Zero-Code Real-Time Data Sync with Flink CDC: A Practical Guide

Problem Statement

Using Flink CDC for data synchronization traditionally requires writing extensive code and manually submitting each job via the command line, which makes maintenance difficult and limits participation to developers.

Feasibility of a Zero‑Code UI

Flink CDC already provides a rich set of connectors (MySQL, MongoDB, Oracle, SQL Server, PostgreSQL, DB2, etc.) and supports full‑load, incremental, point‑in‑time, and whole‑database synchronization. The community is active, and Flink’s extensible source/sink API allows custom integration, e.g., writing directly to Apache Doris.

Core System Features

Metadata Management UI – Create data sources, select databases/tables, and trigger metadata synchronization without writing code.

Data Sync UI – Configure end‑to‑end sync tasks through a graphical console; all parameters are set with mouse clicks.

Table & Field Mapping – Define one‑to‑one or many‑to‑many mappings when source and target schemas differ.

Rich Source Support – Currently includes MySQL and Apache Doris; additional CDC connectors can be added as needed.

Task Lifecycle & Monitoring – Leverage Flink savepoints for checkpoint‑based resume, expose start/stop APIs, and monitor jobs in Flink Application mode.

Traffic Control via Kafka – Use Kafka as a buffering layer to smooth spikes, avoid back‑pressure, and reduce data loss.

Data Synchronization Workflow

The workflow consists of four stages:

Metadata Synchronization – Add a data source (host, authentication, connection parameters) and synchronize its schema into the metadata repository.

Data‑Source Management – List, test, edit, or delete sources; trigger metadata sync on demand.

Table & Field Inspection – Lazy‑loaded tree view shows databases, tables, and column details; supports future data‑lineage extensions.

Task Creation – Select input and output sources, map tables/fields, and configure runtime parameters.

Task Configuration Details

When creating a task, the UI collects:

Input source (e.g., MySQL) and selected tables.

Output sink (e.g., Doris) and target tables.

Mapping definitions for each table and column.

Job name.

Scheduler choice (YARN or Kubernetes).

Window interval, maximum data volume, and maximum record count – these three throttling parameters control the flow rate during execution.

Task Submission Process

Upon submission, the backend generates two JSON payloads that launch two Flink jobs: {"jobType":"cdc-to-kafka", ...} – A Flink CDC job reads the source change log and writes records to a Kafka topic, providing a buffer layer. {"jobType":"kafka-to-sink", ...} – A second Flink job consumes the Kafka topic and writes the data to the target warehouse (e.g., Doris).

The separation isolates source‑side CDC processing from sink‑side ingestion, improving fault tolerance and allowing independent scaling.

Group Management

Tasks can be grouped; each group corresponds to a single Flink job that runs multiple logical sync pipelines. Grouping reduces cluster resource consumption and lays the groundwork for future alerting mechanisms. Memory settings for TaskManager and JobManager can be customized per group.

Operational Benefits

By abstracting CDC job definition into a visual interface, non‑technical users can create and manage real‑time data pipelines without writing code. The system still exploits Flink CDC’s high‑performance change‑data‑capture capabilities while simplifying deployment, monitoring, and scaling.