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

Gnosis Chain is a smart contract platform for decentralized applications (a blockchain). Gnosis was founded by Martin Köppelmann and Stefan George and launched in 2015.

GNO holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). 

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Blockchains (L1)

Gnosis

# Gnosis Active addresses (daily) Methodology

## 1. Overview

The Gnosis active addresses daily metric measures the number of unique addresses that initiated at least one transaction on the Gnosis blockchain each day. This metric serves as a key indicator of daily user activity and network adoption. The calculation uses HyperLogLog data structures to efficiently count distinct transaction senders across multiple dimensional breakdowns while maintaining scalability.

## 2. Data sources

- **Gnosis blockchain transactions**: Complete transaction records from the Gnosis blockchain including sender addresses, timestamps, gas usage, and transaction metadata
- **Token metadata**: WXDAI token address (`0xe91d153e0b41518a2ce8dd3d7944fa863463a97d`) used as the native gas token
- **Chain availability data**: Configuration data that defines valid time periods for each blockchain to ensure data quality

## 3. Step-by-step calculation

### Component A: Transaction fact preparation
- **Purpose**: Clean and standardize raw Gnosis transaction data for analysis
- **Logic**: Extract all transactions from Gnosis blockchain records, applying standardized field mappings and decimal corrections. Classify all transactions with market sector as `blockchains_l1` and business segment as `blockchain`. Convert gas prices from wei to standard units by dividing by 10^18. Set the native token address to WXDAI for all transactions.
- **Key fields**: `block_timestamp` for time boundaries, `from_address` for sender identification, `transaction_hash` for deduplication, gas-related fields with decimal corrections

### Component B: Daily user identification  
- **Purpose**: Identify unique transaction senders per day
- **Logic**: Group transactions by day using timestamp truncation to day boundaries. Extract distinct `from_address` values as active users, classified as "Transaction sender" user type. Apply incremental processing logic to handle new data efficiently while maintaining historical accuracy.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing sender addresses, `chain` set to 'gnosis', `user_type` as 'Transaction sender'

### Component C: HyperLogLog aggregation
- **Purpose**: Create scalable distinct address counting across multiple dimensions
- **Logic**: Initialize HyperLogLog sketches for each day to enable efficient distinct counting. Aggregate addresses across four dimensional breakdowns: project-level (all addresses), chain-level (by blockchain), user-type-level (by sender category), and product-level (by product classification). Use HyperLogLog merge operations to combine sketches and extract final distinct counts.
- **Key fields**: `hll` sketches for distinct counting, `dimensions` field indicating aggregation level, `user_dau` containing final distinct address counts

### Final aggregation
- **Combining components**: Select user DAU counts from the HyperLogLog aggregation, specifically filtering for 'chain' and 'project_dbt_tag' dimensions to focus on overall and chain-specific metrics
- **Currency conversion**: Not applicable - this metric counts addresses, not financial values
- **Time aggregation**: Daily boundaries defined by timestamp truncation to day level, ensuring consistent 24-hour periods
- **Final calculation**: Rename `user_dau` field to `active_addresses_daily` and apply chain availability filters to exclude periods with incomplete data. Fill missing days with zero values to maintain time series continuity.

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

Unique active addresses over a fixed 24-hour window.

GnosisGNO

Active addresses (daily)

Active addresses (daily)

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