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.

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Blockchains (L1)

Avalanche

# Avalanche Active users (daily) Methodology

## 1. Overview

Daily Active Users (DAU) for Avalanche measures the number of unique addresses that initiated at least one transaction on the Avalanche blockchain on a given day. This metric provides insight into user engagement and adoption by counting distinct transaction senders. The calculation uses HyperLogLog sketches for scalable approximate distinct counting and provides multiple dimensional breakdowns including project-level, chain-level, user type, and product-level views.

## 2. Data sources

- **Avalanche blockchain transactions**: Raw transaction records from the Avalanche blockchain containing transaction metadata including timestamps, transaction hashes, sender addresses (`from_address`), and other transaction details
- **Chain availability configuration**: Configuration data that determines which blockchain networks are available for analysis during specific time periods
- **Table modification metadata**: Timestamp tracking for incremental processing to optimize data pipeline performance

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify all unique addresses that sent transactions on Avalanche
- **Logic**: Extract transaction sender addresses (`from_address`) from all Avalanche blockchain transactions. Each transaction sender represents an active user. The component adds contextual metadata including chain classification as 'avalanche', market sector as 'blockchains_l1', business segment as 'blockchain', user type as 'Transaction sender', and preserves the original `block_timestamp` and `transaction_hash`
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `chain` set to 'avalanche', `user_type` set to 'Transaction sender'

### Component B: Daily user aggregation  
- **Purpose**: Aggregate unique users by day and prepare for scalable distinct counting
- **Logic**: Truncate transaction timestamps to daily boundaries using day-level truncation. Collect distinct user addresses for each day, creating one record per unique user per day. Initialize HyperLogLog sketches for each user address to enable efficient approximate distinct counting at scale. Apply incremental processing logic that processes only recent data based on table modification timestamps and includes a configurable lookback window
- **Key fields**: `timestamp` truncated to day boundaries, `user_dau` containing the user address, `timestamp_grain` set to 'day', contextual fields like `chain`, `user_type`, and `product_id`

### Component C: Dimensional breakdowns
- **Purpose**: Create multiple views of DAU data across different dimensions
- **Logic**: Generate four distinct dimensional aggregations by merging HyperLogLog sketches. Project-level aggregation provides total DAU across all dimensions. Chain-level breakdown shows DAU by blockchain network. User-type breakdown segments DAU by different user classifications. Product-level breakdown shows DAU by specific products or services. Each breakdown uses HLL merge operations to combine user sketches appropriately while maintaining dimension-specific groupings
- **Key fields**: `dimensions` indicating the breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), dimensional identifier fields (`chain_id`, `product_id`, `user_type`), merged HLL sketch results

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing DAU for a specific dimension combination on a specific day
- **HyperLogLog processing**: Extract final distinct counts from merged HyperLogLog sketches using `hll_count.extract()` to get approximate unique user counts
- **Schema standardization**: Apply the `finalize_metric_user_dau` macro to standardize the output schema, ensure consistent field naming, and implement a fill-zeros strategy for missing days
- **Time boundaries**: Daily aggregation boundaries determined by timestamp truncation to day level
- **Final calculation**: The final `user_dau` value represents the approximate count of distinct user addresses that sent transactions on Avalanche for each day and dimensional combination, filtered by chain availability rules

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

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

AvalancheAVAX

Active users (daily)

Active users (daily)

Competitive landscape