Jump Crypto: How to Build a Layer 1 Analysis Framework
Original title: "
Original title: "A Framework for Analyzing L1s》
Related Reading
Related Reading
Jump Crypto: In-depth analysis of blockchain infrastructure segmentation track
introduce
In the previous article, we introduced some components related to the first layer (L1) in the blockchain infrastructure. Let's take a closer look at these L1s. In this article, we define a concise yet powerful framework for:
Efficiently analyze the performance of L1.
Determine the commercial viability of their ecosystem based on clearly defined attributes and measurable metrics.
What does the ecosystem look like?
What does the ecosystem look like?
How does this network scale?
Does the chain support composability?
However, these questions do not address the key to a particular L1 performing better than its close competitors. Let's develop a concise framework that allows us to be more specific and structured when analyzing L1 performance.
Let's start with some basic definitions.
technical indicators
technical indicators
Node processing requirements:The minimum CPU/computing resources required to effectively operate a node.
Transactions per second (TPS):Transactions processed and verified on-chain per second.
Chain growth:The average growth rate of the longest chain.
Chain quality:The proportion of honest blocks in the longest chain.
Final time (Time to Finality):The time from transaction submission to on-chain confirmation.
Number of nodes:The number of nodes participating in consensus, execution, or both.
block size:safety
technical attributes
safetyeffectiveness
effectivenessscalability
scalability- The speed and ability of the network to verify or process transactions.
node requirements- Barriers to entry for users to run nodes and participate in governance decisions.
Satoshi CoefficientUpgradability
UpgradabilityEcosystem Growth Indicators
Ecosystem Growth Indicators
Total Value Locked (TVL)- The total value of assets on the chain.
daily trading volumeecosystem properties
ecosystem properties
Ease of Integration/Composabilityuser experience
user experience- Common users can easily understand and participate in on-chain applications.
Community Involvement- The extent to which project stakeholders interact with the application, other users, and developers.
Let's see how these properties fit together to advance the understanding of network evaluation. We can better understand an ecosystem’s success, and its potential for future growth, through a number of metrics, including metrics around community engagement and financial metrics such as protocol revenue and total value locked (TVL).
Tier 1 Performance Stack
Ecosystem properties:Community Engagement｜User Experience/UI｜Ease of Integration/DApp Portability
Ecosystem Growth Indicators:Total Value Locked (TVL)｜Daily Transaction Volume｜Social Media Growth (Discord/Telegram/Twitter)｜Number of Developers｜Protocol Revenue
Infrastructure requirements:Data Availability｜Cross-chain Interoperability｜Searchability/Indexing｜Developer Tools
Technical properties:Technical indicators:
Technical indicators:Node Processing Requirements｜Number of Nodes｜Transactions Per Second (TPS)｜Chain Growth｜Chain Quality｜Block Size｜Latency｜Downtime｜Propagation Time｜Satoshi Coefficient
In conclusion
scalability
scalability
horizontal scalability- The processing power of the network (e.g. transactions per second) should increase with the number of participating nodes. An ideal L1 would have its TPS scale linearly with the number of nodes (n). However, a slightly sublinear extension is acceptable. (We acknowledge that linear scaling is more of a desirable property, and that most L1 scaling is sublinear.)
low overhead- The computational cost of achieving consensus, security, and all the other properties on this list should be minimal relative to the cost of processing each transaction. To achieve sublinear scaling, we need the amount of resources dedicated to validating state updates (q) to be sublinear in the amount of computing resources devoted to computing state transitions (p).
short completion timedecentralized
decentralized
Composability/AtomicityFinality
Finalitysafety
safety
security/robustnessCensorship resistance
Censorship resistancefault tolerance
fault toleranceeffectiveness
effectiveness- Ensuring that honest messages are included/available to block producers. Consensus protocols achieve security fundamentally based on the validity of the chain - validators cannot verify messages to which they do not have access. For some consensus mechanisms (such as PoW), metrics such as chain quality and chain growth can be useful indicators of this property.
tradeoffs to consider
The above overview provides a taxonomy for evaluating L1, but does not provide a truly efficient way of evaluating the relative merits of different networks. Below, we introduce a set of key tradeoffs, discuss the relationship between these different terms, and provide a clear way to understand which chains can best serve a specific use case.
1. Consensus overhead vs. security vs. scalability- The more nodes/computers that participate in the consensus or verification state transition process, the better the security of the network. This is evident, for example, in PoW models, where the longest chain becomes the canonical chain or "true state" of the network. However, if a large subset of these nodes use up their computing resources instead of dedicating them to computing state transitions, throughput will be limited and the network will slow down.
2. Time-to-Finality vs. TPS vs. Security- The faster blocks are completed, the less time validators have to agree on the state. Faster block times can lead to higher TPS, but if there is not enough time to effectively reach a consensus, rollbacks may become more common, compromising the security of the system.
3. Node requirements and scalability- For a blockchain to be truly decentralized, everyone should be able to easily access/participate in the network. In order for the system to be as permissionless as possible, the minimum requirements to run a node should be relatively low. However, as node requirements decrease, so does the total computing power available to the network. More nodes may join the network as a result, but the increase in the number of nodes must compensate for the loss of computational bandwidth caused by less powerful machines. Striking the right balance therefore remains a key challenge.
4. Data availability and indexability- As the amount of data on-chain grows, it becomes more difficult to efficiently parse or filter the data. DApps need to be able to query on-chain data in real-time in order to serve large or fast request sets to their users.
5. Horizontal scalability and atomicity- Sharding requires maintaining different parts of the on-chain state across multiple subnets. While this allows transactions to be processed in parallel, it increases the risk that users may get stuck. There are ways to maintain atomicity between shards, but all of them require some additional overhead.
Application level impact
The infrastructure parameters we have discussed can greatly affect the types of applications built on a particular chain. Consider the following example:
Bandwidth limits support for high-throughput applications, whereas higher TPS limits enable higher frequency transactions and real-time updates.
Longer finalization times may be less useful for payment or other applications that require fast settlement.
High on-chain resource costs (i.e. gas costs) can hinder application development. (For example, the traditional centralized limit order book (CLOB) is not feasible on Ethereum because of the high gas cost, so automatic market makers (AMM) such as Uniswap are popular. On L1 with lower fees such as Solana, And on L2 on chains such as Ethereum, CLOB can be very practical.)
Above, we showed a framework for analyzing L1 performance. Below, we provide a more in-depth analysis on the process of how to better evaluate L1 from its ecosystem/set of projects built on-chain.
We group these items into four main categories:
A blockchain’s ability to integrate these fundamental elements is critical to its short-term growth and long-term sustainability.
In our view, there are five main steps to the development of a high-growth ecosystem:
1. Realize cross-chain communication through assets or universal bridges.
2. Bring liquidity to platforms by integrating DeFi primitives (such as money market lending platforms and exchanges). This incentivizes the core developer community to build better tools and abstract assumptions, allowing less sophisticated developers to build more consumer-facing products.
3. Incentivize user adoption through DApp growth.
4. Focus on bringing high-fidelity data on-chain through oracles or dedicated data availability layers.
in conclusion
in conclusion
It is undeniable that the crypto space has experienced rapid growth since the introduction of Bitcoin in 2009. Much of this growth has been shaped by the advent of the new L1. In 2011, Ethereum introduced Turing's complete architecture through the Ethereum Virtual Machine (EVM), enabling the blockchain not only as a static distributed ledger, but also as a global state machine that runs and executes arbitrary expressive programs. This opens the door to more general DApp development, bringing ordinary retail users into the blockchain ecosystem, as demonstrated by movements like DeFi Summer.
However, as adoption increases, new challenges in scalability emerge, forcing builders to find new ways to help alleviate capacity constraints. This has been seen in the development of chains such as Solana and other L1/L2 which attempt to increase throughput by off-chaining computation.
Now, as the new L1 explores new architectures around "scalability utilizing better consensus mechanisms and cryptographic primitives," effectively assessing its value remains a daunting task. We hope this article gives you a more structured way to more fully evaluate such L1s by showing how core, measurable technical metrics correlate to ecosystem growth and ultimately help determine the market value of a particular network.
