Validators process transactions and receive fees from users (supply-side fees).

Astar is a collective driving web3 adoption by connecting people through blockchain technology, business innovation, decentralized applications, and seamless digital experiences in the Polkadot ecosystem. Astar was founded by Sota Watanabe and launched in 2021.

ASTR tokenholders own and govern the protocol. Part of the transaction fees is burned (revenue). Astar’s investors include Binance, Polychain Capital, Crypto.com, etc.

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Blockchains (L2)

Astar

# Astar Active users (daily) Methodology

## 1. Overview

Daily Active Users (DAU) measures the number of unique wallet addresses that performed transactions on the Astar network within a given day. This metric captures user activity across both Astar's native substrate layer and its Ethereum-compatible EVM layer, providing a comprehensive view of daily user engagement. The calculation uses HyperLogLog sketches for efficient distinct counting and provides multiple dimensional breakdowns for analysis.

## 2. Data sources

- **Astar substrate extrinsics**: Native Astar blockchain transaction records containing transaction sender addresses and block timestamps
- **Astar EVM execution events**: Ethereum-compatible layer transaction events from the ethereum pallet, specifically `Executed` events containing user addresses in JSON format
- **Chain availability configuration**: Internal configuration data used to filter results based on data completeness and quality standards

## 3. Step-by-step calculation

### Component A: User extraction from blockchain data
- **Purpose**: Identifies all unique user addresses that performed transactions on Astar networks
- **Logic**: Extracts user addresses from two distinct data streams. From native Astar substrate extrinsics, uses the `id` field as the user address along with block timestamps. From Astar EVM events, filters for ethereum pallet `Executed` events and extracts user addresses from the first element of the `event_data` JSON array. Both streams are labeled with appropriate chain identifiers (astar for substrate, astarevm for EVM layer) and classified as "Transaction sender" user types with "Astar Substrate" product identification
- **Key fields**: `block_timestamp` for timing, `id` and `event_data` for user addresses, `chain` for network identification

### Component B: Daily user aggregation with HyperLogLog
- **Purpose**: Aggregates users to daily granularity while maintaining efficient distinct counting capabilities
- **Logic**: Truncates block timestamps to day boundaries and creates daily distinct user records. Each user address becomes a unique daily active user record with associated metadata including chain, user type, and product identification. Uses HyperLogLog sketch initialization for scalable distinct counting across different dimensional groupings
- **Key fields**: `timestamp` truncated to day, `user_dau` containing individual user addresses, `chain` and `user_type` for dimensional analysis

### Component C: Multi-dimensional breakdown generation
- **Purpose**: Creates different analytical perspectives of the DAU metric across various dimensions
- **Logic**: Generates four distinct dimensional views using HyperLogLog sketch merging. Creates project-level aggregation (all users combined), chain-level breakdown (separate counts for astar and astarevm), user-type breakdown (Transaction sender category), and product-level breakdown (Astar Substrate product). Each breakdown uses HyperLogLog merge operations to combine daily sketches and extract final distinct counts while setting unused dimension fields to null
- **Key fields**: `dimensions` indicating breakdown type, `chain_id`, `product_id`, `user_type` for dimensional filtering, `user_dau` containing final distinct counts

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a unified dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily boundary logic uses timestamp truncation to day granularity with incremental processing based on partition dates
- **Final calculation**: Applies the `finalize_metric_user_dau` macro for schema standardization, fills missing days with zero values, and applies chain availability filtering to ensure data quality. The final `user_dau` value represents the distinct count of unique wallet addresses active on each day

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

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

AstarASTR

Active users (daily)

Active users (daily)

Competitive landscape

Chain breakdown