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 addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique wallet addresses that performed any blockchain activity on ImmutableX zkEVM during a 30-day rolling window. This metric tracks user engagement and network adoption by counting distinct addresses that either sent transactions or interacted with ImmutableX's Passport identity system. The calculation uses a 30-day rolling window approach, meaning each day's value represents the count of unique addresses active in the current day plus the previous 29 days.

## 2. Data sources

The calculation draws from two main blockchain data sources:

- **ImmutableX zkEVM transaction records**: Complete transaction history including transaction senders, timestamps, and transaction metadata for all network activity
- **ImmutableX Passport wallet event logs**: Parsed `Wallet_NonceChange_event` logs that capture user interactions with ImmutableX's identity and wallet system, including wallet addresses and interaction timestamps

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all unique user addresses that showed activity on ImmutableX zkEVM on each day
- **Logic**: Combines two types of user activity from blockchain data. First, extracts transaction senders by taking the `from_address` field from all ImmutableX zkEVM transactions, classifying these as "Transaction sender" user types. Second, extracts Passport wallet users by taking the `address` field from parsed `Wallet_NonceChange_event` logs, classifying these as "Passport wallet user" types. Both activity types are timestamped using `block_timestamp` and truncated to daily granularity. The component unions these two user types together and applies metadata including chain identifier, market sector, business segment, and product identifiers.
- **Key fields**: `block_timestamp` for timing, `from_address` for transaction senders, `address` for wallet users, `user_type` for classification, `user_address` for the final user identifier

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active users using probabilistic counting for efficiency
- **Logic**: Takes the extracted user activity and groups by day, creating distinct daily user counts. For each day, converts the list of unique `user_address` values into a HyperLogLog sketch using BigQuery's `hll_count.init` function with 15-bit precision. This probabilistic data structure allows efficient storage and merging of large distinct count sets. Groups data by timestamp (daily), chain, user type, and product identifiers to maintain dimensional breakdowns.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing the user address, `hll` containing the HyperLogLog sketch

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for monthly active user calculation
- **Logic**: Generates a complete datespine covering all days from the earliest user activity to yesterday. For each day in this datespine, creates associations with the previous 29 days plus the current day (30 total days). This creates a many-to-many relationship where each target day is associated with 30 historical days. Joins this window structure with the daily HyperLogLog sketches, ensuring each day's final MAU calculation includes user activity from its full 30-day lookback period.
- **Key fields**: `timestamp` for the target date, `joinday` for the historical dates in each window, `hll` for the daily user sketches

### Component D: Monthly active user calculation
- **Purpose**: Calculates the final monthly active user count by merging daily user sets within each 30-day window
- **Logic**: For each target day and its associated 30-day window, merges all daily HyperLogLog sketches using `hll_count.merge_partial`. This combines the distinct user sets from all 30 days while maintaining accuracy of the distinct count. Extracts the final distinct user count using `hll_count.extract`. Creates multiple dimensional breakdowns: project-level (all dimensions nulled except dimension type), chain-level (preserving chain ID), user type level (preserving user type classification), and product level (preserving product ID). Each breakdown represents the same MAU calculation grouped by different business dimensions.
- **Key fields**: `user_mau` containing the final monthly active user count, `dimensions` identifying the aggregation level, dimension-specific fields like `chain_id` and `user_type`

### Final aggregation
- **Combining components**: The monthly active user results from different dimensional breakdowns are combined using UNION ALL operations, creating a single dataset with MAU values broken down by chain, project, user type, and product dimensions
- **Currency conversion**: No currency conversion required as this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows updated daily, where each day's value represents unique addresses active in that day plus the previous 29 days
- **Final calculation**: The target model simply renames the `user_mau` field to `active_addresses_monthly` and filters to include only chain-level and project-level dimensional breakdowns, applying chain availability filtering to ensure data quality

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

Unique addresses active over a rolling 30-day window.

Immutable zkEVMIMX

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown