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# Prime Numbers Active users (daily) Methodology

## 1. Overview

The Primenumbers daily active users (DAU) metric measures the number of unique users who interact with the Primenumbers lending protocol each day. This metric tracks user engagement across borrowing, lending, repayment, withdrawal, and flash loan activities on the XDC Network. The calculation uses HyperLogLog (HLL) algorithms for efficient distinct user counting and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The DAU calculation relies on parsed XDC Network blockchain event logs from the Primenumbers lending pool smart contract:

- **Lending pool events**: Parsed `LendingPool_Borrow_event`, `LendingPool_Repay_event`, `LendingPool_Deposit_event`, `LendingPool_Withdraw_event`, and `LendingPool_FlashLoan_event` logs from XDC Network
- **Event metadata**: Block timestamps, transaction hashes, and user addresses from each lending activity
- **Contract interactions**: All user interactions with the Primenumbers v1 lending pool contract on XDC Network

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identify all users who interact with the Primenumbers lending protocol and classify them by activity type
- **Logic**: Extract user addresses from five types of lending pool events on XDC Network. Borrowers are identified from `Borrow` events using the `user` field. Flash loan users are captured from `FlashLoan` events using the `initiator` field. Repayers come from `Repay` events, depositors from `Deposit` events, and withdrawers from `Withdraw` events, all using the `user` field. Each interaction is tagged with chain (`xdc`), market sector (`lending`), business segment (`lending`), and specific product identifiers (`PrimeFi Lending` for borrowing, `primenumbersv1` for other activities)
- **Key fields**: `user_address` (the wallet address performing the action), `block_timestamp` (when the transaction occurred), `transaction_hash` (unique transaction identifier), `user_type` (Borrower, Repayer, Depositor, Withdrawer), `product_id` (PrimeFi Lending or primenumbersv1)

### Component B: Daily user aggregation  
- **Purpose**: Convert individual user events into daily distinct user counts
- **Logic**: Truncate all block timestamps to daily boundaries using day-level precision. Group users by day, chain, user type, and product, then deduplicate user addresses to count each unique user only once per day regardless of how many transactions they performed. Preserve dimensional attributes including user type classification and product identification for downstream breakdowns
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` (set to 'day'), `user_dau` (distinct user address), dimensional fields for grouping

### Component C: HyperLogLog initialization
- **Purpose**: Prepare efficient data structure for large-scale distinct user counting across multiple dimensions
- **Logic**: Initialize HyperLogLog sketches for each daily user count using 15-bit precision. Group data by timestamp, chain, user type, and product dimensions. Apply incremental processing logic that looks back one month plus a configurable number of days from the maximum partition to handle late-arriving data and ensure accurate counts during incremental updates
- **Key fields**: `hll` (HyperLogLog sketch containing user count estimates), all dimensional grouping fields preserved

### Component D: Multi-dimensional breakdowns
- **Purpose**: Generate DAU metrics across different analytical dimensions simultaneously
- **Logic**: Create four separate aggregation views by merging HyperLogLog sketches and extracting final counts. Project-level aggregation combines all users regardless of other dimensions. Chain-level breakdown preserves chain identity while nullifying other dimensions. User-type breakdown groups by user activity type (Borrower, Repayer, etc.) while nullifying chain and product fields. Product-level breakdown separates PrimeFi Lending and primenumbersv1 products while nullifying other dimensions. Each breakdown uses the same timestamp and applies HLL merge operations to maintain accuracy
- **Key fields**: `dimensions` (identifies breakdown type), `chain_id`, `product_id`, `user_type` (with appropriate null values), `user_dau` (extracted from merged HLL sketches)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, maintaining consistent schema across all breakdown types
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Daily boundaries are maintained throughout, with monthly partitioning for query performance
- **Final calculation**: Apply the `finalize_metric_user_dau` macro to standardize the metric schema, set the data identifier to 'primenumbers', and fill missing days with zero values using the 'fill_zeros' strategy. Apply chain availability filtering to ensure only supported chain data is included in final results

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

Unique revenue-generating users over a fixed 24-hour window.

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Active users (daily)

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Chain breakdown