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Blockchains (L2)

Soneium

# Soneium Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who performed at least one transaction on the Soneium blockchain within a rolling 30-day period. This metric provides insights into user engagement and adoption by tracking unique transaction senders over monthly windows. The calculation uses HyperLogLog (HLL) data structures for efficient cardinality estimation of unique users across large datasets and time periods.

## 2. Data sources

- **Soneium blockchain transactions**: Raw transaction records from the Soneium Layer 2 blockchain containing transaction metadata including sender addresses and timestamps
- **Chain availability data**: Token Terminal's internal chain availability filters to ensure data quality and exclude periods of incomplete data
- **Table modification tracking**: Internal metadata tracking when source data tables were last modified for efficient incremental processing

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identifies active users from raw Soneium blockchain transactions
- **Logic**: Extracts transaction sender addresses from Soneium blockchain transaction records. Each transaction sender (`from_address`) is classified as a "Transaction sender" user type. The component applies chain availability filters to ensure data quality and exclude incomplete data periods. All users are categorized under the "blockchains_l2" market sector and "blockchain" business segment.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identification, `from_address` as the user identifier, and `user_type` classification

### Component B: Daily active users calculation
- **Purpose**: Aggregates unique users per day using HyperLogLog for efficient cardinality estimation
- **Logic**: Groups user transactions by day using timestamp truncation to daily boundaries. Creates distinct records for each unique user address per day, effectively calculating Daily Active Users (DAU). Uses incremental processing to only recalculate recent days when new data arrives. Maintains dimensional breakdowns by chain, user type, and product identifiers.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` as unique user identifier, `chain` and `user_type` for dimensional analysis

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user counts into HyperLogLog sketches for efficient monthly aggregation
- **Logic**: Initializes HyperLogLog data structures for each day's unique user set using precision parameter 15. HLL sketches allow efficient merging of user counts across multiple days without double-counting users who appear on multiple days. Groups data by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` sketch containing compressed representation of unique users, dimensional grouping fields

### Component D: Rolling window creation
- **Purpose**: Creates a 30-day rolling window framework for MAU calculation
- **Logic**: Generates a complete datespine covering all days from the earliest available data to the current date minus one day. For each date in the datespine, creates join relationships with the previous 29 days (total 30-day window). This ensures MAU is calculated consistently even for days with no transaction activity. Uses incremental processing boundaries to optimize computation.
- **Key fields**: `timestamp` for the target date, `joinday` representing each day within the 30-day window

### Component E: Window data joining
- **Purpose**: Combines the rolling window framework with daily HLL sketches
- **Logic**: Joins the 30-day window structure with daily HLL sketches to create a comprehensive dataset where each target date has associated HLL sketches for all days within its 30-day lookback period. Applies incremental filtering with safety margins to ensure data completeness during updates.
- **Key fields**: `timestamp` as target date, `hll` sketches from contributing days, dimensional grouping fields

### Component F: Dimensional MAU calculation
- **Purpose**: Calculates MAU across different dimensional breakdowns
- **Logic**: Performs four separate MAU calculations: 1) Project-level MAU aggregating all dimensions, 2) Chain-level MAU broken down by blockchain, 3) User-type level MAU segmented by user classification, 4) Product-level MAU segmented by product identifier. For each breakdown, merges HLL sketches across the 30-day window using `hll_count.merge_partial()` and extracts the final unique count using `hll_count.extract()`. This approach ensures users active on multiple days within the 30-day period are counted only once.
- **Key fields**: `user_mau` containing the unique monthly active user count, `dimensions` indicating the breakdown type, dimensional identifier fields

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, ensuring each MAU calculation represents a different dimensional view of the same underlying user activity
- **Time boundaries**: Uses daily timestamps with MAU representing unique users active in the 30 days ending on each date
- **Incremental processing**: Applies month-level partitioning with safety margins to recalculate affected periods when source data changes
- **Final calculation**: The `user_mau` field contains the count of unique users who performed at least one transaction within the 30-day rolling window, calculated using HyperLogLog merge operations to ensure accurate cardinality estimation without double-counting users active on multiple days

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

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

Soneium

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown