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Blockchains (L2)

Settlus

# Settlus Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Settlus measures the number of unique addresses that have sent transactions on the Settlus blockchain within any 30-day rolling window. This metric provides insight into user engagement and platform adoption by tracking distinct addresses that actively initiate transactions over monthly periods. The calculation uses a rolling window approach where each day's MAU represents the count of unique users who were active during that day and the previous 29 days.

## 2. Data sources

- **Settlus blockchain transactions**: Raw transaction records from the Settlus blockchain network containing transaction metadata, block timestamps, and sender addresses
- **Transaction sender addresses**: The `from_address` field identifying the wallet addresses that initiated transactions on Settlus
- **Block timestamps**: Timestamp data from Settlus blocks to establish when transactions occurred

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identify unique addresses that sent transactions each day on Settlus
- **Logic**: Extract transaction records from Settlus blockchain data and identify unique sender addresses per day. Each transaction's `from_address` field represents a user, and the `block_timestamp` determines the activity date. Users are classified as "Transaction sender" type since they actively initiated transactions. The calculation truncates timestamps to day granularity and removes duplicate addresses within each day using distinct operations.
- **Key fields**: `block_timestamp` (truncated to day), `from_address` as `user_address`, `user_type` set to "Transaction sender", static values for `chain` ("settlus"), `market_sector_id` ("blockchains_l2"), and `business_segment_id` ("blockchain")

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into memory-efficient data structures for large-scale unique counting
- **Logic**: Transform the daily distinct user lists into HyperLogLog (HLL) sketches using BigQuery's HLL functions. Each day's unique user set is initialized as an HLL sketch with 15-bit precision, allowing efficient approximate counting when merging across multiple days. This approach enables scalable computation of unique users across 30-day windows without storing individual addresses.
- **Key fields**: `timestamp` at day granularity, dimensional grouping fields (`chain_id`, `user_type`, `product_id`), and `hll` sketch containing the day's unique users

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each date to capture monthly activity periods
- **Logic**: Generate a continuous date spine covering the full data range to handle missing dates, then create rolling 30-day windows by joining each date with its previous 29 days. For each target date, the system identifies all days within the 30-day lookback period (current day plus previous 29 days) and prepares to aggregate user activity across this window.
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window

### Component D: Window-based user aggregation
- **Purpose**: Calculate unique user counts across each 30-day window using HLL sketch merging
- **Logic**: For each 30-day window, collect the HLL sketches from all constituent days and merge them using HLL merge operations. This produces an approximate count of unique users who were active during any day within the 30-day period. The merging process automatically handles overlapping users across multiple days within the window.
- **Key fields**: Window `timestamp`, merged `hll` sketches, dimensional breakdown fields

### Component E: Dimensional breakdowns
- **Purpose**: Calculate MAU across different analytical dimensions for flexible reporting
- **Logic**: Create separate aggregations for project-level (all data combined), chain-level, user-type-level, and product-level breakdowns. Each breakdown groups the window data by the relevant dimensions and applies HLL extraction to convert merged sketches into final user counts. Non-relevant dimension fields are set to null for each breakdown type.
- **Key fields**: `dimensions` indicating breakdown type, relevant grouping fields populated while others set to null, `user_mau` as the final extracted count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set containing MAU values across different analytical levels
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Each row represents a single day's MAU value (30-day rolling window ending on that date), with monthly partitioning for efficient storage and querying
- **Final calculation**: The `user_mau` field contains the approximate count of unique addresses that sent transactions during the 30-day window ending on each date, calculated by extracting the final count from merged HLL sketches using `hll_count.extract(hll_count.merge_partial(hll))`

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

Unique revenue-generating users over a rolling 30-day window.

Settlus

Active users (monthly)

Active users (monthly)

Competitive landscape