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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Blockchains (L1)

Mina Protocol

# Mina Protocol Active users (weekly) Methodology

## 1. Overview

Weekly active users (WAU) measures the number of unique addresses that initiated at least one transaction on the Mina blockchain within a rolling 7-day period. This metric answers the business question "How many distinct users are actively transacting on Mina each week?" and uses a sliding window approach where each day's WAU count includes users who were active on that day or any of the previous 6 days. The calculation uses HyperLogLog probabilistic data structures to efficiently handle unique user counting across large datasets.

## 2. Data sources

- **Mina blockchain transaction records**: Raw transaction data from the Mina blockchain containing transaction metadata, timestamps, sender addresses, and transaction hashes
- **Chain availability configuration**: Internal Token Terminal configuration data that defines valid date ranges and operational status for the Mina blockchain
- **Table modification timestamps**: Internal metadata tracking when data tables were last updated, used for incremental processing and ensuring data freshness

## 3. Step-by-step calculation

### Component A: Daily user extraction
- **Purpose**: Identifies unique user addresses that initiated transactions each day
- **Logic**: Processes raw Mina blockchain transaction records to extract the sender address from each transaction. Each transaction sender is classified as a user of type "Transaction sender" on the Mina chain. Transactions are grouped by day using timestamp truncation to daily boundaries. All users are categorized under the "blockchains_l1" market sector and "blockchain" business segment, with no specific product segmentation
- **Key fields**: `block_timestamp` for temporal grouping, `sender` as the user identifier, `transaction_hash` for transaction uniqueness, `chain` set to "mina"

### Component B: Daily hyperLogLog sketch creation  
- **Purpose**: Creates memory-efficient daily unique user counts using probabilistic data structures
- **Logic**: Takes the daily user addresses and creates HyperLogLog sketches with 15-bit precision for each day. These sketches allow efficient counting of unique users while using minimal memory. The sketches are grouped by timestamp, chain, user type, and product dimensions to support multiple analytical breakdowns
- **Key fields**: `timestamp` truncated to day granularity, `hll` containing the HyperLogLog sketch of unique users, dimensional fields like `chain_id`, `user_type`, and `product_id`

### Component C: Date spine generation
- **Purpose**: Creates a complete sequence of dates to ensure no gaps in the time series
- **Logic**: Generates a continuous array of daily timestamps from the earliest transaction date to yesterday. For incremental processing, the start date is adjusted to include a 29-day lookback period plus configurable additional days to handle late-arriving data. This ensures every day has a corresponding record even if no transactions occurred
- **Key fields**: `timestamp` representing each calendar day in the analysis period

### Component D: Seven-day rolling window creation
- **Purpose**: Establishes the 7-day sliding window for each date to capture weekly activity patterns
- **Logic**: For each date in the spine, creates associations with that date plus the previous 6 days (7 days total). Uses array generation to create these 7-day windows and unnests them to create individual date pairs. Each date is linked to 7 different "join days" representing the days whose activity should be counted toward that date's WAU
- **Key fields**: `timestamp` as the target date, `joinday` representing each of the 7 days in the rolling window

### Component E: Window data aggregation
- **Purpose**: Collects daily HyperLogLog sketches for each 7-day window period
- **Logic**: Joins the 7-day windows with the daily HLL sketches, matching each join day to its corresponding daily user data. This creates a dataset where each target date has up to 7 rows of daily HLL sketches (one for each day in its window). Includes incremental processing filters with 60-day safety margins to handle data updates
- **Key fields**: `timestamp` as the target date, `hll` containing daily user sketches, dimensional grouping fields

### Final aggregation
- **Combining components**: HyperLogLog sketches are merged across the 7-day windows using HLL merge operations, then extracted to get final unique user counts. Four separate dimensional breakdowns are calculated: project-level (all dimensions null), chain-level (grouped by chain), user-type-level (grouped by user type), and product-level (grouped by product)
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily grain with 7-day rolling windows, where each day represents users active in that day plus the previous 6 days
- **Final calculation**: The WAU for any given date equals the count of unique user addresses that initiated at least one transaction on that date or any of the previous 6 days, calculated by merging and extracting HyperLogLog sketches. Missing days are filled with zeros, and the result includes multiple dimensional cuts of the same underlying user activity data

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

Unique revenue-generating users over a rolling 7-day window.

Mina ProtocolMINA

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Active users (weekly)

Active users (weekly)

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