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。
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-1.4.2.jar，
否则使用flink-connector-redis-1.4.2-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>1.4.2</version>
</dependency>
打包命令： mvn package,将生成的包放入flink lib中即可，无需其它设置。
-- 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'
| 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/查询失败重试次数 |
| 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 |
| 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
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.
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.
| 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 |
| 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 |
| 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 |
| 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)) |
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', '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
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]
-
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_SINGLE);
configuration.setString(REDIS_COMMAND, RedisCommand.HSET.name());
configuration.setInteger(TTL, 10);
RedisSinkMapper redisMapper = new RowRedisSinkMapper(RedisCommand.HSET, configuration);
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
BinaryRowData binaryRowData = new BinaryRowData(3);
BinaryRowWriter binaryRowWriter = new BinaryRowWriter(binaryRowData);
binaryRowWriter.writeString(0, StringData.fromString("tom"));
binaryRowWriter.writeString(1, StringData.fromString("math"));
binaryRowWriter.writeString(2, StringData.fromString("152"));
DataStream<BinaryRowData> dataStream = env.fromElements(binaryRowData, binaryRowData);
List<String> columnNames = Arrays.asList("name", "subject", "scope");
List<DataType> columnDataTypes =
Arrays.asList(DataTypes.STRING(), DataTypes.STRING(), DataTypes.STRING());
ResolvedSchema resolvedSchema = ResolvedSchema.physical(columnNames, columnDataTypes);
FlinkConfigBase conf =
new FlinkSingleConfig.Builder()
.setHost(REDIS_HOST)
.setPort(REDIS_PORT)
.setPassword(REDIS_PASSWORD)
.build();
RedisSinkFunction redisSinkFunction =
new RedisSinkFunction<>(conf, redisMapper, resolvedSchema, configuration);
dataStream.addSink(redisSinkFunction).setParallelism(1);
env.execute("RedisSinkTest");
-
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();
ide: IntelliJ IDEA
code format: google-java-format + Save Actions
flink 1.12/1.13/1.14+
jdk1.8 Lettuce 6.2.1
Pull Request需要提交至dev分支
提交前请使用mvn spotless:apply进行代码格式化,然后使用maven package打包确认所有测试用例能通过。
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>
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