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Blockchains (L2)

Unichain

# Unichain Active users (monthly) Methodology

## 1. Overview

The Unichain Monthly Active Users (MAU) metric measures the number of unique users who performed at least one transaction on the Unichain blockchain within any 30-day rolling window. This metric provides insight into user engagement and adoption trends by tracking distinct transaction senders over monthly periods. The calculation uses HyperLogLog probabilistic data structures for memory-efficient approximate distinct counting across large datasets, with results broken down by multiple dimensions including chain, user type, and product categories.

## 2. Data sources

The MAU calculation relies on the following raw data sources:
- **Unichain transaction records**: Complete transaction logs from the Unichain blockchain containing transaction metadata, timestamps, and sender addresses
- **Chain availability metadata**: System metadata used to filter results based on chain operational status and data availability periods

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify unique users from raw transaction activity
- **Logic**: Extract transaction senders from Unichain blockchain transaction logs. Each unique `from_address` represents a distinct user performing blockchain activity. Transactions are classified with standardized metadata including chain identifier ('unichain'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender'). The `product_id` field is set to null as Unichain operates as a general-purpose blockchain without specific product categorization.
- **Key fields**: `block_timestamp` for activity timing, `transaction_hash` for transaction identification, `from_address` as the user identifier, and classification fields for dimensional analysis

### Component B: Daily active users calculation  
- **Purpose**: Aggregate users into daily distinct counts
- **Logic**: Group transaction activity by day using timestamp truncation to day boundaries. Count distinct user addresses (`user_address`) for each day to create Daily Active User (DAU) counts. Apply incremental processing logic to efficiently update only recent time periods, using a configurable lookback window to ensure data consistency. Filter results based on chain availability to exclude periods when data may be incomplete or unreliable.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing the distinct user address, `chain` identifier, `user_type` classification, and `product_id` for dimensional breakdowns

### Component C: HyperLogLog initialization
- **Purpose**: Prepare DAU data for efficient approximate distinct counting across rolling windows
- **Logic**: Initialize HyperLogLog sketches for each day's user set using 15-bit precision, which provides approximately 0.65% standard error for distinct counting. Group DAU records by timestamp, chain, user type, and product dimensions, then apply HyperLogLog initialization to the `user_dau` field. This creates compact probabilistic data structures that can be efficiently merged across time windows without storing individual user identifiers.
- **Key fields**: `hll` containing the initialized HyperLogLog sketch, along with all dimensional grouping fields (`timestamp`, `chain_id`, `user_type`, `product_id`)

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest DAU record to the current date minus one day. For each date in the datespine, create join relationships with the previous 29 days using timestamp array generation, resulting in 30-day windows (including the current day). This approach ensures no gaps in the time series and enables consistent MAU calculation even when certain days have no activity. Apply incremental processing filters to optimize performance by limiting the window creation to recently modified time periods.
- **Key fields**: `timestamp` representing the window end date, `joinday` representing each day within the 30-day window

### Component E: Window aggregation
- **Purpose**: Combine daily HyperLogLog sketches across each 30-day window
- **Logic**: Join the rolling window structure with the HyperLogLog-initialized DAU data, matching each day in the 30-day window with its corresponding DAU sketch. For each window end date, merge all HyperLogLog sketches from the 30 constituent days using partial merge operations. Extract the final distinct count estimate from the merged sketch to obtain the MAU value. This approach provides memory-efficient approximate distinct counting without requiring storage of individual user identifiers across the full time range.
- **Key fields**: `hll` sketches merged across the window period, with the final `user_mau` extracted as the distinct count estimate

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives on MAU data
- **Logic**: Generate four distinct dimensional views from the window-aggregated data: project-level aggregation (combining all dimensions), chain-level breakdown (preserving chain identity), user type breakdown (preserving user classification), and product-level breakdown (preserving product categorization). Each breakdown merges HyperLogLog sketches across the non-preserved dimensions while maintaining the specified grouping. Set non-applicable dimension fields to null in each breakdown to create a uniform schema structure.
- **Key fields**: `dimensions` field indicating the breakdown type, with relevant dimensional fields populated and others set to null, plus the calculated `user_mau` value

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, creating a comprehensive view with multiple analytical perspectives in one dataset
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: MAU values are calculated for each day representing the trailing 30-day period ending on that date, providing daily granularity of the monthly rolling metric
- **Final calculation**: Apply the finalize_metric_user_mau macro to standardize the output schema, implement zero-filling strategy for days with missing data, and add consistent metadata fields including data source identification ('unichain'). The final metric represents the approximate count of distinct users active within any 30-day trailing window.

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

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

UnichainUNI

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown