Base batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Base is an Ethereum Layer 2 (L2) offering a secure, low-cost, builder-friendly way for anyone, anywhere, to build onchain.

Coinbase team owns and governs the protocol. Coinbase is a public company and its investors include Andreessen Horowitz, Vanguard Group, ARK Investment Management, etc.

github

twitter

discord

website

Blockchains (L2)

Base

# Base Active addresses (weekly) Methodology

## 1. Overview

Active addresses weekly measures the number of unique wallet addresses that initiated at least one transaction on the Base L2 network within any rolling 7-day period. This metric tracks user activity and engagement by counting distinct transaction senders over weekly windows, providing insight into the size and growth of the Base ecosystem's active user base. The calculation uses HyperLogLog data structures for efficient distinct counting across large transaction volumes.

## 2. Data sources

- **Base blockchain transactions**: Raw transaction records from the Base L2 network containing transaction hashes, sender addresses, timestamps, and block information
- **Transaction metadata**: Block timestamps and transaction identifiers used for temporal aggregation and deduplication

## 3. Step-by-step calculation

### Component A: Transaction user extraction
- **Purpose**: Identifies unique transaction senders as active users from Base blockchain activity
- **Logic**: Extracts transaction sender addresses from Base transaction records, casting the `from_address` field as the user identifier and `block_timestamp` for temporal grouping. Each transaction sender is classified as a "Transaction sender" user type. Additional metadata includes chain identification as "base", market sector as "blockchains_l2", and business segment as "blockchain"
- **Key fields**: `block_timestamp` for timing, `from_address` as the user address, `transaction_hash` for transaction identification

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct active users using HyperLogLog sketches for efficient counting
- **Logic**: Groups transactions by day using timestamp truncation to day granularity. For each day, creates a HyperLogLog sketch initialized with all unique sender addresses that were active on that day. This approach enables memory-efficient distinct counting while preserving the ability to merge counts across time periods
- **Key fields**: `timestamp` truncated to day, `user_dau` as the user address, `hll` as the HyperLogLog sketch

### Component C: Rolling weekly window creation
- **Purpose**: Establishes 7-day rolling windows for weekly active user calculation
- **Logic**: Creates a datespine covering all dates from the earliest transaction to the present. For each date in the datespine, generates an array of the 7 days ending on that date (including the date itself plus the preceding 6 days). This creates overlapping 7-day windows that slide forward one day at a time
- **Key fields**: `timestamp` as the window end date, `joinday` as each day within the 7-day window

### Component D: Weekly active user aggregation
- **Purpose**: Merges daily HyperLogLog sketches within each 7-day window to count distinct weekly active users
- **Logic**: For each 7-day window, joins the window dates with the daily HyperLogLog sketches. Merges all HyperLogLog sketches within each 7-day period using `hll_count.merge_partial()` to combine the distinct counts, then extracts the final distinct count using `hll_count.extract()`. This produces the number of unique addresses that were active on any day within each 7-day period
- **Key fields**: `user_wau` as the weekly active user count, merged from individual daily HLL sketches

### Component E: Dimensional breakdowns
- **Purpose**: Creates the metric across multiple analytical dimensions
- **Logic**: Calculates weekly active users across four dimension levels: project-level aggregation (all Base activity combined), chain-level (by individual chain), user-type-level (by transaction sender type), and product-level (by specific products). Each breakdown groups the data differently while maintaining the same HyperLogLog merging logic
- **Key fields**: `dimensions` field indicates the grouping level, with corresponding dimension identifiers for chain, user type, and product

### Final aggregation
- **Combining components**: The final metric selects weekly active user counts specifically from chain-level and project-level dimensional breakdowns, filtering to these relevant dimensions
- **Time aggregation**: Data is aggregated using rolling 7-day windows that slide forward daily, with each window representing a distinct weekly measurement period
- **Final calculation**: The weekly active addresses count represents the distinct number of wallet addresses that initiated at least one transaction within any 7-day rolling period on the Base network, calculated through HyperLogLog sketch merging for computational efficiency

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

Unique active addresses over a rolling 7-day window.

Base

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown