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Blockchains (L1)

Sonic

# Sonic Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that sent at least one transaction on the Sonic blockchain within a rolling 7-day period. This metric provides insights into user engagement and network activity by tracking distinct addresses that actively participate in transactions over weekly timeframes. The calculation uses HyperLogLog algorithms for efficient distinct counting across large transaction datasets.

## 2. Data sources

- **Sonic blockchain transactions**: Raw transaction records from the Sonic network containing transaction metadata and sender addresses
- **Transaction sender addresses**: The `from_address` field from each Sonic transaction, representing the address initiating the transaction
- **Daily transaction activity**: Time-series data of all transactions processed on the Sonic blockchain

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identifies and classifies unique user addresses from Sonic blockchain transactions
- **Logic**: Extracts the `from_address` field from each Sonic transaction record and classifies it as a "Transaction sender" user type. Each address is associated with the transaction timestamp, chain identifier (sonic), market sector (blockchains_l1), and business segment (blockchain). The component ensures proper data typing and creates a standardized user fact table.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for deduplication, `from_address` as the user identifier, and classification fields for dimensional analysis

### Component B: Daily active user calculation
- **Purpose**: Calculates daily distinct active users by aggregating unique addresses per day
- **Logic**: Groups transactions by day using timestamp truncation and identifies distinct user addresses within each 24-hour period. Uses HyperLogLog initialization to create efficient sketches for distinct counting. The component filters data incrementally to process only recent changes while maintaining historical accuracy.
- **Key fields**: `timestamp` truncated to day grain, `user_address` as the distinct identifier for counting, and dimensional attributes like `chain`, `user_type`, and `product_id`

### Component C: Weekly active user aggregation
- **Purpose**: Creates rolling 7-day windows and aggregates daily active users into weekly active users
- **Logic**: Generates a complete datespine of all days and creates 7-day rolling windows by joining each day with its 6 preceding days. For each window, merges HyperLogLog sketches from the daily active user data and extracts the distinct count. Calculates breakdowns across multiple dimensions including chain-level, project-level, user type, and product categories. Uses incremental processing with safety margins to handle data updates efficiently.
- **Key fields**: `timestamp` for the window end date, `user_wau` as the weekly distinct count, and `dimensions` to categorize different aggregation levels

### Final aggregation
- **Combining components**: Selects weekly active user data specifically for chain-level and project-level dimensions, filtering out other dimensional breakdowns
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Maintains daily granularity with each day representing the end of a 7-day rolling window
- **Final calculation**: Renames `user_wau` to `active_addresses_weekly` and applies standardized metric schema with zero-filling for missing days to ensure complete time series coverage

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

Unique active addresses over a rolling 7-day window.

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Active addresses (weekly)

Active addresses (weekly)

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