zkSync Era batches users' transactions and submits them to Ethereum together with a zero-knowledge proof, paying a share of the transaction fees to Ethereum validators (expense).

zkSync Era is an L2 smart contract platform for decentralized applications on Ethereum. zkSync Era was founded by Matter Labs and launched in 2023.

Matter Labs team owns and governs the protocol. Matter Labs' investors include a16z, Lightspeed Venture Partners, Blockchain Capital, etc.

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Blockchains (L2)

zkSync Era

# zkSync Era Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for zkSync Era measures the number of distinct users who sent at least one transaction on the zkSync Era blockchain within any 7-day rolling window. This metric answers the business question: "How many unique users are actively transacting on zkSync Era each week?" The calculation uses HyperLogLog sketches to efficiently count distinct users across rolling 7-day periods, providing multiple dimensional breakdowns (project-level, chain-level, user type, and product).

## 2. Data sources

The calculation relies on these raw data sources:
- **Blockchain transaction data**: zkSync Era transaction records containing sender addresses, block timestamps, and transaction hashes
- **Transaction metadata**: Block-level timing information for temporal aggregation
- **User identification**: Transaction sender addresses (`from_address` field) representing active users on the network

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify all active users from zkSync Era transaction activity
- **Logic**: Extract all transaction sender addresses from zkSync Era blockchain transactions. Each unique `from_address` represents a user who sent a transaction. Users are classified as 'Transaction sender' type and assigned to the 'blockchains_l2' market sector and 'blockchain' business segment. Block timestamps are preserved for temporal analysis.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for timing, `transaction_hash` for deduplication

### Component B: Daily active users aggregation  
- **Purpose**: Calculate daily distinct user counts as building blocks for weekly metrics
- **Logic**: Group identified users by day using timestamp truncation to daily boundaries. For each day, create distinct lists of user addresses who sent transactions. Apply chain availability filtering to ensure data quality. Users maintain their type classification and dimensional attributes.
- **Key fields**: `timestamp` (truncated to day), `user_address` for distinct counting, dimensional attributes like `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into efficient data structures for set operations
- **Logic**: Transform daily user address lists into HyperLogLog sketches using 15-bit precision. This allows efficient merging and distinct counting across large datasets without storing individual addresses. Each daily sketch represents the unique users for that specific day.
- **Key fields**: `hll` sketch containing compressed user set, `timestamp` for temporal joining

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine from the earliest available data to yesterday to ensure no missing dates. For each date in the spine, create a 7-day window including the current day plus the 6 preceding days. Use array generation to create join keys for each day within the window, enabling efficient window-based aggregation.
- **Key fields**: `timestamp` (target date), `joinday` (days within the 7-day window)

### Component E: Weekly user aggregation
- **Purpose**: Merge daily HLL sketches across 7-day windows to count distinct weekly users
- **Logic**: For each date and its 7-day window, merge the HLL sketches from all days within that window. The merged sketch represents all unique users who were active on any day within the 7-day period. Extract the final count from the merged sketch to get the weekly active user number.
- **Key fields**: `user_wau` (distinct user count), dimensional grouping attributes

### Component F: Dimensional breakdowns
- **Purpose**: Provide multiple analytical perspectives on weekly active users
- **Logic**: Calculate separate WAU metrics across four dimensions: project-level (overall zkSync Era), chain-level (by blockchain), user-type level (transaction senders), and product-level (specific products). Each breakdown aggregates the HLL sketches differently, nullifying irrelevant dimensional fields while preserving the relevant grouping dimension.
- **Key fields**: `dimensions` (breakdown type), relevant dimensional field (e.g., `chain_id`, `user_type`), `user_wau` count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each record representing WAU for a specific date and dimensional combination
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily timestamps with 7-day rolling window logic, ensuring complete date coverage through datespine generation
- **Final calculation**: The final `user_wau` value represents the distinct count of users who sent at least one transaction within any 7-day period ending on the given date, calculated through HLL sketch merging and extraction

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

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

zkSync EraZK

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown