Here’s why Solana-based Jupiter sees nearly half of transactions fail in the last month

Jupiter DEX is facing increasing scrutiny as users have noticed a transaction failure rate of nearly 50%, raising concerns and questions about the platform’s performance. Many are looking for explanations and wondering what measures are being taken to address the issue. In this article, we will explore the situation in detail, examining the factors contributing to the high failure rate and what measures are being taken to improve the user experience on the platform.

High Failure Rate: Causes and Concerns

Over the past 30 days, excluding missing data from August 2nd and 3rd, the average failure rate on Jupyter was 42.89%. This has led to an increase in users wondering what the reasons behind these crashes are and seeking clarification on what measures are being taken to improve the platform’s performance.

One frustration for many users is that they are still paying fees for failed transactions. While this may seem unfair at first glance, it is an inherent aspect of blockchain technology. Every transaction, whether successful or unsuccessful, uses network resources such as computing power and block space. Even if a transaction fails, the verifier still processes it until a problem causes it to go wrong. Since the network is still being used to process the request, the fees compensate for those computing resources.

Increasing slip resistance is a risky solution.

To avoid duplicate fees, users often increase their tolerance for slippage to ensure their transactions go through. This increase increases the probability of a successful transaction because it gives the network permission to complete the swap even if the price changes slightly from the original price.

But increased slippage opens the door to another risk: bots rushing in. These bots can spot high-slippage transactions and execute their trades just before the user does, buying the asset at a lower price and selling it back at a higher price determined by the user’s slippage. This results in users getting less favorable prices on their swaps, which in effect costs them more than just transaction fees.

How Pre-Operations Work on Blockchain Networks for Smart Contracts

The diagram from Hacken shows how front-running works on Ethereum, but the concept also applies to Solana and other smart blockchains.

• Step 1A user initiates a transaction on the network, with the aim of interacting with a smart contract.
• Step 2The first contestant (usually a bot) monitors the network and detects the user’s transaction.
• Step 3The leader contestant creates a new transaction with a higher gas price. The higher gas price encourages investigators to prioritize processing the leader contestant’s transaction over the original user’s transaction.
• Step 4The blockchain network prioritizes transactions based on gas price. Since the leader’s transaction offers a higher gas price than the user’s, it is processed first.
• Step 5The user’s transaction gets less favorable terms or even fails, resulting in financial losses or missed opportunities.

On Jupiter My own words:

The majority of these failed transactions come from arbitrage bots that use the software when an arbitrage opportunity approaches, hoping to complete a transaction when the opportunity arises – this results in a high failure rate. For our users on Jupiter UI, transaction success rates are actually over 90%!

However, preemption relies heavily on the reliability of the RPC (Remote Procedure Call) providers used to interact with the network. An RPC provider is an intermediary between the user and the blockchain and transmits transaction data to the network. If an RPC provider is not reputable, it may be able to enable or even participate in preemption by sharing transaction details with bots or manipulating the order in which transactions are submitted. On the other hand, reputable RPC providers are expected to adhere to ethical standards and ensure that they do not exploit users or allow such behavior to occur.

Another reason for the high rate of failed transactions is the ongoing craze for meme coins, with tens of thousands of new tokens being created every day. Many of these coins lack sufficient liquidity, meaning there aren’t enough tokens on the market to complete transactions. When users try to buy or sell these low-liquidity tokens, transactions can fail because the transaction can’t be completed.

Productivity constraints and delays in processing orders

While the memecoin price surge contributes to the failure rate, Jupiter’s automated slippage and gas calculation features also play a role. These features, which generally work well in stable market conditions, struggle during periods of high volatility. Additionally, the platform has struggled with issues with its free API, which has been exploited by users who bypass price limits by running new machines. This exploitation has increased operational costs and risked degrading service for legitimate users.

Furthermore, Jupyter’s throughput is currently insufficient, especially since it is handling a huge volume of requests, causing the retry logic to slow down to more than 25 seconds.

conclusion

Jupiter DEX faces some tough challenges, including a high transaction failure rate, proactive risk, and infrastructure bottlenecks. These aren’t just minor issues — they directly impact user trust and the platform’s ability to perform well. The team is working hard to fix these issues, but one key question remains: Can Jupiter not only solve these immediate problems but also keep up with the growing demands of the DeFi space?