSchema()
        //
        //    val resultDStream = tableEnv.toAppendStream[Row](table)
        //    resultDStream.print()
        //    env.execute("flink sql kafka 2 hive")
      }
    }

pom

<?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">
    
        <artifactId>realtime-flink-platform-test</artifactId>
        <groupId>com.sm.com.sm.job</groupId>
        <version>1.0-SNAPSHOT</version>
        <modelVersion>4.0.0</modelVersion>
    
    
        <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>
                
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-runtime-web_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-core</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
    
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-scala_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-table-common</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-table-api-scala-bridge_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-clients_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-json</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-orc_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                <scope>provided</scope>
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-sql-connector-kafka_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-sql-connector-hive-2.2.0_${scala.binary.version}</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.hive</groupId>
                <artifactId>hive-serde</artifactId>
                <version>${hive.version}</version>
                
                <exclusions>
                    <exclusion>
                        <groupId>org.apache.logging.log4j</groupId>
                        <artifactId>log4j-slf4j-impl</artifactId>
                    </exclusion>
                    <exclusion>
                        <groupId>org.apache.hive</groupId>
                        <artifactId>hive-common</artifactId>
                    </exclusion>
                    <exclusion>
                        <groupId>org.apache.hive</groupId>
                        <artifactId>hive-service-rpc</artifactId>
                    </exclusion>
                    <exclusion>
                        <groupId>org.apache.parquet</groupId>
                        <artifactId>parquet-hadoop-bundle</artifactId>
                    </exclusion>
                </exclusions>
            </dependency>
            <dependency>
                <groupId>org.apache.hive</groupId>
                <artifactId>hive-exec</artifactId>
                <version>${hive.version}</version>
                
                <exclusions>
                    <exclusion>
                        <groupId>org.apache.logging.log4j</groupId>
                        <artifactId>log4j-slf4j-impl</artifactId>
                    </exclusion>
                    <exclusion>
                        <groupId>org.apache.hive</groupId>
                        <artifactId>hive-llap-tez</artifactId>
                    </exclusion>
                </exclusions>
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-common</artifactId>
                <version>${hadoop.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-hdfs</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>
        </dependencies>
    
        <build>
            <plugins>
                
                <plugin>
                    <groupId>net.alchim31.maven</groupId>
                    <artifactId>scala-maven-plugin</artifactId>
                    <version>4.2.0</version>
                    <executions>
                        <execution>
                            <goals>
                                
                                <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>

提交集群时，在 $FLINK\_HOME/lib 下放入

flink-sql-connector-hive-2.2.0_2.11-1.11.2.jar

[Hive Streaming]: https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/hive/hive_streaming.html