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 (monthly) Methodology

## 1. Overview

The active addresses monthly metric measures the number of unique addresses that have sent at least one transaction on the Metis blockchain within a rolling 30-day period. This metric answers the business question: "How many distinct addresses are actively transacting on Metis each month?" The calculation uses a rolling 30-day window approach rather than calendar months, providing a smoother trend line that updates daily. For blockchain networks, active addresses and active users are considered equivalent concepts since each address represents a potential user.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Metis blockchain transaction records**: Core transaction data including sender addresses, timestamps, and transaction identifiers
- **Transaction timestamps**: Block timestamps indicating when transactions were processed on the Metis blockchain
- **Transaction sender addresses**: The `from_address` field identifying which addresses initiated transactions
- **Transaction metadata**: Additional fields like transaction hashes for data integrity and tracking

## 3. Step-by-step calculation

### Component A: Transaction user extraction
- **Purpose**: Identifies and standardizes transaction senders as active users
- **Logic**: Extracts sender addresses from Metis transaction records, filtering out transactions with invalid timestamps (specifically '1970-01-01 00:00:00 UTC' entries). Casts all relevant fields to appropriate data types and adds standardized metadata including chain identifier ('metis'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender'). Each transaction sender address becomes a potential active user in the system.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` for timing, `transaction_hash` for uniqueness, and various metadata fields for categorization

### Component B: Daily active user calculation  
- **Purpose**: Calculates daily snapshots of unique active addresses
- **Logic**: Groups transaction data by day using timestamp truncation and counts distinct sender addresses for each day. Creates daily records with timestamp grain set to 'day' and preserves all relevant metadata fields. Applies incremental processing to handle new data efficiently while maintaining historical records.
- **Key fields**: `timestamp` (truncated to day), `user_address` as `user_dau`, `timestamp_grain`, and metadata fields for dimensional analysis

### Component C: HyperLogLog daily aggregation
- **Purpose**: Prepares daily address counts for efficient monthly aggregation using probabilistic counting
- **Logic**: Converts daily unique address counts into HyperLogLog (HLL) data structures with precision parameter 15. Groups by timestamp, chain, user type, and product ID, initializing HLL sketches for each combination. This probabilistic approach enables efficient merging of large sets while maintaining accuracy for distinct counting operations.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling 30-day window creation
- **Purpose**: Establishes sliding 30-day periods for monthly active address calculation
- **Logic**: Creates a datespine covering all dates from the earliest transaction to yesterday, then generates join tables that map each date to its corresponding 30-day lookback period (29 days prior plus the current day). Joins this window structure with daily HLL data to ensure complete coverage even for days with no activity. Applies incremental processing logic to limit historical reprocessing.
- **Key fields**: `timestamp` (target date), `joinday` (dates within window), `hll` (daily sketches)

### Component E: Monthly active user aggregation
- **Purpose**: Calculates monthly active addresses across different dimensional breakdowns
- **Logic**: Merges HLL sketches within each 30-day window to count distinct addresses, then extracts final counts using HLL functions. Creates separate breakdowns for project-level ('project_dbt_tag'), chain-level ('chain'), user-type-level ('user_type'), and product-level ('product_id') dimensions. Each breakdown nullifies non-relevant dimensions while preserving the target dimension for grouping.
- **Key fields**: `user_mau` (extracted HLL count), `dimensions` (breakdown type), relevant dimension fields, nullified non-target dimensions

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, creating multiple rows per timestamp representing different analytical perspectives (chain-level, project-level, etc.)
- **Metric transformation**: Filters the combined results to include only 'chain' and 'project_dbt_tag' dimensions, then renames `user_mau` field to `active_addresses_monthly` to align with blockchain terminology where addresses represent users
- **Schema finalization**: Applies the `finalize_metric_active_addresses_monthly` macro to standardize the output schema, fill missing time periods with zero values, and ensure consistent formatting across all metrics
- **Final calculation**: The result represents the count of unique addresses that have sent at least one transaction within any rolling 30-day period, updated daily and available at both chain and project aggregation levels

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

Unique addresses active over a rolling 30-day window.

MetisMETIS

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown