Metis batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Metis is an L2 smart contract platform for decentralized applications on Ethereum. Metis was founded by Kevin Liu, Elena Sinelnikova, and Yuan Su, and launched in 2021.

METIS tokenholders own and govern the protocol. They currently run the only sequencer and take a cut of the total transaction fees paid by users (revenue). Metis' investors include Master Ventures, Digital Finance Group, Momentum 6, etc.

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Blockchains (L2)

Metis

# Metis Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Metis measures the number of unique addresses that initiated at least one transaction on the Metis blockchain within a 30-day rolling window. This metric uses HyperLogLog probabilistic counting to efficiently calculate unique users across overlapping time periods. The calculation provides dimensional breakdowns at project, chain, user type, and product levels to enable comprehensive analysis of user engagement patterns.

## 2. Data sources

- **Metis blockchain transactions**: Raw transaction records from the Metis blockchain containing transaction metadata including sender addresses, timestamps, and transaction hashes
- **Chain availability data**: Configuration data that defines valid data coverage periods for the Metis blockchain to ensure accurate metric boundaries

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and classify active users from raw transaction data
- **Logic**: Processes raw Metis blockchain transactions to identify unique user addresses. Filters out invalid timestamps (specifically '1970-01-01 00:00:00 UTC' records) to ensure data quality. Each transaction's `from_address` field represents a user who initiated the transaction. All users are classified with the user type 'Transaction sender' and associated with market sector 'blockchains_l2' and business segment 'blockchain'. The `product_id` is set to null as Metis operates as a single product blockchain.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction tracking, and classification fields for dimensional analysis

### Component B: Daily active users aggregation
- **Purpose**: Calculate daily unique active users from the identified user base
- **Logic**: Groups the transaction senders by day using timestamp truncation to day boundaries. For each day, counts distinct user addresses that initiated at least one transaction. Preserves dimensional metadata including chain, user type, market sector, business segment, and product identifiers. Applies incremental processing logic to efficiently update recent data while maintaining historical accuracy. Chain availability filters ensure only valid data periods are included in the calculation.
- **Key fields**: `timestamp` truncated to day, `user_address` for unique user identification, and dimensional classification fields

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient MAU calculation
- **Logic**: Transforms the daily active user counts into HyperLogLog sketches using `hll_count.init` with precision parameter 15. These sketches enable efficient approximate counting of unique users across overlapping time periods without storing individual user identifiers. Groups data by timestamp, chain, user type, and product dimensions to maintain proper sketch boundaries.
- **Key fields**: `hll` sketch containing probabilistic representation of daily unique users, maintaining dimensional groupings

### Component D: 30-day rolling window creation
- **Purpose**: Establish consistent 30-day measurement periods for MAU calculation
- **Logic**: Creates a datespine covering all days from the earliest DAU record to the current date minus one day. For each day in the datespine, generates a 30-day lookback window (current day minus 29 days through current day) using timestamp arrays. Joins this window structure with the daily HLL sketches to ensure complete coverage even for days with no activity. This approach prevents gaps in the rolling window calculation that could lead to incorrect MAU values.
- **Key fields**: `timestamp` for the measurement date, `joinday` for the window boundaries, and HLL sketches aligned to the window periods

### Component E: Dimensional MAU calculations
- **Purpose**: Calculate MAU across four different dimensional breakdowns
- **Logic**: Processes the windowed HLL data to create four distinct MAU calculations. Project-level MAU merges all HLL sketches across the 30-day window regardless of other dimensions. Chain-level MAU groups by chain identifier. User-type-level MAU groups by user classification. Product-level MAU groups by product identifier. Each breakdown uses `hll_count.merge_partial` to combine sketches within the window, then `hll_count.extract` to get the final unique user count. Non-applicable dimension fields are set to null for each breakdown type.
- **Key fields**: `dimensions` to identify breakdown type, dimensional grouping fields, and `user_mau` containing the final unique user count

### Final aggregation
- **Combining components**: The four dimensional MAU calculations are combined using UNION ALL operations to create a comprehensive dataset containing all breakdown perspectives in a single table structure
- **Time boundaries**: Uses 30-day rolling windows where each day's MAU represents unique users from that day back 29 days, providing consistent monthly measurement periods
- **Data quality**: Applies incremental processing with configurable lookback periods and table modification timestamps to ensure fresh data while maintaining efficiency
- **Final calculation**: The `finalize_metric_user_mau` macro applies schema standardization, fills missing periods with zeros according to the fill_zeros strategy, and adds metadata fields including data source identifier 'metis' for proper attribution

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

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

MetisMETIS

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown