Mantra is a Decentralized Exchange (DEX), initially built on Ethereum and upgraded to the Mantra Chain, a blockchain built on the Cosmos SDK.

website

twitter

discord

github

Blockchains (L1)

MANTRA

# MANTRA Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interacted with the Mantra protocol within any 30-day period. This metric answers the business question: "How many distinct users used Mantra's blockchain or DEX services in the past 30 days?" The calculation uses a rolling 30-day window approach with HyperLogLog data structures for efficient unique user counting, providing MAU breakdowns across multiple dimensions including project level, chain level, user type, and product.

## 2. Data sources

- **Mantra blockchain message events**: Event logs from the Mantra blockchain containing transaction sender information and metadata for general blockchain activity
- **Mantra WASM contract execution events**: Smart contract execution events from the Mantra DEX contract containing swap operation details and user addresses  
- **Event attribute data**: JSON-structured event attributes containing detailed transaction information including sender addresses, pool identifiers, token amounts, and swap parameters
- **Transaction metadata**: Block timestamps, transaction hashes, and event indices for proper temporal ordering and deduplication

## 3. Step-by-step calculation

### Component A: General blockchain user identification
- **Purpose**: Identifies users performing general blockchain transactions on Mantra
- **Logic**: Extract user addresses from Mantra blockchain message events where the `event_type` equals 'message'. Parse the `sender` field from event attributes JSON to identify transaction originators. Classify these users as 'Transaction sender' type and assign them to the 'Mantra Chain' product with 'blockchain' business segment and 'blockchains_l1' market sector.
- **Key fields**: `block_timestamp` for temporal analysis, `sender` address for user identification, `transaction_hash` for deduplication

### Component B: DEX user identification  
- **Purpose**: Identifies users performing decentralized exchange operations on Mantra DEX
- **Logic**: Extract user addresses from WASM contract execution events where `event_type` equals 'wasm', the contract address matches the specific Mantra DEX contract ('mantra1466nf3zuxpya8q9emxukd7vftaf6h4psr0a07srl5zw74zh84yjqagspfm'), and the action equals 'execute_swap_operations'. Parse the `sender` field from swap event attributes JSON. Classify these users as 'DEX user' type and assign them to 'Mantra Zone' product with 'dex' business segment and 'exchanges' market sector.
- **Key fields**: `sender` address from swap events, detailed swap information including `pool_identifier`, `amount_in`, `amount_out`, token addresses, and fee structures

### Component C: Daily active user aggregation
- **Purpose**: Aggregates distinct users by day across both user sources
- **Logic**: Combine general blockchain users and DEX users, truncating timestamps to day granularity. Create distinct daily user counts grouped by `timestamp`, `chain`, `user_type`, and `product_id`. This eliminates duplicate user addresses within the same day while preserving user type and product classifications.
- **Key fields**: Daily truncated `timestamp`, `chain` identifier ('mantra'), `user_type` classification, `product_id` designation, unique `user_address`

### Component D: HyperLogLog sketch creation
- **Purpose**: Converts daily active user data into HyperLogLog sketches for efficient unique counting
- **Logic**: Transform the daily active user data into HyperLogLog sketches using precision level 15. Group by `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, and `product_id`, initializing HLL sketches for the user addresses within each group. This enables efficient approximate unique user counting across time windows.
- **Key fields**: `hll` sketch containing encoded user address data, dimensional grouping fields

### Component E: Rolling window creation
- **Purpose**: Creates 30-day rolling windows for each timestamp to calculate MAU
- **Logic**: Generate a complete datespine covering all days from the earliest DAU data to the current date. For each day in the datespine, create associations with the 29 preceding days plus the current day to form 30-day windows. Join this window structure with the daily HLL sketches to collect all relevant user data for each 30-day period.
- **Key fields**: `timestamp` for the window end date, `joinday` for days within the window, associated HLL sketches

### Component F: MAU calculation by dimension
- **Purpose**: Calculates unique user counts within each 30-day window across multiple dimensional breakdowns
- **Logic**: For each 30-day window, merge all HLL sketches using `hll_count.merge_partial` and extract the unique user count using `hll_count.extract`. Create separate dimensional breakdowns: project level (all users combined), chain level (grouped by chain), user type level (grouped by user type), and product level (grouped by product). Each breakdown nullifies non-applicable dimension fields and aggregates accordingly.
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown type, relevant dimensional identifiers

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with standardized schema including timestamp, dimensions, and applicable identifiers
- **Schema standardization**: Apply the `finalize_metric_user_mau` macro to ensure consistent field names, data types, and metadata across the final output
- **Missing data handling**: Fill periods with zero MAU where no user activity occurred using the 'fill_zeros' strategy to maintain complete time series
- **Final calculation**: The result provides daily MAU values representing unique users active in the 30-day period ending on each date, broken down by the specified dimensional categories

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

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

MANTRAMANTRA

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown