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Blockchains (L2)

Boba Network

# Boba Network Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the Boba Network in a given 7-day period. This metric tracks user engagement and adoption on the Layer 2 blockchain by counting distinct transaction senders over rolling weekly windows. The calculation uses HyperLogLog sketches for efficient aggregation of large user sets and provides breakdowns at both chain and project levels.

## 2. Data sources

- **Boba Network transaction records**: Raw blockchain transaction data containing transaction metadata, timestamps, and sender addresses
- **Transaction senders**: The `from_address` field from each transaction, representing the address that initiated the transaction

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Extract and standardize user activity data from raw Boba Network transactions
- **Logic**: Pull transaction records from Boba Network blockchain data, extracting the transaction sender address (`from_address`), block timestamp, and transaction hash. Add standardized metadata including chain identifier ('bobanetwork'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender'). Set `product_id` to null as this is a chain-level metric
- **Key fields**: `block_timestamp` for temporal analysis, `from_address` as the user identifier, `transaction_hash` for transaction reference

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily distinct active users from transaction activity
- **Logic**: Group transactions by day using timestamp truncation to day granularity. For each day, count distinct transaction sender addresses to get daily active users. Include metadata dimensions like chain, user type, market sector, business segment, and product identifier for downstream aggregation
- **Key fields**: `timestamp` (truncated to day), `user_address` (renamed from `from_address`), dimensional metadata for breakdown analysis

### Component C: Weekly aggregation with HyperLogLog
- **Purpose**: Aggregate daily users into weekly active user counts using efficient probabilistic data structures
- **Logic**: Initialize HyperLogLog sketches for each day's unique user set to enable efficient set union operations. Create a continuous datespine covering all dates, then generate a 7-day rolling window by joining each date with the 6 preceding days. For each weekly window, merge the HyperLogLog sketches from all 7 days and extract the distinct user count, providing an estimate of weekly active users
- **Key fields**: `timestamp` for the window end date, `hll` sketches for efficient user set operations, `user_wau` as the weekly distinct user count

### Component D: Dimensional breakdown
- **Purpose**: Provide weekly active user metrics across different analytical dimensions
- **Logic**: Calculate weekly active users for multiple breakdowns including chain-level aggregation (all Boba Network activity), project-level aggregation (project_dbt_tag dimension), user type breakdown, and product-level breakdown. Each breakdown uses the same HyperLogLog merging logic but groups by different dimensional attributes
- **Key fields**: `dimensions` field indicating the breakdown type ('chain', 'project_dbt_tag', 'user_type', 'product_id'), `user_wau` as the weekly count for each dimension

### Final aggregation
- **Combining components**: Filter the weekly active user results to include only chain-level and project-level breakdowns, excluding user type and product breakdowns for this specific metric
- **Currency conversion**: Not applicable for this count-based metric
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day and the preceding 6 days
- **Final calculation**: Rename `user_wau` to `active_addresses_weekly` to align with the metric naming convention. Apply the finalization macro with 'fill_zeros' strategy to ensure complete time series coverage and apply chain availability filters

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

Unique active addresses over a rolling 7-day window.

Boba NetworkBOBA

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown