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：
代码语言：javascript复制#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-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-USER-LOGIN-TOPIC --partitions 3 --replication-factor 3
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DWS-USER-LOGIN-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_MEMBER_INFO (
id string,
user_id string,
member_growth_score string,
member_level string,
balance string,
gmt_create string,
gmt_modified string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_MEMBER_INFO/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE ODS_MEMBER_ADDRESS (
id string,
user_id string,
province string,
city string,
area string,
address string,
log string,
lat string,
phone_number string,
consignee_name string,
gmt_create string,
gmt_modified string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_MEMBER_ADDRESS/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE ODS_USER_LOGIN (
id string,
user_id string,
ip string,
login_tm string,
logout_tm string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_USER_LOGIN/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE DWD_USER_LOGIN (
id string,
user_id string,
ip string,
login_tm string,
logout_tm string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/DWD_USER_LOGIN/'
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);
CREATE TABLE DWS_USER_LOGIN (
user_id string,
ip string,
gmt_create string,
login_tm string,
logout_tm string,
member_level string,
province string,
city string,
area string,
address string,
member_points string,
balance string,
member_growth_score string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/DWS_USER_LOGIN/'
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_user_login_info(
dt String,
province String,
city String,
user_id String,
login_tm String,
gmt_create String
) engine = MergeTree() order by dt;二、启动Flink代码
依次启动如下Flink代码：”ProduceKafkaDBDataToODS.scala”、“DimDataToHBase.scala”、“ProduceKafkaODSDataToDWD.scala”、“ProduceUserLogInToDWS.scala”、“ProcessUserLoginInfoToDM.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：-Xmx300m -Xms300m
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”。
