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Blockchains (L2)

Boba Network

# Boba Network Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique addresses that have initiated at least one transaction on the Boba Network blockchain within a rolling 30-day window. This metric provides insight into user engagement and adoption of the Boba Network Layer 2 scaling solution. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across time periods while maintaining accuracy for large datasets.

## 2. Data sources

The calculation relies on the following data sources:
- **Boba Network transaction data**: Complete transaction records from the Boba Network Layer 2 blockchain, including transaction metadata and sender addresses
- **Chain availability data**: Configuration data that defines operational periods for the Boba Network to ensure data quality and exclude periods with incomplete data collection

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses from Boba Network transaction activity
- **Logic**: Extracts the sender address (`from_address`) from each transaction record on Boba Network, treating each unique sender as an active user. Each transaction represents a user interaction with the Boba Network blockchain. Users are classified as "Transaction sender" type, with chain identifier "bobanetwork", market sector "blockchains_l2", and business segment "blockchain"
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction tracking

### Component B: Daily active user calculation  
- **Purpose**: Aggregates unique users per day using probabilistic counting
- **Logic**: Groups user activity by day using timestamp truncation to day boundaries. Creates HyperLogLog sketches for each day to efficiently count distinct user addresses. Maintains dimensional breakdowns by `user_type`, `chain`, and `product_id` for flexible analysis. Applies chain availability filtering to exclude periods with unreliable data collection
- **Key fields**: `timestamp` truncated to day grain, `user_dau` containing the user address, HyperLogLog sketches for efficient distinct counting

### Component C: Rolling window MAU aggregation
- **Purpose**: Calculates 30-day rolling monthly active users from daily data
- **Logic**: Creates a complete date spine to ensure no missing days in the time series. For each day, generates a 30-day lookback window by joining with the previous 29 days of daily active user data. Merges HyperLogLog sketches across the 30-day window to calculate unique monthly active users, accounting for users who may be active on multiple days within the month
- **Key fields**: Rolling window timestamps, merged HyperLogLog sketches, dimensional groupings

### Final aggregation
- **Combining components**: Calculates MAU across multiple dimensional breakdowns including project-level totals, chain-level breakdowns, user type segments, and product-level analysis. Each breakdown maintains the same 30-day rolling window logic but aggregates at different granularities
- **Currency conversion**: Not applicable - this is a user count metric
- **Time aggregation**: Uses daily boundaries with 30-day rolling windows. Each day's MAU represents unique users active in the trailing 30-day period ending on that day
- **Final calculation**: Extracts the final count from merged HyperLogLog sketches using `hll_count.extract()`, unions all dimensional breakdowns, and applies schema standardization with zero-filling for periods without activity

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

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

Boba NetworkBOBA

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown