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

Mina is a smart contract platform for decentralized applications (a blockchain). Mina was founded by Evan Shapiro & Izaak Meckler and launched in 2017.

Mina’s investors include Pantera Capital, FTX Ventures, Circle Ventures, etc.

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Mina Protocol

# Mina Protocol Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Mina Protocol measures the total number of unique addresses that sent at least one transaction within a 30-day rolling window. This metric provides insight into user engagement and adoption trends on the Mina blockchain. The calculation uses HyperLogLog probabilistic counting to efficiently handle large user sets while maintaining accuracy, and employs a rolling window approach where each day's MAU represents users active in the current day plus the previous 29 days.

## 2. Data sources

- **Mina blockchain transaction records**: Raw transaction data from the Mina Protocol blockchain containing block timestamps, transaction hashes, and sender addresses
- **Chain availability configuration**: Metadata used to filter data based on blockchain availability and quality standards

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique users per day from Mina blockchain transactions
- **Logic**: Extracts all transaction records from Mina blockchain data, identifying the transaction sender (`sender` field) as the active user. Each transaction includes a block timestamp that indicates when the transaction was included in a block. The system truncates block timestamps to day boundaries to group transactions by calendar day. For each day, it counts distinct user addresses to determine Daily Active Users (DAU). All users are classified as "Transaction sender" user type since Mina's user activity is measured through transaction sending behavior.
- **Key fields**: `block_timestamp` (truncated to day), `sender` (user address), `transaction_hash`, `chain` (set to "mina")

### Component B: HyperLogLog sketch creation  
- **Purpose**: Creates probabilistic data structures for efficient user counting across time windows
- **Logic**: Takes the daily user data and initializes HyperLogLog sketches for each day's user set. HyperLogLog is a probabilistic algorithm that allows efficient counting of distinct elements (users) while using minimal memory. Each daily user set is converted into an HLL sketch with precision parameter 15, which provides high accuracy for cardinality estimation. The sketches are grouped by timestamp, chain, user type, and product dimensions to enable flexible aggregation.
- **Key fields**: `timestamp`, `hll` (HyperLogLog sketch), `user_type`, `chain_id`, `product_id`

### Component C: Datespine and rolling window generation
- **Purpose**: Creates a complete time series framework and 30-day rolling windows for MAU calculation
- **Logic**: Generates a continuous datespine covering every day from the earliest available data to the most recent complete day (yesterday). This ensures no gaps in the time series even if certain days had no transactions. For each day in the datespine, creates a 30-day rolling window by generating timestamps for the current day plus the previous 29 days. This sliding window approach means each day's MAU calculation includes users active within the past 30 days ending on that day.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 30-day window)

### Component D: Window aggregation and HLL merging
- **Purpose**: Combines user activity across 30-day windows to calculate MAU
- **Logic**: For each day and its corresponding 30-day window, joins the window dates with the daily HLL sketches to collect all relevant user data. Merges the HyperLogLog sketches from all days within each 30-day window using HLL merge operations. This probabilistic merging maintains the distinct user count properties while combining data across multiple days. The merged HLL sketch represents all unique users who were active at any point during the 30-day window.
- **Key fields**: `hll` (merged HyperLogLog sketches across 30-day windows)

### Component E: Dimensional breakdowns
- **Purpose**: Generates MAU metrics across different analytical dimensions
- **Logic**: Extracts the final user counts from merged HLL sketches and creates separate views for different dimensional breakdowns. Generates four distinct breakdown levels: project-level aggregation (all Mina users combined), chain-level aggregation (by blockchain), user type-level aggregation (by user classification), and product-level aggregation (by specific products or features). Each breakdown uses appropriate grouping and sets non-relevant dimensions to null values to maintain consistent schema structure.
- **Key fields**: `user_mau` (final MAU count), `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical perspectives in a single table structure
- **Currency conversion**: Not applicable as this metric counts users rather than financial values
- **Time aggregation**: Uses daily granularity with 30-day rolling windows, where each day represents the MAU for the trailing 30-day period ending on that date
- **Final calculation**: The metric applies standardization through the finalize_metric_user_mau macro, which ensures consistent schema, fills missing time periods with zero values, and applies proper data typing and metadata. Incremental processing includes lookback periods to handle late-arriving data and maintains data freshness while optimizing performance.

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

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

Mina ProtocolMINA

Archived

Active users (monthly)

Active users (monthly)

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