The Evolution of Blockchain Data Indexing Technology: From Nodes to AI-Powered Full Chain Data Services

1. Introduction

The development of blockchain technology has led to the rise of decentralized applications ( dApp ), from the initial Etheroll, ETHLend, and CryptoKitties, to the numerous financial, gaming, and social dApps that now permeate various blockchains. In this process, the data sources that dApps rely on have become increasingly important.

In 2024, artificial intelligence and Web3 have become hot topics. In the field of AI, data is like the source of life, driving the learning and evolution of systems. Without the support of massive amounts of data, even the most advanced AI algorithms struggle to exhibit their true intelligence.

This article will delve into the development of blockchain data accessibility, analyze the evolution of data indexing technology, and compare several major blockchain data service protocols, with a particular focus on their innovations in the application of AI technology.

2. The Evolution of Data Indexing: From Blockchain Nodes to Full Chain Databases

2.1 Data Source: Blockchain Node

Blockchain is referred to as a decentralized ledger, and its core is a distributed network maintained by numerous nodes. Each node keeps a complete copy of the blockchain data, ensuring the decentralization feature of the network. However, for ordinary users, building and maintaining a node not only has a high technical threshold but also requires bearing high hardware and bandwidth costs.

To solve this problem, RPC node providers have emerged. They are responsible for the operation and maintenance of nodes, providing data access services through RPC endpoints. Although public RPC endpoints are free to use, there are rate limits that may affect the user experience of dApps. Private RPC endpoints, while performing better, still show inefficiency for complex queries, and there are issues with scalability and cross-network compatibility.

2.2 Data Parsing: From Raw Data to Usable Data

The raw data provided by blockchain nodes is often encrypted and encoded, making it difficult for average users and developers to process this data directly. The data parsing process converts complex raw data into a format that is easier to understand and manipulate, which is a critical step in the data indexing process.

2.3 The Evolution of Data Indexers

With the explosive growth of Blockchain data, the demand for data indexers is becoming increasingly prominent. Indexers organize on-chain data and store it in databases to achieve efficient querying. They provide a unified query interface, allowing developers to quickly retrieve the required information using standardized query languages.

Different types of indexers each have their advantages:

1. Full Node Indexer: Extract data directly from full nodes to ensure data integrity and accuracy.
2. Lightweight Indexer: Obtains specific data on demand from full nodes, reducing storage requirements but potentially increasing query time.
3. Specialized Indexer: Optimized for specific types of data or blockchain, such as NFT data or DeFi transactions.
4. Aggregated Indexer: Integrates multi-chain and off-chain data, providing a unified query interface, suitable for multi-chain dApps.

Faced with a massive amount of data, mainstream indexer protocols not only support multi-chain indexing but also customize data parsing frameworks for different applications. The emergence of indexers has significantly improved data indexing and query efficiency, supporting complex queries and data filtering, bringing important innovations to Blockchain data access.

2.4 Full-chain Database: Aligning to Stream Priority

As application demands become increasingly complex, standardized API index formats struggle to meet diverse query needs. The "stream-first" approach in modern data pipeline architectures has become a solution, enabling real-time data processing and analysis. Blockchain data service providers are also gradually moving towards building data streams, launching various real-time data lake products.

These services aim to provide real-time Blockchain transaction analysis and comprehensive query capabilities. Redefining on-chain data management through a modern data pipeline perspective opens up new possibilities for custom high-performance datasets.

3. AI + Database: In-depth comparison of The Graph, Chainbase, and Space and Time

3.1 The Graph

The Graph network provides multi-chain data indexing and query services through a decentralized Node network. Its core products include the data query execution market and the data indexing cache market, serving the query needs of users.

Subgraphs ( are the fundamental data structure of The Graph, defining the methods for data extraction and transformation. The network consists of four roles: indexers, curators, delegators, and developers, which together support the data needs of web3 applications.

The Graph is actively embracing AI technology. Its core development team, Semiotic Labs, has developed tools such as AutoAgora, Allocation Optimizer, and AgentC, which optimize index pricing, resource allocation, and user query experience respectively.

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) 3.2 Chainbase

Chainbase, as a full-chain data network, integrates data from multiple blockchains. Its features include:

• Real-time data lake: Provides instant access to Blockchain data streams.
• Dual-chain architecture: Built on Eigenlayer AVS to enhance cross-chain data processing capabilities.
• Innovative data format: Introducing the "manuscripts" standard to optimize data structuring.
• Encrypted World Model: Combining AI technology, it has developed a model called Theia that can understand and predict Blockchain transactions.

Chainbase's AI model Theia is based on NVIDIA's DORA model, providing intelligent data services by analyzing on-chain and off-chain data.

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) 3.3 Space and Time

Space and Time###SxT( is committed to building a verifiable computation layer that expands zero-knowledge proof technology. Its core innovation is Proof of SQL, a ZKP technology that ensures the verifiability of SQL query results.

SxT collaborates with Microsoft AI Lab to develop generative AI tools that simplify Blockchain data processing. Users can query using natural language, and AI automatically converts it to SQL and executes it.

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Conclusion and Prospects

Blockchain data indexing technology has evolved from the initial node data, through parsers and indexers, to ultimately an AI-enabled full-chain data service. This process has continuously improved data access efficiency and the level of intelligence.

In the future, with the advancement of technologies such as AI and zero-knowledge proofs, blockchain data services will become more intelligent and secure. As infrastructure, it will continue to support industry innovation.