Name: Ethereum Active addresses (monthly) Metrics
Creator: Token Terminal
License: https://tokenterminal.com/terms

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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etherscan

website

Blockchains (L1)

Ethereum

# Ethereum Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique Ethereum addresses that have sent at least one transaction in a rolling 30-day period. This metric serves as a proxy for user activity and adoption on the Ethereum network, since each address represents a distinct user account. The calculation uses a 30-day rolling window approach with HyperLogLog approximation for efficient computation of unique address counts across overlapping time periods.

## 2. Data sources

- **Ethereum transaction records**: Complete transaction history including sender addresses (`from_address`), transaction hashes, and block timestamps
- **Table metadata**: Incremental processing metadata for performance optimization

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract all unique addresses that have sent transactions on Ethereum
- **Logic**: Process all Ethereum transaction records to identify transaction senders. Each transaction's `from_address` field represents a user address that initiated blockchain activity. Each record is tagged as a "Transaction sender" user type and categorized under the "blockchains_l1" market sector and "blockchain" business segment. The data is partitioned by day based on `block_timestamp` for efficient incremental processing.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for timing, `transaction_hash` for deduplication

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique active addresses by removing intraday duplicates
- **Logic**: Group transaction sender data by day using timestamp truncation to day granularity. Apply DISTINCT operation on `user_address` to ensure each address is counted only once per day, regardless of how many transactions they sent. This creates daily snapshots of unique addresses that were active on each specific day.
- **Key fields**: `timestamp` (truncated to day), `user_address` as `user_dau`, chain and product metadata

### Component C: Rolling window MAU calculation
- **Purpose**: Compute 30-day rolling monthly active users using HyperLogLog approximation
- **Logic**: Create HyperLogLog sketches for each day's unique active addresses using a precision of 15 bits. Generate a datespine covering all days in the analysis period, then for each day create a 30-day rolling window by looking back 29 days plus the current day. Join the datespine with daily HLL sketches across the rolling window timeframe. Merge HLL sketches within each 30-day window using HyperLogLog merge operations, then extract the final approximate unique count. Create multiple dimensional breakdowns including project-level (`project_dbt_tag`) and chain-level (`chain`) aggregations.
- **Key fields**: `hll` sketches for efficient unique counting, `timestamp` for window boundaries, `user_mau` as the final count

### Final aggregation
- **Combining components**: The MAU calculation flows directly from daily active users through HyperLogLog processing to produce rolling 30-day unique address counts
- **Currency conversion**: Not applicable - this is a count-based metric
- **Time aggregation**: Uses 30-day rolling windows where each day represents the count of unique addresses active in the previous 30 days (including current day)
- **Final calculation**: The `user_mau` metric is directly mapped to `active_addresses_monthly` since addresses and users are equivalent concepts for blockchain metrics. The calculation filters for chain-level and project-level dimensions, applies schema finalization through the `finalize_metric_active_addresses_monthly` macro with zero-fill strategy for missing dates, and applies chain availability filtering.

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