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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for zkSync Era Bridge measures the number of unique users who interacted with the zkSync Era Bridge protocol within any 7-day rolling window. This metric captures user engagement across bridge operations including token deposits from Ethereum to zkSync Era and withdrawals from zkSync Era back to Ethereum. The calculation uses probabilistic counting with HyperLogLog data structures to efficiently handle large-scale distinct user counting across rolling time windows.

## 2. Data sources

The metric is built from the following raw blockchain data sources:

- **zkSync Era Bridge contract events**: Parsed smart contract events from the zkSync Era Bridge contracts deployed on Ethereum mainnet
- **Deposit events**: `DepositInitiated` events from the L1ERC20Bridge contract capturing users bridging ERC20 tokens to zkSync Era
- **Withdrawal events**: `WithdrawalFinalized` events from the L1ERC20Bridge contract capturing users completing token withdrawals to Ethereum
- **ETH withdrawal events**: `EthWithdrawalFinalized` events from the Diamond contract for ETH-specific withdrawals
- **Transaction function calls**: `requestL2Transaction` function calls from the Diamond contract for additional deposit interactions
- **Ethereum transaction metadata**: Block timestamps and transaction hashes for temporal context

## 3. Step-by-step calculation

### Component A: User event collection
- **Purpose**: Identify and categorize all user interactions with the zkSync Era Bridge protocol
- **Logic**: Extract user addresses from bridge-related blockchain events and function calls. Parse `DepositInitiated` events to identify depositors using the `from` field, parse `WithdrawalFinalized` and `EthWithdrawalFinalized` events to identify withdrawers using the `to` field. Include successful `requestL2Transaction` function calls using the `from_address` field. Each interaction is tagged with metadata including the interaction type, chain identifier, and timestamp.
- **Key fields**: User addresses are captured in `user_address`, interaction types are classified as either `Depositor` or `Withdrawer` in `user_type`, all events are tagged with `chain` as 'ethereum', `market_sector_id` as 'bridges', and `business_segment_id` as 'mint_and_burn'

### Component B: Daily user aggregation  
- **Purpose**: Aggregate user interactions to daily unique user counts while preserving dimensional attributes
- **Logic**: Group all user events by calendar day using timestamp truncation to day boundaries. For each day, create distinct user records preserving the dimensional attributes including chain, user type, market sector, business segment, and product identifiers. This creates a comprehensive daily snapshot of unique bridge users across different interaction categories.
- **Key fields**: Daily timestamps in `timestamp`, time granularity marked as 'day' in `timestamp_grain`, unique user addresses in `user_dau`, and preserved dimensional fields for downstream aggregation

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user lists into probabilistic data structures for efficient distinct counting across time windows
- **Logic**: Transform daily user address lists into HyperLogLog data structures using BigQuery's `hll_count.init` function with precision parameter 15. This creates compact probabilistic representations of user sets that can be efficiently merged across multiple days to calculate distinct counts in rolling windows.
- **Key fields**: HyperLogLog sketches stored in `hll` field, maintaining all dimensional groupings for subsequent window calculations

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each day to calculate weekly active user metrics
- **Logic**: Generate a complete datespine covering all days from the minimum date in the dataset to yesterday. For each day in the datespine, create associations with the previous 6 days using cross-join operations with timestamp arrays. This creates a comprehensive mapping where each target day connects to its 7-day lookback window, ensuring no gaps in the time series even for days with no user activity.
- **Key fields**: Target calculation dates in `timestamp`, lookup days for window calculation in `joinday`, maintaining the complete temporal relationship for window aggregation

### Component E: Multi-dimensional WAU calculation
- **Purpose**: Calculate weekly active users across different dimensional breakdowns
- **Logic**: For each day and its associated 7-day window, merge the HyperLogLog structures from all days in the window using `hll_count.merge_partial`, then extract the distinct count using `hll_count.extract`. Calculate separate breakdowns for project-level (all users combined), chain-level aggregation, user-type segmentation (depositors vs withdrawers), and product-level groupings. Each breakdown nullifies irrelevant dimensional fields while preserving the relevant grouping dimension.
- **Key fields**: Weekly active user counts in `user_wau`, dimensional classification in `dimensions`, relevant dimensional values in `chain_id`, `user_type`, and `product_id` depending on breakdown type

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, creating a comprehensive view of weekly active users across all dimensional cuts
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro with data identifier 'zksyncerabridge' and fill strategy 'fill_zeros' to standardize output schema and fill missing days with zero values
- **Time boundaries**: Use daily boundaries for window calculations, with each WAU value representing unique users in the 7-day period ending on that date
- **Final calculation**: The final WAU metric represents the exact count of distinct user addresses that interacted with the zkSync Era Bridge within any 7-day rolling window, calculated using probabilistic HyperLogLog merging for computational efficiency

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

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

zkSync Era Bridge

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown