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

Ink is an Ethereum Layer 2 (L2) blockchain built on Optimism's Superchain, focused on providing a secure, scalable infrastructure for Web3 applications.

Kraken team owns and governs the protocol. Kraken's investors include RIT Capital Partners, Hummingbird Ventures, Money Partners Group, etc.

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Blockchains (L2)

# Ink Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed at least one transaction on the Ink blockchain within a 7-day rolling window. This metric provides insights into sustained user engagement and protocol adoption over weekly periods. The calculation uses HyperLogLog data structures for efficient processing of large-scale user activity data, enabling accurate cardinality estimation across multiple dimensional breakdowns.

## 2. Data sources

- **Ink blockchain transactions**: Raw transaction records from the Ink L2 blockchain containing sender addresses, timestamps, and transaction hashes
- **Transaction metadata**: Block timestamps for temporal aggregation and transaction hashes for deduplication
- **User identification**: Transaction sender addresses (`from_address`) used to identify unique active users
- **Chain classification**: Protocol metadata identifying Ink as an L2 blockchain in the blockchains market sector

## 3. Step-by-step calculation

### Component A: Daily active user foundation

- **Purpose**: Establishes the base layer of daily unique active users from transaction activity
- **Logic**: Extracts all transaction records from Ink blockchain data and identifies unique user addresses per day. Transaction timestamps are truncated to day granularity, and each unique `from_address` (transaction sender) is counted as an active user for that day. The system classifies all users as "Transaction sender" type and applies Ink-specific metadata including chain identifier and market sector classification.
- **Key fields**: `block_timestamp` (truncated to day), `user_address` (from `from_address`), `chain` (set to 'ink'), `user_type` (set to 'Transaction sender')

### Component B: HyperLogLog aggregation for daily users

- **Purpose**: Converts daily user lists into HyperLogLog sketches for efficient cardinality estimation
- **Logic**: Takes the daily active user data and applies HyperLogLog initialization with 15-bit precision to create compact probabilistic data structures. Each day's unique user addresses are processed through `hll_count.init()` to create HLL sketches that can be efficiently merged across time windows. This aggregation maintains accuracy while reducing memory requirements for large user sets.
- **Key fields**: `timestamp` (day), `hll` (HyperLogLog sketch of user addresses), dimensional attributes like `chain_id`, `user_type`, `product_id`

### Component C: Seven-day rolling window creation

- **Purpose**: Establishes a complete 7-day sliding window framework that handles missing dates
- **Logic**: Creates a comprehensive datespine covering all days from the earliest available data to yesterday, then generates a cartesian product where each date is joined with its preceding 6 days to form complete 7-day windows. This approach uses `generate_timestamp_array()` to create both the main datespine and the 7-day lookback periods, ensuring no gaps in the time series that could skew weekly calculations.
- **Key fields**: `timestamp` (target date), `joinday` (each of the 7 days in the rolling window)

### Component D: Weekly window HLL merging

- **Purpose**: Combines daily HLL sketches across each 7-day window to calculate weekly active users
- **Logic**: Joins the 7-day window structure with daily HLL sketches, matching each window day with its corresponding HLL data. For each target date, collects HLL sketches from the current day plus the previous 6 days. The HLL sketches are then merged using `hll_count.merge_partial()` to combine the probabilistic user counts across the full weekly period, maintaining the accuracy of cardinality estimation while handling overlapping users correctly.
- **Key fields**: `timestamp` (target date), `hll` (merged HLL sketch for 7-day period), dimensional breakdowns

### Component E: Dimensional breakdown generation

- **Purpose**: Creates multiple analytical views of WAU data across different business dimensions
- **Logic**: Processes the merged weekly HLL data through four distinct grouping strategies. Project-level breakdown aggregates all activity without dimensional filters. Chain-level breakdown groups by `chain_id`. User type breakdown groups by `user_type`. Product-level breakdown groups by `product_id`. Each breakdown uses `hll_count.extract()` to convert the final merged HLL sketches into actual user counts, while setting unused dimensional fields to NULL for clean data structure.
- **Key fields**: `user_wau` (extracted cardinality), `dimensions` (breakdown type), relevant dimensional identifiers

### Final aggregation

- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset containing project-level, chain-level, user-type-level, and product-level WAU metrics in a single unified structure
- **Time aggregation**: Daily boundary logic uses day-truncated timestamps with 7-day rolling windows, ensuring consistent weekly periods that update daily
- **Final calculation**: Weekly Active Users equals the cardinality of unique user addresses who performed at least one transaction within any 7-day rolling period, calculated as the extracted value from merged HyperLogLog sketches across the weekly window

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

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

Ink

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown