Infrastructure

bloXroute

# bloXroute Active users (monthly) Methodology

## 1. Overview

The BloxRoute Monthly Active Users (MAU) metric measures the number of unique users who have interacted with BloxRoute services within a rolling 30-day window. This metric identifies users through fee payment transactions on the Solana blockchain and uses HyperLogLog data structures for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Solana native transfers**: On-chain SOL transfer events from the Solana blockchain that represent fee payments to BloxRoute services
- **Transaction signatures**: Solana transaction metadata including transaction signers who initiate fee payments  
- **Block timestamps**: Temporal data from Solana blocks to establish when fee payments occurred
- **BloxRoute fee addresses**: Specific Solana addresses that collect service fees (`HWEoBxYs7ssKuudEjzjmpfJVX7Dvi7wescFsVx2L5yoY`)

## 3. Step-by-step calculation

### Component A: Fee payment identification
- **Purpose**: Identifies user interactions with BloxRoute services through fee payments
- **Logic**: Filters Solana native transfer events to isolate transfers sent to BloxRoute fee collection addresses. Excludes internal transfers where the fee collection addresses are also the senders to avoid counting operational transfers. Each transfer represents a user interaction with BloxRoute services and includes the transaction signer as the user identifier.
- **Key fields**: `block_timestamp` for temporal boundaries, `transaction_signer` as user identifier, `to_address` filtered to BloxRoute fee addresses, `amount` representing fee payments in SOL

### Component B: Daily active user calculation  
- **Purpose**: Converts fee payment transactions into daily distinct user counts
- **Logic**: Groups fee payment transactions by day-truncated timestamps and extracts unique transaction signers per day. Each unique signer on a given day represents one daily active user. Maintains dimensional attributes including chain identifier (Solana), user type classification as 'User', and product identifiers for service categorization.
- **Key fields**: `timestamp` truncated to day granularity, `signer` as unique user identifier, `chain` set to 'solana', `user_type` classified as 'User', `product_id` for service categorization

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user lists into efficient data structures for distinct counting across time windows
- **Logic**: Transforms daily active user data into HyperLogLog sketches using a precision parameter of 15 bits. Groups data by timestamp, timestamp grain, chain, user type, and product dimensions. Each HLL sketch represents the distinct users for a specific day and dimension combination.
- **Key fields**: `hll` sketches initialized from daily user data, dimensional grouping keys preserved for downstream aggregation

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a complete datespine covering the full time range of available data. For each day in the datespine, creates a 30-day lookback window using timestamp array generation. This approach ensures consistent window boundaries and handles missing days properly by creating explicit date ranges for window aggregation.
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day lookback window

### Component E: Window aggregation
- **Purpose**: Combines HLL sketches across 30-day windows to calculate MAU
- **Logic**: Joins the rolling window structure with daily HLL sketches, matching each window day with corresponding daily HLL data. Merges HLL sketches across the entire 30-day period for each target date and dimension combination. Extracts the final distinct user count from the merged HLL sketch.
- **Key fields**: `timestamp` as target date, merged `hll` sketches across window period, dimensional attributes preserved

### Final aggregation
- **Combining components**: Creates separate MAU calculations for four dimensional breakdowns: project-level (all dimensions aggregated), chain-level, user-type-level, and product-level. Each breakdown nullifies unused dimensional attributes and maintains distinct user counts through HLL merging.
- **Time aggregation**: Uses rolling 30-day windows calculated daily, where each day represents the MAU for the preceding 30-day period including that day
- **Final calculation**: Extracts distinct user counts from merged HLL sketches and combines all dimensional breakdowns into a unified result set. Applies schema standardization and fills missing time periods with zero values to ensure complete time series data.

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

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

bloXroute

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown