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 addresses (daily) Methodology

## 1. Overview

Active addresses daily measures the number of unique addresses that performed at least one transaction on the Injective network each day. This metric combines activity from both Injective EVM (Ethereum Virtual Machine compatible layer) and Injective Cosmos (native Cosmos SDK layer) to provide a comprehensive view of network adoption and usage. The calculation identifies all transaction senders across both execution environments and counts them as distinct active addresses using HyperLogLog aggregation for efficient processing of large datasets.

## 2. Data sources

The metric draws from two primary blockchain data sources:

- **Injective EVM transaction data**: Complete transaction records from Injective's EVM-compatible layer, including sender addresses (`from_address`), transaction hashes, and block timestamps
- **Injective Cosmos message events**: Parsed message events from Injective's native Cosmos SDK layer, specifically `message` event types that contain sender information in JSON-formatted event attributes
- **Chain availability configuration**: Metadata defining data availability periods for filtering results to valid time ranges

## 3. Step-by-step calculation

### Component A: Injective EVM user identification
- **Purpose**: Extract all unique transaction senders from Injective's EVM layer
- **Logic**: Query Injective EVM transaction data to identify all addresses that initiated transactions. Extract the `from_address` field as the user address, along with block timestamps and transaction hashes. Apply safe type casting to ensure data consistency and classify these users as `Transaction sender` type belonging to the `Injective EVM` product and `blockchains_l1` market sector
- **Key fields**: `from_address` (sender address), `block_timestamp` (transaction timing), `transaction_hash` (unique transaction identifier)

### Component B: Injective cosmos user identification  
- **Purpose**: Extract all unique message senders from Injective's Cosmos SDK layer
- **Logic**: Query Injective Cosmos message events filtered to `message` event types. Parse the JSON-formatted `event_attributes` to extract sender addresses using the `sender` key. Associate each sender with block timestamps and classify as `Transaction sender` type belonging to the `Injective Cosmos` product and `blockchains_l1` market sector
- **Key fields**: `sender` (extracted from JSON event attributes), `block_timestamp` (event timing), `transaction_hash` (associated transaction)

### Component C: User combination and daily aggregation
- **Purpose**: Combine users from both execution layers and aggregate by day
- **Logic**: Union the EVM and Cosmos user datasets, then truncate block timestamps to day boundaries using timestamp truncation. Select distinct combinations of date, chain, user type, and user address to eliminate duplicate counting of the same address performing multiple transactions on the same day
- **Key fields**: `timestamp` (truncated to day), `user_address` (unique sender), `chain` (execution layer identifier)

### Component D: HyperLogLog sketch creation
- **Purpose**: Create efficient data structures for scalable distinct counting
- **Logic**: Generate HyperLogLog sketches for each day, grouped by timestamp, chain, user type, and product ID. HLL sketches enable approximate distinct counting with minimal memory usage, essential for processing large volumes of blockchain data while maintaining accuracy
- **Key fields**: `hll` (HyperLogLog sketch), grouping dimensions for aggregation

### Component E: Multi-dimensional aggregation
- **Purpose**: Create different analytical views of the user activity data
- **Logic**: Generate four separate dimensional breakdowns by merging HLL sketches: project-level aggregation combining all chains and user types, chain-level breakdown separating EVM and Cosmos activity, user type breakdown (currently all Transaction senders), and product ID breakdown distinguishing between `Injective EVM` and `Injective Cosmos` components. Each breakdown extracts the final count from merged HLL sketches
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_dau` (distinct user count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single dataset, then filter to include only 'chain' and 'project_dbt_tag' dimensions for the active addresses metric
- **Time aggregation**: Data is already aggregated to daily boundaries through timestamp truncation in earlier steps
- **Final calculation**: Rename the `user_dau` field to `active_addresses_daily` to reflect that in blockchain contexts, active users are conceptualized as active addresses. Apply the finalization macro with zero-filling strategy to ensure continuous time series with zero values for days with no activity
- **Chain availability filtering**: Apply availability filters to exclude data from periods where chain data may be incomplete or unreliable

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

Unique active addresses over a fixed 24-hour window.

InjectiveINJ

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown