Validators process transactions and receive validator rewards (supply-side fees).

AVAX is a smart contract platform for decentralized applications (a blockchain). Avalanche was founded by Emin Gün Sirer and launched in 2019.

AVAX holders own and govern the protocol. They currently take (burn) all of the total transaction fees paid by users (revenue). AVAXs investors include a16z, Polychain, Galaxy Digital, etc.

github

twitter

discord

etherscan

website

Blockchains (L1)

Avalanche

# Avalanche Active users (monthly) Methodology

## 1. Overview

The Avalanche Monthly Active Users (MAU) metric measures the number of unique addresses that have sent at least one transaction on the Avalanche blockchain within a 30-day rolling window. This metric uses HyperLogLog probabilistic counting to efficiently calculate approximate unique user counts across different dimensional breakdowns including project-level, chain-level, user-type, and product-level views. The calculation employs a rolling 30-day window approach, meaning each day's MAU represents unique users from that day plus the previous 29 days.

## 2. Data sources

- **Avalanche blockchain transaction records**: Raw transaction data from the Avalanche blockchain containing transaction details, sender addresses (`from_address`), timestamps (`block_timestamp`), and transaction hashes
- **Internal tracking systems**: Token Terminal's table modification tracking system for optimizing incremental data processing and determining processing boundaries

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique transaction senders on a daily basis and prepares them for monthly aggregation
- **Logic**: Extracts all transaction sender addresses from Avalanche blockchain transactions, truncates timestamps to daily granularity, and creates HyperLogLog sketches for efficient approximate counting. Each user is classified as a "Transaction sender" user type and associated with the Avalanche chain and blockchain business segment. The data is grouped by date, chain, user type, and product dimensions to prepare for the rolling window calculation.
- **Key fields**: `timestamp` (truncated to day), `user_address` (from `from_address`), `chain` (set to 'avalanche'), `user_type` (set to 'Transaction sender'), `market_sector_id` (set to 'blockchains_l1'), `business_segment_id` (set to 'blockchain')

### Component B: Date spine and rolling window creation  
- **Purpose**: Creates a complete time series and establishes 30-day rolling windows for MAU calculation
- **Logic**: Generates a continuous date spine covering the entire analysis period to ensure no dates are missing from the final output. For each date in the spine, creates a 30-day rolling window by joining with the previous 29 days of data. This approach ensures that every day has a complete 30-day lookback period for calculating monthly active users, even if some intermediate days had no activity.
- **Key fields**: `timestamp` (spine date), `joinday` (each day within the 30-day window)

### Component C: HyperLogLog aggregation across rolling windows
- **Purpose**: Calculates approximate unique user counts for each 30-day window using probabilistic counting
- **Logic**: For each date and its corresponding 30-day window, merges the HyperLogLog sketches from all days within that window. The HyperLogLog algorithm provides memory-efficient approximate counting of unique users across the rolling window. This approach handles the challenge of counting distinct users across multiple days without requiring exact deduplication, which would be computationally expensive for large datasets.
- **Key fields**: `hll` (HyperLogLog sketch), `user_mau` (extracted unique count from merged sketches)

### Component D: Dimensional breakdown creation
- **Purpose**: Creates multiple views of the MAU metric across different business dimensions
- **Logic**: Generates four separate dimensional breakdowns of the MAU data. The project-level breakdown aggregates all users regardless of other dimensions. The chain-level breakdown groups by blockchain (in this case, just Avalanche). The user-type breakdown separates different categories of users. The product-level breakdown groups by specific products or services. Each breakdown maintains the same time-based aggregation logic but groups and nullifies different dimensional fields.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_mau` (unique user count)

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a single comprehensive dataset containing all perspective views of the MAU metric
- **Time aggregation**: Each record represents a daily snapshot of the 30-day rolling MAU calculation, with timestamps indicating the end date of each 30-day window
- **Schema standardization**: The finalize_metric_user_mau macro applies consistent schema formatting, data type standardization, and gap-filling logic to ensure missing dates are handled appropriately with zero values
- **Final calculation**: The final MAU value for any given date represents the approximate count of unique addresses that sent at least one transaction within the 30-day period ending on that date, calculated using HyperLogLog probabilistic counting for computational efficiency

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

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

AvalancheAVAX

Active users (monthly)

Active users (monthly)

Competitive landscape