“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
└─────────────────┘
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:
- Performance: Trillion-record sync capability
- Usability: 5-minute setup, 30-minute deployment
- Extensibility: Connector development via minimal class implementations
- Stability: 24/7 operation
- 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 │ │
│ └────────┘ └─────────┘ └────────┘ │
└───────────────────────────────────────────
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 Layer
┌──────────┬──────────┬──────────┐
│ Spark │ Flink │ Zeta │
│Translator│Translator│Translator│
└──────────┴──────────┴──────────┘
2. Intelligent Connection Pooling
Before
Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)
After
Tables ─► Dynamic Pool (100 tables ≈ 10 connections)
3. Zero-Copy Data Transfer
Traditional
Source → Memory → Transform → Memory → Sink
SeaTunnel
Source ═════► Transform ═════► Sink
4. Adaptive Backpressure
Fast Producer Slow Consumer
│ │
▼ ▼
[||||||||] → [|||] (Automatic throttling)
5. Dynamic Thread Scheduling
Traditional Pool SeaTunnel Pool
│││││││││││ (100) │││││ (10-50 adaptive)
└─────────┘ └───┘
6. Plugin Architecture
ClassLoader Isolation
Bootstrap CL → System 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 — 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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