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 addresses (daily) Methodology

## 1. Overview

The Ethereum active addresses daily metric measures the number of unique addresses that initiated at least one transaction on the Ethereum blockchain within a 24-hour period. This metric serves as a key indicator of daily user activity and engagement on the Ethereum network. The calculation identifies all transaction sender addresses and counts distinct addresses per day using probabilistic HyperLogLog counting for scalability.

## 2. Data sources

The metric relies on the following raw blockchain data sources:

- **Ethereum transaction records**: Complete transaction history from the Ethereum blockchain, including all successful and failed transactions
- **Transaction metadata**: Block timestamps, transaction hashes, and sender addresses (`from_address`) from each transaction
- **Address data**: Ethereum addresses that serve as transaction initiators (senders)

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies all addresses that initiated transactions on Ethereum
- **Logic**: Extracts the sender address (`from_address`) from every Ethereum transaction record along with the block timestamp and transaction hash. Each transaction sender is classified with metadata including chain identifier (ethereum), market sector (blockchains_l1), business segment (blockchain), and user type (Transaction sender). This creates the foundational dataset of all addresses that demonstrated activity by sending transactions.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp`, `transaction_hash`, and standardized metadata fields

### Component B: Daily user deduplication  
- **Purpose**: Creates distinct daily active address counts
- **Logic**: Groups the transaction sender data by truncating timestamps to daily boundaries and identifying unique combinations of date, chain, user type, and user address. This deduplication step ensures that addresses initiating multiple transactions on the same day are only counted once per day. The process creates a clean dataset of unique active addresses per day.
- **Key fields**: `timestamp` (truncated to day), `user_address` (deduplicated), `chain`, `user_type`

### Component C: HyperLogLog sketch creation
- **Purpose**: Enables efficient probabilistic distinct counting for large-scale data
- **Logic**: Converts the daily distinct user addresses into HyperLogLog sketches with precision parameter 15. This probabilistic data structure allows for memory-efficient approximate distinct counting, which is essential for handling Ethereum's large transaction volumes. Individual daily sketches are created for each combination of date, chain, user type, and product.
- **Key fields**: `hll` sketch objects containing probabilistic distinct count information

### Component D: Dimensional aggregation
- **Purpose**: Creates multiple breakdowns of the active address count
- **Logic**: Merges the daily HLL sketches across different dimensional breakdowns to provide various views of the data. Creates four distinct aggregations: overall project-level counts, chain-level breakdowns, user-type-level breakdowns, and product-level breakdowns. Each aggregation merges the appropriate HLL sketches and extracts the final distinct count estimate.
- **Key fields**: `dimensions` (indicating breakdown type), `chain_id`, `user_type`, `product_id`, `user_dau` (distinct count)

### Final aggregation
- **Combining components**: The dimensional aggregations are combined using union operations to create a single dataset with multiple breakdown perspectives. The target model filters for 'chain' and 'project_dbt_tag' dimensions to focus on the most relevant breakdowns.
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day granularity, ensuring consistent 24-hour measurement periods
- **Final calculation**: The `user_dau` field containing the HyperLogLog distinct count estimate is renamed to `active_addresses_daily` to reflect the final metric. Days with no activity are filled with zeros using the finalization macro, and chain availability filtering is applied to ensure data quality across different time periods.

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

Unique active addresses over a fixed 24-hour window.

EthereumETH

Active addresses (daily)

Active addresses (daily)

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