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Building a Trillion-Scale Data Sync System: The Untold Story of Apache SeaTunnel

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

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

1. Multi-Engine Support Evolution

Historical Context

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

SeaTunnel API Layer
                   ▲
         Translation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘
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2. Intelligent Connection Pooling

Before

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

Tables ─► Dynamic Pool (100 tables ≈ 10 connections)
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3. Zero-Copy Data Transfer

Traditional

Source → Memory → Transform → Memory → Sink
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SeaTunnel

Source ═════► Transform ═════► Sink
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4. Adaptive Backpressure 

Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)
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5. Dynamic Thread Scheduling 

Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)
└─────────┘            └───┘
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6. Plugin Architecture

ClassLoader Isolation

Bootstrap CL → System CL → SeaTunnel CL → Plugin CL
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Loading Process

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

The Memory Leak Mystery
A persistent memory creep traced to special character handling — was found after 72 hours 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.

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