This content originally appeared on DEV Community and was authored by 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
└─────────────────┘
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.
This content originally appeared on DEV Community and was authored by Apache SeaTunnel
Apache SeaTunnel | Sciencx (2025-02-19T07:56:32+00:00) Building a Trillion-Scale Data Sync System: The Untold Story of Apache SeaTunnel. Retrieved from https://www.scien.cx/2025/02/19/building-a-trillion-scale-data-sync-system-the-untold-story-of-apache-seatunnel/
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