When it comes to on-chain application development, many people overlook a long-standing issue: data and applications aging together.

Game assets, NFT metadata, AI inference results—these things accumulate every day. Over a year, just the core state data can grow to 20—40GB. What's more frustrating is that they need to be accessed, modified, and verified frequently. In the later stages, developers are often forced to choose the old methods: backup, migration, rebuild indexes. These processes are costly and inefficient, and most importantly, they can't guarantee the integrity of historical data.

Recently, a new approach has emerged that changes this deadlock.

The key difference is that it’s not just about stuffing data in; instead, it binds data and verification capabilities together. Each object is assigned a stable identity at the moment of creation, and subsequent state changes happen within this object without destroying the original structure.

What’s the result? No matter how large the data volume or how frequent the updates, the system can ensure these points: the object address remains constant, the entire history is fully traceable, overall availability exceeds 99% under multi-node redundancy architecture, and parallel read latency stays at the second level.

This has a significant impact on developers. When your data is stored in such a system, you can design iteration logic more confidently, without constantly worrying that a single modification will break the entire on-chain state.

There are several practical benefits:

**Lower costs** Once data is stored, it gains long-term verifiability, saving a lot of trouble related to migration, backup, and version management.

**Access frequency is no longer a bottleneck** High-frequency read/write operations are natively supported in this architecture, without triggering new objects or additional on-chain operations due to updates.

**Historical traceability is no longer difficult** The complete chain of state evolution is preserved, eliminating the need for extra index maintenance when querying historical data.

From another perspective, this changes developers’ mindset towards data management. It used to be defensive—preventing data corruption and exploding maintenance costs. Now, it becomes proactive, because the underlying storage logic has already helped solve these pain points.