目录
1. 准备工作
生成数据
创建数据表
2. 创建数据表
创建数据源表
创建数据目标表
3. 计算
WITH子句
1. 准备工作
生成数据
source kafka json 数据格式 ：
topic case_kafka_mysql：
{"ts": "20201011","id": 8,"price_amt":211}
topic flink_test_2：
{"id": 8,"coupon_price_amt":100}
注意：针对双流中的每条记录都发触发
topic: case_kafka_mysql
docker exec -it 192d1369463a bash
bash-5.1# cd /opt/kafka_2.12-2.5.0/bin
bash-5.1# ./kafka-console-producer.sh --broker-list localhost:9092 --topic case_kafka_mysql
>{"ts": "20201011","id": 8,"price_amt":211}
topic: flink_test_2
docker exec -it 192d1369463a bash
bash-5.1# cd /opt/kafka_2.12-2.5.0/bin
bash-5.1# ./kafka-console-producer.sh --broker-list localhost:9092 --topic flink_test_2
>{"id": 8,"coupon_price_amt":100}
创建数据表
mysql 建表语句
CREATE TABLE `sync_test_2` (
`id` bigint(11) NOT NULL AUTO_INCREMENT,
`ts` varchar(64) DEFAULT NULL,
`total_gmv` bigint(11) DEFAULT NULL,
PRIMARY KEY (`id`),
UNIQUE KEY `uidx` (`ts`) USING BTREE
) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8mb4;
CREATE TABLE `sync_test_22` (
`id` bigint(11) NOT NULL AUTO_INCREMENT,
`ts` varchar(64) DEFAULT NULL,
`coupon_ratio` double DEFAULT NULL,
PRIMARY KEY (`id`),
UNIQUE KEY `uidx` (`ts`) USING BTREE
) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8mb4;
2. 创建数据表
创建数据源表
create table flink_test_2_1 (
id BIGINT,
ts VARCHAR,
price_amt BIGINT,
proctime AS PROCTIME ()
)
with (
'connector' = 'kafka',
'topic' = 'case_kafka_mysql',
'properties.bootstrap.servers' = '127.0.0.1:9092',
'properties.group.id' = 'flink_gp_test2-1',
'scan.startup.mode' = 'earliest-offset',
'format' = 'json',
'json.fail-on-missing-field' = 'false',
'json.ignore-parse-errors' = 'true',
'properties.zookeeper.connect' = '127.0.0.1:2181/kafka'
);
create table flink_test_2_2 (
id BIGINT,
coupon_price_amt BIGINT,
proctime AS PROCTIME ()
)
with (
'connector' = 'kafka',
'topic' = 'flink_test_2',
'properties.bootstrap.servers' = '127.0.0.1:9092',
'properties.group.id' = 'flink_gp_test2-2',
'scan.startup.mode' = 'earliest-offset',
'format' = 'json',
'json.fail-on-missing-field' = 'false',
'json.ignore-parse-errors' = 'true',
'properties.zookeeper.connect' = '127.0.0.1:2181/kafka'
);
关键配置的说明：
json.fail-on-missing-field：在json缺失字段时是否报错
json.ignore-parse-errors：在解析json失败时是否报错
一般无法保证json格式，所以以上两个配置是比较重要的。
创建数据目标表
CREATE TABLE sync_test_2 (
ts string,
total_gmv bigint,
PRIMARY KEY (ts) NOT ENFORCED
) WITH (
'connector' = 'jdbc',
'url' = 'jdbc:mysql://127.0.0.1:3306/db01?characterEncoding=UTF-8',
'table-name' = 'sync_test_2',
'username' = 'root',
'password' = 'Admin'
);
CREATE TABLE sync_test_22 (
ts string,
coupon_ration bigint,
PRIMARY KEY (ts) NOT ENFORCED
) WITH (
'connector' = 'jdbc',
'url' = 'jdbc:mysql://127.0.0.1:3306/db01?characterEncoding=UTF-8',
'table-name' = 'sync_test_2',
'username' = 'root',
'password' = 'Admin'
);
3. 计算
一个作业中写入一个Sink或多个Sink。
说明 写入多个Sink语句时，需要以BEGIN STATEMENT SET;开头，以END;结尾。
BEGIN STATEMENT SET; --写入多个Sink时，必填。
INSERT INTO sync_test_2
SELECT
ts,
SUM(price_amt - coupon_price_amt) AS total_gmv
FROM
(
SELECT
a.ts as ts,
a.price_amt as price_amt,
b.coupon_price_amt as coupon_price_amt
FROM
flink_test_2_1 as a
LEFT JOIN flink_test_2_2 b on b.id = a.id
)
GROUP BY ts;
INSERT INTO sync_test_22
SELECT
ts,
sum(coupon_price_amt)/sum(amount) AS coupon_ration
FROM
(
SELECT
a.ts as ts,
a.price_amt as price_amt,
b.coupon_price_amt as coupon_price_amt
FROM
flink_test_2_1 as a
LEFT JOIN flink_test_2_2 b on b.id = a.id
)
GROUP BY ts;;
END; --写入多个Sink时，必填。
WITH子句
WITH提供了一种编写辅助语句以用于更大的查询的方法。这些语句通常被称为公共表表达式（CTE），可以被视为定义仅针对一个查询存在的临时视图。
改写上述查询：
BEGIN STATEMENT SET; --写入多个Sink时，必填。
with orders_with_coupon AS (
SELECT
a.ts as ts,
a.price_amt as price_amt,
b.coupon_price_amt as coupon_price_amt
FROM
flink_test_2_1 as a
LEFT JOIN flink_test_2_2 b on b.id = a.id
)
INSERT INTO sync_test_2
SELECT
ts,
SUM(price_amt - coupon_price_amt) AS total_gmv
FROM orders_with_coupon
GROUP BY ts;
INSERT INTO sync_test_22
SELECT
ts,
coupon_price_amt/price_amt AS coupon_ration
FROM orders_with_coupon
GROUP BY ts;;
END; --写入多个Sink时，必填。
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