Parameter Description

• n_days: Loockback period. If set to 0, start_date and end_date are used.

• start_date: Data collection start date.

• end_date: Data collection end date.

Transaction Fees

Every Solana transaction requires a base fee (SOL) to compensate validators for processing the transaction. An optional prioritization fee is also available to increase the probability that the transaction is processed by the current leader (validator).

After SIMD-0096 100% of the priority fee is paid directly to the validator processing the transaction.

Base fee is instead 50% burnt and 50% distributed to validators.

Studying transaction dynamics is crucial to assess the impact of changes in the percentage of fees burned. Analyzing the ratio between vote and non-vote transactions, the TPS distribution, and the priority fees per transaction (PF/tx) provides key insights into network behavior. Shifts in these metrics help evaluate whether fee adjustments influence transaction inclusion, validator incentives, and overall network efficiency. Understanding these trends ensures that changes in Solana’s fee structure optimize resource allocation without negatively impacting transaction throughput or user costs.

Solana Transaction Topology

Solana transactions are categorized into two types: vote and non-vote. Vote transactions are executed by validators to participate in consensus, while non-vote transactions represent standard user activity, such as transfers, swaps, and program interactions.

Cross-Program Invocations and Priority Fees

Tracking Cross-Program Invocations (CPIs) is essential for understanding the dynamics of Priority Fees (PFs) on Solana, especially within the framework of local fee markets. CPIs occur when a transaction calls multiple smart contracts, increasing compute unit (CU) consumption and influencing how fees are distributed across different parts of the network.

Since PFs are tied to CU usage, transactions with high CPI depth can significantly impact block congestion and fee competition. By analyzing CPIs, we gain insights into:

• How fees concentrate across different programs
• Which transactions contribute most to network congestion
• The impact of complex contract interactions on fee dynamics

This helps optimize fee structures, ensuring that local fee markets effectively prioritize high-value transactions without disproportionately disadvantaging smaller users.