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 (daily) Methodology

## 1. Overview

The Blast Daily Active Users (DAU) metric measures the number of unique addresses that initiated transactions on the Blast blockchain each day. This metric provides insight into daily user engagement and activity levels on the Blast Layer 2 network. The calculation uses HyperLogLog (HLL) probabilistic data structures to efficiently count distinct users across multiple dimensions while handling large-scale blockchain data.

## 2. Data sources

- **Blast blockchain transactions**: Complete transaction records from the Blast Layer 2 network, including sender addresses, timestamps, and transaction metadata
- **Chain availability configuration**: Network availability periods and operational status data
- **Incremental processing metadata**: Table modification timestamps and cache data for efficient data updates

## 3. Step-by-step calculation

### Component A: Daily user extraction
- **Purpose**: Identifies unique user addresses that initiated transactions on each day
- **Logic**: Queries Blast transaction records and extracts the `from_address` field as the user identifier. The `block_timestamp` is truncated to daily granularity using day-level time boundaries. Each unique `from_address` per day represents one daily active user. Users are classified as 'Transaction sender' type and assigned to the 'blockchains_l2' market sector and 'blockchain' business segment
- **Key fields**: `from_address` as the unique user identifier, `block_timestamp` for temporal grouping, static classification fields for categorization

### Component B: HyperLogLog sketch creation
- **Purpose**: Converts daily user data into probabilistic data structures for efficient distinct counting
- **Logic**: Creates HyperLogLog sketches using the `hll_count.init` function with 15-bit precision for each combination of timestamp, timestamp grain, chain, user type, and product identifier. This enables efficient storage and computation of distinct user counts across large datasets while maintaining acceptable accuracy
- **Key fields**: `user_dau` field containing the user address, HLL sketches stored for each dimensional combination

### Component C: Multi-dimensional breakdowns
- **Purpose**: Generates user counts across different analytical dimensions
- **Logic**: Creates four separate breakdown views: project-level aggregation (overall Blast DAU), chain-level breakdown (by blockchain), user-type breakdown (transaction senders), and product-level breakdown (currently null for Blast). Each breakdown uses `hll_count.merge_partial` to combine HLL sketches and `hll_count.extract` to produce the final distinct user counts. Unused dimension fields are set to null for each breakdown type
- **Key fields**: `dimensions` field indicating breakdown type, `chain_id`, `product_id`, `user_type` for dimensional grouping, `user_dau` as the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a unified dataset with consistent schema across all dimension types
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day granularity, ensuring consistent daily user counting periods
- **Final calculation**: The `finalize_metric_user_dau` macro standardizes the output schema, applies data quality filters, and implements a fill-zeros strategy to populate missing days with zero user counts, ensuring complete time series data for analytical consistency

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

Unique revenue-generating users over a fixed 24-hour window.

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Active users (daily)

Active users (daily)

Competitive landscape

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