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Settlus

# Settlus Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed at least one transaction on the Settlus blockchain within any 7-day rolling window. This metric provides insight into user engagement and platform adoption by tracking distinct users over weekly periods. The calculation uses HyperLogLog sketches for efficient distinct user counting across large datasets and multiple dimensional breakdowns.

## 2. Data sources

- **Settlus blockchain transaction records**: Contains transaction data including sender addresses, block timestamps, transaction hashes, and transaction metadata from the Settlus Layer 2 blockchain
- **Generated timestamp sequences**: Complete daily date ranges created programmatically to ensure no gaps in the rolling window calculations

## 3. Step-by-step calculation

### Component A: User transaction extraction
- **Purpose**: Identifies all user addresses that initiated transactions on any given day
- **Logic**: Extracts transaction sender addresses from Settlus blockchain transaction records. Each transaction sender (`from_address`) is classified as a "Transaction sender" user type. Transactions are grouped by day using timestamp truncation to daily granularity. The system captures the block timestamp, transaction hash, and user address for each transaction, associating users with the Settlus chain and blockchain business segment.
- **Key fields**: `user_address` (transaction sender), `block_timestamp` (transaction timing), `transaction_hash` (unique transaction identifier), `chain` (set to "settlus"), `user_type` (set to "Transaction sender")

### Component B: Daily active user aggregation
- **Purpose**: Creates daily snapshots of unique active users with HyperLogLog optimization
- **Logic**: Processes the user transaction data to identify distinct user addresses per day. Groups data by timestamp (truncated to day), chain, user type, and product dimensions. Initializes HyperLogLog sketches for each daily user set to enable efficient distinct counting in subsequent aggregations. This creates the foundation for rolling window calculations by providing daily user activity summaries.
- **Key fields**: `timestamp` (daily granularity), `user_dau` (unique user addresses), `hll` (HyperLogLog sketch), `chain_id`, `user_type`, `product_id`

### Component C: Date spine generation  
- **Purpose**: Creates a complete sequence of daily timestamps to handle missing data periods
- **Logic**: Generates a continuous daily timestamp array from the earliest available data date through the current date minus one day. This ensures that rolling window calculations don't skip days that may have zero user activity. Creates join tables that map each date to its preceding 6 days, forming the foundation for 7-day rolling windows.
- **Key fields**: `timestamp` (daily sequence), `joinday` (dates within each 7-day window)

### Component D: Rolling window construction
- **Purpose**: Builds 7-day rolling windows for each date using the date spine
- **Logic**: Joins the date spine with daily user data to create rolling 7-day windows. For each date, includes user data from that date plus the previous 6 days. Uses inner joins between the date spine join tables and daily HLL data to ensure complete window coverage. Maintains dimensional groupings (chain, user type, product) throughout the window construction process.
- **Key fields**: `timestamp` (window end date), `hll` (daily HyperLogLog sketches), dimensional grouping fields

### Final aggregation
- **Combining components**: Merges HyperLogLog sketches across each 7-day window using HLL merge operations to calculate distinct users over the rolling period. Creates four separate dimensional breakdowns: project-level (all dimensions null), chain-level (grouped by chain), user type-level (grouped by user type), and product-level (grouped by product). Combines all breakdowns using union operations.
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the previous 6 days. Windows are calculated using daily granularity with timestamp truncation.
- **Final calculation**: Extracts the final distinct user count from merged HyperLogLog sketches using HLL extract functions. Applies schema standardization through the finalize metric macro, fills missing periods with zeros, and uses incremental processing with partition-based updates for efficiency. The result is daily WAU values showing unique users active in each 7-day period ending on that date.

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

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

Settlus

Active users (weekly)

Active users (weekly)

Competitive landscape