前言

上文提到使用Flink SQL写入hadoop catalog 的iceberg table 的简单示例，这次我就flink 消费kafka 流式写入iceberg table做一个验证，现记录如下：

环境：本地测试环境 JDK1.8  、Flink 1.11.2  、Hadoop3.0.0 、Hive2.1.1

一、前置说明

本文记录了使用HDFS的一个路径作为iceberg 的结果表，使用Flink实时消费kafka中的数据并写入iceberg表，并且使用Hive作为客户端实时读取。

因为iceberg强大的读写分离特性，新写入的数据几乎可以实时读取。参考 数据湖技术Iceberg的探索与实践.pdf 

 

二、使用步骤

1.创建Hadoop Catalog的Iceberg 表

代码如下（示例）：

        System.out.println("---> 1. create iceberg hadoop catalog table  .... ");
        // create hadoop catalog
        tenv.executeSql("CREATE CATALOG hadoop_catalog WITH (\n" +
                "  'type'='iceberg',\n" +
                "  'catalog-type'='hadoop',\n" +
                "  'warehouse'='hdfs://nameservice1/tmp',\n" +
                "  'property-version'='1'\n" +
                ")");

        // change catalog
        tenv.useCatalog("hadoop_catalog");
        tenv.executeSql("CREATE DATABASE if not exists iceberg_hadoop_db");
        tenv.useDatabase("iceberg_hadoop_db");
        // create iceberg result table
        tenv.executeSql("drop table hadoop_catalog.iceberg_hadoop_db.iceberg_002"); 
        tenv.executeSql("CREATE TABLE  hadoop_catalog.iceberg_hadoop_db.iceberg_002 (\n" +
                "    user_id STRING COMMENT 'user_id',\n" +
                "    order_amount DOUBLE COMMENT 'order_amount',\n" +
                "    log_ts STRING\n" +
                ")");

 

2.使用Hive Catalog创建Kafka流表

代码如下（示例）：

        System.out.println("---> 2. create kafka Stream table  .... ");
        String HIVE_CATALOG = "myhive";
        String DEFAULT_DATABASE = "tmp";
        String HIVE_CONF_DIR = "/xx/resources";
        Catalog catalog = new HiveCatalog(HIVE_CATALOG, DEFAULT_DATABASE, HIVE_CONF_DIR);
        tenv.registerCatalog(HIVE_CATALOG, catalog);
        tenv.useCatalog("myhive");
        // create kafka stream table
        tenv.executeSql("DROP TABLE IF EXISTS ods_k_2_iceberg");
        tenv.executeSql(
                "CREATE TABLE ods_k_2_iceberg (\n" +
                        " user_id STRING,\n" +
                        " order_amount DOUBLE,\n" +
                        " log_ts TIMESTAMP(3),\n" +
                        " WATERMARK FOR log_ts AS log_ts - INTERVAL '5' SECOND\n" +
                        ") WITH (\n" +
                        "  'connector'='kafka',\n" +
                        "  'topic'='t_kafka_03',\n" +
                        "  'scan.startup.mode'='latest-offset',\n" +
                        "  'properties.bootstrap.servers'='xx:9092',\n" +
                        "  'properties.group.id' = 'testGroup_01',\n" +
                        "  'format'='json'\n" +
                        ")");

3. 使用SQL连接kafka流表和iceberg 目标表

代码如下（示例）：

        System.out.println("---> 3. insert into iceberg  table from kafka stream table .... ");
        tenv.executeSql(
                "INSERT INTO  hadoop_catalog.iceberg_hadoop_db.iceberg_002 " +
                        " SELECT user_id, order_amount, DATE_FORMAT(log_ts, 'yyyy-MM-dd') FROM myhive.tmp.ods_k_2_iceberg");

4.  数据验证

bin/kafka-console-producer.sh --broker-list xx:9092 --topic t_kafka_03
{"user_id":"a1111","order_amount":11.0,"log_ts":"2020-06-29 12:12:12"}
{"user_id":"a1111","order_amount":11.0,"log_ts":"2020-06-29 12:15:00"}
{"user_id":"a1111","order_amount":11.0,"log_ts":"2020-06-29 12:20:00"}
{"user_id":"a1111","order_amount":11.0,"log_ts":"2020-06-29 12:30:00"}
{"user_id":"a1111","order_amount":13.0,"log_ts":"2020-06-29 12:32:00"}
{"user_id":"a1112","order_amount":15.0,"log_ts":"2020-11-26 12:12:12"}


hive> add jar /home/zmbigdata/iceberg-hive-runtime-0.10.0.jar;
hive> CREATE EXTERNAL TABLE tmp.iceberg_002(user_id STRING,order_amount DOUBLE,log_ts STRING)
STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION '/tmp/iceberg_hadoop_db/iceberg_002';
 
hive> select * from tmp.iceberg_002  limit 5;
a1111	11.0	2020-06-29
a1111	11.0	2020-06-29
a1111	11.0	2020-06-29
a1111	11.0	2020-06-29
a1111	13.0	2020-06-29
Time taken: 0.108 seconds, Fetched: 5 row(s)

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总结

本文仅仅简单介绍了使用Flink Table API 消费kafka并实时写入基于HDFS Hadoop Catalog的iceberg 结果表中，初步验证了该方案的可行性，当然鉴于该示例比较单一未经过线上验证，所以仅供参考。
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