spark+kafka+hive

ゞ浴缸里的玫瑰 2022-05-14 07:21 274阅读 0赞

使用spark streaming消费kafka消息，并通过hql同步到hive中

用到的pom：

<dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-core_2.11</artifactId>
                <version>2.3.1</version>
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-sql_2.11</artifactId>
                <version>2.3.1</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming_2.11</artifactId>
                <version>2.3.1</version>
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-hive_2.11</artifactId>
                <version>2.3.1</version>
            </dependency>
    
    
            
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka_2.11</artifactId>
                <version>1.6.3</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
                <version>2.3.1</version>
            </dependency>
    
            <dependency>
                <groupId>junit</groupId>
                <artifactId>junit</artifactId>
                <version>4.12</version>
                <scope>compile</scope>
            </dependency>

*  hive可以通过更改配置来支持行级别的更新和删除，这里通过曲线（比如更新行：先删除原有行再插入新行）实现，效率不高；
 *  这里消费的消息是通过canal解析sql的binlog得到的json格式，canal解析并通过kafka生产消息参照另一篇文章：[canal+kafka实践——实时etl][canal_kafka_etl]

package com.kexin.etl;
    
    import com.google.gson.Gson;
    import org.apache.kafka.clients.consumer.ConsumerRecord;
    import org.apache.spark.api.java.JavaRDD;
    import org.apache.spark.api.java.JavaSparkContext;
    import org.apache.spark.sql.SparkSession;
    import org.apache.spark.streaming.Durations;
    import org.apache.spark.streaming.api.java.JavaInputDStream;
    import org.apache.spark.streaming.api.java.JavaStreamingContext;
    import org.apache.spark.streaming.kafka010.ConsumerStrategies;
    import org.apache.spark.streaming.kafka010.KafkaUtils;
    import org.apache.spark.streaming.kafka010.LocationStrategies;
    
    import java.io.File;
    import java.util.*;
    import java.util.regex.Pattern;
    
    /**
     * @Author KeXin
     * @Date 2018/9/4 上午10:05
     **/
    public class KafkaEtl {
    
        static String warehouseLocation = new File("spark-warehouse").getAbsolutePath();
        static SparkSession spark = SparkSession.builder()
                .config("spark.sql.warehouse.dir", warehouseLocation)
                .master("local[2]")
                .appName("etl")
                .enableHiveSupport()
                .getOrCreate();
        public static void main(String[] args) throws InterruptedException {
    
            String brokers = "127.0.0.1:9092";
            String topics = "etl_timely.*";
            Pattern pattern = Pattern.compile(topics);
    
            JavaStreamingContext jssc = new JavaStreamingContext(JavaSparkContext.fromSparkContext(spark.sparkContext()), Durations.seconds(2));
    
            Map<String, Object> kafkaParams = new HashMap<>();
            kafkaParams.put("bootstrap.servers", brokers);
            kafkaParams.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
            kafkaParams.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
            kafkaParams.put("serializer.class", "kafka.serializer.StringEncoder");
            kafkaParams.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            kafkaParams.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            kafkaParams.put("group.id", "helloSpark");
    
            JavaInputDStream<ConsumerRecord<String, String>> messages = KafkaUtils.createDirectStream(jssc, LocationStrategies.PreferConsistent(), ConsumerStrategies.SubscribePattern(pattern, kafkaParams));
    
            //{event_op_type=insert, database_name=etl_kafka, event_timestamp=1536029791000, table_name=persons
            // , map_info={Address=, FirstName=Bill, LastName=Gates, City=, P_Id=4}}
    
            //获取每条消息的offset
            messages.foreachRDD((rdd, time) -> {
                System.out.println("========= " + time + "=========");
                JavaRDD<String> rowRDD = rdd.map(record -> {
                    String result;
                    Gson gson = new Gson();
                    Map<String, Object> map = new HashMap<>();
                    map = gson.fromJson(record.value(), map.getClass());
                    Map<String, Object> map_info = (Map<String, Object>) map.get("map_info");
                    String eventType = (String) map.get("event_op_type");
                    switch (eventType) {
                        case "delete":
                            result = "insert overwrite table " + map.get("table_name")+" select * from "+map.get("table_name")+" where ";
                            for (String key : map_info.keySet()) {
                                result += key + "!='" + map_info.get(key) + "' or ";
                            }
                            result = result.substring(0, result.lastIndexOf("or "));
                            break;
                        case "insert":
                            String stemp = " values(";
                            result = "insert into " + map.get("table_name");
                            for (String key : map_info.keySet()) {
                                stemp += "'" + map_info.get(key) + "',";
                            }
                            stemp = stemp.substring(0, stemp.lastIndexOf(','));
                            result += stemp + ")";
                            break;
                        default:
                            //update
                            Map<String, Object> map_before = (Map<String, Object>) map.get("beforeColumns");
                            result = "insert overwrite table " + map.get("table_name")+" select * from "+map.get("table_name")+" where ";
                            for (String key : map_before.keySet()) {
                                result += key + "!='" + map_before.get(key) + "' or ";
                            }
                            result = result.substring(0, result.lastIndexOf("or "));
                            spark.sql(result);
    
                            String stemp1 = " values(";
                            result = "insert into " + map.get("table_name");
                            for (String key : map_info.keySet()) {
                                stemp1 += "'" + map_info.get(key) + "',";
                            }
                            stemp1 = stemp1.substring(0, stemp1.lastIndexOf(','));
                            result += stemp1 + ")";
                            break;
                    }
                    System.out.println(result);
                    String stable = "CREATE TABLE IF NOT EXISTS " + map.get("table_name")+" (";
                    for(String key:map_info.keySet()){
                        stable  += key+" varchar(255),";
                    }
                    stable = stable.substring(0,stable.lastIndexOf(","))+") USING hive";
                    spark.sql(stable);
                    spark.sql(result);
                    spark.sql("select * from "+map.get("table_name")).show();
    
                    return result;
                });
                rowRDD.saveAsTextFile("./src/main/resources/output/operations");
            });
    
    
            // Start the computation
            jssc.start();
            jssc.awaitTermination();
            jssc.stop(false);
        }
    }

[canal_kafka_etl]: https://blog.csdn.net/qq_29721419/article/details/82388090

spark+kafka+hive

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