Starknet is a Validity-Rollup (ZK-Rollup) Layer 2 network that operates on top of Ethereum, enabling dApps to massively scale without compromising on security. It achieves this by bundling transactions into an off-chain computed STARK proof. This proof is then submitted to Ethereum as a single transaction, resulting in significantly higher throughput, faster processing times, and much lower costs, all while retaining the robust security of the Ethereum settlement layer.

Starknet aims to be governed by a decentralized, community-driven process centered around STRK token holders. While StarkWare originally developed the technology, the network is transitioning to a completely decentralized model where the broader community makes decisions regarding protocol upgrades, development, and the future of the network.

Starknet is a Layer 2 that uses STARK technology to validate off-chain transactions, enabling unlimited scale while preserving Ethereum's security and decentralization.

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# Starknet Active users (weekly) Methodology

## 1. Overview

The Starknet Weekly Active Users (WAU) metric measures the number of unique addresses that initiated or received transactions on the Starknet blockchain within a rolling 7-day period. This metric provides insight into user engagement and activity levels on the Starknet Layer 2 network. The calculation uses HyperLogLog data structures to efficiently count unique users across overlapping time windows while providing multiple dimensional breakdowns for analysis.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Starknet transaction records**: Complete transaction data from the Starknet blockchain including transaction hashes, sender addresses, transaction types, block timestamps, and gas fees
- **Starknet receipt events**: Transaction receipt event logs that contain recipient address information and transaction execution details
- **Starknet block data**: Block timestamp information used for daily time period aggregation

## 3. Step-by-step calculation

### Component A: Transaction data processing
- **Purpose**: Extract and standardize transaction data from Starknet blockchain
- **Logic**: Raw Starknet transaction data is processed to extract key fields including block timestamps, transaction hashes, sender addresses, and transaction types. Recipient addresses are extracted from the first element of the receipt events array when available. Gas fees are converted from wei to ETH units by dividing by 10^18. The data is categorized with Starknet as a Layer 2 blockchain in the 'blockchains_l2' market sector and 'blockchain' business segment
- **Key fields**: `block_timestamp` for daily aggregation, `sender_address` as transaction initiator, `to_address` extracted from receipt events as recipient, `transaction_type` used as user type classification

### Component B: Daily active users calculation  
- **Purpose**: Calculate unique active users per day from transaction activity
- **Logic**: Daily active users are identified by finding distinct addresses that either sent or received transactions on each day. The calculation groups transactions by day using block timestamp truncation and identifies unique user addresses using the sender address or recipient address (whichever is available). Users are further classified by transaction type, which becomes the user type dimension
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing unique user addresses, `user_type` derived from transaction type, `chain` identifier

### Component C: HyperLogLog conversion and windowing
- **Purpose**: Convert daily user counts to HyperLogLog structures and create rolling 7-day windows
- **Logic**: Daily active user data is converted to HyperLogLog data structures to enable efficient unique counting across overlapping time periods. A continuous datespine is generated covering all days from the earliest transaction date to the current date. For each day in the datespine, a 7-day window is created that includes the current day plus the previous 6 days. This creates overlapping windows where each day participates in multiple weekly calculations
- **Key fields**: `hll` containing HyperLogLog representation of daily users, `timestamp` for window anchor dates, window join logic spanning 7 consecutive days

### Component D: Weekly active user aggregation by dimensions
- **Purpose**: Calculate unique weekly active users across multiple dimensional breakdowns
- **Logic**: Within each 7-day window, HyperLogLog structures are merged and extracted to count unique users across the rolling period. This calculation is performed separately for four different dimensional breakdowns: project-level aggregation (all users regardless of other dimensions), chain-level aggregation (grouping by Starknet chain), user-type aggregation (grouping by transaction type), and product-level aggregation (though product_id is null for Starknet). Each breakdown merges the appropriate HLL structures and extracts the final unique count
- **Key fields**: `user_wau` containing unique weekly active user counts, `dimensions` indicating the aggregation level, dimension-specific fields like `chain_id` and `user_type`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple aggregation levels in a single table
- **Time boundaries**: Daily boundaries are determined by block timestamp truncation to day granularity, with rolling 7-day windows that update daily to provide continuous weekly metrics
- **Final calculation**: The final WAU count represents the number of unique addresses that were active (sent or received transactions) within any 7-day rolling window, calculated using HyperLogLog merge operations for computational efficiency and accuracy across large datasets

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

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

StarknetSTRK

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown