实时任务执行流程
目前暂时将项目在本地执行，执行顺序如下：
一、准备环境
这里默认HDFS、Hive、HBase、Kafka环境已经准备，启动maxwell组件监控mysql业务库数据：
#在Kafka中创建好对应的kafka topic（已创建的topic，可忽略，避免重复创建）
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-USER-LOG-DATA --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DB-BUSSINESS-DATA --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-ODS-TOPIC --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DIM-TOPIC --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DWD-BROWSE-LOG-TOPIC --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DWS-BROWSE-LOG-WIDE-TOPIC --partitions 3 --replication-factor 3
#启动maxwell
[root@node3 ~]# cd /software/maxwell-1.28.2/bin
[root@node3 bin]# maxwell --config ../config.properties
#在Hive中创建好需要的Iceberg各层的表
add jar /software/hive-3.1.2/lib/iceberg-hive-runtime-0.12.1.jar;
add jar /software/hive-3.1.2/lib/libfb303-0.9.3.jar;CREATE TABLE ODS_PRODUCT_CATEGORY (
id string,
p_id string,
name string,
pic_url string,
gmt_create string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_PRODUCT_CATEGORY/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE ODS_PRODUCT_INFO (
product_id string,
category_id string,
product_name string,
gmt_create string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_PRODUCT_INFO/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE ODS_BROWSELOG (
log_time string,
user_id string,
user_ip string,
front_product_url string,
browse_product_url string,
browse_product_tpcode string,
browse_product_code string,
obtain_points string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_BROWSELOG/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE DWD_BROWSELOG (
log_time string,
user_id string,
user_ip string,
front_product_url string,
browse_product_url string,
browse_product_tpcode string,
browse_product_code string,
obtain_points string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/DWD_BROWSELOG/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE DWS_BROWSE_INFO (
log_time string,
user_id string,
user_ip string,
product_name string,
front_product_url string,
browse_product_url string,
first_category_name string,
second_category_name string,
obtain_points string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/DWS_BROWSE_INFO/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);#启动Clickhouse
[root@node1 ~]# service clickhouse-server start#在Clickhouse中创建好对应表
create table dm_product_visit_info(
current_dt String,
window_start String,
window_end String,
first_cat String,
second_cat String,
product String,
product_cnt UInt32
) engine = MergeTree() order by current_dt;二、启动Flink代码
依次启动如下Flink代码：”ProduceKafkaDBDataToODS.scala”、“ProduceKafkaLogDataToODS.scala”、“DimDataToHBase.scala”、“ProduceKafkaODSDataToDWD.scala”、“ProduceBrowseLogToDWS.scala”、“ProcessBrowseLogInfoToDM.scala”代码。各个代码中Kafka Connector属性“scan.startup.mode”设置为“latest-offset”，从最新位置消费数据。
注意：代码执行时可以设置使用内存参数：-Xmx500m -Xms500m
三、启动数据采集接口代码
启动项目“LakeHouseDataPublish”发布数据。
四、启动模拟数据代码
启动项目“LakeHouseMockData”中模拟向数据库中生产数据代码“RTMockDBData.java”，此代码中只需要向MySQL生产用户登录数据即可。
启动项目“LakeHouseMockData”中向日志采集接口生产日志的代码“RTMockUserLogData.java”。
这里如果想和第一个业务一起运行还需要将第一个业务“ProduceUserLogInToDWS.scala”、“ProcessUserLoginInfoToDM.scala”两个代码。
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