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.

governance

github

twitter

etherscan

website

Blockchains (L1)

Ethereum

# Ethereum Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Ethereum measures the number of unique addresses that have sent at least one transaction on the Ethereum network within a rolling 30-day period. This metric tracks user engagement and adoption by identifying active participants in the Ethereum ecosystem. The calculation uses HyperLogLog data structures to efficiently count unique users across sliding 30-day windows while providing breakdowns by different dimensions such as chain, user type, and product.

## 2. Data sources

- **Ethereum transaction records**: Complete transaction history from the Ethereum blockchain including sender addresses, transaction hashes, block timestamps, and transaction metadata
- **Chain availability configuration**: Data quality and completeness filters specific to Ethereum network operations

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identifies all unique active users on Ethereum by extracting transaction senders from raw blockchain data
- **Logic**: Processes Ethereum transaction records to identify unique addresses that have initiated transactions. Each transaction's `from_address` field represents a user address that actively participated in the network. All identified users are classified as 'Transaction sender' user type and mapped to the Ethereum chain with 'blockchains_l1' market sector classification. The `product_id` field is set to null since this represents base Ethereum network activity rather than specific application usage.
- **Key fields**: `block_timestamp` for temporal analysis, `from_address` as the user identifier, `transaction_hash` for transaction uniqueness, and `chain` classification as 'ethereum'

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active users to serve as the foundation for rolling MAU calculations
- **Logic**: Groups the identified users by day using timestamp truncation to daily granularity. Applies distinct counting on user addresses to ensure each user is counted only once per day, regardless of how many transactions they sent. Users are further grouped by chain, user type, and product identifiers to enable dimensional breakdowns in the final metric. Chain availability filters are applied to ensure data quality and completeness.
- **Key fields**: `timestamp` truncated to day, `user_address` for unique user counting, `chain`, `user_type`, and `product_id` for dimensional grouping

### Component C: HyperLogLog daily conversion
- **Purpose**: Converts daily unique user counts into HyperLogLog sketches for efficient unique counting across time periods
- **Logic**: Takes the daily active user data and creates HyperLogLog sketches using `hll_count.init()` function with precision 15. This probabilistic data structure enables efficient approximate counting of unique elements across large datasets and time ranges. The HLL sketches preserve the ability to merge user sets from different days while avoiding double-counting users who appear on multiple days.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id` for grouping, and `hll` containing the HyperLogLog sketch of daily users

### Component D: Date spine and rolling window creation
- **Purpose**: Ensures complete date coverage and creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a complete date spine covering the entire analysis period to prevent missing timestamps from causing gaps in the rolling window calculations. For each date in the spine, creates a 30-day lookback window that includes the current date plus the 29 previous days. This approach ensures consistent 30-day periods for MAU calculation even when some days have no user activity. The window generation uses timestamp array functions to create all necessary date combinations.
- **Key fields**: `timestamp` for the target date and `joinday` for each day within the 30-day window

### Component E: Rolling window user aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 30-day windows to calculate monthly active users
- **Logic**: Joins the rolling date windows with daily HyperLogLog sketches to create complete 30-day user datasets for each date. For each 30-day window, merges the HyperLogLog sketches from all days using `hll_count.merge_partial()` function. This operation combines the probabilistic user sets while maintaining uniqueness across the entire period. The merged sketches are then processed to extract the final unique user count using `hll_count.extract()`.
- **Key fields**: `timestamp` for the calculation date, dimensional grouping fields, and merged `hll` sketches containing 30-day user sets

### Component F: Dimensional breakdowns
- **Purpose**: Creates MAU calculations across different analytical dimensions for comprehensive reporting
- **Logic**: Generates separate MAU calculations for four different dimension levels: overall project level, chain level, user type level, and product level. Each breakdown groups the rolling window data by the relevant dimensions while setting other dimensional fields to null. This approach enables analysis of MAU trends across different cuts of the data while maintaining consistent calculation methodology. All breakdowns use the same HyperLogLog merging and extraction logic to ensure comparable results.
- **Key fields**: `dimensions` to identify the breakdown type, relevant dimensional fields (`chain_id`, `user_type`, `product_id`), and `user_mau` containing the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive MAU dataset with multiple analytical perspectives. Each record includes the calculation timestamp, dimension type, relevant dimensional values, and the unique user count for that specific breakdown.
- **Currency conversion**: Not applicable as this is a user count metric rather than a financial metric
- **Time aggregation**: Uses rolling 30-day windows calculated daily, providing a smooth trend line of monthly active users over time. Each day's MAU represents unique users active in the current day plus the previous 29 days.
- **Final calculation**: The final MAU value for each dimension and date represents the approximate count of unique Ethereum addresses that sent at least one transaction within the specified 30-day rolling period, calculated using HyperLogLog probabilistic counting methods for scalability and performance.

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

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

EthereumETH

Active users (monthly)

Active users (monthly)

Competitive landscape