flink-connector-redis支持select查询

1 项目介绍

基于bahir-flink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select/维表join查询支持
3.增加关联查询缓存(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
8.支持flink cdc删除及其它RowKind.DELETE
9.支持select查询

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。

注：redis不支持两段提交无法实现刚好一次语义。

2 使用方法:

2.1 工程直接引用

项目依赖Lettuce(6.2.1)及netty-transport-native-epoll(4.1.82.Final),如flink环境有这两个包,则使用flink-connector-redis-1.3.2.jar，
否则使用flink-connector-redis-1.4.1-jar-with-dependencies.jar。

<dependency><groupId>io.github.jeff-zou</groupId><artifactId>flink-connector-redis</artifactId><!-- 没有单独引入项目依赖Lettuce netty-transport-native-epoll依赖时 --><!--            <classifier>jar-with-dependencies</classifier>--><version>1.4.1</version>
</dependency>

2.2 自行打包

打包命令： mvn package -DskipTests,将生成的包放入flink lib中即可，无需其它设置。

2.3 使用示例

-- 创建redis表示例
create table redis_table (name varchar, age int) with ('connector'='redis', 'host'='10.11.69.176', 'port'='6379','password'='test123', 'redis-mode'='single','command'='set');
-- 写入  insert into redis_table select * from (values('test', 1));-- 查询  insert into redis_table select name,age + 1 from redis_table /*+ options('scan.key'='test') */create table gen_table (age int , level int, proctime as procTime()) with ('connector'='datagen','fields.age.kind' = 'sequence','fields.age.start' = '2','fields.age.end' = '2','fields.level.kind' = 'sequence','fields.level.start' = '10','fields.level.end' = '10'); -- 关联查询 
insert into redis_table select 'test', j.age + 10 from gen_table s left join redis_table  for system_time as of proctime as j
on j.name = 'test'

3 参数说明：

3.1 主要参数：

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 sentinel
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.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
max.retries	1	Integer	写入失败重试次数
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
set.if.absent	false	Boolean	在key不存在时才写入,只对set hset有效
io.pool.size	(none)	Integer	Lettuce内netty的io线程池大小,默认情况下该值为当前JVM可用线程数，并且大于2
event.pool.size	(none)	Integer	Lettuce内netty的event线程池大小 ,默认情况下该值为当前JVM可用线程数，并且大于2
scan.key	(none)	String	查询时redis key
scan.addition.key	(none)	String	查询时限定redis key,如map结构时的hashfield
scan.range.start	(none)	Integer	查询list结构时指定lrange start
scan.range.stop	(none)	Integer	查询list结构时指定lrange start
scan.count	(none)	Integer	查询set结构时指定srandmember count

3.1.1 command值与redis命令对应关系：

command值	写入	查询	维表关联	删除(Flink CDC等产生的RowKind.delete)
set	set	get	get	del
hset	hset	hget	hget	hdel
get	set	get	get	del
hset	hset	hget	hget	hdel
rpush	rpush	lrange
lpush	lpush	lrange
incrBy incrByFloat	incrBy incrByFloat	get	get	写入相对值，如:incrby 2 -> incryby -2
hincrBy hincryByFloat	hincrBy hincryByFloat	hget	hget	写入相对值，如:hincrby 2 -> hincryby -2
zincrby	zincrby	zscore	zscore	写入相对值，如:zincrby 2 -> zincryby -2
sadd	sadd	srandmember 10		srem
zadd	zadd	zscore	zscore	zrem
pfadd(hyperloglog)	pfadd(hyperloglog)
publish	publish
zrem	zrem	zscore	zscore
srem	srem	srandmember 10
del	del	get	get
hdel	hdel	hget	hget
decrBy	decrBy	get	get

注：为空表示不支持

3.1.2 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.

3.1.3 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.

3.2 sink时ttl相关参数

Field	Default	Type	Description
ttl	(none)	Integer	key过期时间(秒),每次sink时会设置ttl
ttl.on.time	(none)	String	key的过期时间点,格式为LocalTime.toString(), eg: 10:00 12:12:01,当ttl未配置时才生效
ttl.key.not.absent	false	boolean	与ttl一起使用,当key不存在时才设置ttl

3.3 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	是否打开限制
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

3.4 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	sentinel进程密码

4 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))

5 使用示例:

5.1 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 intosink_table
selects.username,s.level,d.age
fromsource_table s
left join dim_table for system_time as of s.proctime as d ond.name = s.usernameand d.level = s.level;
-- username为3那一行会关联到redis内的值，输出为： 3,3,100

5.2 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 --
insertintoresult_table
selects.uid,s.username,j.score, -- 来自维表j.score2 -- 来自维表
fromsource_table as s
join join_table for system_time as of s.proc_time as j onj.uid = s.uidresult:
2> +I[2, 1e0fe885a2990edd7f13dd0b81f923713182d5c559b21eff6bda3960cba8df27c69a3c0f26466efaface8976a2e16d9f68b3, null, null]
1> +I[1, 30182e00eca2bff6e00a2d5331e8857a087792918c4379155b635a3cf42a53a1b8f3be7feb00b0c63c556641423be5537476, 10.3, 10.1]

5.3 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<GenericRowData> dataStream = env.fromElements(genericRowData, genericRowData);RedisSinkOptions redisSinkOptions =new RedisSinkOptions.Builder().setMaxRetryTimes(3).build();FlinkConfigBase conf =new FlinkSingleConfig.Builder().setHost(REDIS_HOST).setPort(REDIS_PORT).setPassword(REDIS_PASSWORD).build();RedisSinkFunction redisSinkFunction =new RedisSinkFunction<>(conf, redisMapper, redisSinkOptions, resolvedSchema);dataStream.addSink(redisSinkFunction).setParallelism(1);env.execute("RedisSinkTest");

5.4 redis-cluster写入示例

示例代码路径: src/test/java/org.apache.flink.streaming.connectors.redis.table.SQLInsertTest.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();

6 解决问题联系我

https://github.com/jeff-zou/flink-connector-redis.git

7 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

8 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)

<dependency><groupId>io.github.jeff-zou</groupId><artifactId>flink-connector-redis</artifactId><version>1.1.1-1.12</version>
</dependency>