Difference between revisions of "L1 comparison"

Revision as of 13:59, 14 December 2022

Whether they were writing games, operating systems or text editing applications, in the 70s, 80s and early 90s, developers always had to face limitations imposed by hardware. Applications were constrained to accessing a few kilobytes of memory through small stacks and heaps, using limited (and constantly changing) instruction sets, and using significant amounts of power to run instructions. The history repeats itself in the blockchain landscape these days. Application developers are limited to stack sizes of a few kilobytes to several megabytes at best. Persistent storage is expensive and limited. Programmers are bound to using cumbersome APIs that make hidden assumptions in terms of numbers of executed instructions. And, moreover, most chains operate inefficiently, burning too much power per executed transaction. This not only limits the types of applications that can be deployed on chain, but also increases development and testing time (and cost).

As opposed to all existing blockchains, the IC brings modern programming to on-chain developers, allowing them to use time for creativity rather than fixing memory packing issues or spreading computation in small iterations that do not hit instruction limits. The IC programming model offers orthogonal persistence, large stack and heap spaces (4GB), stable storage of 48GB (with plans for increase) in mainstream languages, such as Rust, or even Python.

To get a view of the top performers in the blockchain industry, it's useful to compare across common metrics. Here we build a table that does such a comparison. All data correct as of December 2022.

Metrics explanations and references are given below.

L1 Comparisons
Metrics / L1	ICP	ADA	AVAX	ALGO	ETH	NEAR	SOL
Average TPS	9’720	2.37	49.52	15.5	11.1	6	286
Average Finality	0.96secs		2.3secs	3.5secs	15mins	2.4secs
Average Tx Cost	$0.0000022	$0.1	0.01	$0.00025	$2.39	$0.0031	$0.000026
Fixed tx cost	Yes	no	no	no	no	no	no
Energy Consumption	0.008	51.59	4.76	2.7			0.166
On-Chain Storage	$5 (3.95T cycles x 1XDR)	$17,035 - $113,507 (53,236 – 354708ADA)	$206,875 (15,62 5AVAX)		15,494,409 (12,643.75 ETH)		48,625 (3,477.69 SOL)
Nodes / Validators	549	1050	1195	1530		798	1872
Nakamoto coefficient		22
repos, DAOs, dapps, tokens	full web3	nft & defi		y		y
Safety threshold		51%
Burnt fees	443ICP		2,047,382.09AVAX
Max stack size	4 GiB			4 MB	32 KiB	256 KiB
Max persisted memory	52 GiB			1 MB	2^261 B (however, 15,494,409$ per GiB)	32 KiB

Metrics

TPS measures the transactions processed per second - note that the interval over which these are measured does vary across chains.
Finality refers to the amount of time that passes between the proposal of a new valid block containing transactions until the block has been finalized and its content is guaranteed to not be reversed or modified (for some blockchains, e.g., Bitcoin, this guarantee can only be probabilistic).
Tx Cost measures the cost of a transaction (Cardano and Ethereum figures found in Messari dashboard)
Energy Consumption measures the energy consumption
On-Chain Storage measures the cost of storing data on-chain
Nodes/Validators measures the number of nodes
Nakamoto Coefficient is the count of malicious nodes needed to collude to prevent a blockchain from functioning properly
Market Cap is the price of the coin times the number of coins
Price denotes the dollar price of the coin
Circulating Supply is the count of coins in circulation
Volume is the amount transferred in a given period
Users counts the users on the network
Repos, DAOs, Dapps, Tokens showcases the leading applications supported by the network
Safety Threshold is the assumption underlying the safety property of a blockchain
Burnt Fees counts the amount of tokens burnt by fees

References

ICP : IC Dashboard
ADA : Cardano explorer and cexplorer
AVAX : Snowtrace and Avalanche explorer
ALGO : Algorand website and Algorand metrics site
ETH : Etherscan
NEAR : Near explorer
SOL : Solana website and Solana beach

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+Whether they were writing games, operating systems or text editing applications, in the 70s, 80s and early 90s, developers always had to face limitations imposed by hardware. Applications were constrained to accessing a few kilobytes of memory through small stacks and heaps, using limited (and constantly changing) instruction sets, and using significant amounts of power to run instructions. The history repeats itself in the blockchain landscape these days. Application developers are limited to stack sizes of a few kilobytes to several megabytes at best. Persistent storage is expensive and limited. Programmers are bound to using cumbersome APIs that make hidden assumptions in terms of numbers of executed instructions. And, moreover, most chains operate inefficiently, burning too much power per executed transaction. This not only limits the types of applications that can be deployed on chain, but also increases development and testing time (and cost).
+As opposed to all existing blockchains, the IC brings modern programming to on-chain developers, allowing them to use time for creativity rather than fixing memory packing issues or spreading computation in small iterations that do not hit instruction limits. The IC programming model offers orthogonal persistence, large stack and heap spaces (4GB), stable storage of 48GB (with plans for increase) in mainstream languages, such as Rust, or even Python.
 To get a view of the top performers in the blockchain industry, it's useful to compare across common metrics. Here we build a table that does such a comparison. All data correct as of December 2022.