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Photon

# Photon Active users (monthly) Methodology

## 1. Overview

The Photon Monthly Active Users (MAU) metric measures the number of unique users who have interacted with the Photon protocol on Solana over a rolling 30-day period. This metric uses a sliding window approach where each day's MAU represents the count of distinct users who performed fee-paying transactions within the previous 30 days. The calculation employs HyperLogLog data structures for memory-efficient distinct user counting and provides breakdowns across multiple dimensions including chain, user type, and product level.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Solana native token transfers**: Native SOL transfer transactions on the Solana blockchain, specifically transfers directed to designated addresses
- **Solana transaction metadata**: Transaction signatures, timestamps, and signer addresses from Solana blockchain records
- **Photon fee collection addresses**: Hardcoded list of specific Solana addresses that receive protocol fees (`AVUCZyuT35YSuj4RH7fwiyPu82Djn2Hfg7y2ND2XcnZH`)
- **Chain availability filters**: Configuration data for determining which chains are active and available for analysis

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identify user interactions with the Photon protocol by tracking fee-paying transactions
- **Logic**: Filter Solana native token transfers to isolate only those transfers where the recipient address matches the designated Photon fee collection address. Each transfer represents a user interaction that incurred a protocol fee. Extract the transaction signer as the user identifier, along with transaction timestamp and amount details.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` as user identifier, `amount` for fee value, `token_address` standardized to SOL token address

### Component B: Daily active users calculation  
- **Purpose**: Aggregate fee-paying transactions into daily unique user counts
- **Logic**: Group the filtered fee transactions by day using timestamp truncation. For each day, extract the distinct set of transaction signers who paid fees to the protocol. Classify all users as 'Trader' user type since they are engaging in fee-paying activities. Apply chain availability filters to ensure only data from active chains is included.
- **Key fields**: `timestamp` truncated to day granularity, `user_type` set to 'Trader', `chain` identifier, `product_id` for Photon, `user_dau` containing the unique user address

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user counts into memory-efficient data structures for distinct counting across time windows
- **Logic**: Transform the daily active user data into HyperLogLog sketches using an initialization function with 15-bit precision. Group by timestamp, chain, user type, and product to create separate HLL sketches for each dimensional combination. This allows efficient merging of user counts across multiple days without storing individual user addresses.
- **Key fields**: `hll` sketch containing the compressed user count data, dimensional grouping fields preserved from DAU calculation

### Component D: Rolling window construction
- **Purpose**: Create a 30-day sliding window framework for calculating monthly active users
- **Logic**: Generate a datespine covering all days from the earliest available data through yesterday. For each day in the datespine, create join relationships to the 30 previous days (including the current day) using timestamp arithmetic. This creates a comprehensive mapping where each target day is associated with all days in its 30-day lookback period.
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window

### Component E: Monthly active user aggregation
- **Purpose**: Calculate the distinct user count across each 30-day rolling window
- **Logic**: Join the rolling window framework with the daily HLL sketches to associate each target day with its corresponding 30-day period of user data. Merge the HyperLogLog sketches across all days within each window using partial merge functions, then extract the final distinct count. This produces a MAU value for each day representing unique users active in the preceding 30 days.
- **Key fields**: `timestamp` for the target date, merged `hll` sketch, dimensional breakdown fields

### Component F: Multi-dimensional breakdowns
- **Purpose**: Provide MAU calculations across different analytical dimensions
- **Logic**: Create separate aggregations of the monthly active user data across four dimensional breakdowns: project-level (overall totals), chain-level (by blockchain), user-type-level (by user classification), and product-level (by specific product). Each breakdown nullifies non-relevant dimensional fields while preserving the target dimension. Union all dimensional views together to create a comprehensive dataset.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` with nulls for non-applicable dimensions, `user_mau` containing the final count

### Final aggregation  
- **Combining components**: Union the four dimensional breakdown tables (project, chain, user type, product level) into a single comprehensive result set containing all MAU calculations
- **Schema standardization**: Apply the finalization macro to ensure consistent schema structure, proper data types, and standardized metadata fields across all dimensional breakdowns
- **Missing data handling**: Fill any missing days with zero values using the specified fill strategy to ensure continuous time series data
- **Final calculation**: Each row represents the count of unique users who interacted with Photon within a 30-day rolling window ending on the specified timestamp, broken down by the indicated dimensional grouping

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 30-day window.

Photon

Active users (monthly)

Active users (monthly)

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