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 (weekly) Methodology

## 1. Overview

The Avalanche Weekly Active Users (WAU) metric measures the count of unique user addresses that have sent at least one transaction on the Avalanche blockchain within a rolling 7-day window. This metric uses HyperLogLog (HLL) probabilistic data structures for efficient distinct counting and provides multiple dimensional breakdowns including chain-level, user type, and product-level aggregations. The calculation answers the business question: "How many unique users are actively transacting on Avalanche each week?"

## 2. Data sources

The calculation relies on the following raw data sources:
- **Avalanche transaction records**: Raw Avalanche blockchain transaction data containing transaction metadata, sender addresses (`from_address`), and block timestamps
- **Table modification metadata**: Internal tracking data for incremental processing optimization, containing last modified timestamps for efficient data refresh cycles

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Extract and classify unique user addresses from Avalanche transaction data
- **Logic**: The system identifies all transaction senders from Avalanche blockchain records, extracting the `from_address` field as the user identifier. Each user is classified with the user type "Transaction sender" and associated with chain metadata including "avalanche" as chain, "blockchains_l1" as market sector, and "blockchain" as business segment. The `block_timestamp` provides the temporal context for user activity.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for timing, `chain` identifier, `user_type` classification, and business categorization fields

### Component B: Daily active user aggregation
- **Purpose**: Aggregate transaction senders to daily unique user counts using HyperLogLog structures
- **Logic**: Raw user addresses are aggregated to daily granularity by truncating `block_timestamp` to day precision. The system creates distinct user counts per day, maintaining dimensional attributes like chain, user type, market sector, business segment, and product identifiers. HyperLogLog sketches are initialized at this stage with 15-bit precision for efficient approximate distinct counting across large datasets.
- **Key fields**: `timestamp` (day-truncated), `timestamp_grain` (set to "day"), `user_address` as distinct user identifier, and dimensional attributes

### Component C: Time spine generation and rolling window creation
- **Purpose**: Create complete date ranges and establish 7-day rolling windows for WAU calculation
- **Logic**: A date spine is generated covering the full range from the earliest DAU timestamp to the current date minus one day, ensuring no missing timestamps that could break rolling window calculations. For each date in the spine, the system creates a 7-day window by generating an array of the current date plus the 6 preceding days. This creates a "joinday" relationship that maps each target date to its constituent 7-day period.
- **Key fields**: `timestamp` for target dates, `joinday` for the 7-day window dates, enabling proper window-based aggregation

### Component D: Weekly active user window aggregation
- **Purpose**: Merge daily HLL sketches across 7-day rolling windows to calculate weekly unique users
- **Logic**: The system joins the time spine joinday mappings with the daily HLL data, creating a comprehensive dataset where each target timestamp is associated with all daily HLL sketches from its 7-day window. Daily HLL sketches are merged using HLL partial merge operations across each 7-day period, then extracted to produce the final distinct user count. This approach handles overlapping users across multiple days within the week while maintaining approximation accuracy.
- **Key fields**: `timestamp` for the target date, dimensional attributes (`chain_id`, `user_type`, `product_id`), and merged `hll` sketches

### Component E: Dimensional breakdown creation
- **Purpose**: Generate multiple aggregation levels for different analytical perspectives
- **Logic**: The system creates four distinct dimensional breakdowns from the windowed data. The project-level breakdown aggregates all users regardless of other dimensions. The chain-level breakdown maintains chain-specific counts. The user type breakdown preserves user classification distinctions. The product breakdown maintains product-level granularity. Each breakdown nullifies non-relevant dimensional fields and extracts final counts from the merged HLL sketches using HLL extract operations.
- **Key fields**: `dimensions` field indicating breakdown type, relevant dimensional identifiers (preserved), nullified irrelevant dimensions, and `user_wau` containing final distinct counts

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations, creating a unified dataset with different aggregation levels distinguished by the `dimensions` field and populated dimensional attributes
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily timestamps with 7-day rolling windows, partitioned by month for efficient incremental processing
- **Final calculation**: The final WAU value represents the distinct count of user addresses that sent transactions within any 7-day rolling period, calculated using HyperLogLog probabilistic counting with approximately 0.65% standard error at 15-bit precision

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

Unique revenue-generating users over a rolling 7-day window.

AvalancheAVAX

Active users (weekly)

Active users (weekly)

Competitive landscape