“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: 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            │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. “How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” “How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” “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 Key Metrics Daily Data Volume: 100+ TB Concurrent Sync Jobs: 3,000+ tables (batch & streaming) Latency SLA: Seconds Current State: 3+ hours behind, worsening 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
└─────────────────┘ ┌───────────────────┐
│ 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 Market Solutions Analysis Solution A: High performance but heavyweight deployment Solution B: Lightweight but unstable, single-node Solution C: High maintenance costs, inflexible Solution A : High performance but heavyweight deployment Solution A Solution B : Lightweight but unstable, single-node Solution B Solution C : High maintenance costs, inflexible Solution C 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 Performance : Trillion-record sync capability Performance Usability : 5-minute setup, 30-minute deployment Usability Extensibility : Connector development via minimal class implementations Extensibility Stability : 24/7 operation Stability Efficiency : 50%+ resource reduction vs alternatives Efficiency Core Architecture After months of community collaboration: ┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└─────────────────────────────────────────── ┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└─────────────────────────────────────────── Technical Breakthroughs 1. Multi-Engine Support Evolution Historical Context Historical Context 2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine 2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine Translation Layer Innovation Translation Layer Innovation SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘ SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘ 2. Intelligent Connection Pooling Before Before Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections) Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections) After After Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections) Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections) 3. Zero-Copy Data Transfer Traditional Traditional Source → Memory → Transform → Memory → SinkTransform → Memory → Sink Source → Memory → Transform → Memory → SinkTransform → Memory → Sink SeaTunnel SeaTunnel Source ═════► Transform ═════► SinkTransform ═════► Sink Source ═════► Transform ═════► SinkTransform ═════► Sink 4. Adaptive Backpressure Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling) Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling) 5. Dynamic Thread Scheduling Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘ Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘ 6. Plugin Architecture ClassLoader Isolation ClassLoader Isolation Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL Loading Process Loading Process 1. Scan Plugins → 2. Create Loaders → 3. Load Config → 4. Init 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. 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.” “Talk is cheap. Show me the code.” But today we say: “Code is cheap. Show me the value.” “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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