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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Metis

# Metis Active addresses (weekly) Methodology

## 1. Overview

The Metis weekly active addresses metric measures the number of unique addresses that initiated transactions on the Metis blockchain within rolling 7-day windows. This metric provides insights into user engagement and platform activity levels on the Metis L2 network. The calculation uses probabilistic counting techniques to efficiently aggregate unique addresses across overlapping weekly periods.

## 2. Data sources

- **Metis blockchain transactions**: Raw transaction records from the Metis blockchain containing transaction metadata, timestamps, sender addresses, and transaction hashes
- **Transaction timing data**: Block timestamps and transaction execution timestamps from Metis blockchain data

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique daily active users from raw Metis transaction data
- **Logic**: Raw Metis transaction records are processed to extract unique user addresses per day. Each transaction's `from_address` field represents the transaction sender and is treated as a unique user identifier. Transactions are filtered to exclude invalid records with timestamps set to '1970-01-01 00:00:00 UTC'. Block timestamps are truncated to daily granularity to group transactions by calendar day. Each unique `from_address` appearing on a given day constitutes one daily active user.
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as user identifier, `transaction_hash` for transaction tracking, `chain` identifier set to 'metis'

### Component B: Weekly active user aggregation using HyperLogLog
- **Purpose**: Calculate rolling 7-day unique user counts efficiently using probabilistic data structures
- **Logic**: Daily unique users are aggregated into weekly active users using HyperLogLog (HLL) probabilistic counting with 15-bit precision. A datespine is generated covering all days from the earliest transaction date to the current date. For each day in the datespine, a 7-day rolling window is created by joining with the previous 6 days plus the current day. HLL sketches from each of the 7 days in each window are merged to produce the unique count of users active within that 7-day period. This approach handles overlapping windows efficiently while maintaining accuracy for unique counting across large datasets.
- **Key fields**: `timestamp` for daily boundaries, `hll` sketches for probabilistic counting, `user_wau` as the final weekly active user count

### Component C: Dimensional breakdowns
- **Purpose**: Provide weekly active user metrics across different analytical dimensions
- **Logic**: The weekly active user calculation is broken down into multiple dimensions: chain-level aggregation (grouping all users under the Metis chain), project-level aggregation (grouping under 'project_dbt_tag' dimension), user type breakdowns (currently 'Transaction sender' type), and product-level breakdowns (currently null for Metis). Each breakdown maintains the same HLL-based unique counting methodology but groups results differently for various analytical perspectives.
- **Key fields**: `dimensions` field indicating breakdown type ('chain', 'project_dbt_tag', 'user_type', 'product_id'), `chain_id` for chain-specific grouping

### Final aggregation
- **Combining components**: The target metric selects only chain-level and project-level dimensional breakdowns from the weekly active user aggregations, filtering where `dimensions` is 'chain' or 'project_dbt_tag'
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Uses rolling 7-day windows calculated daily, where each day represents the count of unique addresses active over the trailing 7-day period including that day
- **Final calculation**: The `user_wau` field is directly mapped to `active_addresses_weekly`, representing the number of unique addresses that initiated at least one transaction within each 7-day rolling window period

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

Unique active addresses over a rolling 7-day window.

MetisMETIS

Active addresses (weekly)

Active addresses (weekly)

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