Validators process transactions and receive fees from users (supply-side fees).

Ethereum is a smart contract platform for decentralized applications (a blockchain). Ethereum was founded by Vitalik Buterin, Anthony Di Iorio, Mihai Alisie, etc. and launched in 2015.

ETH holders own and govern the protocol. They currently take a cut of the total transaction fees (base fee) paid by users (revenue). Ethereum’s investors include KR1, Block Ventures, 8 Decimal Capital, etc.

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Blockchains (L1)

Ethereum

# Ethereum Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Ethereum measures the count of unique addresses that sent at least one transaction on the Ethereum blockchain within any rolling 7-day period. This metric provides insight into user engagement and activity levels on the Ethereum network over weekly timeframes. The calculation uses HyperLogLog data structures for efficient cardinality estimation of unique users across overlapping time windows.

## 2. Data sources

- **Ethereum transaction records**: Raw blockchain transaction data from the Ethereum network containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Chain availability configuration**: System configuration data that determines which chains and time periods to include in calculations

## 3. Step-by-step calculation

### Component A: User extraction
- **Purpose**: Identifies all unique transaction senders from raw Ethereum blockchain data
- **Logic**: Extracts the sender address from each Ethereum transaction along with the block timestamp and transaction hash. Each transaction sender is classified as user type "Transaction sender" and categorized under market sector "blockchains_l1" and business segment "blockchain". The chain is identified as "ethereum" and no specific product categorization is applied
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for timing, `transaction_hash` for transaction identification, and metadata fields for classification

### Component B: Daily active users aggregation  
- **Purpose**: Aggregates unique user addresses into daily counts to create the foundation for weekly calculations
- **Logic**: Groups the extracted user data by day using timestamp truncation to day level. Removes duplicate user addresses within each day to ensure each unique address is counted only once per day. Applies chain availability filtering to ensure only valid time periods and chains are included
- **Key fields**: `timestamp` truncated to day granularity, `chain` identifier, `user_type` classification, and `user_dau` containing unique user addresses

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user sets into HyperLogLog data structures for efficient cardinality estimation
- **Logic**: For each day's unique user set, initializes a HyperLogLog sketch with precision 15 bits. Groups by timestamp, chain, user type, and product dimensions while creating the HyperLogLog representation of the daily active user set
- **Key fields**: `hll` containing the HyperLogLog sketch of daily users, along with all dimensional grouping fields

### Component D: Rolling window creation
- **Purpose**: Creates overlapping 7-day windows for each day to calculate weekly active users
- **Logic**: Generates a complete date spine covering the full time range of available data. For each date in the spine, creates a 7-day window by generating timestamps for the 6 preceding days plus the current day. This creates overlapping windows where each day appears in 7 different weekly calculations
- **Key fields**: `timestamp` for the window end date and `joinday` representing each day within the 7-day window

### Component E: Window aggregation
- **Purpose**: Combines daily HyperLogLog sketches within each 7-day window
- **Logic**: Joins the rolling window dates with the daily HyperLogLog data to collect all daily sketches that fall within each 7-day window. This creates the foundation for merging user sets across the weekly timeframe
- **Key fields**: Window `timestamp`, dimensional groupings, and `hll` sketches from each day in the window

### Component F: Dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Creates separate aggregations for four different breakdowns: overall project level (project_dbt_tag), by chain, by user type, and by product. For each breakdown, merges the HyperLogLog sketches within each 7-day window and extracts the final cardinality estimate. Uses null values for dimensions not relevant to each breakdown
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimensional fields, and `user_wau` containing the final weekly active user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with consistent schema across all breakdown types
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 7-day windows with daily granularity, where each day represents the end of a 7-day period
- **Final calculation**: The final WAU value is extracted from merged HyperLogLog sketches using `hll_count.extract(hll_count.merge_partial(hll))`, providing an approximate count of unique users active within each 7-day window

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

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

EthereumETH

Active users (weekly)

Active users (weekly)

Competitive landscape