NEAR Performance
Q5. How does NEAR stock up to other L1s in terms of speed and performance? How fast is NEAR, and compare it with other blockchain speeds in terms of transactions per minute and percentage of transactions that fail.
Introduction
In this dashboard, I will investigate how NEAR is doing in comparison to other L1 networks, namely Solana, Ethereum and Avalanche.
This dashboard includes the following ==metrics==:
- The total number of transactions for each network
- The number of transactions per minute for each network
- The total daily count for successful and for failed transactions per network
- The % of failed transactions for each network
A few notes on ==methodology==:
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In order to calculate the number of transactions per minute for each blockchain, I took the number of daily transactions and divided it by the number of minutes - this way we can see the average number of transactions per minute.
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I have only included data from the past 30 days, due to Avalanche’s tables still being backfilled.
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I have excluded Solana from the percentage of failed transactions comparison, as Solana has way too many transactions overall, resulting in a very high fail %. The comparison just didn’t seem fair enough, given that Solana tends to double-count transactions (with cases where connecting a wallet to a protocol counts as a transaction sometimes).
The huge dominance of Solana’s transactions makes it quite difficult to compare the networks fairly. What we can see here is that over 96% of daily transactions belong to Solana - in order to see Near’s performance better, we need to exclude Solana from the rest of the graphs.
By looking at the transactions executed per minute for each network, we can see that Solana is still the winner here - but the comparison is not fair due to the reasons mentioned above. The next step is to look at the same metrics again, but without Solana.
What does the more clear data tell us? We can see that NEAR has quite a big number of daily transactions and that it is a fairly fast network. By comparing it to the other two chains, we can see that it has more daily transactions than Avalanche, and it is also ‘faster’ than Avalanche - as it has more transactions per minute. We can also see that NEAR has fewer daily transactions than Ethereum, and so fewer transactions per minute executed. However, given that it is a smaller chain than Ethereum, these results make sense - in my opinion, beating Avalanche on these numbers is a big achievement for NEAR already!
The next point of comparison is to look at the counts of failed versus successful transactions - again, sadly excluding Solana.
Failed transactions
Firstly, we can see that NEAR still beats Avalanche when it comes to the daily number of successful transactions, with Ethereum having 5x more successful transactions on average compared to NEAR. We can also see that NEAR has more failed transactions than both Ethereum and Avalanche - which is not good news. In the past 30 days, NEAR had almost 2M of failed transactions, in comparison to 1.4M for Ethereum and only 677k for Avalanche… Given that the total numbers of transactions vary for each network, the most fair comparison is by looking at the percentage of failed transactions per each network, which is shown below.
What does the % tell us? Sadly, NEAR has the biggest percentage of failed transactions, followed by Avalanche with slightly less and Ethereum with the smallest proportion of failed transactions.
Why do NEAR transactions seem to fail so much, in comparison to other networks?
I have queried all of NEAR’s failed transactions, shown in the table on the left. By investigating them one by one on NEAR Explorer, we can see some of the main reasons why transactions fail. One of the main reasons for failure is "error": {"type": "lackBalanceForState"…
which translates to Error: The account wouldn't have enough balance to cover storage
.
Another popular error is the "error": {"type": "functionCallError"…
, which translates to `Type erased error from `External trait implementation
. More error messages can be found on NEAR’s GitHub - and it is quite interesting to see all of the reasoning behind failed transactions!
Conclusion
This dashboard has investigated NEAR’s Performance from a few different angles. I have looked into the total number of daily transactions in the past month and compared it to other blockchains. I have also done the comparison for the transactions executed per minute, as well as the overall counts of successful and failed transactions. The results were quite surprising to me, with NEAR having the biggest percentage of failed transactions - and in order to understand why this is the case, I have looked into the failed transactions and tried to understand the error messages.