# flink-connector-redis
**Repository Path**: hyb11122/flink-connector-redis
## Basic Information
- **Project Name**: flink-connector-redis
- **Description**: redis connector for flink
- **Primary Language**: Java
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 6
- **Created**: 2022-11-01
- **Last Updated**: 2022-11-01
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[EN](README-en.md)
### 项目介绍
基于[bahir-flink](https://github.com/apache/bahir-flink.git)二次开发，相对bahir调整的内容有：
```
1.使用Lettuce替换Jedis,同步读写改为异步读写，大幅度提升了性能
2.增加了Table/SQL API，增加维表查询支持
3.增加查询缓存(支持增量与全量)
4.增加支持整行保存功能，用于多字段的维表关联查询
5.增加限流功能，用于Flink SQL在线调试功能
6.增加支持Flink高版本（包括1.12,1.13,1.14+）
7.统一过期策略等。
```
因bahir使用的flink接口版本较老，所以改动较大，开发过程中参考了腾讯云与阿里云两家产商的流计算产品，取两家之长，并增加了更丰富的功能。
### 支持功能对应redis的操作命令有：
| 插入 | 维表查询 |
|---------------------------------------------------------|------|
| set | get |
| hset | hget |
| rpush lpush | |
| incrBy incrByFloat decrBy hincrBy hincryByFloat zincrby | |
| sadd zadd pfadd(hyperloglog) | |
| publish | |
| zrem srem | |
| del hdel | |
### 使用方法:
在命令行执行 mvn package -DskipTests打包后，将生成的包flink-connector-redis-1.2.1.jar引入flink lib中即可，无需其它设置。
项目依赖Lettuce 6.2.1,如flink环境无lettuce,则使用flink-connector-redis-1.2.1-jar-with-dependencies.jar
开发环境工程直接引用：
```
io.github.jeff-zou
flink-connector-redis
1.2.1
```
### 使用说明：
#### value.data.structure = column(默认)
无需通过primary key来映射redis中的Key，直接由ddl中的字段顺序来决定Key,如：
```
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.
```
#### value.data.structure = row
整行内容保存至value并以'\01'分割
```
create table sink_redis(username VARCHAR, passport VARCHAR) with ('command'='set')
其中username为key, username\01passport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, name\01subject\01score为value.
```
#### with参数说明：
| 字段 | 默认值 | 类型 | 说明 |
| --------------------- | ------ | ------- |--------------------------------------------------------------------------------------------------|
| connector | (none) | String | `redis` |
| host | (none) | String | Redis IP |
| port | 6379 | Integer | Redis 端口 |
| password | null | String | 如果没有设置，则为 null |
| database | 0 | Integer | 默认使用 db0 |
| timeout | 2000 | Integer | 连接超时时间，单位 ms，默认 1s |
| cluster-nodes | (none) | String | 集群ip与端口，当redis-mode为cluster时不为空，如：10.11.80.147:7000,10.11.80.147:7001,10.11.80.147:8000 |
| command | (none) | String | 对应上文中的redis命令 |
| redis-mode | (none) | Integer | mode类型： single cluster |
| lookup.cache.max-rows | -1 | Integer | 查询缓存大小,减少对redis重复key的查询 |
| lookup.cache.ttl | -1 | Integer | 查询缓存过期时间，单位为秒， 开启查询缓存条件是max-rows与ttl都不能为-1 |
| lookup.max-retries | 1 | Integer | 查询失败重试次数 |
| lookup.cache.load-all | false | Boolean | 开启全量缓存,当命令为hget时,将从redis map查询出所有元素并保存到cache中,用于解决缓存穿透问题 |
| sink.max-retries | 1 | Integer | 写入失败重试次数 |
| sink.parallelism | (none) | Integer | 写入并发数 |
| value.data.structure | column | String | column: value值来自某一字段 (如, set: key值取自DDL定义的第一个字段, value值取自第二个字段)
row: 将整行内容保存至value并以'\01'分割 |
##### 在线调试SQL时，用于限制sink资源使用的参数:
| Field | Default | Type | Description |
|-----------------------|---------|---------|----------------------------------------|
| sink.limit | false | Boolean | 限制开头 |
| sink.limit.max-num | 10000 | Integer | taskmanager内每个slot可以写的最大数据量 |
| sink.limit.interval | 100 | String | taskmanager内每个slot写入数据间隔 milliseconds |
| sink.limit.max-online | 30 * 60 * 1000L | Long | taskmanager内每个slot最大在线时间, milliseconds |
#### 集群类型为sentinel时额外连接参数:
| 字段 | 默认值 | 类型 | 说明 |
| ------------------ | ------ | ------ |-----|
| master.name | (none) | String | 主名 |
| sentinels.info | (none) | String | 如：10.11.80.147:7000,10.11.80.147:7001,10.11.80.147:8000 |
| sentinels.password | none) | String | |
### 数据类型转换
| flink type | redis row converter |
| ------------ | ------------------------------------------------------------ |
| CHAR | String |
| VARCHAR | String |
| String | String |
| BOOLEAN | String String.valueOf(boolean val)
boolean Boolean.valueOf(String str) |
| BINARY | String Base64.getEncoder().encodeToString
byte[] Base64.getDecoder().decode(String str) |
| VARBINARY | String Base64.getEncoder().encodeToString
byte[] Base64.getDecoder().decode(String str) |
| DECIMAL | String BigDecimal.toString
DecimalData DecimalData.fromBigDecimal(new BigDecimal(String str),int precision, int scale) |
| TINYINT | String String.valueOf(byte val)
byte Byte.valueOf(String str) |
| SMALLINT | String String.valueOf(short val)
short Short.valueOf(String str) |
| INTEGER | String String.valueOf(int val)
int Integer.valueOf(String str) |
| DATE | String the day from epoch as int
date show as 2022-01-01 |
| TIME | String the millisecond from 0'clock as int
time show as 04:04:01.023 |
| BIGINT | String String.valueOf(long val)
long Long.valueOf(String str) |
| FLOAT | String String.valueOf(float val)
float Float.valueOf(String str) |
| DOUBLE | String String.valueOf(double val)
double Double.valueOf(String str) |
| TIMESTAMP | String the millisecond from epoch as long
timestamp TimeStampData.fromEpochMillis(Long.valueOf(String str)) |
### 使用示例:
- ##### 维表查询：
```
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
```
- #### 多字段的维表关联查询
很多情况维表有多个字段,本实例展示如何利用'value.data.structure'='row'写多字段并关联查询。
```
-- 创建表
create table sink_redis(uid VARCHAR,score double,score2 double )
with ( 'connector' = 'redis',
'host' = '10.11.69.176',
'port' = '6379',
'redis-mode' = 'single',
'password' = '****',
'command' = 'SET',
'value.data.structure' = 'row'); -- 'value.data.structure'='row':整行内容保存至value并以'\01'分割
-- 写入测试数据，score、score2为需要被关联查询出的两个维度
insert into sink_redis select * from (values ('1', 10.3, 10.1));
-- 在redis中，value的值为: "1\x0110.3\x0110.1" --
-- 写入结束 --
-- create join table --
create table join_table with ('command'='get', 'value.data.structure'='row') like sink_redis
-- create result table --
create table result_table(uid VARCHAR, username VARCHAR, score double, score2 double) with ('connector'='print')
-- create source table --
create table source_table(uid VARCHAR, username VARCHAR, proc_time as procTime()) with ('connector'='datagen', 'fields.uid.kind'='sequence', 'fields.uid.start'='1', 'fields.uid.end'='2')
-- 关联查询维表，获得维表的多个字段值 --
insert
into
result_table
select
s.uid,
s.username,
j.score, -- 来自维表
j.score2 -- 来自维表
from
source_table as s
join join_table for system_time as of s.proc_time as j on
j.uid = s.uid
result:
2> +I[2, 1e0fe885a2990edd7f13dd0b81f923713182d5c559b21eff6bda3960cba8df27c69a3c0f26466efaface8976a2e16d9f68b3, null, null]
1> +I[1, 30182e00eca2bff6e00a2d5331e8857a087792918c4379155b635a3cf42a53a1b8f3be7feb00b0c63c556641423be5537476, 10.3, 10.1]
```
- ##### DataStream查询方式
示例代码路径: src/test/java/org.apache.flink.streaming.connectors.redis.datastream.DataStreamTest.java
hset示例，相当于redis命令：*hset tom math 150*
```
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 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");
```
- ##### redis-cluster写入示例
示例代码路径: src/test/java/org.apache.flink.streaming.connectors.redis.table.SQLTest.java
set示例，相当于redis命令： *set test test11*
```
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();
```
### 开发与测试环境
ide: IntelliJ IDEA
code format: google-java-format + Save Actions
code check: CheckStyle
flink 1.12/1.13/1.14+
jdk1.8 Lettuce 6.2.1
### 如果需要flink 1.12版本支持，请切换到分支flink-1.12(注：1.12使用jedis)
```
io.github.jeff-zou
flink-connector-redis
1.1.1-1.12
```