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Blockchains (L1)

Sonic

# Sonic Active users (weekly) Methodology

## 1. Overview

The Sonic weekly active users (WAU) metric measures the number of unique addresses that initiated at least one transaction on the Sonic blockchain within a rolling 7-day window. This metric provides insight into user engagement and platform adoption by tracking distinct transaction senders over weekly periods. The calculation uses HyperLogLog algorithms for efficient cardinality estimation and creates multiple dimensional breakdowns to analyze user activity across different segments.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Sonic blockchain transaction records**: Complete transaction history from the Sonic blockchain including transaction metadata, timestamps, and participant addresses
- **Transaction sender addresses**: The `from_address` field identifying the originator of each transaction
- **Block timestamps**: Transaction timing data used to determine daily and weekly activity periods
- **Transaction hashes**: Unique transaction identifiers used for data integrity and deduplication

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify unique users from raw blockchain transaction data
- **Logic**: Processes all Sonic blockchain transactions to identify distinct users. Each transaction's `from_address` is treated as a user, and all users are classified as "Transaction sender" type. The component associates each user activity with the Sonic chain and categorizes the protocol under the "blockchains_l1" market sector and "blockchain" business segment. No product-level differentiation is applied at this stage.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identity, `from_address` as `user_address` for user identification, `user_type` set to "Transaction sender"

### Component B: Daily active user calculation
- **Purpose**: Aggregate unique users by day using HyperLogLog for efficient cardinality estimation
- **Logic**: Groups user activity by calendar day using timestamp truncation to daily boundaries. For each day, creates an HLL sketch containing all unique user addresses that initiated transactions. This approach allows efficient storage and combination of large user sets without storing individual addresses. The component maintains dimensional attributes (chain, user type, product) alongside the HLL sketches for downstream breakdowns.
- **Key fields**: `timestamp` truncated to day granularity, `timestamp_grain` set to "day", `user_dau` as the unique user address, HLL sketch initialization for cardinality estimation

### Component C: Weekly rolling window construction
- **Purpose**: Create 7-day rolling windows for each date to calculate weekly active users
- **Logic**: Generates a complete date spine covering the full time range of available data to ensure no missing dates in the final output. For each date in the spine, creates a 7-day window by joining with the current day plus the previous 6 days. This windowing approach uses timestamp array generation to create all relevant date combinations. The component then joins these date windows with the daily HLL sketches to create a comprehensive view of user activity across rolling weekly periods.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window, HLL sketches from daily calculations

### Component D: Dimensional breakdown calculation
- **Purpose**: Calculate WAU across multiple dimensional views of the data
- **Logic**: Creates four distinct dimensional breakdowns of the weekly active user metric. For each dimension, merges HLL sketches across the 7-day windows and extracts the final cardinality estimate. The breakdowns include: overall project-level WAU (no dimensional filtering), chain-level WAU (grouped by blockchain), user type-level WAU (grouped by user classification), and product-level WAU (grouped by product, though currently null for Sonic). Each breakdown maintains its dimensional context while calculating the same underlying weekly unique user count.
- **Key fields**: `dimensions` indicating the breakdown type, dimensional fields (`chain_id`, `user_type`, `product_id`) with appropriate nulls, `user_wau` as the final unique user count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset containing WAU metrics across all dimension combinations
- **Time aggregation**: Each record represents WAU for a specific date, where the date indicates the end of the 7-day rolling window (e.g., a record dated 2024-01-07 shows unique users active from 2024-01-01 through 2024-01-07)
- **HLL processing**: Final user counts are extracted from merged HLL sketches using HLL extraction functions, providing approximate but highly accurate cardinality estimates
- **Final calculation**: The weekly active user count for any given date represents the distinct number of addresses that initiated at least one transaction within the 7-day period ending on that date, calculated by merging HLL sketches across all days in the window

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

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

SonicS

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape