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 (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the Blast blockchain during a 7-day rolling window period. For blockchain networks, active addresses directly correspond to active users, as each transaction originates from a unique wallet address. This metric provides insight into user engagement and network adoption by tracking weekly participation levels while accounting for users who are active on multiple days within the same week.

## 2. Data sources

- **Blast blockchain transactions**: Complete transaction records from the Blast blockchain, including transaction metadata such as sender addresses (`from_address`), timestamps (`block_timestamp`), and block information
- **Table modification metadata**: System metadata tracking when underlying data tables were last updated, used for efficient incremental processing of new transactions

## 3. Step-by-step calculation

### Component A: Daily active users extraction

- **Purpose**: Identifies unique addresses that sent transactions on each day
- **Logic**: Processes Blast blockchain transaction records to extract unique transaction sender addresses (`from_address`) grouped by day. Each transaction record provides the originating wallet address and block timestamp. The timestamp is truncated to day boundaries to group transactions by calendar day. Only the `from_address` field is used to identify active users, as this represents the actual transaction initiator. Each unique address is counted only once per day regardless of how many transactions they sent.
- **Key fields**: `from_address` (transaction sender address), `block_timestamp` (transaction timestamp), truncated `timestamp` (day boundary)

### Component B: HyperLogLog sketch creation  

- **Purpose**: Converts daily user lists into efficient probabilistic data structures for set operations
- **Logic**: Takes the daily active user addresses and creates HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. This probabilistic data structure allows for efficient counting of unique elements across multiple sets without storing all individual addresses. Each day's unique addresses are converted into a single HyperLogLog sketch that can later be merged with other days.
- **Key fields**: `hll` (HyperLogLog sketch of daily users), `timestamp` (day), `chain_id` (Blast), `user_type` (Transaction sender)

### Component C: Weekly window generation

- **Purpose**: Creates 7-day rolling windows for calculating weekly active users
- **Logic**: Generates a complete date spine covering all days from the earliest transaction date to yesterday. For each date in this spine, creates a 7-day window by generating timestamps for the target date and the 6 preceding days. This creates overlapping 7-day windows where each day serves as the end date of a weekly period. The windowing ensures that weekly active user counts capture all users who were active at any point during each 7-day period.
- **Key fields**: `timestamp` (window end date), `joinday` (each day within the 7-day window)

### Component D: Weekly active user calculation

- **Purpose**: Counts unique users across 7-day periods using set union operations
- **Logic**: For each 7-day window, collects the HyperLogLog sketches from all days within that window and merges them using `hll_count.merge_partial`. This efficiently performs set union operations to count unique addresses that were active on any day within the 7-day period, automatically handling users who were active on multiple days. The final count is extracted using `hll_count.extract` to get the estimated number of unique weekly active users.
- **Key fields**: `user_wau` (weekly active user count), `timestamp` (week end date)

### Component E: Dimensional breakdown

- **Purpose**: Calculates weekly active users across different analytical dimensions
- **Logic**: Aggregates the weekly active user counts across four different dimensions: project level (all Blast data combined), chain level (Blast blockchain specifically), user type level (transaction senders), and product level (currently null for Blast). Each breakdown involves grouping the HyperLogLog sketches by the relevant dimension and merging sketches within each group. The results are combined using union operations to create a comprehensive dataset with multiple analytical perspectives.
- **Key fields**: `dimensions` (breakdown type: chain, project_dbt_tag, user_type, product_id), dimension-specific identifiers

### Final aggregation

- **Combining components**: The weekly active user data filtered to include only chain-level and project-level dimensions, as these correspond to active addresses for blockchain analysis. The `user_wau` field is directly mapped to `active_addresses_weekly` since blockchain active users equal active addresses
- **Currency conversion**: Not applicable - this is a count metric
- **Time aggregation**: Uses 7-day rolling windows where each date represents the end of a weekly period containing that date plus the previous 6 days
- **Final calculation**: The final metric value equals the weekly active user count (`user_wau`) for the specified dimensions, with missing periods filled with zeros and schema standardization applied through the finalization macro

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

Unique active addresses over a rolling 7-day window.

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Active addresses (weekly)

Active addresses (weekly)

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