# Using Flink CDC to synchronize data from MySQL sharding tables and build real-time data lake
For OLTP databases, to deal with a huge number of data in a single table, we usually do database and table sharding to get better throughput.
But sometimes, for convenient analysis, we need to merge them into one table when loading them to data warehouse or data lake.
This tutorial will show how to use Flink CDC to build a real-time data lake for such a scenario.
You can walk through the tutorial easily in the docker environment. The entire process uses standard SQL syntax without a single line of Java/Scala code or IDE installation.
The following sections will take the pipeline from MySQL to [Iceberg](https://iceberg.apache.org/) as an example. The overview of the architecture is as follows:
You can also use other data sources like Oracle/Postgres and sinks like Hudi to build your own pipeline.
## Preparation
Prepare a Linux or MacOS computer with Docker installed.
### Starting components required
The components required in this tutorial are all managed in containers, so we will use `docker-compose` to start them.
Create `docker-compose.yml` file using following contents:
```
version: '2.1'
services:
sql-client:
user: flink:flink
image: yuxialuo/flink-sql-client:1.13.2.v1
depends_on:
- jobmanager
- mysql
environment:
FLINK_JOBMANAGER_HOST: jobmanager
MYSQL_HOST: mysql
volumes:
- shared-tmpfs:/tmp/iceberg
jobmanager:
user: flink:flink
image: flink:1.13.2-scala_2.11
ports:
- "8081:8081"
command: jobmanager
environment:
- |
FLINK_PROPERTIES=
jobmanager.rpc.address: jobmanager
volumes:
- shared-tmpfs:/tmp/iceberg
taskmanager:
user: flink:flink
image: flink:1.13.2-scala_2.11
depends_on:
- jobmanager
command: taskmanager
environment:
- |
FLINK_PROPERTIES=
jobmanager.rpc.address: jobmanager
taskmanager.numberOfTaskSlots: 2
volumes:
- shared-tmpfs:/tmp/iceberg
mysql:
image: debezium/example-mysql:1.1
ports:
- "3306:3306"
environment:
- MYSQL_ROOT_PASSWORD=123456
- MYSQL_USER=mysqluser
- MYSQL_PASSWORD=mysqlpw
volumes:
shared-tmpfs:
driver: local
driver_opts:
type: "tmpfs"
device: "tmpfs"
```
The Docker Compose environment consists of the following containers:
- SQL-Client: Flink SQL Client, used to submit queries and visualize their results.
- Flink Cluster: a Flink JobManager and a Flink TaskManager container to execute queries.
- MySQL: mainly used as a data source to store the sharding table.
***Note:***
1. To simply this tutorial, the jar packages required has been packaged into the SQL-Client container. You can see how it's built in [GitHub](https://github.com/luoyuxia/flink-cdc-tutorial/tree/main/flink-cdc-iceberg-demo/sql-client).
If you want to run with your own Flink environment, remember to download the following packages and then put them to `FLINK_HOME/lib/`.
Currently, the Iceberg official `iceberg-flink-runtime` jar that supports Flink 1.13 isn't released.
Here, we provide a `iceberg-flink-runtime` jar supporting Flink 1.13, which is built based on the master branch of Iceberg.
You can download the `iceberg-flink-runtime` jar from the [apache official repository](https://repo.maven.apache.org/maven2/org/apache/iceberg/iceberg-flink-runtime/) once Iceberg 0.13.0 is released.
2. All the following commands involving `docker-compose` should be executed in the directory of the `docker-compose.yml` file.
To start all containers, run the following command in the directory that contains the `docker-compose.yml` file:
```shell
docker-compose up -d
```
This command automatically starts all the containers defined in the Docker Compose configuration in a detached mode. Run `docker ps` to check whether these containers are running properly.
We can also visit [http://localhost:8081/](http://localhost:8081/) to see if Flink is running normally.
Checkpoint is disabled by default, we need to enable it to commit Iceberg transactions.
Besides, the beginning of mysql-cdc binlog phase also requires waiting a complete checkpoint to avoid disorder of binlog records.
```sql
-- Flink SQL
Flink SQL> SET execution.checkpointing.interval = 3s;
```
2. Create MySQL sharding source table
Create a source table that captures the data from the logical sharding table `user`. Here, we use regex to match all the physical tables.
Besides, the table defines metadata column to identify which database/table the record comes from.
```sql
-- Flink SQL
Flink SQL> CREATE TABLE user_source (
database_name STRING METADATA VIRTUAL,
table_name STRING METADATA VIRTUAL,
`id` DECIMAL(20, 0) NOT NULL,
name STRING,
address STRING,
phone_number STRING,
email STRING,
PRIMARY KEY (`id`) NOT ENFORCED
) WITH (
'connector' = 'mysql-cdc',
'hostname' = 'mysql',
'port' = '3306',
'username' = 'root',
'password' = '123456',
'database-name' = 'db_[0-9]+',
'table-name' = 'user_[0-9]+'
);
```
3. Create Iceberg sink table
Create a sink table `all_users_sink` used to load data to Iceberg.
We define `database_name`, `table_name` and `id` as a combined primary key, because `id` maybe not unique across different databases and tables.
```sql
-- Flink SQL
Flink SQL> CREATE TABLE all_users_sink (
database_name STRING,
table_name STRING,
`id` DECIMAL(20, 0) NOT NULL,
name STRING,
address STRING,
phone_number STRING,
email STRING,
PRIMARY KEY (database_name, table_name, `id`) NOT ENFORCED
) WITH (
'connector'='iceberg',
'catalog-name'='iceberg_catalog',
'catalog-type'='hadoop',
'warehouse'='file:///tmp/iceberg/warehouse',
'format-version'='2'
);
```
## Streaming to Iceberg
1. Streaming write data from MySQL to Iceberg using the following Flink SQL:
```sql
-- Flink SQL
Flink SQL> INSERT INTO all_users_sink select * from user_source;
```
It will start a streaming job which will synchronize historical and incremental data from MySQL to Iceberg continuously.
The running job can be found in [Flink UI](http://localhost:8081/#/job/running), and it looks like:
Then, we can use the following command to see the files written to Iceberg:
```shell
docker-compose exec sql-client tree /tmp/iceberg/warehouse/default_database/
```
It should look like:
The actual files may differ in your environment, but the structure of the directory should be similar.
2. Use the following Flink SQL to query the data written to `all_users_sink`:
```sql
-- Flink SQL
Flink SQL> SELECT * FROM all_users_sink;
```
We can see the data queried in the Flink SQL CLI:
3. Make some changes in the MySQL databases, and then the data in Iceberg table `all_users_sink` will also change in real time.
(3.1) Insert a new user in table `db_1.user_1`
```sql
--- db_1
INSERT INTO db_1.user_1 VALUES (111,"user_111","Shanghai","123567891234","user_111@foo.com");
```
(3.2) Update a user in table `db_1.user_2`
```sql
--- db_1
UPDATE db_1.user_2 SET address='Beijing' WHERE id=120;
```
(3.3) Delete a user in table `db_2.user_2`
```sql
--- db_2
DELETE FROM db_2.user_2 WHERE id=220;
```
After executing each step, we can query the table `all_users_sink` using `SELECT * FROM all_users_sink` in Flink SQL CLI to see the changes.
The final query result is as follows:
From the latest result in Iceberg, we can see that there is a new record of `(db_1, user_1, 111)`, and the address of `(db_1, user_2, 120)` has been updated to `Beijing`.
Besides, the record of `(db_2, user_2, 220)` has been deleted. The result is exactly the same with the changes we did in MySQL.
## Clean up
After finishing the tutorial, run the following command in the directory of `docker-compose.yml` to stop all containers:
