Users deposit and lock assets on Ethereum in exchange for an equivalent amount of a corresponding token on zkSync Era. Conversely, users can withdraw assets from zkSync Era in exchange for the same amount on Ethereum.

zkSync Era Bridge is an asset bridge built on Ethereum and zkSync Era. zkSync Era Bridge was developed 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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zkSync Era Bridge

# zkSync Era Bridge Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for zkSyncEra Bridge measures the number of unique addresses that performed bridge operations within any 30-day rolling period. This metric tracks user engagement with the zkSyncEra Bridge infrastructure by identifying users who deposit assets from Ethereum to zkSyncEra or withdraw assets back to Ethereum. The calculation uses HyperLogLog probabilistic counting for efficient computation across large datasets and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

- **Ethereum zkSyncEra Bridge events**: Parsed event logs from zkSyncEra Bridge contracts deployed on Ethereum L1, including deposit initiation events, withdrawal completion events, and ETH withdrawal events
- **Ethereum zkSyncEra Bridge function calls**: Successful function call traces for L2 transaction requests initiated through the zkSyncEra Bridge
- **Blockchain transaction data**: Ethereum transaction metadata including block timestamps and transaction hashes for event context
- **Token metadata**: Information about bridged tokens and contract addresses for proper event classification

## 3. Step-by-step calculation

### Component A: User event identification
- **Purpose**: Identifies all user addresses that interact with zkSyncEra Bridge contracts
- **Logic**: Extracts user addresses from specific zkSyncEra Bridge events on Ethereum. For `L1ERC20Bridge_DepositInitiated` events, captures the `from` address as a Depositor. For `L1ERC20Bridge_WithdrawalFinalized` and `Diamond_EthWithdrawalFinalized` events, captures the `to` address as a Withdrawer. For `Diamond_requestL2Transaction` function calls, captures the `from_address` as a Depositor. Only includes successful transactions with confirmed status.
- **Key fields**: `block_timestamp` for timing, `user_address` for unique identification, `user_type` for categorization (Depositor/Withdrawer), `chain` identifier, and transaction metadata

### Component B: Daily active users calculation  
- **Purpose**: Aggregates user interactions into daily unique user counts
- **Logic**: Groups user events by day using timestamp truncation to day boundaries. Ensures each user address is counted only once per day regardless of multiple bridge transactions. Creates HyperLogLog sketches of daily user sets with 15-bit precision for efficient storage and subsequent MAU calculations.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `timestamp_grain` set to 'day', dimensional attributes like `chain`, `user_type`, and `product_id`

### Component C: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a datespine of all dates from the earliest data point to yesterday. For each date, creates associations with the previous 30 days (including the current date) to establish rolling windows. Uses timestamp array generation to create systematic date ranges, ensuring no missing dates in the calculation period.
- **Key fields**: `timestamp` for the target date, `joinday` for each day in the 30-day lookback period

### Component D: MAU window aggregation
- **Purpose**: Combines daily user data across 30-day windows
- **Logic**: Joins the rolling window dates with daily HLL user data. For each target date, collects HLL sketches from all 30 days in the rolling window. Maintains dimensional attributes (chain, user type, product ID) throughout the join process to preserve breakdowns.
- **Key fields**: `timestamp` for the target date, `hll` sketches from contributing days, dimensional attributes preserved from daily data

### Component E: Dimensional MAU calculation
- **Purpose**: Calculates MAU across multiple dimensional breakdowns
- **Logic**: Creates separate MAU calculations for four different dimensional views: project-level (overall), chain-level, user type-level, and product ID-level. For each breakdown, groups data by the relevant dimensions and merges HLL sketches using `hll_count.merge_partial()` to combine user sets from the 30-day window. Extracts final unique user counts using `hll_count.extract()`. Sets non-applicable dimension fields to null for each breakdown.
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown type, dimensional identifiers (`chain_id`, `user_type`, `product_id`) with appropriate null values

### Final aggregation
- **Combining components**: Unions all dimensional MAU calculations into a single result set, creating a comprehensive view with project-level, chain-level, user type-level, and product ID-level breakdowns
- **Time aggregation**: Maintains daily granularity with each record representing MAU as of that specific date, calculated using the trailing 30-day window including the target date
- **Final calculation**: The MAU value represents the estimated number of unique addresses that performed any bridge operation (deposit or withdrawal) within the 30-day period ending on the target date. HyperLogLog provides approximate but highly accurate counts with minimal memory usage, suitable for large-scale user tracking across extended time periods

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

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

zkSync Era Bridge

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown