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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for ImmutableX zkEVM measures the number of unique users who performed any activity on the ImmutableX zkEVM blockchain within a rolling 7-day period. This metric captures both transaction senders and Passport wallet users, providing a comprehensive view of user engagement. The calculation uses HyperLogLog sketches for memory-efficient approximate counting across multiple dimensional breakdowns (overall project, chain, user type, and product level).

## 2. Data sources

The metric relies on the following raw data sources:
- **ImmutableX zkEVM transaction records**: Contains transaction data including sender addresses (`from_address`) and block timestamps
- **ImmutableX zkEVM event logs**: Specifically parsed `Wallet_NonceChange` events from the Immutable Passport system, containing user wallet addresses and activity timestamps
- **Metadata classifications**: Static mapping of ImmutableX zkEVM to market sector (`blockchains_l2`), business segment (`blockchain`), and product identifiers

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Identifies and classifies all unique user addresses active on ImmutableX zkEVM
- **Logic**: Extracts user addresses from two distinct sources. From transaction records, identifies "Transaction sender" users using the `from_address` field. From parsed `Wallet_NonceChange` event logs, identifies "Passport wallet user" users using the `address` field. Each user record includes the block timestamp, transaction hash, and user classification metadata (chain, market sector, business segment, product ID, and user type).
- **Key fields**: `block_timestamp`, `from_address`/`address` (mapped to `user_address`), `user_type`, `chain`, `product_id`

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct active users as input for weekly calculations
- **Logic**: Truncates block timestamps to day boundaries using timestamp truncation. Groups user records by day, chain, user type, and product ID. Applies distinct counting to the `user_address` field to eliminate duplicate users within each day. Creates daily user counts segmented by the various dimensional attributes. Includes incremental processing logic to only update recent data.
- **Key fields**: `timestamp` (day-truncated), `user_dau` (distinct user addresses), `timestamp_grain`, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog initialization and datespine creation
- **Purpose**: Prepares daily user data for efficient weekly aggregation using probabilistic counting
- **Logic**: Initializes HyperLogLog sketches for each day's user set with precision 15, enabling memory-efficient approximate counting of unique users. Creates a continuous datespine covering the full time range to ensure no gaps in the weekly calculations, even for days with no activity. Generates 7-day rolling windows by creating timestamp arrays spanning from 6 days before through the current day for each date in the spine.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `joinday` (window dates)

### Component D: Weekly window aggregation
- **Purpose**: Combines daily HLL sketches across 7-day rolling windows
- **Logic**: Joins the 7-day rolling windows with the daily HLL sketches to capture all users active within each weekly period. For each target date, merges HLL sketches from that date and the previous 6 days using `hll_count.merge_partial()` to handle user overlaps between days. Maintains all dimensional breakdowns (chain, user type, product) throughout the aggregation process.
- **Key fields**: `timestamp`, `hll` (merged sketches), dimensional attributes

### Final aggregation
- **Combining components**: Creates four separate dimensional breakdowns of the weekly data. The project-level breakdown aggregates across all dimensions. Chain-level breakdown groups by `chain_id`. User type breakdown groups by `user_type`. Product breakdown groups by `product_id`. Each breakdown merges the appropriate HLL sketches and extracts final user counts using `hll_count.extract()`.
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the previous 6 days. Daily boundaries are determined by timestamp truncation to day granularity.
- **Final calculation**: Extracts unique user counts from merged HyperLogLog sketches for each dimensional breakdown. Combines all breakdowns using union operations. Applies final formatting through the `finalize_metric_user_wau` macro, which standardizes the schema and fills missing days with zero values to create a complete time series.

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

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

Immutable zkEVMIMX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown