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). 

governance

github

twitter

discord

etherscan

website

Blockchains (L1)

Gnosis

# Gnosis Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Gnosis chain measures the number of unique addresses that initiated at least one transaction on the Gnosis blockchain within a 30-day rolling window. This metric provides insight into user engagement and network adoption by tracking unique transaction senders over time. The calculation uses HyperLogLog sketches for efficient processing of large-scale unique counting operations across multiple dimensional breakdowns.

## 2. Data sources

- **Gnosis blockchain transactions**: Raw transaction records from the Gnosis blockchain containing transaction metadata including sender addresses, timestamps, block information, and gas usage details
- **Chain availability metadata**: Control data that ensures accurate time-based filtering and data quality across the Gnosis network timeline
- **Incremental processing controls**: Internal tracking tables that manage data freshness and coordinate incremental updates with appropriate lookback periods

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique users (transaction senders) active each day on Gnosis chain
- **Logic**: Extracts all transaction records from Gnosis blockchain data and identifies unique `from_address` values for each day. Each unique sender address is classified as a 'Transaction sender' user type. The data is partitioned by day and includes market sector classification as 'blockchains_l1' and business segment as 'blockchain'. Chain availability filtering is applied to ensure data quality
- **Key fields**: Daily timestamps (`timestamp`), unique sender addresses (`user_dau`), user classification (`user_type`), chain identifier (`chain`), and dimensional metadata (`product_id`)

### Component B: HLL sketch creation
- **Purpose**: Converts daily user data into HyperLogLog sketches for efficient unique counting
- **Logic**: Takes the daily active user data and creates HLL sketches with 15-bit precision using `hll_count.init()`. This enables scalable unique user counting across large datasets. Data is grouped by timestamp, timestamp grain, chain, user type, and product to maintain dimensional structure
- **Key fields**: HLL sketches (`hll`), temporal grouping keys, and dimensional attributes

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Builds a complete datespine covering the full analysis period using `generate_timestamp_array()` to ensure no missing timestamps. For each day in the datespine, generates an array of the previous 30 days (including the current day) using another timestamp array. This creates joinday relationships that enable rolling window aggregation
- **Key fields**: Base timestamps (`timestamp`) and rolling window join keys (`joinday`)

### Component D: Window-based HLL aggregation
- **Purpose**: Aggregates user activity across 30-day windows
- **Logic**: Joins the rolling window structure with daily HLL data, matching each rolling window day with corresponding HLL sketches. For each 30-day window, merges HLL sketches using `hll_count.merge_partial()` to combine unique user counts across the window period. This creates MAU values while preserving dimensional structure
- **Key fields**: Window timestamps, merged HLL sketches, and dimensional breakdowns

### Final aggregation
- **Combining components**: Creates separate dimensional views by grouping merged HLL data by different dimensions (project_dbt_tag level, chain level, user_type level, and product_id level). Each breakdown nullifies non-relevant dimensions using `safe_cast(null as string)`. All dimensional breakdowns are combined using UNION ALL operations to create a unified result set
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses monthly partitioning for storage optimization while maintaining daily granularity in calculations. Rolling 30-day windows ensure smooth MAU trends without month-boundary artifacts
- **Final calculation**: Extracts final unique user counts from merged HLL sketches using `hll_count.extract()`. Applies the `finalize_metric_user_mau()` macro to standardize schema, handle missing data with fill_zeros strategy, and prepare the final output with consistent formatting and metadata

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

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

GnosisGNO

Active users (monthly)

Active users (monthly)

Competitive landscape