Infrastructure

bloXroute

# bloXroute Active users (weekly) Methodology

## 1. Overview

BloxRoute Weekly Active Users (WAU) measures the number of unique users who interacted with BloxRoute services within any 7-day rolling period. The metric is calculated by identifying users through fee payments made to BloxRoute's fee collection accounts on Solana, then creating a rolling 7-day window to count distinct active users. This metric provides insight into BloxRoute's user engagement and adoption patterns over time.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Solana native transfers**: Native SOL transfer events from the Solana blockchain, capturing all token movements
- **BloxRoute fee accounts**: Specific Solana addresses that collect fees for BloxRoute services (`HWEoBxYs7ssKuudEjzjmpfJVX7Dvi7wescFsVx2L5yoY`)
- **Transaction metadata**: Block timestamps and transaction IDs from Solana for temporal aggregation and deduplication
- **Transaction signers**: Wallet addresses that initiated transactions, representing the active users

## 3. Step-by-step calculation

### Component A: Fee collection identification
- **Purpose**: Identify user interactions with BloxRoute services through fee payments
- **Logic**: Extract all native SOL transfers directed to BloxRoute's designated fee collection account. Filter out any transfers where the fee account itself is the transaction signer to avoid counting internal operations. Each qualifying transfer represents a user paying for BloxRoute services, with the transaction signer identified as the active user.
- **Key fields**: `block_timestamp` for temporal grouping, `transaction_signer` as the user identifier, `to_address` filtered to BloxRoute fee accounts, and `amount` for the fee value

### Component B: Daily active user aggregation  
- **Purpose**: Create daily snapshots of distinct active users
- **Logic**: Group the fee payment transactions by day using timestamp truncation to daily boundaries. For each day, count the distinct transaction signers to determine the Daily Active Users (DAU). Convert these daily user sets into HyperLogLog (HLL) sketches for efficient storage and subsequent merging operations.
- **Key fields**: `timestamp` truncated to day granularity, `signer` as the distinct user identifier, `chain` set to 'solana', and `user_type` classified as 'User'

### Component C: Rolling window creation
- **Purpose**: Generate 7-day rolling windows for WAU calculation
- **Logic**: Create a datespine covering all days from the earliest DAU data to the most recent day. For each date in the datespine, generate a 7-day window that includes the current day plus the 6 preceding days. This approach ensures consistent window coverage even for days with no activity.
- **Key fields**: `timestamp` for the window end date and `joinday` representing each day within the 7-day window

### Component D: Window aggregation with HLL merging
- **Purpose**: Calculate distinct users across each 7-day window
- **Logic**: For each 7-day window, merge the HLL sketches from all constituent days to produce a single sketch representing all unique users who were active during that period. Extract the final count from the merged HLL sketch to get the estimated number of distinct weekly active users.
- **Key fields**: `timestamp` for the window date, merged `hll` sketches across the window period, and extracted `user_wau` count

### Component E: Dimensional breakdowns
- **Purpose**: Provide WAU metrics across multiple analytical dimensions
- **Logic**: Calculate separate WAU aggregations for different dimensional views: project-level (overall BloxRoute), chain-level (by blockchain), user-type-level (by user classification), and product-level (by specific products). Each breakdown aggregates the appropriate HLL sketches while setting unused dimension columns to null.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `user_type`, and `product_id` for dimensional grouping, with null values for non-applicable dimensions

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, maintaining the same schema across all breakdown types
- **Schema standardization**: Apply the standard metric finalization macro to ensure consistent field naming, data types, and metadata
- **Missing data handling**: Fill gaps in the time series with zero values for days with no activity, ensuring complete temporal coverage
- **Final calculation**: The final WAU value represents the estimated count of distinct users who were active within any 7-day rolling period, calculated using HyperLogLog sketches for scalable distinct counting

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

Unique revenue-generating users over a rolling 7-day window.

bloXroute

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown