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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Blockchains (L2)

Blast

# Blast Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Blast measures the number of unique addresses that initiated transactions on the Blast blockchain within a 30-day rolling window. This metric uses a sophisticated HyperLogLog-based approach to efficiently count distinct users across multiple dimensional breakdowns, providing insights into user engagement patterns on the Blast Layer 2 network. The calculation employs a rolling 30-day window rather than calendar months to provide smooth, continuous trend analysis.

## 2. Data sources

- **Blast blockchain transactions**: Transaction records from the Blast Layer 2 network containing transaction metadata, timestamps, and sender addresses
- **Blockchain availability metadata**: Chain-specific availability rules and data quality filters to ensure accurate reporting periods
- **Table modification tracking**: Internal system tracking when underlying transaction data is updated to support incremental processing

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identify unique transaction senders per day as the foundation for MAU calculation
- **Logic**: Extract all transaction records from Blast blockchain data and identify unique `from_address` values per day. Each day's timestamp is truncated to day granularity using the `block_timestamp` field. Users are classified as 'Transaction sender' type and associated with the 'blockchains_l2' market sector and 'blockchain' business segment. The system applies incremental processing filters to only process recent data when running updates.
- **Key fields**: `from_address` becomes `user_dau`, `block_timestamp` becomes daily `timestamp`, static classification fields for `user_type`, `market_sector_id`, and `business_segment_id`

### Component B: HyperLogLog sketch initialization
- **Purpose**: Convert daily user sets into HyperLogLog sketches for efficient distinct counting across time windows
- **Logic**: Take the daily active users and create HyperLogLog sketches with 15-bit precision for each unique combination of timestamp, chain, user type, and product. The HyperLogLog algorithm enables memory-efficient storage and merging of large distinct count estimates. Group the daily user data by all dimensional attributes and initialize HyperLogLog sketches for each group.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id` as grouping dimensions, `hll` as the HyperLogLog sketch containing user counts

### Component C: Datespine and rolling window creation
- **Purpose**: Create a comprehensive time framework that handles missing data and generates 30-day rolling windows
- **Logic**: Generate a complete datespine covering every day from the earliest transaction data to the current date minus one day. For each day in the datespine, create a 30-day window by joining with the previous 29 days using timestamp array generation. This approach ensures no gaps in the timeline and provides consistent 30-day periods for each reporting date. The system handles incremental updates by focusing on recent time periods while maintaining historical continuity.
- **Key fields**: `timestamp` as the reporting date, `joinday` representing each day within the 30-day window

### Component D: Rolling window aggregation
- **Purpose**: Merge HyperLogLog sketches within each 30-day window to count distinct users
- **Logic**: Join the 30-day rolling windows with the daily HyperLogLog sketches, matching each window day with the corresponding daily sketch. For each reporting timestamp and dimensional combination, merge all HyperLogLog sketches within the 30-day period using HyperLogLog merge operations. This produces a distinct count estimate of users who were active at any point during the 30-day period ending on each reporting date.
- **Key fields**: Merged `hll` sketches containing 30-day distinct user estimates, preserved dimensional attributes `chain_id`, `user_type`, `product_id`

### Component E: Dimensional breakdown creation
- **Purpose**: Generate separate views of MAU data across different analytical dimensions
- **Logic**: Create four distinct dimensional breakdowns from the rolling window data. The 'project_dbt_tag' breakdown aggregates all users regardless of other dimensions. The 'chain' breakdown preserves chain-level detail. The 'user_type' breakdown focuses on user classification. The 'product_id' breakdown segments by product usage. Each breakdown nullifies non-relevant dimensions and extracts final counts from merged HyperLogLog sketches using the extract function.
- **Key fields**: `dimensions` indicating breakdown type, preserved relevant dimension fields, nullified irrelevant dimensions, `user_mau` as the final distinct user count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, maintaining separate rows for each dimension type and reporting date
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows with daily reporting granularity, ensuring smooth trending without calendar month boundaries
- **Final calculation**: Extract distinct user counts from merged HyperLogLog sketches, apply schema standardization through the `finalize_metric_user_mau` macro, and implement zero-filling strategy for missing data points to ensure complete time series

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

Unique revenue-generating users over a rolling 30-day window.

BlastBLAST

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown