This Data Sync Disaster Sparked an Open-Source Revolution

“How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …”

The Midnight SOS

One late night in 2021, just as I was about to shut down my computer, an urgent call came from operations:

“Help! The entire data sync system has crashed. Over 3,000 table synchronizations are backlogged, and business systems are triggering alarms…”

The voice on the line belonged to a business line tech lead, thick with anxiety. This wasn’t our first emergency, but the scale was unprecedented:

Key Metrics

• Daily Data Volume: 100+ TB
• Concurrent Sync Jobs: 3,000+ tables (batch & streaming)
• Latency SLA: Seconds
• Current State: 3+ hours behind, worsening

“System resource usage?”
“A nightmare! Database connections maxed out, CPU at 80%, memory alerts…”

An emergency patch deployed overnight provided temporary relief. Post-mortem analysis and community discussions revealed this wasn’t an isolated incident but an industry-wide pain point.

Why Existing Solutions Failed

┌───────────────────┐
│ 1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections
├──────────────────┤
│ 2. Poor performance & scalability │──► Performance cannot keep up, and adding new data sources requires changing a lot of code
├─────────────────┤
│ 3. Poor stability │──► Synchronization crashes occur several times a year, and often when others are celebrating a holiday, we are recovering
├─────────────────┤
│ 4. Poor batch and stream integration │──► Batch and stream integration is not supported, batch and stream need to be written separately
├─────────────────┤
│ 5. Poor monitoring │──► Real-time synchronization progress, synchronization rate, etc. cannot be seen
└─────────────────┘

Market Solutions Analysis

• Solution A: High performance but heavyweight deployment
• Solution B: Lightweight but unstable, single-node
• Solution C: High maintenance costs, inflexible

These limitations sparked the creation of SeaTunnel’s new engine — affectionately called “Ultraman Zeta” by the community for bringing light to data integration.

Architectural Evolution

Design Goals

We set audacious objectives:

1. Performance: Trillion-record sync capability
2. Usability: 5-minute setup, 30-minute deployment
3. Extensibility: Connector development via minimal class implementations
4. Stability: 24/7 operation
5. Efficiency: 50%+ resource reduction vs alternatives

Core Architecture

After months of community collaboration:

┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└───────────────────────────────────────────

Technical Breakthroughs

1. Multi-Engine Support Evolution

Historical Context

2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine

Translation Layer Innovation

SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘

2. Intelligent Connection Pooling

Before

Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections)

After

Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections)

3. Zero-Copy Data Transfer

Traditional

Source → Memory → Transform → Memory → SinkTransform → Memory → Sink

SeaTunnel

Source ═════► Transform ═════► SinkTransform ═════► Sink

4. Adaptive Backpressure

Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling)

5. Dynamic Thread Scheduling

Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘

6. Plugin Architecture

ClassLoader Isolation

Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL

Loading Process

1. Scan Plugins → 2. Create Loaders → 3. Load Config → 4. Init

War Stories

• The Memory Leak Mystery: A persistent memory creep traced to special character handling — found after 72hrs of stack analysis.
• Phantom Data Phenomenon: Intermittent data duplicates caused by batch boundary conditions — solved with transaction isolation improvements.
• Performance Cliff: 40% throughput drops with specific data patterns — resolved through adaptive batching.

Epilogue

As Linus Torvalds said: “Talk is cheap. Show me the code.”

But today we say: “Code is cheap. Show me the value.”

SeaTunnel proves that elegant solutions emerge when solving real-world problems at scale. The true measure of technology lies not in its complexity, but in its ability to make developers’ lives easier.