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.

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Blockchains (L1)

Cosmos Hub

# Cosmos Hub Active addresses (daily) Methodology

## 1. Overview

The Cosmos active addresses daily metric measures the number of unique addresses that performed at least one transaction on the Cosmos Hub blockchain each day. This metric serves as a key indicator of user adoption and daily network activity. The calculation identifies unique transaction senders from blockchain message events and aggregates them daily using probabilistic counting techniques to ensure accurate distinct address counting at scale.

## 2. Data sources

The metric relies on the following underlying data sources:

- **Cosmos Hub message events**: Raw blockchain events from the Cosmos Hub that contain transaction details and metadata
- **Transaction attributes**: JSON-formatted event attributes that include sender address information and transaction context
- **Block timestamps**: Blockchain timestamp data used to aggregate transactions into daily time periods

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies unique addresses that initiated transactions on Cosmos Hub
- **Logic**: Filters Cosmos Hub blockchain events for `message` event types and extracts the sender address from the JSON-formatted `event_attributes` field using the `sender` key. Each extracted address represents a user who initiated a transaction and is classified as a "Transaction sender" user type. The data includes metadata categorizing Cosmos as a layer-1 blockchain in the blockchains market sector.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identification, `user_address` containing the sender address, and classification fields like `chain`, `user_type`, and `business_segment_id`

### Component B: Daily user aggregation with HyperLogLog
- **Purpose**: Creates daily distinct counts of active addresses using probabilistic counting
- **Logic**: Truncates block timestamps to daily boundaries and creates HyperLogLog sketches for each unique combination of date, chain, user type, and product. The HyperLogLog approach enables approximate distinct counting that scales efficiently with large datasets while maintaining high accuracy. This prevents double-counting users who perform multiple transactions within the same day.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing the address, and `hll` containing the HyperLogLog sketch data

### Component C: Multi-dimensional aggregation
- **Purpose**: Calculates active address counts across different analytical dimensions
- **Logic**: Merges HyperLogLog sketches and extracts distinct counts across four dimensional breakdowns: overall project level (project_dbt_tag), individual chain level, user type level, and product level. Each dimension provides different analytical perspectives on the active address data. The HyperLogLog merge operations combine sketches while preserving the distinct counting properties.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id` for chain-specific counts, `user_type` for user classification, `product_id` for product-specific analysis, and `user_dau` containing the final distinct address count

### Final aggregation
- **Combining components**: Filters the multi-dimensional user DAU results to include only 'chain' and 'project_dbt_tag' dimensions, which represent chain-level and overall project-level active address counts
- **Metric transformation**: Renames the `user_dau` field to `active_addresses_daily` to reflect the specific metric being calculated
- **Time aggregation**: Applies daily boundary logic with the finalization macro that fills missing days with zeros to ensure complete time series data
- **Final calculation**: The result is the count of distinct addresses that performed at least one transaction on each given day, aggregated using HyperLogLog for scalable distinct counting and filtered by chain availability rules

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

Unique active addresses over a fixed 24-hour window.

Cosmos HubATOM

Active addresses (daily)

Active addresses (daily)

Competitive landscape