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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for zkSync Era measures the number of unique user addresses that have initiated at least one transaction on the zkSync Era network within a rolling 30-day period. This metric answers the business question: "How many distinct users are actively engaging with zkSync Era on a monthly basis?" The calculation uses a rolling window approach where each day's MAU represents the count of unique users who were active in the preceding 30 days, providing a smooth trend line that avoids the artificial spikes and drops associated with calendar month boundaries.

## 2. Data sources

The calculation relies on the following raw data sources:

- **zkSync Era transaction logs**: Core blockchain transaction data from the zkSync Era network containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Transaction sender addresses**: Ethereum-format addresses (`from_address` field) representing users who initiated transactions on zkSync Era
- **Block timestamps**: Precise timestamps indicating when transactions were included in blocks on the zkSync Era network
- **Incremental processing metadata**: System metadata tracking table modification times to enable efficient incremental data updates

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Identifies all unique transaction senders on zkSync Era and classifies them as active users
- **Logic**: Extracts all transaction records from zkSync Era blockchain data and identifies the `from_address` field as the user address for each transaction. Each transaction sender is classified with a `user_type` of "Transaction sender" and assigned to the "blockchains_l2" market sector and "blockchain" business segment. The system captures the `block_timestamp` to enable time-based analysis
- **Key fields**: `from_address` (user address), `block_timestamp` (transaction time), `transaction_hash` (unique identifier), `user_type` (classification as transaction sender)

### Component B: Daily active users (DAU) calculation  
- **Purpose**: Aggregates unique users per day using probabilistic counting for memory efficiency
- **Logic**: Groups transaction data by day using timestamp truncation to daily boundaries. For each day, creates HyperLogLog sketches to efficiently count distinct user addresses. This probabilistic data structure allows accurate estimation of unique users while using minimal memory, which is crucial for large-scale blockchain data processing
- **Key fields**: `timestamp` (daily boundary), `user_address` (distinct user identifier), HyperLogLog sketch for user counting, dimensional attributes like `chain`, `user_type`, and `product_id`

### Component C: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for each date to enable MAU calculation
- **Logic**: Generates a comprehensive date spine covering all dates from the earliest available data to the current date minus one day. For each date in the spine, creates a 30-day lookback window by generating an array of the 30 dates ending on that date. This creates a join structure where each target date is associated with all dates in its 30-day window
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 30-day window)

### Component D: Window-based MAU aggregation
- **Purpose**: Combines daily HyperLogLog sketches within each 30-day window to calculate unique monthly active users
- **Logic**: Joins the rolling window structure with the daily HyperLogLog data, matching each day in a 30-day window to its corresponding DAU sketch. Within each 30-day window, merges all daily HyperLogLog sketches using HLL merge operations, then extracts the final count of unique users. This approach ensures that a user active on multiple days within the 30-day period is counted only once
- **Key fields**: Merged HyperLogLog sketches, `user_mau` (final unique user count for the 30-day period)

### Component E: Dimensional breakdowns
- **Purpose**: Provides MAU calculations across multiple analytical dimensions
- **Logic**: Creates separate aggregations for different analytical perspectives: project-level MAU (overall zkSync Era activity), chain-level MAU (enabling cross-chain comparisons), user-type-level MAU (comparing different types of users), and product-level MAU (for future product categorization). Each breakdown maintains the same core HyperLogLog merging logic while grouping by different dimensional attributes
- **Key fields**: `dimensions` (breakdown category), dimension-specific identifiers like `chain_id`, `user_type`, and `product_id`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a single comprehensive dataset containing MAU calculations across all analytical dimensions
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 30-day windows calculated daily, with timestamp truncation to daily boundaries for consistent time-based analysis
- **Final calculation**: The final MAU value for any given date represents the count of unique user addresses that initiated at least one transaction within the 30-day period ending on that date, calculated by merging daily HyperLogLog sketches and extracting the probabilistic unique count estimate

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

Unique revenue-generating users over a rolling 30-day window.

zkSync EraZK

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown