Flink SQL -- CheckPoint

1、开启CheckPoint

checkpoint可以定时将flink任务的状态持久化到hdfs中，任务执行失败重启可以保证中间结果不丢失

# 修改flink配置文件
vim flink-conf.yaml# checkppint 间隔时间
execution.checkpointing.interval: 1min
# 任务手动取消时保存checkpoint
execution.checkpointing.externalized-checkpoint-retention: RETAIN_ON_CANCELLATION
# 同时允许1个checkpoint执行
execution.checkpointing.max-concurrent-checkpoints: 1
execution.checkpointing.min-pause: 0
# 数据处理的语义
execution.checkpointing.mode: EXACTLY_ONCE
# checkpoint超时时间
execution.checkpointing.timeout: 10min
execution.checkpointing.tolerable-failed-checkpoints: 0
execution.checkpointing.unaligned: false
# 状态后端（保存状态的位置，hashmap：内存）
state.backend: hashmap
# checkpoint路径
state.checkpoints.dir: hdfs://master:9000/flink/checkpoint

2、编写一个Flnik SQL 脚本：

vim word_count.sql

-- 实时从kafka中读取单词，统计单词的数量，将结果保存到mysql中-- 1、创建source表
CREATE TABLE words (word STRING
) WITH ('connector' = 'kafka','topic' = 'words', -- 数据的topic'properties.bootstrap.servers' = 'master:9092,node1:9092,node2:9092', -- broker 列表'properties.group.id' = 'testGroup', -- 消费者组'scan.startup.mode' = 'earliest-offset', -- 读取数据的位置earliest-offset latest-offset'format' = 'csv' -- 读取数据的格式
);-- 2、创建sink表
CREATE TABLE word_count (word STRING,num BIGINT,PRIMARY KEY (word) NOT ENFORCED -- 主键
) WITH ('connector' = 'jdbc','url' = 'jdbc:mysql://master:3306/student','table-name' = 'word_count', -- 需要手动到mysql中创建表'username' ='root','password' ='123456'
);-- 3、编写sql处理数据将结果保存到sink表中
insert into word_count
select 
word,
count(1) as num
from
words
group by word;

3、使用sq-client.sh -f 启动脚本

sql-client.sh -f word_count.sql

 4、当任务失败的时候再重新启动任务：

-- 1、获取checkpoint的路径
/file/checkpoint/47ee348d8c9edadadfc770cf7de8e7ee/chk-23-- 2、再sql脚本中增加参数，增加到sql脚本的insert into 的前面
-- 指定任务会的checkpoint的地址
SET'execution.savepoint.path'='hdfs://master:9000/file/checkpoint/47ee348d8c9edadadfc770cf7de8e7ee/chk-23';-- 3、启动sql任务
sql-client.sh -f word_count.sql