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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Linea measures the number of unique addresses that sent at least one transaction on the Linea blockchain within a rolling 7-day window. This metric tracks user engagement and activity levels on the Linea Layer 2 network. The calculation uses HyperLogLog data structures to efficiently count unique users across rolling time windows while providing dimensional breakdowns by project, chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Linea blockchain transaction records**: Complete transaction data from the Linea network including sender addresses, timestamps, and transaction hashes
- **Transaction metadata**: Block timestamps for temporal aggregation and transaction hash identifiers for data integrity
- **Address data**: Transaction sender addresses (`from_address`) that represent active users on the network

## 3. Step-by-step calculation

### Component A: User facts extraction
- **Purpose**: Extract and classify transaction senders as active users with standardized metadata
- **Logic**: Select all transactions from Linea blockchain data and extract the sender address (`from_address`) as the user identifier. Assign each user the classification "Transaction sender" as the `user_type`. Apply standard categorization with `chain` set to "linea", `market_sector_id` as "blockchains_l2", `business_segment_id` as "blockchain", and `product_id` as null. Preserve the original `block_timestamp` and `transaction_hash` for downstream processing.
- **Key fields**: `block_timestamp` for temporal aggregation, `from_address` as the unique user identifier, `user_type` for user classification, and chain metadata for dimensional analysis

### Component B: Daily active users calculation  
- **Purpose**: Aggregate unique users per day and prepare data for weekly window calculations
- **Logic**: Group transaction data by truncated day timestamps (`timestamp_trunc(block_timestamp, day)`) and select distinct user addresses per day. Preserve dimensional attributes including `chain`, `user_type`, and `product_id` for downstream breakdowns. Convert the daily unique user data into HyperLogLog (HLL) structures using `hll_count.init()` with precision 15 for efficient approximate counting in subsequent window operations.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing unique user addresses, and `hll` structures for efficient aggregation

### Component C: Date spine and window creation
- **Purpose**: Create complete time series and establish 7-day rolling windows for WAU calculation
- **Logic**: Generate a continuous date spine covering all dates from the earliest available data to yesterday using `generate_timestamp_array()` with daily intervals. For each date in the spine, create a 7-day window by generating timestamps from 6 days prior through the current date. Join this window structure with the daily HLL data to ensure complete coverage of all user activity within each 7-day period.
- **Key fields**: `timestamp` for the target date and `joinday` representing each day within the 7-day lookback window

### Component D: Weekly active user aggregation
- **Purpose**: Calculate unique users across 7-day windows using HLL merging
- **Logic**: For each target date, merge HLL structures from all days within the 7-day window using `hll_count.merge_partial()`. This combines the approximate unique user counts across the entire week while avoiding double-counting users who were active on multiple days. Extract the final unique user count using `hll_count.extract()` to get the approximate count of distinct users who were active at least once during the 7-day period.
- **Key fields**: Merged `hll` structures containing user activity data and `user_wau` as the final weekly active user count

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives of the WAU metric
- **Logic**: Generate four separate dimensional views of the weekly active user data. Create breakdowns by project tag (project_dbt_tag dimension with all other dimensions set to null), by chain (`chain_id` dimension), by user type (`user_type` dimension), and by product (`product_id` dimension). For each breakdown, aggregate HLL structures at the appropriate dimensional level and extract unique user counts. Combine all dimensional views using union operations to create a comprehensive analytical dataset.
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimensional fields (`chain_id`, `user_type`, `product_id`), and `user_wau` counts for each dimensional slice

### Final aggregation
- **Combining components**: Union all dimensional breakdown views into a single result set with consistent schema structure
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to ensure consistent field naming, data types, and metadata including project identification as "linea"  
- **Missing data handling**: Fill any missing dates in the time series with zero values using the "fill_zeros" strategy to maintain complete temporal continuity
- **Final calculation**: The result provides daily WAU values representing the count of unique addresses that sent at least one transaction within each 7-day rolling window, broken down by multiple dimensions for comprehensive analysis

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

Unique revenue-generating users over a rolling 7-day window.

LineaLINEA

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown