🏆 TiDB Hackathon 2022

A detailed analysis of TiDB Hackathon 2022’s Application Track, focusing on the top three award-winning projects—DataDance, Yunji, and Mirror. This write-up summarizes their architectures, design trade-offs, and engineering challenges from academic, system-engineering, and participant perspectives.

1. Competition Overview

Competition: TiDB Hackathon 2022
Track: Application Group
Theme: Possibility at Scale
Official page: https://tidb.net/events/hackathon2022

TiDB Hackathon is the flagship annual event organized by PingCAP and the TiDB open-source community.
It is widely regarded as one of the most influential hackathons in the database and distributed-systems ecosystem.

The 2022 edition brought together:

• 303 participants
• 86 teams
• engineers, researchers, database kernel developers, and open-source contributors

The Application Track challenges participants to build real-world, large-scale distributed applications powered by TiDB’s HTAP capabilities, TiFlash, TiKV, and related ecosystem tools.

The hackathon requires teams to:

• Propose an idea (RFC)
• Build a working prototype in 48 hours
• Demonstrate scalability & real engineering depth
• Deliver a polished live demo

Evaluation criteria:

1. Innovation
2. Engineering completeness
3. Scalability
4. Architectural soundness
5. Community usefulness
6. Demo quality

2. The Winning Projects (Application Track)

This write-up examines three award-winning projects:

🥇 First Prize — DataDance

https://github.com/datadance-fun/DataDance

🥈 Second Prize — Yunji

https://github.com/VelocityLight/yunji

🥈 Second Prize — Mirror

https://github.com/mirror-data/mirror

3. Project Analyses

3.1 First Prize — DataDance

Goal:
A real-time data transformation platform (dbt-like + incremental ETL) built on TiDB.

Key Features

• SQL-based workflow definitions
• DAG transformation graph
• Incremental recomputation with minimal deltas
• TiCDC-triggered updates
• Materialized outputs stored in TiDB/TiFlash
• Web UI for pipeline visualization

Architecture Overview

User SQL → Parser → DAG Builder → Task Scheduler → Executors ↓ ↑ Metadata (TiDB)

Strengths

• Very strong engineering completeness for a 48-hour hackathon
• Clear real-world use case
• Seamless integration with TiDB HTAP
• Professional UI + strong system story

3.2 Second Prize — Yunji

https://github.com/VelocityLight/yunji

Goal:
A multi-tenant, cloud-native user analytics platform for real-time metrics & behavioral analysis.

Key Features

• Multi-tenant isolation
• Event ingestion → normalization → TiDB
• Real-time user profile store
• Metrics dashboard backed by TiFlash
• Plugin-based analytics

Architecture Overview

Event Stream → Collector → Normalizer → TiDB ↓ Analytics API ↓ Dashboard / UI

Strengths

• Clean schema design for event analytics
• HTAP-aware architecture
• Balanced full-stack system
• High practical value

3.3 Second Prize — Mirror

https://github.com/mirror-data/mirror

Goal:
A developer-friendly visualization and introspection tool for TiDB clusters.

Key Features

• Visual schema explorer
• Column statistics & profiling
• Query insight dashboards
• Real-time table previews
• ER diagram & dependency mapping

Architecture Overview

Frontend (React) → API Gateway → TiDB Information Schema / Stats ↓ Profiling & Insights Engine

Strengths

• Excellent UX and presentation polish
• Fills a needed gap in TiDB developer tooling
• Lightweight, usable, easy to integrate

4. Technical Analysis

4.1 Professor / Research Perspective

A researcher may focus on:

• HTAP execution patterns (TiDB + TiFlash)
• DAG-based incremental view maintenance (DataDance)
• Multi-tenant metadata modeling (Yunji)
• Schema and stats introspection (Mirror)
• Hybrid streaming/batch pipelines
• Query pushdown and cost-based execution

These projects represent modern directions in distributed SQL systems.

4.2 Systems Engineer Perspective

Engineering topics of interest:

• Pipeline ingestion architectures
• Metadata schema design
• Consistency models for incremental pipelines
• Avoiding full recomputation via deltas
• TiFlash as analytical accelerator
• Performance considerations under concurrency

Common design trade-offs:

• SQL vs DSL for workflow definitions
• Metadata stored inside TiDB vs external store
• Push vs pull data refresh
• Horizontal scaling patterns

4.3 Contestant Perspective

Things a participant would care about:

• What project ideas are competitive?
• What can be realistically built in 48 hours?
• UI vs backend effort allocation
• How much demo polish is needed?
• What factors matter most to judges?

Observed Winning Patterns

• Clean, end-to-end user story
• Practical use case deeply tied to TiDB
• Strong visual demo
• Real code, not just concepts
• Architectural clarity

5. High-Scoring Architecture Patterns

Across DataDance, Yunji, and Mirror, common patterns emerge:

1. TiDB as the primary metadata & transactional store

Used for: schemas, pipeline state, configs, offsets, profiles.

2. TiFlash as analytical compute

Used for: aggregations, segmentation, statistics, dashboards.

3. DAG or streaming execution models

(DataDance and Yunji)

4. Strong frontend polish

Yunji & Mirror show UI matters.

5. Cloud-native deployment

Most teams used Docker to simplify demo.

6. Community Code References

🥇 DataDance (First Prize)

https://github.com/datadance-fun/DataDance

🥈 Yunji (Second Prize)

https://github.com/VelocityLight/yunji

🥈 Mirror (Second Prize)

https://github.com/mirror-data/mirror

These repositories serve as excellent references for:

• incremental computation
• schema design
• clean data ingestion pipelines
• scalable HTAP architectures
• developer-oriented tooling

7. Reflection & Conclusion

TiDB Hackathon 2022 Application Track brings together innovation in:

• distributed SQL
• HTAP workloads
• real-time data transformations
• cloud-native SaaS analytics
• developer observability tools

From a research angle, these projects demonstrate practical prototypes of active research areas such as incremental view maintenance, hybrid streaming workloads, and HTAP optimization.

From a systems-engineering angle, they illustrate clean architecture boundaries, correct use of TiDB/TiFlash, and realistic distributed workload patterns.

From a contestant angle, the lesson is clear:

The strongest projects do not reinvent TiDB—they extend it in impactful, meaningful, and highly usable ways.

TiDB Hackathon remains a unique environment where distributed systems innovation meets real-world engineering constraints, producing ideas and prototypes that often evolve into community contributions.