Validators process & store the uploaded data and collect upload & storage fees from data uploaders (supply-side fees).

Swarm is a data storage network built on Gnosis. Swarm was founded by Viktor Tron, Jeffrey Wilcke and Daniel A. Nagy and launched in 2016.

BZZ tokenholders own and govern the protocol. They currently don’t take a cut of the fees paid by data uploaders.

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Infrastructure

Swarm

# Swarm Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Swarm measures the distinct number of users who interacted with the Swarm decentralized storage protocol within any 30-day rolling window. This metric tracks user engagement with Swarm's postage stamp system, which is the economic mechanism users employ to store data on the network. The calculation uses HyperLogLog sketches to efficiently compute distinct user counts across rolling 30-day windows, with breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The metric relies on the following raw data sources:
- **Gnosis blockchain event logs**: Parsed `BatchCreated` events from the Swarm PostageStamp contract, capturing when users create new postage stamp batches
- **Gnosis blockchain event logs**: Parsed `BatchTopUp` events from the Swarm PostageStamp contract, identifying when users add value to existing batches  
- **Gnosis token transfer events**: BZZ token transfers to the PostageStamp contract address, used to identify users who top up batches
- **Chain availability metadata**: Configuration data for filtering and validating supported blockchain networks

## 3. Step-by-step calculation

### Component A: User interaction identification
- **Purpose**: Identifies distinct user interactions with the Swarm protocol from blockchain events
- **Logic**: Extracts user addresses from two types of Swarm protocol interactions on Gnosis chain. First, parses `BatchCreated` events from the PostageStamp contract to identify batch creators using the `owner` parameter. Second, identifies batch top-uppers by joining `BatchTopUp` events with BZZ token transfers to the PostageStamp contract address, extracting the `from_address` from the token transfers as the user address. Each interaction records the block timestamp, transaction hash, log index, and classifies users into types like "Batch Creator" or "Batch TopUpper"
- **Key fields**: `user_address` (the interacting wallet), `user_type` (classification of interaction), `block_timestamp` (when interaction occurred), `chain` (always "gnosis" for Swarm), hardcoded market sector as "infrastructure" and business segment as "other"

### Component B: Daily active user calculation  
- **Purpose**: Aggregates user interactions into daily distinct active user counts
- **Logic**: Groups the user interactions by truncating timestamps to daily boundaries and creates distinct counts of active users per day. Groups data by timestamp (daily), chain, user type, market sector, business segment, and product ID to create the foundation for MAU calculation. Each user address is treated as a distinct active user for that day regardless of how many interactions they had
- **Key fields**: `timestamp` (daily boundary), `user_dau` (distinct user address), `user_type` (batch creator vs batch top-upper), `chain` (gnosis), `product_id` (null for Swarm)

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user counts into HyperLogLog sketches for efficient distinct counting across time windows
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches with precision 15 for each combination of day, chain, user type, and product. HLL sketches enable efficient merging of user counts across multiple days without double-counting users who appear on multiple days within the window
- **Key fields**: `hll` (HyperLogLog sketch of user addresses), same dimensional fields as DAU data for grouping

### Component D: Rolling window creation
- **Purpose**: Generates 30-day rolling windows for MAU calculation using a datespine approach
- **Logic**: Creates a complete datespine from the earliest available data date to yesterday, then for each date generates a joinable array of the previous 30 days (including the current date). This ensures every day has a complete 30-day lookback window even if some days have no user activity. Joins this datespine with the HLL sketch data to create the rolling window dataset
- **Key fields**: `timestamp` (the end date of each 30-day window), `joinday` (each of the 30 days in the window), maintains all dimensional breakdowns from the HLL data

### Component E: Multi-dimensional MAU aggregation
- **Purpose**: Calculates MAU across different dimensional breakdowns by merging HLL sketches within each 30-day window
- **Logic**: For each 30-day window, merges the HyperLogLog sketches across all days in the window to get distinct user counts. Creates four separate dimensional views: project-level (all dimensions null), chain-level (grouped by chain), user type-level (grouped by user type), and product-level (grouped by product ID). Uses HLL merge and extract functions to get final distinct user counts while avoiding double-counting users who were active on multiple days
- **Key fields**: `user_mau` (distinct user count for the 30-day window), `dimensions` (indicating the breakdown type), dimensional fields set to null except for the relevant grouping dimension

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project-level, chain-level, user type, product ID) into a single result set using UNION ALL operations
- **Schema standardization**: Applies the `finalize_metric_user_mau` macro to standardize the output schema, ensuring consistent column types and naming conventions across all dimensional breakdowns
- **Missing data handling**: Fills days with no activity with zero values using the "fill_zeros" strategy, ensuring continuous time series data
- **Final calculation**: The final MAU value represents the distinct count of users who had at least one interaction with Swarm's postage stamp system within any 30-day rolling window, calculated using HyperLogLog sketches to ensure accuracy and efficiency

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

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

SwarmBZZ

Active users (monthly)

Active users (monthly)

Competitive landscape