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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the approximate number of distinct users who performed at least one transaction on the Metis blockchain within a 7-day rolling window. This metric provides insight into user engagement and adoption patterns by tracking unique addresses that interact with the Metis network over weekly periods. The calculation uses HyperLogLog probabilistic data structures for efficient approximate distinct counting across multiple analytical dimensions.

## 2. Data sources

- **Metis blockchain transactions**: Raw transaction records from the Metis blockchain containing transaction hashes, timestamps, and sender addresses (`from_address`)
- **Chain availability metadata**: Token Terminal's internal metadata tracking periods when blockchain data is considered complete and reliable for analysis

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Identifies active users from raw Metis transaction data
- **Logic**: Extracts each transaction sender address as an active user, along with the transaction timestamp. Filters out invalid timestamps (specifically excluding '1970-01-01 00:00:00 UTC' records). Classifies all users as 'Transaction sender' type and assigns Metis-specific metadata including chain identifier, market sector ('blockchains_l2'), and business segment ('blockchain')
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction tracking, and classification metadata

### Component B: Daily user aggregation  
- **Purpose**: Calculates Daily Active Users (DAU) as the foundation for WAU calculation
- **Logic**: Groups transaction data by day using timestamp truncation to day boundaries. Counts distinct user addresses within each day, creating a daily snapshot of unique active users. Maintains dimensional attributes including chain, user type, and product identifiers for later analysis
- **Key fields**: `timestamp` truncated to day granularity, distinct `user_address` counts, `chain` and `user_type` for dimensional grouping

### Component C: HyperLogLog conversion
- **Purpose**: Converts daily user counts to probabilistic data structures for efficient weekly aggregation
- **Logic**: Transforms daily distinct user counts into HyperLogLog (HLL) data structures using BigQuery's `hll_count.init()` function with precision parameter 15. This enables approximate distinct counting when merging data across multiple days without storing individual user addresses
- **Key fields**: HLL-encoded user counts (`hll`) grouped by timestamp, chain, user type, and product dimensions

### Component D: Rolling window creation
- **Purpose**: Creates 7-day rolling windows for each date to calculate weekly metrics
- **Logic**: Generates a datespine covering all dates from the earliest DAU data to yesterday. For each date in the spine, creates join relationships with the current date plus the previous 6 days (7 days total). This windowing approach ensures consistent 7-day periods even when some days have no transaction activity
- **Key fields**: `timestamp` for the target date, `joinday` for each day within the 7-day window

### Component E: Window aggregation
- **Purpose**: Combines daily HLL data within each 7-day window
- **Logic**: Joins the rolling window structure with daily HLL data, collecting all HLL structures that fall within each 7-day period. Groups by the target timestamp and dimensional attributes, then merges HLL structures using `hll_count.merge_partial()` to calculate approximate distinct users across the entire week
- **Key fields**: Weekly `timestamp`, merged `hll` structures, dimensional groupings for analysis

### Component F: Dimensional breakdowns
- **Purpose**: Creates multiple analytical views of WAU data across different dimensions
- **Logic**: Generates four separate breakdowns of the WAU data: project-level totals (aggregating across all dimensions), chain-level breakdowns (grouping by chain), user type breakdowns (grouping by user type), and product-level breakdowns (grouping by product). Each breakdown uses appropriate grouping and HLL merging to maintain distinct user counting accuracy
- **Key fields**: Dimension identifiers (`dimensions`), dimensional values (`chain_id`, `user_type`, `product_id`), and extracted user counts (`user_wau`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL to create a comprehensive dataset with multiple analytical perspectives in a single table
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily boundaries with 7-day rolling windows, ensuring consistent weekly measurement periods
- **Final calculation**: The final WAU value is extracted from merged HyperLogLog structures using `hll_count.extract()`, providing an approximate count of distinct users who were active during each 7-day period. The `finalize_metric_user_wau` macro applies standardized schema formatting and fills missing periods with zero values to ensure complete time series data

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

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

MetisMETIS

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown