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 addresses (monthly) Methodology

## 1. Overview

Monthly active addresses for zkSync Era measures the number of unique addresses that have sent at least one transaction within any 30-day rolling period. This metric answers the business question of how many distinct addresses are actively using the zkSync Era blockchain on a monthly basis. The calculation uses a probabilistic approach with HyperLogLog sketches to efficiently count unique addresses across rolling 30-day windows, treating transaction senders as active addresses.

## 2. Data sources

The metric relies on the following raw data sources:
- **zkSync Era transaction records**: Complete transaction history from the zkSync Era blockchain, including transaction hashes, block timestamps, and sender addresses
- **Transaction metadata**: Block-level timestamp information for accurate daily aggregation and time-based filtering

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies all addresses that have initiated transactions on zkSync Era
- **Logic**: Extracts the `from_address` field from all zkSync Era transaction records, representing the address that initiated each transaction. Each record includes the block timestamp, transaction hash, chain identifier, market sector classification, and user type classification. Transaction senders are classified as 'Transaction sender' user type within the 'blockchains_l2' market sector and 'blockchain' business segment
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for temporal analysis, `transaction_hash` for transaction identification

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active addresses
- **Logic**: Groups transaction sender addresses by day using daily timestamp truncation. For each day, identifies all unique addresses that sent at least one transaction. Each unique address-day combination becomes a daily active user record with 'day' timestamp grain. Records maintain chain, user type, market sector, business segment, and product classifications
- **Key fields**: Daily truncated `timestamp`, `user_address` as `user_dau`, classification dimensions for grouping

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily active users into memory-efficient probabilistic data structures for unique counting
- **Logic**: Transforms daily active user records into HyperLogLog sketches using precision parameter 15. Groups records by timestamp, chain, user type, and product dimensions. Each group's unique addresses are converted into a single HyperLogLog sketch that enables efficient approximate unique counting operations
- **Key fields**: `hll` sketch containing compressed unique address information, grouped by dimensional attributes

### Component D: 30-day rolling window construction
- **Purpose**: Creates the temporal framework for calculating monthly active addresses
- **Logic**: Generates a datespine covering all days from the first available data to yesterday. For each day in the datespine, creates join relationships to the current day plus the previous 29 days, forming 30-day rolling windows. Joins HyperLogLog sketches from daily aggregates to window periods, ensuring complete coverage even for days with no transaction activity
- **Key fields**: `timestamp` for the window end date, `joinday` for dates within each 30-day window, associated HyperLogLog sketches

### Component E: Monthly active address calculation by dimensions
- **Purpose**: Calculates unique address counts across different analytical dimensions
- **Logic**: For each 30-day window, merges all HyperLogLog sketches within the window period using partial merge operations. Extracts the final unique count from merged sketches. Calculates separate breakdowns for project-level analysis (project_dbt_tag dimension), chain-level analysis (chain dimension), user type analysis, and product analysis. Each breakdown groups and merges sketches according to its dimensional focus
- **Key fields**: `user_mau` containing the estimated unique address count, `dimensions` indicating the analytical perspective, relevant dimensional identifiers

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, with each record representing monthly active addresses from a specific analytical perspective
- **Currency conversion**: Not applicable for this address counting metric
- **Time aggregation**: Uses 30-day rolling windows calculated daily, where each day represents the unique addresses active in the trailing 30-day period
- **Final calculation**: The target model filters to only 'chain' and 'project_dbt_tag' dimensions and renames `user_mau` to `active_addresses_monthly`, representing the count of unique addresses that sent transactions within the 30-day period ending on each date

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

Unique addresses active over a rolling 30-day window.

zkSync EraZK

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown