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 addresses (daily) Methodology

## 1. Overview

Daily active addresses measures the number of unique addresses that initiated at least one transaction on the Metis blockchain within a 24-hour period. This metric provides insights into user engagement and network activity on the Metis Layer 2 blockchain. The calculation leverages HyperLogLog probabilistic counting for efficient processing of large datasets and treats transaction senders as active addresses, which is the standard approach for blockchain analytics.

## 2. Data sources

- **Metis transaction records**: Raw transaction data from the Metis blockchain containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Chain availability configuration**: Data quality filters that define periods when Metis blockchain data is considered complete and reliable

## 3. Step-by-step calculation

### Component A: User identification and extraction
- **Purpose**: Identify unique users (addresses) from Metis blockchain transactions
- **Logic**: Extract transaction sender addresses from Metis blockchain transaction records. Filter out transactions with invalid timestamps, specifically excluding records timestamped as '1970-01-01 00:00:00 UTC' which represent data quality issues. Cast all relevant fields to appropriate data types for consistency. Classify each address as a 'Transaction sender' user type and associate with Metis chain metadata including market sector 'blockchains_l2' and business segment 'blockchain'
- **Key fields**: `from_address` (transaction sender), `block_timestamp` (transaction time), `transaction_hash` (unique transaction identifier)

### Component B: Daily user aggregation with probabilistic counting
- **Purpose**: Aggregate unique users by day using efficient probabilistic counting
- **Logic**: Group transaction data by day using timestamp truncation to day granularity. Apply HyperLogLog probabilistic counting algorithm with precision parameter 15 to efficiently count distinct user addresses per day. This approach handles large datasets while maintaining accuracy for distinct counting. Create incremental processing logic to optimize data pipeline performance by only processing recent data when updating existing results
- **Key fields**: `timestamp` (day-level), `user_address` (distinct users), `hll` (HyperLogLog sketch)

### Component C: Multi-dimensional breakdown creation
- **Purpose**: Create different aggregation levels for flexible analysis
- **Logic**: Generate four distinct dimensional views using HyperLogLog merge operations. Create project-level aggregation combining all dimensions, chain-level breakdown showing Metis-specific metrics, user-type breakdown showing transaction sender activity, and product-level breakdown (null for blockchain-level analysis). Use HyperLogLog merge and extract functions to combine probabilistic counts across dimensions while maintaining accuracy
- **Key fields**: `dimensions` (aggregation level), `chain_id` (blockchain identifier), `user_type` (address classification), `user_dau` (distinct daily active users)

### Final aggregation
- **Combining components**: Select user DAU values specifically for 'chain' and 'project_dbt_tag' dimensions, which represent the core active address metrics. Map `user_dau` field directly to `active_addresses_daily` since blockchain active addresses are equivalent to daily active users
- **Time aggregation**: Daily boundaries are defined using timestamp truncation to day granularity in UTC timezone
- **Data quality**: Apply chain availability filters to exclude time periods when Metis blockchain data is incomplete or unreliable. Fill missing days with zero values using the fill_zeros strategy to ensure continuous time series
- **Final calculation**: The final metric value represents the count of distinct addresses that sent at least one transaction on the Metis blockchain within each 24-hour period, calculated using probabilistic HyperLogLog counting for efficiency and accuracy

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

Unique active addresses over a fixed 24-hour window.

MetisMETIS

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown