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.

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Blockchains (L2)

Base

# Base Active users (weekly) Methodology

## 1. Overview

The Base Weekly Active Users (WAU) metric measures the number of unique addresses that initiated at least one transaction on the Base blockchain within a 7-day rolling window. This metric answers the business question "How many distinct users are actively transacting on Base over a weekly timeframe?" The calculation uses HyperLogLog sketches for efficient approximate distinct counting across rolling 7-day windows, with results broken down by multiple dimensions including chain, user type, and product.

## 2. Data sources

- **Base blockchain transactions**: Raw transaction records from the Base blockchain containing transaction hashes, sender addresses, and block timestamps
- **Transaction metadata**: Block timestamp data used for temporal analysis and daily aggregation
- **Address data**: Transaction sender addresses (`from_address`) that represent active users on the Base network

## 3. Step-by-step calculation

### Component A: Daily active users extraction
- **Purpose**: Identify unique active users per day from Base transaction data
- **Logic**: Extract distinct combinations of transaction sender addresses and dates from Base blockchain transactions. Each transaction sender address (`user_address`) represents a unique user, and block timestamps are truncated to day granularity to create daily user activity records. The system classifies all users as "Transaction sender" type and assigns them to the "blockchains_l2" market sector and "blockchain" business segment.
- **Key fields**: `block_timestamp` (truncated to day), `user_address` (transaction sender), `chain` ("base"), `user_type` ("Transaction sender")

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily user counts into HyperLogLog sketches for efficient approximate distinct counting
- **Logic**: Transform the daily distinct user data into HyperLogLog sketches using 15-bit precision. This enables efficient merging of user counts across time windows while maintaining reasonable accuracy for distinct counting. Each day's unique users are encoded into an HLL sketch that can be efficiently combined with other days.
- **Key fields**: `hll` (HyperLogLog sketch of daily users), `timestamp` (day granularity), `chain_id`, `user_type`, `product_id`

### Component C: Datespine and window creation
- **Purpose**: Create complete date coverage and 7-day rolling windows for WAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest transaction date to yesterday to ensure no missing days in the time series. For each day in the datespine, create a 7-day rolling window that includes the current day plus the 6 preceding days. This windowing approach ensures each WAU value represents users active within the most recent 7-day period ending on that date.
- **Key fields**: `timestamp` (current day), `joinday` (days within the 7-day window)

### Component D: HLL sketch aggregation across windows
- **Purpose**: Merge HLL sketches across 7-day windows to calculate weekly distinct users
- **Logic**: For each 7-day window, merge the HLL sketches from all days within that window using HyperLogLog merge operations. Extract the final distinct count from the merged sketch to get the approximate number of unique users active within that 7-day period. This approach efficiently handles overlapping time windows while maintaining distinct user counts.
- **Key fields**: `hll` (merged HLL sketches), `user_wau` (extracted distinct count)

### Component E: Dimensional breakdowns
- **Purpose**: Create separate WAU calculations for different analytical dimensions
- **Logic**: Break down the WAU results across four key dimensions: 1) Project level aggregation (all chains combined), 2) Chain-level breakdown (by individual blockchain), 3) User type breakdown (by user classification), and 4) Product level breakdown (by specific products). Each breakdown aggregates the HLL sketches appropriately and extracts distinct counts. Null values are assigned to non-relevant dimension fields in each breakdown.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau` (final distinct count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set, with each row representing WAU for a specific dimension combination on a specific date
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily boundary logic with 7-day rolling windows, where each day's WAU represents unique users active in the 7-day period ending on that date
- **Final calculation**: The final `user_wau` value represents the approximate distinct count of unique addresses that initiated at least one transaction on Base within the 7-day rolling window, calculated using HyperLogLog merge and extract operations across the time window

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

Unique revenue-generating users over a rolling 7-day window.

Base

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown