Next‑Gen Visual Drag‑Drop Data Flow Platform: Features, Architecture, and Performance

Highlights

Stream‑Batch Integration – a single system handles both real‑time stream processing and batch jobs.

Version Control – data‑flow definitions are versioned like source code; any faulty change can be rolled back with one click.

Distributed Auto‑Fault Tolerance – failed nodes are automatically recovered without manual intervention.

Alerting – built‑in notifications surface issues immediately.

Configurable Data Permissions – fine‑grained access control determines who can view or edit specific data.

Architecture

The platform supports dynamic horizontal scaling. During low‑traffic periods fewer machines are provisioned to save resources, while peak events (e.g., large sales campaigns) can trigger the addition of extra nodes to sustain higher loads.

UI Overview

Home Dashboard

The landing page presents a consolidated view of system health, data‑flow execution status, source connectivity, and active alerts.

Data Flow Statistics

Shows the smoothness of data‑flow execution and server load; clicking CPU or memory panels reveals detailed node metrics.

Query Template Statistics

Displays invocation counts for each query template together with current server load.

Data Management

Data Flow

The core feature allows users to configure synchronization, cleaning, filtering, and reporting steps via a drag‑and‑drop canvas, requiring zero code.

When a flow is published, resources are controllable, nodes auto‑recover on failure, and the system can scale out dynamically. Multiple versions are retained; a faulty version can be rolled back with a single click.

Real‑time execution logs stream live status of running tasks, and a publish‑record list records historical versions for rollback.

Data Sources

Supported sources are added through a plug‑in design, making future extensions straightforward. Consoles are provided for relational databases (MySQL, StarRocks, Doris, Oracle), message queues (Kafka), and search engines (Elasticsearch).

Data Alignment

The alignment module automatically validates and repairs data consistency between heterogeneous sources. Strategies include count matching, content matching, and random sampling. Users can configure trigger timing, alignment strategy, and comparison windows. Alignment logs detail mismatched rows and fields.

Query Templates

Query templates expose data via configurable APIs. Features include version control, secret‑key management, permission settings, dynamic conditions, sharding, rate limiting, logging, and caching. A preview page shows example documentation and allows quick testing; publishing makes the API callable externally. Call logs record each invocation with detailed request/response information.

Performance

Single‑instance benchmarks on a 6 CPU × 12 GB machine:

Listening‑stream processing: 30,058 ops/s

Batch processing: 60,268 ops/s

Code example

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