FlinkSQL 平台

背景

由于公司内部需求较多，并不想每次都写一个 streaming 程序，故而开始搭建 flinksql 平台，基于 jdk1.8，flink1.12.x

效果

传一个 sql 文件给 jar 包，然后 sql 文件内的 sql 将自动执行

jar 包 vs web 界面

调研了基于 web 的 zeppline

1. zeppline 设计的初衷其实是为了交互式分析
2. 基于 zeppline rest api 与现有的监控不兼容，需要修改现有监控的代码
3. 虽然带有 web 界面的对用户很是友好，对于分析人员来说，是一个不错的选择，但对于开发人员来说，真正的线上长时间的运行程序，开发成 HA 的 server 还是有必要的

基于以上 3 点最终选择 jar 作为最终的方式

使用

1. 将 sql 写入 xxx.sql 文件中，如

CREATE TEMPORARY FUNCTION MillisecondsToDateStr AS 'io.github.shengjk.udf.MillisecondsToDateStr' LANGUAGE JAVA;


-- ExecutionCheckpointingOptions
set execution.checkpointing.mode=EXACTLY_ONCE;
set execution.checkpointing.timeout=30 min;--  30min
set execution.checkpointing.interval=1 min ; -- 1min
set execution.checkpointing.externalized-checkpoint-retention=RETAIN_ON_CANCELLATION;

-- ExecutionConfigOptions
set table.exec.state.ttl=1 day;  -- 1 day
set table.exec.mini-batch.enabled=true; -- enable mini-batch optimization
set table.exec.mini-batch.allow-latency=1 s; -- 1s
set table.exec.mini-batch.size=1000;
set table.exec.sink.not-null-enforcer=drop;

-- -- dadadadadada
CREATE TABLE orders
(
   status      int,
   courier_id  bigint,
   id          bigint,
   finish_time BIGINT
)
WITH (
   'connector' = 'kafka','topic' = 'canal_monitor_order',
   'properties.bootstrap.servers' = 'localhost:9092','properties.group.id' = 'testGroup',
   'format' = 'ss-canal-json','ss-canal-json.table.include' = 'orders','scan.startup.mode' = 'earliest-offset');

-- flink.partition-discovery.interval-millis;
CREATE TABLE infos
(
   info_index int,
   order_id   bigint
)
WITH (
   'connector' = 'kafka','topic' = 'canal_monitor_order',
   'properties.bootstrap.servers' = 'localhost:9092','properties.group.id' = 'testGroup',
   'format' = 'ss-canal-json','ss-canal-json.table.include' = 'infos','scan.startup.mode' = 'earliest-offset');


CREATE TABLE redisCache
(
   finishOrders BIGINT,
   courier_id   BIGINT,
   dayStr       String
)
WITH (
   'connector' = 'redis',
   'hostPort'='localhost:6400',
   'keyType'='hash',
   'keyTemplate'='test2_${courier_id}',
   'fieldTemplate'='${dayStr}',
   'valueNames'='finishOrders',
   'expireTime'='259200');

create view temp as
select o.courier_id,
      (CASE
           WHEN sum(infosMaxIndex.info_index) is null then 0
           else sum(infosMaxIndex.info_index) end) finishOrders,
      o.status,
      dayStr
from ((select courier_id,
             id,
             last_value(status)                             status,
             MillisecondsToDateStr(finish_time, 'yyyyMMdd') dayStr
      from orders
      where status = 60
      group by courier_id, id, MillisecondsToDateStr(finish_time, 'yyyyMMdd'))) o
        left join (select max(info_index) info_index, order_id
                   from infos
                   group by order_id) infosMaxIndex on o.id = infosMaxIndex.order_id
group by o.courier_id, o.status, dayStr;


INSERT INTO redisCache SELECT finishOrders,courier_id,dayStr FROM temp;

2. 将 flinksql-platform 打包并上传至服务器
3. 将必要的 connector jar 放入到相应的目录下
4. 执行，如

flink-1.12.0/bin/flink  run -p 3 -yt ./flinkjar/  -C file:///home/shengjk/flinkjar/test-udf.jar -C file:///home/shengjk/flinkjar/jedis-2.10.2.jar  -m yarn-cluster -ynm sqlDemo  -c io.github.shengjk.Main ./flinksql-platform-1.0-SNAPSHOT.jar --sqlPath ./xxx.sql

其中
-C 添加 udfJar 等第三方 jar 包 -C 参数apply到了client端生成的JobGraph里，然后提交JobGraph来运行的
-yt 目录 将 udfJar 等第三方 jar 包提交到 TaskManager 上

总括

更详细的内容，请移步 flinksql-platform