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Dolomite

# Dolomite Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Dolomite measures the distinct count of users who interacted with the Dolomite margin trading protocol within any 30-day rolling window. This metric tracks user engagement across multiple blockchains and interaction types, providing insights into platform adoption and user retention. The calculation uses HyperLogLog algorithms for efficient distinct user counting across large datasets and maintains dimensional breakdowns by chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Blockchain event logs**: Parsed event logs from Dolomite's `DolomiteMargin` smart contracts deployed on Arbitrum, Base, Ink, Berachain, and Ethereum networks
- **Transaction data**: Blockchain transaction timestamps and hashes for temporal ordering and deduplication
- **Contract events**: Eight specific event types from the margin trading protocol: `LogDeposit`, `LogWithdraw`, `LogSell`, `LogBuy`, `LogTrade`, `LogTransfer`, `LogLiquidate`, and `LogVaporize` events
- **Chain metadata**: Chain availability filters to ensure accurate cross-chain calculations

## 3. Step-by-step calculation

### Component A: User interaction capture
- **Purpose**: Identifies and categorizes all user interactions with the Dolomite protocol across supported blockchains
- **Logic**: Extracts user addresses from eight different event types emitted by Dolomite's margin trading contracts. Each event type captures different user roles: `LogDeposit` and `LogWithdraw` events identify depositors and withdrawers through the `accountOwner` field, `LogSell` and `LogBuy` events capture traders via `accountOwner`, `LogTrade` events identify traders through `takerAccountOwner`, `LogTransfer` events track margin users via `accountOneOwner`, and `LogLiquidate` and `LogVaporize` events capture liquidators through `solidAccountOwner`. All interactions are tagged with chain identifier, business segment as lending, and user type classification.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for deduplication, `chain` for multi-chain aggregation, `user_type` for behavioral categorization, `user_address` as the primary user identifier

### Component B: Daily user aggregation  
- **Purpose**: Aggregates user interactions into daily distinct active user counts using efficient probabilistic data structures
- **Logic**: Groups all user interactions by day using `timestamp_trunc` to the day granularity. Creates HyperLogLog sketches with precision 15 for each combination of date, chain, user type, and product to enable memory-efficient distinct user counting. Applies chain availability filters to ensure accurate cross-chain calculations and exclude data from chains that may be temporarily unavailable.
- **Key fields**: `timestamp` truncated to daily boundaries, `timestamp_grain` set to day, `chain` for network identification, `user_type` for interaction classification, `user_dau` containing the distinct user address, `hll` containing the HyperLogLog sketch

### Component C: Rolling window creation
- **Purpose**: Generates 30-day sliding windows for each day to enable monthly active user calculations
- **Logic**: Creates a continuous date spine from the earliest data date to the current date minus one day using `generate_timestamp_array` with daily intervals. For each date in the spine, generates a 30-day lookback period by creating an array of the 30 days ending on that date. Joins this expanded date range with the daily user data to create a comprehensive dataset where each day has associations with all its contributing days within the 30-day window.
- **Key fields**: `timestamp` as the target date for MAU calculation, `joinday` representing each contributing day within the 30-day window, `hll` sketches for efficient aggregation

### Component D: Dimensional MAU calculation
- **Purpose**: Calculates monthly active users across multiple business dimensions while maintaining data structure for reporting
- **Logic**: Merges HyperLogLog sketches within each 30-day window and extracts the distinct count to produce MAU values. Creates four separate dimensional views: project-level MAU aggregates all chains and user types, chain-level MAU breaks down by individual blockchain, user-type-level MAU segments by interaction behavior, and product-level MAU analyzes by specific product offerings. Each dimension sets non-applicable fields to null and includes a dimensions identifier for downstream processing.
- **Key fields**: `timestamp` for the MAU measurement date, `dimensions` indicating the breakdown level, `chain_id`, `product_id`, `user_type` as dimensional attributes, `user_mau` containing the final distinct user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, maintaining separate records for each dimensional view while using null values for non-applicable dimension fields
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows calculated daily, with monthly partitioning for efficient data storage and querying
- **Final calculation**: Applies the `finalize_metric_user_mau` macro to standardize the output schema, implement zero-filling strategies for missing data points, and add metadata fields including the data identifier set to "dolomite"

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

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

DolomiteDOLO

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown