Skip to main content
版本:1.5.0

使用示例

为了更容易创建 InLong Sort 作业,这里我们列出了一些数据流配置示例。下面将介绍 InLong Sort 的 SQL、Dashboard、Manager 客户端工具的使用。

环境要求

  • Apache Flink 1.13.5
  • MySQL
  • Apache Kafka
  • Apache Hadoop
  • Apache Hive 3.x

准备 InLong Sort 和 Connectors

你可以通过参考部署指引准备 InLong Sort 和数据节点 Connectors。

使用 SQL API 方式

示例构建了 MySQL --> Kafka --> Hive 的数据流,为了便于理解流程执行过程进行了拆解。

读 MySQL 写 Kafka

单表同步配置示例如下:

./bin/flink run -c org.apache.inlong.sort.Entrance apache-inlong-[version]-bin/inlong-sort/sort-dist-[version].jar \
--sql.script.file mysql-to-kafka.sql
  • mysql-to-kafka.sql
CREATE TABLE `table_1`(
PRIMARY KEY (`id`) NOT ENFORCED,
`id` BIGINT,
`name` STRING,
`age` INT,
`salary` FLOAT,
`ts` TIMESTAMP(2),
`event_type` STRING)
WITH (
'append-mode' = 'true',
'connector' = 'mysql-cdc-inlong',
'hostname' = 'localhost',
'username' = 'root',
'password' = 'password',
'database-name' = 'dbName',
'table-name' = 'tableName'
);

CREATE TABLE `table_2`(
`id` BIGINT,
`name` STRING,
`age` INT,
`salary` FLOAT,
`ts` TIMESTAMP(2))
WITH (
'topic' = 'topicName',-- Your kafka topic
'properties.bootstrap.servers' = 'localhost:9092',
'connector' = 'kafka',
'json.timestamp-format.standard' = 'SQL',
'json.encode.decimal-as-plain-number' = 'true',
'json.map-null-key.literal' = 'null',
'json.ignore-parse-errors' = 'true',
'json.map-null-key.mode' = 'DROP',
'format' = 'json',
'json.fail-on-missing-field' = 'false'
);

INSERT INTO `table_2`
SELECT
`id` AS `id`,
`name` AS `name`,
`age` AS `age`,
CAST(NULL as FLOAT) AS `salary`,
`ts` AS `ts`
FROM `table_1`;

读 Kafka 写 Hive

警告

需要在 hive 中先创建 user 表。

./bin/flink run -c org.apache.inlong.sort.Entrance apache-inlong-[version]-bin/inlong-sort/sort-dist-[version].jar \
--sql.script.file kafka-to-hive.sql
  • kafka-to-hive.sql
CREATE TABLE `table_1`(
`id` BIGINT,
`name` STRING,
`age` INT,
`salary` FLOAT,
`ts` TIMESTAMP(2)
WITH (
'topic' = 'topicName',-- Your kafka topic
'properties.bootstrap.servers' = 'localhost:9092',
'connector' = 'kafka',
'scan.startup.mode' = 'earliest-offset',
'json.timestamp-format.standard' = 'SQL',
'json.encode.decimal-as-plain-number' = 'true',
'json.map-null-key.literal' = 'null',
'json.ignore-parse-errors' = 'true',
'json.map-null-key.mode' = 'DROP',
'format' = 'json',
'json.fail-on-missing-field' = 'false',
'properties.group.id' = 'groupId'-- Your group id
);

CREATE TABLE `user`(
`id` BIGINT,
`name` STRING,
`age` INT,
`salary` FLOAT,
`ts` TIMESTAMP(9))
WITH (
'connector' = 'hive',
'default-database' = 'default',
'hive-version' = '3.1.2',
'hive-conf-dir' = 'hdfs://ip:9000/.../hive-site.xml' -- Put your hive-site.xml into HDFS
);

INSERT INTO `user`
SELECT
`id` AS `id`,
`name` AS `name`,
`age` AS `age`,
CAST(NULL as FLOAT) AS `salary`,
`ts` AS `ts`
FROM `table_1`;

其它 Connectors

Extract NodeLoad Node 部分,有更丰富的 connector 可以使用,可根据使用场景参考配置。