flink中自定义检查点存储为mysql

在 Flink 中，我们可以通过实现 `CheckpointStorage` 接口来自定义检查点存储。下面是一个将检查点存储到 MySQL 数据库的示例：

首先，我们需要实现 `CheckpointStorage` 接口：

public class MySQLCheckpointStorage implements CheckpointStorage {

    private final String jdbcUrl;
    private final String username;
    private final String password;

    public MySQLCheckpointStorage(String jdbcUrl, String username, String password) {
        this.jdbcUrl = jdbcUrl;
        this.username = username;
        this.password = password;
    }

    @Override
    public CheckpointStorageAccess createCheckpointStorage(JobID jobId) throws IOException {
        Connection conn = DriverManager.getConnection(jdbcUrl, username, password);
        return new MySQLCheckpointStorageAccess(conn, jobId);
    }

    @Override
    public boolean supportsHighlyAvailableStorage() {
        return false;
    }

    @Override
    public RecoverableWriter createRecoverableWriter() throws Exception {
        throw new UnsupportedOperationException();
    }
}

在 `createCheckpointStorage` 方法中，我们创建一个 MySQL 数据库连接，并返回 `MySQLCheckpointStorageAccess` 对象，它实现了 `CheckpointStorageAccess` 接口：

public class MySQLCheckpointStorageAccess implements CheckpointStorageAccess {

    private final Connection connection;
    private final JobID jobId;

    public MySQLCheckpointStorageAccess(Connection connection, JobID jobId) {
        this.connection = connection;
        this.jobId = jobId;
    }

    @Override
    public CompletableFuture<CompletedCheckpoint> addCheckpoint(
            long checkpointId,
            CheckpointProperties props,
            TaskStateSnapshot checkpointState) throws Exception {
        // 将检查点状态序列化为字节数组
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        ObjectOutputStream oos = new ObjectOutputStream(out);
        oos.writeObject(checkpointState);
        byte[] stateBytes = out.toByteArray();
        oos.close();

        // 插入检查点数据到 MySQL 数据库
        PreparedStatement stmt = connection.prepareStatement(
                "INSERT INTO checkpoints (id, job_id, properties, state) VALUES (?, ?, ?, ?)");
        stmt.setLong(1, checkpointId);
        stmt.setString(2, jobId.toString());
        stmt.setString(3, props.toUnmodifiableMap().toString());
        stmt.setBytes(4, stateBytes);
        stmt.executeUpdate();
        stmt.close();

        // 返回 CompletedCheckpoint 对象
        return CompletableFuture.completedFuture(new CompletedCheckpoint(
                jobId,
                checkpointId,
                System.currentTimeMillis(),
                System.currentTimeMillis(),
                props,
                checkpointState));
    }

    @Override
    public void shutdown(JobStatus jobStatus) throws Exception {
        connection.close();
    }
}

在 `addCheckpoint` 方法中，我们将检查点状态序列化为字节数组，并将其插入到 MySQL 数据库中。

现在，我们可以在 Flink 作业中使用自定义的检查点存储。例如：

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStateBackend(new FsStateBackend("file:///tmp/checkpoints"));
env.enableCheckpointing(1000, CheckpointingMode.EXACTLY_ONCE);
env.getCheckpointConfig().setCheckpointStorage(new MySQLCheckpointStorage(
        "jdbc:mysql://localhost:3306/flink_checkpoints",
        "root",
        "password"));

在上面的例子中，我们将检查点存储设置为 MySQL 数据库，并将检查点间隔设置为 1000 毫秒。当 Flink 作业启动时，它将创建一个名为 `flink_checkpoints` 的数据库，并将检查点数据插入到其中。