This GIP outlines a comprehensive upgrade to The Graph, transforming it into a dynamic, self-optimizing decentralized network that can efficiently scale to meet the growing demands of decentralized applications (dApps). The proposal introduces adaptive indexing mechanisms, auction-based query fees, privacy-preserving zero-knowledge techniques, and programmable liquidity management via veGRT. These innovations are designed to enhance scalability, economic alignment, and privacy, allowing The Graph to evolve into a more flexible and efficient data network for the decentralized web. The use of veNFTs, representing locked veGRT tokens, will enable dynamic liquidity management, ensuring that liquidity is allocated in real-time to high-demand subgraphs while simultaneously providing governance participation rights. Inspired by Uniswap v3-style concentrated liquidity pools, locked veGRT will dynamically manage liquidity allocation in real time, responding to network demand while integrating a query price floor system to ensure that query fees remain viable for indexers. This proposal ensures that The Graph can dynamically scale to meet the evolving demands of decentralized applications (dApps), securing economic incentives and liquidity for both clients and indexers.
Motivation
The Graph, as a foundational component of the Web3 infrastructure, must continuously evolve to meet the growing complexity and data demands of decentralized applications (dApps). While the current models have served well, certain areas may face challenges as the ecosystem scales, motivating the need for this proposal:
- Scalability Considerations: As query volumes and complexity increase, the network could face challenges in scaling efficiently to meet the growing demand.
- Static Query Fees: The current system may lack the flexibility needed to dynamically adjust query fees in response to varying traffic levels, potentially leading to inefficiencies during peak and low traffic periods.
- Liquidity Management Optimization: Real-time liquidity management could be enhanced to better match resource allocation with actual query demand, preventing potential bottlenecks and ensuring fair compensation for indexers and participants.
- Economic Alignment: Aligning the incentives for indexers, stakers, and participants more closely with network conditions could help maintain a balanced and efficient system as the network grows and evolves.
- Privacy Enhancements: Sectors like healthcare and finance increasingly require advanced privacy-preserving techniques, which present an opportunity for development in the current system.
This GIP proposes a comprehensive upgrade to address these considerations. By evolving The Graph into a self-regulating network that dynamically adapts to changing demands and market conditions, this proposal ensures that resources, liquidity, and governance are optimized in real time to better serve the decentralized ecosystem.
The Graph as The Conductor of a Decentralized Orchestra
The Graph operates not merely as a passive data infrastructure but as the conductor of the decentralized Web3 ecosystem, orchestrating real-time data flow between decentralized applications (dApps) and blockchain networks. Just as a conductor in an orchestra is responsible for guiding musicians, adjusting timing, and balancing sound based on live feedback, The Graph continually tunes the flow of data and resources, creating an intelligent, responsive system.
In the same way a conductor reacts instantly to the nuances of sound—providing real-time feedback to shape the performance—The Graph receives live data and user queries, optimizes indexing and resource allocation, and reacts to changes in query demand or network conditions. This enables developers, users, and applications to adjust their systems dynamically based on real-time insights, optimizing the performance of their decentralized systems.
veNFTs, representing locked veGRT tokens, serve as the core components in this orchestration, acting like dynamic liquidity managers. Rather than passively waiting for demand to arise, they allocate liquidity based on real-time query needs, ensuring resources flow where they are most needed. veNFT holders, much like musicians contributing to a symphony, are active participants in this process, using governance to influence resource distribution, optimize system performance, and decide on the future direction of The Graph.
The key here is that The Graph doesn’t operate like a traditional, static data layer. Instead, it behaves like a reactive, intelligent layer—an evolving network that listens to data patterns, reacts to changes, and optimizes for efficiency and scalability. This responsiveness is critical for the future of decentralized systems, enabling developers and users to build dApps that are continuously optimized, much like how a well-tuned orchestra continuously adapts its sound to create a harmonious performance.
By functioning as this dynamic conductor, The Graph facilitates real-time feedback loops, which empower users and developers to gain actionable insights, enhance query performance, and fine-tune their systems. This creates a future-ready platform that is not just scalable, but adaptable, responsive, and intelligent, forming the backbone of the Web3 data layer. Through this metaphor, The Graph emerges as the critical orchestrator of decentralized data—constantly refining, optimizing, and ensuring that the entire Web3 ecosystem performs at its best.
Key Innovations
1. Adaptive Indexing Mechanisms
Adaptive Indexing introduces a flexible, real-time response to the growing complexity of decentralized data queries. The current static indexing system in The Graph does not efficiently scale with the fluctuating query volumes and data complexity. To address this, we propose the following:
Proposed Enhancements:
- Specialized Indexing Strategies: Indexers will be able to implement customized indexing strategies based on the specific requirements of the data streams they manage. By leveraging advanced techniques such as sharding, parallel processing, and data partitioning, indexers can optimize how they handle large data sets and high-frequency queries.
- Event-Driven Indexing: Indexers will only index data when specific events or thresholds are triggered, such as price changes or significant blockchain events. This reduces unnecessary processing and ensures that indexers only focus on relevant data updates. For example, an indexer tracking token price movements could only update the subgraph when significant price changes occur.
- Context-Aware Subgraphs: Subgraphs will adapt dynamically to the context of incoming queries. For example, when multiple users request data related to a specific contract or event, the subgraph will prioritize and optimize the retrieval of that data while deprioritizing less relevant data streams.
- Lazy Indexing: Lazy indexing defers the indexing of historical data until a query specifically requests it. This strategy optimizes resource usage for dApps that focus on real-time data and only require historical data on an as-needed basis.
Rationale:
Decentralized applications (dApps) require increasingly complex and dynamic data querying. The current static indexing system cannot efficiently handle such fluctuations in query demand, leading to overuse of resources and higher latencies. With adaptive indexing, The Graph can allocate resources dynamically based on network conditions, ensuring indexers can handle real-time shifts in query volume without overwhelming the system. Indexers, much like musicians in an orchestra, adjust their strategies dynamically based on real-time feedback from the network, ensuring the best possible performance.
Adaptive indexing allows The Graph to dynamically allocate resources based on real-time demand, optimizing performance and ensuring that costs are kept in check. This new model ensures that indexers are not overwhelmed by unnecessary queries and can focus on high-value data streams.
Use Case:
Consider a DeFi application that processes high-frequency price data. Adaptive indexing ensures that resources are concentrated on these critical data streams, providing users with real-time data and reducing latency, while lower-priority data is indexed less frequently.
In a DeFi application that tracks token swaps across decentralized exchanges, event-driven indexing could trigger updates only when a significant number of token swaps occur. This would reduce unnecessary data processing while ensuring the accuracy of subgraph data during periods of high trading activity.
2. Zero-Knowledge Indexing for Enhanced Privacy
With rising privacy concerns across industries such as healthcare and finance, The Graph must implement solutions that allow for secure and private data queries. The introduction of zero-knowledge proof (ZKP) techniques provides a privacy layer that ensures users can query data without exposing sensitive information.
Proposed Enhancements:
- Zero-Knowledge Proofs (ZKP) for Query Privacy: Users will be able to query data in The Graph without revealing either the content of their query or their identity. By integrating zero-knowledge proofs into the query process, The Graph will ensure that sensitive data remains secure and private.
- Encrypted Smart Contracts for Data Access: Smart contracts will be enhanced to provide encrypted access to data. Only authorized parties will be able to decrypt and query specific datasets, ensuring that sensitive information remains confidential.
Rationale:
While decentralized systems offer transparency, they often lack sufficient privacy measures. By integrating zero-knowledge indexing, The Graph can offer a more privacy-centric solution that is ideal for sectors requiring confidentiality, such as healthcare, finance, and legal services. This innovation broadens the range of use cases for The Graph, making it suitable for a variety of industries that rely on secure data access.
Data privacy is a growing concern for decentralized applications, particularly in industries like finance, healthcare, and legal services. The zero-knowledge indexing feature will enable these sectors to adopt decentralized technologies while ensuring that data remains private. By allowing users to interact with The Graph without revealing sensitive information, the network becomes more versatile and secure, enabling broader adoption by privacy-sensitive industries.
Use Case:
A healthcare dApp could use zero-knowledge indexing to allow hospitals to query patient data while keeping sensitive medical records confidential. This maintains regulatory compliance and ensures secure, fast data querying in a privacy-preserving manner.
A healthcare dApp could allow medical professionals to query specific patient data without exposing the entire medical record. Using zero-knowledge proofs, the doctor could query only the relevant data (such as current medications) while ensuring that other sensitive details remain encrypted and inaccessible.
3. Modular and Scalable Subgraphs: Metagraphs, Supergraphs, and Micrographs
To support the growing complexity of decentralized applications, The Graph must introduce modular subgraphs that can scale dynamically across various industries. The introduction of metagraphs, supergraphs, and micrographs will provide a flexible, scalable data infrastructure.
Proposed Enhancements:
The Graph must support increasingly complex, cross-chain, and real-time data demands as Web3 matures. To achieve this, Metagraphs, Supergraphs, and Micrographs provide modular solutions that allow The Graph to scale seamlessly, making data querying more efficient, flexible, and adaptable across a range of decentralized applications (dApps).
Metagraphs: Integrating Off-Chain Data
Metagraphs enable the integration of off-chain data into decentralized applications. These metagraphs are particularly valuable for applications that require real-time data from external sources such as currency exchange rates, weather data, and sports scores.
How Metagraphs Work:
Metagraphs provide developers with a unified querying experience by allowing them to query both on-chain and off-chain data streams. Off-chain data is pulled from trusted external feeds, validated, and made available within the same decentralized environment that powers on-chain queries.
Comparison with Chainlink:
While Chainlink focuses on validating the accuracy and security of off-chain data through decentralized oracles, The Graph’s metagraphs emphasize accessibility and queryability. Developers can use metagraphs to interact with external data sources in real-time without needing to rely on separate systems or tools.
Use Cases for Metagraphs:
- DeFi Lending Platforms: A decentralized finance (DeFi) platform could use metagraphs to pull in real-time fiat exchange rates, ensuring accurate collateral-to-loan ratios for users borrowing against crypto assets.
- Prediction Markets: A decentralized prediction market could use metagraphs to query real-world events such as sports scores or election results, enabling automatic contract settlements based on external data.
- Insurance dApps: An insurance protocol could rely on metagraphs to pull in weather data for agricultural insurance claims, triggering automated payouts when predefined weather conditions (e.g., droughts or floods) are met.
Supergraphs: Aggregating Cross-Chain Data
As the blockchain landscape becomes more multi-chain, supergraphs will aggregate data across different blockchains, enabling complex cross-chain queries for decentralized applications.
How Supergraphs Work:
Supergraphs serve as an aggregation layer that allows developers to query data from multiple blockchains within a single subgraph. Using techniques like parallel indexing and sharding, supergraphs enable efficient data retrieval from several blockchains simultaneously, facilitating the creation of cross-chain applications.
Use Cases for Supergraphs:
- Cross-Chain DeFi: A DeFi protocol operating across multiple blockchains (such as Ethereum, Solana, and Binance Smart Chain) can use supergraphs to aggregate liquidity data and execute cross-chain token swaps seamlessly.
- NFT Marketplaces: An NFT marketplace could use supergraphs to query metadata and transaction history from multiple chains, providing users with a unified interface for interacting with NFTs across different networks.
- DEX Aggregators: Decentralized exchanges (DEXs) can use supergraphs to pull in real-time liquidity and price data from multiple blockchains, optimizing trades across chains for the best execution prices.
Micrographs: Optimized for Granular Data Processing
Micrographs are lightweight subgraphs optimized for real-time data processing. They focus on handling high-frequency, low-latency queries, making them ideal for financial applications or supply chain dApps that require granular, event-driven data access.
How Micrographs Work:
Micrographs process specific, real-time data tasks, ensuring that only the most relevant subsets of data are retrieved. This compartmentalized structure reduces the computational overhead associated with broader data processing, making micrographs highly efficient for narrow use cases that require real-time data access.
Use Cases for Micrographs:
- Financial Trading: A decentralized financial trading platform can use micrographs to process real-time token prices, trading volume, and order book data, enabling high-frequency traders to execute strategies based on the latest market data.
- Healthcare Applications: A healthcare dApp could use micrographs to retrieve specific subsets of patient data (e.g., vital signs) in real-time, ensuring that only the necessary data is accessed, which preserves privacy and reduces query costs.
- Supply Chain Tracking: A supply chain platform could use micrographs to query real-time location and environmental data (such as temperature) for assets in transit, ensuring the data remains accurate and up-to-date without overloading the system.
4. Preventing Fragmentation with Dynamic Resource Allocation
One challenge introduced by the specialization of subgraphs is the risk of fragmentation, where different parts of the network become overly specialized, leading to data silos. This fragmentation reduces overall efficiency and accessibility. To mitigate this, The Graph will implement dynamic resource allocation and cross-subgraph integration strategies.
Mitigation Strategies:
- Dynamic Auction-Based Query Fees: Dynamic auction-based query fees will ensure that liquidity flows where it is most needed, preventing the over-specialization of certain subgraphs. Indexers will set query fees based on demand, allowing for real-time adjustments that ensure efficient resource allocation.
- Real-Time Liquidity Allocation via veNFTs: veNFTs will automatically allocate liquidity to under-served subgraphs, balancing resource distribution across the network. This dynamic liquidity allocation will prevent the formation of data silos and ensure that all parts of the network are well-supported.
- Cross-Subgraph Integration: Developers will be encouraged to build applications that span multiple subgraph types (metagraphs, supergraphs, micrographs), ensuring that no subgraph operates in isolation. This integration will maintain a cohesive network where data flows freely between subgraphs.
5. veNFT: The Unified System for Liquidity and Governance
The introduction of veNFTs (vested NFTs) introduces a system where liquidity management and governance are unified into a single framework. veNFTs represent locked veGRT tokens, granting holders governance rights and control over liquidity allocation.
veNFT/veGRT Model:
- Concentrated Liquidity: veNFTs dynamically allocate liquidity to subgraphs based on real-time query demand. This ensures that liquidity flows to where it’s most needed, reducing the risk of query delays or failures.
- Governance Voting: veNFTs also enable governance participation, allowing holders to vote on key network decisions, including query fee floors, liquidity distribution, and network upgrades.
Rationale:
The veNFT system aligns economic incentives with governance by rewarding participants who contribute liquidity to the network. This alignment ensures that governance decisions are made by stakeholders with a vested interest in the network’s long-term health, while also ensuring that liquidity is always available where it is most needed.
Use Case:
A veNFT holder could vote to raise query fees during a period of high network demand, ensuring that indexers are fairly compensated for their work. Simultaneously, veNFTs could allocate additional liquidity to high-demand subgraphs, ensuring that queries are processed smoothly despite the increased traffic.
6. Dynamic Auction-Based Query Fees with a Price Floor
To ensure that indexers are fairly compensated while maintaining network efficiency, The Graph will introduce dynamic auction-based query fees alongside a query price floor.
Proposed Enhancements:
- Auction-Based Query Fees: Indexers will bid on the price they charge for handling queries, allowing for real-time adjustments based on network demand. This auction-based model ensures that indexers are compensated fairly during periods of high demand, while users benefit from lower fees during low-demand periods.
- Query Price Floor: A minimum query price floor will be implemented to ensure that query fees never fall below a certain threshold. This prevents indexers from being under-compensated during periods of low demand and ensures the sustainability of the network.
How It Works:
- Auction-Based Fees: During periods of high query demand, indexers will be able to charge higher fees to reflect the increased network activity. Conversely, during periods of low demand, query fees will automatically decrease, ensuring cost-efficiency for users.
- veNFT Liquidity Management: veNFTs will dynamically allocate liquidity to high-demand subgraphs, ensuring that queries are always processed in a timely manner, even during periods of network congestion.
- Query Price Floor: The minimum price floor will guarantee that indexers are always compensated fairly, ensuring that the network remains sustainable even during periods of low query volume.
Use Case:
During a token launch event, query demand spikes as users query token prices and liquidity. The auction-based query fee system automatically raises query fees to match the increased demand, ensuring that indexers are compensated for their work. At the same time, veNFTs dynamically allocate additional liquidity to the token-related subgraphs, ensuring smooth query execution during the event.
Economic Impact
The introduction of dynamic query fees, veNFT liquidity management, and query price floors ensures a more capital-efficient and economically sustainable network. By enabling dynamic resource allocation, The Graph can handle large-scale query volumes while maintaining stable rewards for indexers and ensuring cost-efficiency for clients.
The combination of dynamic auction-based query fees, veNFT-based liquidity management, and a query price floor will create a more capital-efficient network. By aligning liquidity with demand and ensuring that indexers are compensated fairly, The Graph can scale sustainably while maintaining strong economic incentives for participants.
Key Economic Benefits:
- Incentive Alignment: veNFTs ensure that indexers are rewarded appropriately for handling high-demand queries, while the query price floor prevents underpricing.
- Real-Time Scalability: Dynamic fees and liquidity allocation allow The Graph to scale efficiently, adapting to shifts in network traffic without creating bottlenecks.
- Governance Efficiency: veGRT holders gain greater influence over key decisions, ensuring that long-term participants have a say in the network’s development.
Implementation Plan
Phase 1: Adaptive Indexing and Dynamic Fees
- Research, develop and implement adaptive indexing strategies, including event-driven indexing and context-aware subgraphs.
- Implement auction-based query fees with a price floor to ensure fair pricing and efficient resource allocation.
Phase 2: veGRT & veNFT Prototype Development
- Develop the veNFT liquidity and governance system, ensuring that veNFTs can manage liquidity and enable governance voting.
- Develop the veNFT framework to enable dynamic liquidity concentration and governance voting.
Phase 3: Full Integration
- Roll out veNFTs and dynamic query fees across the network, starting with pilot subgraphs and indexers.
- Prototype and test the auction-based query fee mechanism with selected indexers.
- Continuously optimize performance based on community feedback and data-driven insights.
Conclusion
The proposed upgrades will transform The Graph into a self-regulating network that dynamically responds to real-time demand. By integrating adaptive indexing, zero-knowledge privacy, dynamic auction-based query fees, and programmable liquidity management via veNFTs, The Graph will become more responsive, scalable, and economically sustainable. This ensures that The Graph remains the backbone of decentralized data infrastructure, capable of supporting the most complex and data-intensive dApps while maintaining robust economic incentives.
