Validators process transactions and receive fees from users (supply-side fees). DA costs for users are reflected in the transactions fees paid.

Celestia is a proof of stake blockchain optimized for data availability (DA). Celestia was founded by Mustafa Al-Bassam, etc. and launched in 2023.

TIA holders own and govern the protocol. They currently do not take a cut of the total transaction fees paid by users (revenue). Celestia's investors include Polychain Capital, Placeholder, Coinbase Ventures, etc.

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Blockchains (L1)

Celestia

# Celestia Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Celestia measures the number of unique users who performed transactions on the Celestia blockchain within any 7-day rolling window. This metric answers the business question of user engagement and activity patterns over weekly periods, providing insights into the protocol's user retention and growth trends. The calculation uses HyperLogLog data structures for efficient distinct user counting across multiple dimensional breakdowns including project-level, chain-level, user type, and product ID.

## 2. Data sources

- **Blockchain events**: Celestia blockchain message events containing transaction data and user interaction records
- **Message attributes**: JSON-formatted event attributes that include sender information and transaction metadata
- **Transaction data**: Celestia transaction records with timestamp, hash, and sender address information
- **Table metadata**: Internal table modification timestamps used for incremental processing and data consistency checks

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identify unique user addresses from Celestia blockchain transactions
- **Logic**: Extract user activity from Celestia blockchain message events by parsing JSON attributes within the event data. A JavaScript user-defined function processes the `attributes` array to extract sender information from message events. Filter for events where `event_type` equals 'message' and extract the `sender` value from the parsed JSON attributes. Each transaction sender is classified as 'Transaction sender' user type, with the chain identifier set to 'celestia', market sector as 'blockchains_l1', and business segment as 'blockchain'
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for unique transaction identification, `user_address` as the extracted sender address, and `user_type` classification

### Component B: Daily active user aggregation
- **Purpose**: Calculate daily distinct users from the extracted user addresses
- **Logic**: Aggregate the user addresses into daily active users by truncating timestamps to day level and selecting distinct user addresses per day. Group the data by timestamp (day), chain, user type, market sector, business segment, and product ID to create comprehensive daily user counts. Apply incremental processing logic with lookback periods to ensure data completeness while maintaining efficient processing
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing distinct user addresses, dimensional grouping fields including `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog initialization and datespine creation
- **Purpose**: Prepare DAU data for efficient weekly aggregation using probabilistic data structures
- **Logic**: Convert daily active user data into HyperLogLog structures using `hll_count.init` function for efficient distinct counting operations. Create a complete datespine covering all days from the minimum timestamp in the DAU data to the current date minus one day, generating timestamps at daily intervals. This ensures no missing days in the time series analysis
- **Key fields**: `hll` structures containing user data, `timestamp` for complete date coverage

### Component D: Rolling window generation
- **Purpose**: Create 7-day rolling windows for each day in the analysis period
- **Logic**: Generate rolling windows by creating a cross-join structure where each day is associated with itself and the previous 6 days. Use `generate_timestamp_array` to create 7-day arrays starting from 6 days before each target date through the target date itself. Join this window structure with the HLL-enabled DAU data to associate each day's window with the corresponding user data from constituent days
- **Key fields**: `timestamp` as the target date, `joinday` for the individual days within each 7-day window, associated `hll` data for each component day

### Component E: Weekly active user calculation by dimension
- **Purpose**: Calculate distinct users across 7-day windows for multiple dimensional breakdowns
- **Logic**: Merge HyperLogLog structures within each 7-day window using `hll_count.merge_partial` to combine user sets across the window period, then extract the final distinct count using `hll_count.extract`. Calculate WAU across four dimensional breakdowns: project-level aggregation (all dimensions collapsed), chain-level aggregation (grouped by chain), user type-level aggregation (grouped by user type), and product ID-level aggregation (grouped by product ID). Each breakdown uses appropriate null casting for non-relevant dimensions
- **Key fields**: `user_wau` containing the distinct user count for each 7-day window, `dimensions` indicating the breakdown type, relevant grouping fields for each dimensional level

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type-level, product ID-level) into a single result set with consistent schema structure
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily boundaries with 7-day rolling windows, using timestamp truncation to day level for consistent temporal grouping
- **Final calculation**: The final WAU value represents the distinct count of users who performed transactions within any 7-day rolling window, calculated using HyperLogLog merge operations across the constituent days and extracted as the final distinct user count for each dimensional breakdown

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

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

CelestiaTIA

Active users (weekly)

Active users (weekly)

Competitive landscape