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Blockchains (L2)

Settlus

# Settlus Active users (daily) Methodology

## 1. Overview

Daily Active Users (DAU) measures the number of unique addresses that initiated transactions on the Settlus blockchain within a given day. This metric quantifies user engagement and adoption by counting distinct transaction senders, providing insight into the protocol's daily usage patterns and growth trajectory. The calculation uses HyperLogLog sketching for efficient unique user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Settlus blockchain transactions**: Complete transaction records from the Settlus network containing block timestamps, transaction hashes, sender addresses, and transaction metadata
- **Token Terminal chain availability metadata**: Operational status and data quality indicators used to filter results based on chain availability periods

## 3. Step-by-step calculation

### Component A: User identification and extraction
- **Purpose**: Identify and extract unique users from Settlus blockchain transaction data
- **Logic**: Extract transaction senders from Settlus blockchain transaction records. Each transaction's `from_address` field represents an active user. The system captures the block timestamp, transaction hash, and sender address for each transaction. All transaction senders are classified as "Transaction sender" user type within the "blockchains_l2" market sector and "blockchain" business segment. The chain identifier is set to "settlus" with no specific product differentiation.
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (user identifier), `transaction_hash` (unique transaction reference), `chain` (network identifier), `user_type` (classification as transaction sender)

### Component B: Daily user aggregation
- **Purpose**: Calculate daily unique active users using HyperLogLog sketching for efficient counting
- **Logic**: Aggregate unique users at the daily level by truncating block timestamps to day boundaries. Create distinct combinations of timestamp, chain, user type, and user address to ensure each user is counted once per day. Generate HyperLogLog sketches with precision 15 for scalable unique user counting. Apply incremental processing to handle new data efficiently while maintaining historical accuracy.
- **Key fields**: `timestamp` (daily boundary), `timestamp_grain` (set to "day"), `user_dau` (unique user address), HyperLogLog sketch data for efficient aggregation

### Component C: Dimensional breakdown creation
- **Purpose**: Create multiple aggregation views for flexible reporting across different dimensions
- **Logic**: Generate four distinct dimensional breakdowns using HyperLogLog merge operations. Create project-level aggregation combining all users across the entire protocol. Generate chain-level breakdown showing users by blockchain network. Produce user-type-level aggregation separating different categories of users. Create product-level breakdown for future product differentiation. Each breakdown uses HLL merge operations to combine sketches while preserving uniqueness guarantees.
- **Key fields**: `dimensions` (aggregation level identifier), `chain_id` (chain-specific breakdown), `user_type` (user category breakdown), `product_id` (product-specific breakdown), merged HLL sketches for each dimension

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with standardized schema. Each row represents a unique combination of timestamp, dimension type, and dimensional values with the corresponding unique user count extracted from merged HyperLogLog sketches.
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Daily boundaries determined by truncating block timestamps to day level in the user's timezone
- **Final calculation**: Extract final unique user counts from merged HyperLogLog sketches using the extract function. Apply chain availability filters to ensure results only include data from periods when the Settlus chain was operationally available and data quality was verified. Fill missing days with zero values to maintain complete time series continuity for reporting purposes.

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

Unique revenue-generating users over a fixed 24-hour window.

Settlus

Active users (daily)

Active users (daily)

Competitive landscape