flink 维表查询 redis 之 flink-connector-redis

<dependency>
    <groupId>io.github.jeff-zou</groupId>
    <artifactId>flink-connector-redis</artifactId>
    <version>1.0.10</version>
</dependency>

create table sink_redis(username VARCHAR, passport VARCHAR)  with ('command'='set') 
其中username为key, passport为value.
create table sink_redis(name VARCHAR, subject VARCHAR, score VARCHAR)  with ('command'='hset') 
其中name为map结构的key, subject为field, score为value.

create table sink_redis(name varchar, level varchar, age varchar) with ( 'connector'='redis', 'host'='10.11.80.147','port'='7001', 'redis-mode'='single','password'='******','command'='hset');
-- 先在redis中插入数据,相当于redis命令： hset 3 3 100 --
insert into sink_redis select * from (values ('3', '3', '100'));
                
create table dim_table (name varchar, level varchar, age varchar) with ('connector'='redis', 'host'='10.11.80.147','port'='7001', 'redis-mode'='single', 'password'='*****','command'='hget', 'maxIdle'='2', 'minIdle'='1', 'lookup.cache.max-rows'='10', 'lookup.cache.ttl'='10', 'lookup.max-retries'='3');
    
-- 随机生成10以内的数据作为数据源 --
-- 其中有一条数据会是： username = 3  level = 3, 会跟上面插入的数据关联 -- 
create table source_table (username varchar, level varchar, proctime as procTime()) with ('connector'='datagen',  'rows-per-second'='1',  'fields.username.kind'='sequence',  'fields.username.start'='1',  'fields.username.end'='10', 'fields.level.kind'='sequence',  'fields.level.start'='1',  'fields.level.end'='10');
create table sink_table(username varchar, level varchar,age varchar) with ('connector'='print');
insert into
	sink_table
select
	s.username,
	s.level,
	d.age
from
	source_table s
left join dim_table for system_time as of s.proctime as d on
	d.name = s.username
	and d.level = s.level;
-- username为3那一行会关联到redis内的值，输出为： 3,3,100	

Configuration configuration = new Configuration();
configuration.setString(REDIS_MODE, REDIS_CLUSTER);
configuration.setString(REDIS_COMMAND, RedisCommand.HSET.name());
RedisSinkMapper redisMapper = (RedisSinkMapper)RedisHandlerServices
.findRedisHandler(RedisMapperHandler.class, configuration.toMap())
.createRedisMapper(configuration);
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
GenericRowData genericRowData = new GenericRowData(3);
genericRowData.setField(0, "tom");
genericRowData.setField(1, "math");
genericRowData.setField(2, "152");
DataStream<GenericRowData> dataStream = env.fromElements(genericRowData, genericRowData);
RedisCacheOptions redisCacheOptions = new RedisCacheOptions.Builder().setCacheMaxSize(100).setCacheTTL(10L).build();
FlinkJedisConfigBase conf = getLocalRedisClusterConfig();
RedisSinkFunction redisSinkFunction = new RedisSinkFunction<>(conf, redisMapper, redisCacheOptions);
dataStream.addSink(redisSinkFunction).setParallelism(1);
env.execute("RedisSinkTest");

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, environmentSettings);
String ddl = "create table sink_redis(username VARCHAR, passport VARCHAR) with ( 'connector'='redis', " +
              "'cluster-nodes'='10.11.80.147:7000,10.11.80.147:7001','redis- mode'='cluster','password'='******','command'='set')" ;
tEnv.executeSql(ddl);
String sql = " insert into sink_redis select * from (values ('test', 'test11'))";
TableResult tableResult = tEnv.executeSql(sql);
tableResult.getJobClient().get()
.getJobExecutionResult()
.get();

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flink 1.13
jdk1.8
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