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.

github

twitter

discord

website

Blockchains (L2)

zkSync Era

# zkSync Era Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on zkSync Era within any 7-day period. This metric answers the question "How many distinct users are actively transacting on the zkSync Era network each week?" The calculation uses a rolling 7-day window approach with HyperLogLog probabilistic counting to efficiently handle large datasets while maintaining accuracy for unique address counting across overlapping time periods.

## 2. Data sources

- **zkSync Era transaction records**: Complete transaction history containing sender addresses, transaction hashes, block timestamps, and transaction metadata from the zkSync Era Layer 2 network
- **Chain availability configuration**: Token Terminal's internal data quality framework that defines reliable data periods for each blockchain network
- **Table modification timestamps**: Internal tracking system that monitors when underlying data sources were last updated to enable incremental processing

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Extracts all unique transaction sender addresses from zkSync Era blockchain activity
- **Logic**: Processes zkSync Era transaction records to identify every address that initiated a transaction. Each transaction sender address is captured along with the block timestamp when the transaction occurred. All senders are classified with the user type "Transaction sender" and categorized under the market sector "blockchains_l2" and business segment "blockchain". Product identifiers are set to null since this measures network-level activity rather than specific application usage.
- **Key fields**: `from_address` (transaction sender), `block_timestamp` (when transaction occurred), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user aggregation  
- **Purpose**: Groups transaction senders by day to create daily active user counts
- **Logic**: Takes the user identification data and truncates timestamps to day granularity using timestamp truncation. For each day, identifies all unique addresses that sent at least one transaction during that 24-hour period. Uses DISTINCT operations to ensure each address is counted only once per day regardless of how many transactions they sent. This creates a daily snapshot of unique active addresses.
- **Key fields**: `timestamp` (truncated to day), `user_address` (deduplicated daily), `timestamp_grain` (set to "day")

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily active users into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transforms daily active user data into HyperLogLog sketches, which are probabilistic data structures that enable approximate counting of unique elements. Each day's unique addresses are processed through the HLL initialization function with precision parameter 15, creating compressed representations that can be efficiently merged across time periods. This approach allows for scalable unique counting when creating overlapping weekly windows.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (day), dimensional attributes (`chain_id`, `user_type`, `product_id`)

### Component D: Weekly rolling window generation
- **Purpose**: Creates 7-day rolling windows for weekly active address calculation  
- **Logic**: Generates a complete datespine covering all days from the earliest transaction data to the present day. For each day in this spine, creates a 7-day window that includes the current day plus the previous 6 days. Uses array generation functions to create all possible day combinations within each window. This ensures comprehensive coverage of all weekly periods without gaps, even for days with no transaction activity.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within each 7-day window)

### Component E: Weekly aggregation with HyperLogLog merging
- **Purpose**: Combines HyperLogLog sketches across 7-day windows to count unique weekly active addresses
- **Logic**: For each 7-day window, joins the window dates with the daily HyperLogLog sketches to collect all sketches falling within that period. Merges all HyperLogLog sketches within each window using the merge_partial function, then extracts the approximate count of unique addresses using the extract function. This produces weekly active user counts that account for addresses being active on multiple days within the same week (counted only once per week).
- **Key fields**: `user_wau` (weekly active users count), `timestamp` (week ending date), dimensional breakdowns

### Component F: Dimensional breakdown processing
- **Purpose**: Calculates weekly active addresses across multiple analytical dimensions
- **Logic**: Creates separate aggregations for different analytical perspectives including chain-level (zkSync Era network totals), project-level (Token Terminal's internal project classification), user-type-level (transaction sender breakdowns), and product-level (application-specific if applicable). Each dimension uses the same HyperLogLog merging logic but groups by different categorical attributes. Results are combined using union operations to create a comprehensive dataset supporting various analytical queries.
- **Key fields**: `dimensions` (breakdown type), dimension-specific identifiers, `user_wau` (count for each dimension)

### Final aggregation
- **Combining components**: Weekly active user counts are filtered to include only chain-level and project-level dimensions as specified by the filter criteria. The user WAU metric is directly converted to active addresses weekly since for blockchain networks, active users are equivalent to active addresses.
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 7-day rolling windows where each day represents the end of a weekly period. Weekly boundaries are defined as the current day plus the previous 6 days, creating overlapping windows that update daily.
- **Final calculation**: The final weekly active addresses value equals the weekly active users count extracted from merged HyperLogLog sketches. Chain availability filters are applied to exclude data from periods when zkSync Era data quality was unreliable. Missing periods are filled with zeros to maintain continuous time series, and incremental processing logic ensures efficient updates when new transaction data becomes available.

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

Unique active addresses over a rolling 7-day window.

zkSync EraZK

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown