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

Cosmos Hub is an interoperability & security platform for blockchains and applications. Cosmos was founded by Ethan Buchman & Jae Kwon and launched in 2019.

ATOM holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Cosmos’s investors include Paradigm, IOSG Ventures, 1confirmation, etc.

governance

github

twitter

discord

etherscan

website

Blockchains (L1)

Cosmos Hub

# Cosmos Hub Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Cosmos Hub measures the count of unique addresses that initiated transactions within any 30-day rolling period. This metric provides insight into user engagement and adoption trends by tracking distinct transaction senders over monthly windows. The calculation uses HyperLogLog data structures for efficient unique counting and provides breakdowns across multiple dimensions including project level, chain level, user type, and product categories.

## 2. Data sources

- **Cosmos Hub blockchain events**: Message events from the Cosmos Hub blockchain containing transaction metadata and sender information
- **Transaction attributes**: JSON-formatted event attributes that include sender addresses and other transaction metadata
- **Chain metadata**: Cosmos Hub chain identifiers and classification data
- **User classification data**: User type categorizations and business segment assignments

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique transaction senders on a daily basis from Cosmos Hub blockchain events
- **Logic**: Extracts message events from Cosmos Hub blockchain where the event type equals "message". From each event's JSON attributes, extracts the sender address using the `sender` field. Groups transactions by day using timestamp truncation to day boundaries, creating a daily list of unique user addresses who initiated transactions
- **Key fields**: Uses `block_timestamp` for daily grouping, `transaction_hash` for transaction identification, `event_attributes` JSON field for sender extraction, and creates standardized fields like `chain` (set to "cosmos"), `market_sector_id` ("blockchains_l1"), `business_segment_id` ("blockchain"), and `user_type` ("Transaction sender")

### Component B: HyperLogLog daily aggregation  
- **Purpose**: Creates efficient daily unique user counts using HyperLogLog data structures to enable fast monthly aggregations
- **Logic**: Takes the daily unique user addresses and creates HyperLogLog sketches for each day, grouped by timestamp, chain, user type, and product identifiers. Uses HLL initialization with precision parameter 15 to balance accuracy and memory efficiency. This creates a compressed representation of daily unique users that can be efficiently merged across time periods
- **Key fields**: Groups by `timestamp`, `timestamp_grain` (set to "day"), `chain_id`, `user_type`, `product_id`, and creates `hll` field containing the HyperLogLog sketch of unique user addresses

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each day to calculate monthly active users
- **Logic**: Generates a complete date spine covering the analysis period to ensure no missing days. For each date in the spine, creates joins to the previous 29 days plus the current day (total 30-day window). This approach ensures consistent 30-day rolling periods even when some days have no activity
- **Key fields**: Uses `timestamp` as the primary date and `joinday` to represent each day within the 30-day lookback window

### Component D: Monthly active user calculation by dimension
- **Purpose**: Aggregates unique users across 30-day windows and creates breakdowns by different analytical dimensions
- **Logic**: For each 30-day window, merges the HyperLogLog sketches from all days within that window using `hll_count.merge_partial()`, then extracts the final unique count using `hll_count.extract()`. Creates four separate dimensional breakdowns: project-level (aggregating all dimensions), chain-level (preserving chain breakdowns), user-type-level (preserving user type breakdowns), and product-level (preserving product breakdowns). Each breakdown nullifies non-relevant dimensional fields
- **Key fields**: Maintains `timestamp`, `timestamp_grain`, adds `dimensions` field to identify breakdown type, preserves relevant dimensional fields (`chain_id`, `product_id`, `user_type`) while nullifying others, and calculates `user_mau` as the final unique count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project-level, chain-level, user-type-level, and product-level) into a single result set, allowing for flexible analytical queries across different granularities
- **Time aggregation**: Uses daily timestamps with 30-day rolling windows, ensuring each day represents the unique users active in the 30-day period ending on that date
- **Final calculation**: The final MAU value represents the count of distinct user addresses that initiated at least one transaction within any 30-day rolling period, calculated efficiently using merged HyperLogLog sketches and providing dimensional flexibility for various analytical needs

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

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

Cosmos HubATOM

Active users (monthly)

Active users (monthly)

Competitive landscape