Overview

When a transaction fails on the Ethereum network, it is still charged. Transactions fail when there is not enough gas to pay for a transaction, or when a smart contract rejects a transaction. \n

You are still charged for failed transactions because miners need to confirm transactions to the chain whether they succeed or fail. Therefore, you are paying for that regardless of whether your transaction goes through. \n

If an approval or simple transaction fails, it's probably a gas problem. If your transaction failed in UniSwap or another DEX during a trade, it's probably a slippage problem. \n

If you are getting failed transactions, you need to add more gas by customizing gas before initiating the transaction (you can also "accelerate" a transaction by paying later, e.g. click "accelerate" in MetaMask) and/or, if you are trading on a DEX, you need to increase slippage (done in the configuration of DEXs like Uniswap, SuhiSwap, etc.).

Methodology

The intention of this dashboard is to analyze which user action results in the highest rate of failed transactions related to Sushiswap DEX on Ethereum.

To do so, we are gonna compare the swapping, borrowing, lending, and adding or removing liquidity on Sushiswap-Ethereum. The main metrics analyzed for each of them are:

• Number of succeed and failed transactions
• Failure rate
• Number of transactions vs failure rate

At the end of the analysis all of the actions are compared to each other to see which of them are the most related to a failed transactions.

Borrowing transactions

Looking at the borrowing transactions, we can see how in numbers the transactions increased since the Ethereum merge, passing from below 15k to around 20k. If we consider the succeed and failed transactions, we can see how the proportions seems to be stable over time.

Considering the second chart, we can see how the failure rate is stablished at a range between 10 and 20%, going up and down.

Finally, taking a look at the relationship between the number of transactions and the failure rate, we can see how it seems that there is a low positive correlation showing that when the number of transactions increase in the major of the cases the failure rate increase as well. But, there are some higher points in a low number of transactions.

Lending activity

Looking at the lending transactions, we can see how the numbers have been constant over time. If we consider the succeed and failed transactions, we can see how the proportions seems to be stable over time.

Considering the failure rate, we can see how it decreased from 2.5 to 2% over the past month.

Finally ,in this case, there is a strong positive correlation showing that when the number of transactions increase in the major of the cases the failure rate increase as well.

Liquidity actions: addings and removings activity

Regarding the liquidity actions, we can see how in numbers the transactions increased a little bit since the Ethereum merge, passing from around 40 to around 50. If we consider the succeed and failed transactions, we can see how the amjor fo the days have no failed transactions.

Considering the second chart, we can see how it cannot be evaluate properly due to the aforementioned situation.

Finally, taking a look at the relationship between the number of transactions and the failure rate, we can see that maybe a relationship between both metrics could be stablished but it is not signtificant due to the low number of available days with failed transactions.

Swaps activity

Looking at the swaps, we can see how in numbers the transactions remained constant over time. If we consider the succeed and failed transactions, we can see how the proportions seems to be stable over time.

Considering the second chart, we can see how the failure rate seems to be increasing in a smooth way day by day passing from 2.5 to over 3% since the last month.

Finally, taking a look at the relationship between the number of transactions and the failure rate, here we cannot see any correlation between both parameters.

Comparison between all actions

Finally, taking a global look considering all of the evaluated type of Sushiswap actions on Ethereum network, we can see how the swaps are the most common actions, having more than 600k daily transactions, with a huge difference against lending and borrowing actions having around 10k daily transactions. Low activity has been observed on the liquidity actions.

In terms of failure rate, we can see how in this case, the highest rate of failed transactions is observed in the borrowing actions, with a big difference against the other options, who have similar failure rate. The first one have around 15% of failure rate while the others are below 5%.

Key findings

• The borrowing transactions increased over the past month.
• If we consider the succeed and failed borrowing transactions, we can see how the proportions seems to be stable over time.
• There is a low positive correlation showing that when the number of borrow transactions increase in the major of the cases the failure rate increase as well.
• The number of lending actions have been constant over time.
• If we consider the succeed and failed lending transactions, we can see how the proportions seems to be stable over time.
• There is a strong positive correlation showing that when the number of lendingtransactions increase in the major of the cases the failure rate increase as well.
• The liquidity actions increased a little bit over the past month.
• There is no liquidity failed actions in the major of the days.
• The swaps remained constant over time.
• If we consider the succeed and failed swap transactions, we can see how the proportions seems to be stable over time.
• The swaps are the most common actions, having more than 600k daily transactions, with a huge difference against lending and borrowing actions having around 10k daily transactions. Low activity has been observed on the liquidity actions.
• The highest rate of failed transactions is observed in the borrowing actions, with a big difference against the other options, who have similar failure rate. The first one have around 15% of failure rate while the others are below 5%.

This dashboard can be update by granularity and timeframe. To do so, you can follow these steps:

• Choose the granularity of the analysis: day, week or month
• Choose the number of months to be evaluated: 1,3,6,9,12,18 or 24
• Put the selected options to the parameter buttons situated on the top of the dashboard
• Finally, press the “Apply All Parameters” button to refresh the results