Blast batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense). Bridge depositors deposit ETH or stablecoins to Blast and receive a yield from ETH staking or T-bill strategies.

Blast is an L2 smart contract platform for decentralized applications on Ethereum. Blast has an inherent yield generation mechanism, coming from ETH staking and RWA protocols. Blast was founded by Pacman and launched in 2023.

Blast team and investors currently own and govern the protocol. Blast's investors include Paradigm, Standard Crypto, Santiago, etc.

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Blast

# Blast Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique wallet addresses that have sent at least one transaction on the Blast network within a rolling 30-day window. This metric provides insight into user engagement and network adoption by tracking distinct users who actively interact with the blockchain. The calculation uses a rolling window approach where each day represents the count of unique addresses that were active within the previous 30 days (including the current day).

## 2. Data sources

The calculation relies on the following data sources:
- **Blast transaction records**: Complete transaction history from the Blast blockchain containing sender addresses, recipient addresses, block timestamps, and transaction metadata
- **Internal metadata**: Chain identification, user classification schemes, and business segment categorizations used for dimensional analysis

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identify unique addresses that sent transactions each day
- **Logic**: Extract distinct sender addresses (`from_address`) from Blast transaction records, grouped by daily timestamp boundaries. Each unique address that initiates a transaction on a given day is considered an active user for that day. The system truncates block timestamps to day boundaries to ensure consistent daily aggregation. Users are classified as "Transaction sender" type under the "blockchain" business segment.
- **Key fields**: `from_address` (sender wallet address), `block_timestamp` (transaction timestamp), `timestamp` (daily boundary), `user_dau` (daily active user identifier)

### Component B: HyperLogLog sketch creation
- **Purpose**: Create probabilistic data structures for efficient distinct counting at scale
- **Logic**: Convert daily lists of active user addresses into HyperLogLog probabilistic sketches with precision parameter 15. This approach enables accurate estimation of distinct user counts while managing memory usage for large datasets. Each day's unique user set is represented as an HLL sketch that can be efficiently merged with other sketches.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily boundary), `chain_id` (Blast network identifier), `user_type` (user classification)

### Component C: Rolling window calculation
- **Purpose**: Calculate 30-day rolling monthly active user counts
- **Logic**: Generate a complete datespine covering all days in the analysis period, then for each day, create a 30-day lookback window covering the current day plus the previous 29 days. Join this datespine with daily HLL sketches to ensure no gaps in the rolling window calculation. This approach guarantees that every day has a complete 30-day historical context for MAU calculation.
- **Key fields**: `timestamp` (current day), `joinday` (each day in the 30-day window), `datespine` (continuous daily sequence)

### Component D: Monthly active user aggregation
- **Purpose**: Merge HLL sketches across the 30-day window to count distinct users
- **Logic**: For each day in the datespine, merge all HLL sketches from the corresponding 30-day window using HyperLogLog merge operations. Extract the final distinct count from the merged sketch to produce the monthly active user count. This process efficiently handles the probabilistic counting of unique addresses across the rolling window while maintaining accuracy for large-scale data.
- **Key fields**: `user_mau` (monthly active users count), `hll_count.merge_partial()` (sketch merging function), `hll_count.extract()` (count extraction function)

### Component E: Dimensional breakdown
- **Purpose**: Create separate views of MAU across different analytical dimensions
- **Logic**: Generate four separate breakdowns of the monthly active user data: by chain (network-level view), by project tag (project-level aggregation), by user type (classification-based view), and by product (product-specific metrics). Each breakdown maintains the same 30-day rolling window logic but groups data differently to support various analytical perspectives.
- **Key fields**: `dimensions` (breakdown category), `chain_id` (for chain-level view), `user_type` (for user classification view), `product_id` (for product-specific view)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single comprehensive dataset that provides MAU data across multiple analytical dimensions
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Rolling 30-day windows calculated daily, providing a smooth trend line of monthly active user engagement
- **Final calculation**: Rename the `user_mau` field to `active_addresses_monthly` to reflect that for blockchain networks, active users are represented by their wallet addresses. Apply standardized metric finalization including schema validation and zero-filling for any missing data points. The final metric represents the count of unique wallet addresses that sent at least one transaction within the trailing 30-day period for each date.

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

Unique addresses active over a rolling 30-day window.

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Active addresses (monthly)

Active addresses (monthly)

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