Validators process transactions and receive transactions fees (supply-side fees).

Injective is a smart contract platform for decentralized finance applications (a blockchain) and an orderbook exchange protocol. Injective was founded by Eric Chen, Albert Chon etc. and launched in 2020. Note that this dashboard only tracks Injective's blockchain business.

INJ holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue) via Burn Auctions. Injective's investors include Binance, Jump Crypto, Mark Cuban, etc.

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Blockchains (L1)

Injective

# Injective Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that executed transactions on the Injective protocol within a 7-day rolling window. This metric provides insight into user engagement and adoption trends by tracking distinct transaction senders across both Injective EVM and Injective Cosmos chains. The calculation uses HyperLogLog data structures to efficiently aggregate unique users across multiple dimensional breakdowns while maintaining accuracy for large user sets.

## 2. Data sources

- **Injective EVM transaction records**: All transaction data from the Injective EVM chain, including transaction senders (`from_address`), transaction hashes, block timestamps, and transaction metadata
- **Injective Cosmos message events**: Message event logs from the Injective Cosmos chain containing event attributes with sender information extracted from JSON structures
- **Chain availability configuration**: Internal configuration data that determines which chains are currently active and should be included in metric calculations

## 3. Step-by-step calculation

### Component A: Injective EVM user identification
- **Purpose**: Identifies unique daily active users from Injective EVM transactions
- **Logic**: Extracts all transaction records and identifies the `from_address` field as the user address. Each transaction sender represents a user interaction with the Injective EVM chain. Data is aggregated daily by truncating `block_timestamp` to day boundaries and deduplicating user addresses within each day. Users are classified as "Transaction sender" type and associated with the "Injective EVM" product ID and "blockchains_l1" market sector.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the unique user identifier, `transaction_hash` for transaction reference

### Component B: Injective cosmos user identification  
- **Purpose**: Identifies unique daily active users from Injective Cosmos message events
- **Logic**: Processes message events where `event_type` equals "message" and extracts the sender address from the `event_attributes` JSON field using the `$.sender` path. Similar to EVM users, these are deduplicated daily and classified as "Transaction sender" type. Users are associated with the "Injective Cosmos" product ID and "blockchains_l1" market sector.
- **Key fields**: `block_timestamp` for temporal boundaries, `event_attributes` JSON containing sender information, `event_type` for filtering message events

### Component C: Daily active user aggregation
- **Purpose**: Combines both EVM and Cosmos users into a unified daily active user dataset
- **Logic**: Unions the deduplicated user sets from both Injective chains, creating a comprehensive view of daily active users across the entire Injective ecosystem. Applies chain availability filtering to ensure only data from active chains is included. Each record represents a unique user-day combination with consistent schema across both chains.
- **Key fields**: `timestamp` truncated to daily boundaries, `user_address` as the unique identifier, `chain` indicating the source blockchain, `user_type`, `product_id`, and sector classifications

### Component D: HyperLogLog initialization for efficient aggregation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transforms the daily active user data into HyperLogLog sketches using the `hll_count.init` function with 15-bit precision. This allows for memory-efficient storage and fast aggregation of unique user counts across multiple dimensions without losing accuracy. Each daily user set becomes an HLL sketch that can be merged with other days.
- **Key fields**: `hll` data structure containing the probabilistic user count representation, dimension fields for grouping (`chain_id`, `user_type`, `product_id`)

### Component E: Rolling window creation
- **Purpose**: Establishes 7-day rolling windows for weekly active user calculation
- **Logic**: Creates a datespine covering all days from the earliest DAU record to the current date. For each day in the datespine, generates an array of the 7 preceding days (including the current day) using `generate_timestamp_array` with 6-day lookback. Cross-joins this with the daily HLL data to create records for each day-within-window combination, enabling aggregation across the 7-day periods.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window, `hll` sketches for each component day

### Component F: Dimensional breakdown aggregation
- **Purpose**: Calculates weekly active users across different analytical dimensions
- **Logic**: Groups the rolling window data by dimension type and merges HLL sketches using `hll_count.merge_partial` to combine unique user counts across the 7-day periods. Creates separate breakdowns for project-level (all chains combined), chain-level (per individual chain), user type level, and product ID level. Each breakdown nullifies irrelevant dimension fields while preserving the relevant grouping dimension.
- **Key fields**: `dimensions` indicating the breakdown type, dimension-specific fields (`chain_id`, `user_type`, `product_id`), `user_wau` as the final unique user count extracted from merged HLL sketches

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives in a single table
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamp boundaries with 7-day rolling windows, where each day's WAU represents unique users active in the 7-day period ending on that day
- **Final calculation**: The final weekly active user count is extracted from the merged HyperLogLog sketches using `hll_count.extract`, providing an accurate count of unique addresses that were active within each 7-day window across the specified dimensional breakdown

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

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

InjectiveINJ

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown