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Drift Protocol

# Drift Protocol Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Drift Protocol measures the count of unique users who have interacted with the protocol within any rolling 30-day period. This metric provides insight into user engagement and protocol adoption over time. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across sliding 30-day windows while maintaining accuracy for large datasets.

## 2. Data sources

- **Solana blockchain instruction logs**: Raw instruction execution data from the Solana blockchain containing transaction details and program interactions
- **Transaction signer data**: User wallet addresses that signed and executed transactions on the Solana network
- **Drift Protocol program interactions**: Filtered instruction data specifically targeting the Drift Protocol program address (`dRiftyHA39MWEi3m9aunc5MzRF1JYuBsbn6VPcn33UH`)

## 3. Step-by-step calculation

### Component A: Raw interaction capture
- **Purpose**: Extract all user interactions with Drift Protocol from Solana blockchain data
- **Logic**: Solana instruction logs are filtered to include only transactions that executed against the Drift Protocol program address. Each instruction record contains the transaction signer (user wallet address), transaction timestamp, and transaction identifier. The transaction signer serves as the unique user identifier for activity tracking.
- **Key fields**: `block_timestamp` as the interaction time, `transaction_signer` as the user identifier, `transaction_id` for transaction uniqueness, and `executing_account` for program filtering

### Component B: Daily active user calculation
- **Purpose**: Aggregate unique users per day to establish the foundation for monthly calculations
- **Logic**: Raw interactions are grouped by day using timestamp truncation to daily boundaries. Distinct transaction signers are counted for each day to identify unique daily active users. All users are classified as 'Trader' user type since Drift Protocol primarily serves trading functionality. Each daily record maintains chain identifier ('solana'), product identifier ('driftprotocol'), and timestamp granularity metadata.
- **Key fields**: Daily truncated `timestamp`, `signer` as unique user identifier, `chain_id` for blockchain identification, `user_type` classification, and `product_id` for protocol identification

### Component C: HyperLogLog conversion and windowing preparation  
- **Purpose**: Convert daily user counts to efficient probabilistic data structures and prepare for rolling window calculations
- **Logic**: Daily active user data is converted into HyperLogLog (HLL) structures with 15-bit precision for memory-efficient unique counting. A complete datespine is generated covering all days from the first recorded interaction to the current date minus one day. Cross-joins create all possible 30-day windows by generating 30-day lookback periods for each date in the datespine, ensuring no missing dates in the final calculation.
- **Key fields**: HLL-encoded `user_dau` values, complete date coverage through datespine generation, and 30-day rolling window boundaries

### Component D: Monthly active user window aggregation
- **Purpose**: Calculate unique users across rolling 30-day periods using HLL merge operations
- **Logic**: For each target date, all daily HLL structures within the corresponding 30-day lookback window are merged using HLL merge operations. This produces a single HLL structure representing all unique users who were active within that 30-day period. The process maintains dimensional breakdowns including chain, user type, and product identifiers throughout the aggregation.
- **Key fields**: Merged HLL structures representing 30-day unique user counts, preserved dimensional attributes (`chain_id`, `user_type`, `product_id`)

### Component E: Dimensional breakdown and finalization
- **Purpose**: Create multiple dimensional views of MAU data and extract final user counts
- **Logic**: The merged HLL data is broken down into four dimensional perspectives: project-level (all dimensions null), chain-level (grouping by chain), user type-level (grouping by user type), and product-level (grouping by product). Each breakdown uses HLL extract operations to convert probabilistic structures back to integer user counts. Results from all dimensional breakdowns are combined using union operations to create a comprehensive dataset.
- **Key fields**: Extracted `user_mau` counts, dimensional identifier (`dimensions`), and appropriate grouping fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined through union operations to create a single comprehensive MAU dataset with multiple analytical perspectives
- **Time aggregation**: MAU is calculated using rolling 30-day windows, meaning each date represents unique users active in the 30 days ending on that date
- **Final calculation**: HyperLogLog structures are merged across 30-day periods and extracted to produce integer counts of unique monthly active users, providing approximate but highly accurate unique user counts suitable for large-scale analytics

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

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

Drift ProtocolDRIFT

Active users (monthly)

Active users (monthly)

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