Linea batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Linea is an L2 smart contract platform for decentralized applications on Ethereum. Linea was founded by the ConsenSys team and launched in 2023.

ConsenSys team owns and governs the protocol. ConsenSys's investors include ParaFi Capital, Microsoft, Coinbase Ventures, etc.

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Blockchains (L2)

Linea

# Linea Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Linea measures the count of unique wallet addresses that initiated at least one transaction on the Linea blockchain within any 30-day rolling window. This metric answers the business question: "How many unique users are actively transacting on Linea over monthly periods?" The calculation uses HyperLogLog probabilistic counting to efficiently aggregate unique users across rolling 30-day windows, providing multiple dimensional breakdowns for analysis.

## 2. Data sources

The MAU calculation relies on the following data sources:
- **Linea blockchain transaction data**: Complete transaction records from the Linea blockchain containing transaction metadata, timestamps, and sender addresses
- **Chain availability configuration**: Data quality filters to ensure complete and reliable blockchain data coverage

## 3. Step-by-step calculation

### Component A: Daily user extraction
- **Purpose**: Identify unique users active each day on Linea
- **Logic**: Extract all transactions from Linea blockchain records and identify unique `from_address` values (wallet addresses that initiated transactions) for each calendar day. Each transaction sender address represents an active user. Timestamps are truncated to day-level granularity to group user activity by date.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as user identifier, `transaction_hash` for transaction reference

### Component B: HyperLogLog initialization for daily user sets
- **Purpose**: Prepare daily user data for efficient unique counting across time windows
- **Logic**: Convert each day's unique user set into HyperLogLog probabilistic data structures using 15-bit precision. This enables memory-efficient merging and counting of unique users across multiple days without storing complete user lists.
- **Key fields**: `timestamp` for daily grouping, `user_dau` containing unique user addresses, `hll` as the HyperLogLog sketch

### Component C: Continuous datespine generation
- **Purpose**: Create complete time series without gaps for rolling window calculations
- **Logic**: Generate a continuous sequence of daily timestamps from the earliest transaction date to the most recent complete day. This ensures rolling windows can be calculated consistently even for days with no transaction activity.
- **Key fields**: `timestamp` representing each calendar day in the range

### Component D: Rolling 30-day window creation
- **Purpose**: Define the 30-day periods for monthly active user calculation
- **Logic**: For each day in the datespine, generate the corresponding 30-day window by creating an array of the previous 29 days plus the current day. This creates overlapping rolling windows where each day represents the end of a 30-day MAU measurement period.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 30-day window

### Component E: Window-based user aggregation
- **Purpose**: Combine daily HyperLogLog sketches across 30-day windows
- **Logic**: Join the rolling window definitions with daily HyperLogLog user data, matching each window day with the corresponding daily user sketch. This creates the foundation for merging unique user counts across each 30-day period.
- **Key fields**: `timestamp` as window end date, `hll` containing daily user sketches, dimension fields like `chain_id`, `user_type`, `product_id`

### Component F: Dimensional breakdown calculations
- **Purpose**: Generate MAU metrics across different analytical dimensions
- **Logic**: Create four separate dimensional views by merging HyperLogLog sketches within each 30-day window:
  - Project-level: Overall MAU across all dimensions
  - Chain-level: MAU broken down by blockchain (Linea in this case)
  - User type-level: MAU segmented by user classification (Transaction senders)
  - Product-level: MAU broken down by specific products or applications
For each breakdown, HyperLogLog sketches are merged using `hll_count.merge_partial()` and extracted to get unique user counts using `hll_count.extract()`.
- **Key fields**: `user_mau` as the final unique user count, `dimensions` indicating the breakdown type, relevant dimension identifiers

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical perspectives of the same underlying MAU metric
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Each timestamp represents the end date of a 30-day rolling window, with MAU calculated as the count of unique users active within that window
- **Final calculation**: The final MAU value represents the estimated count of unique wallet addresses that initiated at least one transaction within any 30-day period ending on the specified date, calculated using HyperLogLog probabilistic counting for accuracy and efficiency

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

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

LineaLINEA

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown