Immutable zkEVM batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Immutable zkEVM is an L2 smart contract platform for decentralized applications on Ethereum. Immutable zkEVM was founded by the Immutable team and launched in 2023.

Immutable team owns and governs the protocol. Immutable's investors include Coinbase Ventures, Temasek, Tencent Holdings, etc.

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Immutable zkEVM

# Immutable zkEVM Active users (daily) Methodology

## 1. Overview

Daily Active Users (DAU) measures the number of unique users who interact with the Immutable zkEVM blockchain on a given day. This metric captures user engagement by counting distinct wallet addresses that either send transactions or use Immutable's Passport wallet system. The calculation combines transaction activity with Passport-specific events to provide comprehensive coverage of user activity across the Immutable zkEVM ecosystem, supporting multiple dimensional breakdowns for analysis.

## 2. Data sources

The DAU calculation relies on these blockchain data sources:

- **Transaction records**: Immutable zkEVM blockchain transaction data containing sender addresses, timestamps, and transaction metadata for all on-chain transactions
- **Passport wallet events**: Parsed `Wallet_NonceChange_event` logs from Immutable's Passport wallet system that track wallet state changes and user activity
- **Chain availability metadata**: Data quality indicators used to filter results based on blockchain data availability periods

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify all unique user addresses that show activity on Immutable zkEVM
- **Logic**: Extract user activity from two distinct sources. First, capture transaction senders from all Immutable zkEVM blockchain transactions, recording the `from_address` as active users with user type "Transaction sender". Second, capture Passport wallet users from `Wallet_NonceChange_event` logs, recording the `address` field as active users with user type "Passport wallet user". Both sources include block timestamps and are tagged with consistent metadata including chain identifier, market sector, business segment, and product identifiers
- **Key fields**: `block_timestamp` for activity timing, `user_address` for unique user identification, `user_type` for user classification, `transaction_hash` for transaction linking, `chain` and `product_id` for dimensional analysis

### Component B: Daily aggregation
- **Purpose**: Convert timestamped user activity into daily unique user counts
- **Logic**: Truncate all block timestamps to daily granularity using day-level time boundaries. Within each day, identify distinct user addresses across all activity types, preserving dimensional attributes like user type, chain, and product identifiers. Apply incremental loading logic with configurable lookback days to ensure data completeness while maintaining processing efficiency
- **Key fields**: `timestamp` truncated to day level, `user_address` for deduplication, `user_type`, `chain`, and `product_id` for dimensional breakdowns

### Component C: HyperLogLog aggregation
- **Purpose**: Enable efficient distinct user counting across multiple dimensions using probabilistic data structures
- **Logic**: Initialize HyperLogLog sketches with 15-bit precision for each unique combination of timestamp, chain, user type, and product identifier. Group user addresses by these dimensions and create HLL sketches that can be efficiently merged and queried for distinct counts
- **Key fields**: HLL sketches containing user address information, dimensional grouping keys for breakdown analysis

### Component D: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives of DAU data for different use cases
- **Logic**: Generate four distinct dimensional views by merging HLL sketches and extracting final counts. Create project-level aggregation combining all users regardless of dimension, chain-level breakdown showing DAU by blockchain, user-type breakdown separating transaction senders from Passport users, and product-level breakdown for product-specific analysis. Each breakdown maintains timestamp and appropriate dimensional identifiers while setting non-relevant dimensions to null
- **Key fields**: `user_dau` as the final distinct user count, `dimensions` indicating the breakdown type, `chain_id`, `product_id`, and `user_type` as dimensional identifiers

### Final aggregation
- **Combining components**: Union all dimensional breakdown results into a single comprehensive dataset containing project-level, chain-level, user-type-level, and product-level DAU metrics
- **Schema standardization**: Apply the `finalize_metric_user_dau` macro to ensure consistent output schema, proper data typing, and metadata enrichment with Immutable zkEVM identifiers
- **Missing value handling**: Fill gaps in the time series with zero values for days without user activity, ensuring complete daily coverage for analysis and reporting
- **Data quality filtering**: Apply chain availability filters to exclude periods where blockchain data may be incomplete or unreliable, ensuring metric accuracy and consistency

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

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

Immutable zkEVMIMX

Active users (daily)

Active users (daily)

Competitive landscape

Chain breakdown