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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Celestia

# Celestia Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that have sent at least one transaction on the Celestia network within a 7-day rolling window. This metric answers the question "How many unique users are actively transacting on Celestia each week?" and uses a rolling window approach where each day's value represents the count of unique addresses that were active in that day plus the previous 6 days.

## 2. Data sources

- **Celestia message events**: Raw blockchain events from the Celestia network containing transaction metadata and message attributes
- **Transaction sender data**: Sender addresses extracted from JSON-formatted message event attributes
- **Celestia blockchain metadata**: Block timestamps and transaction hashes for proper temporal ordering

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies unique addresses that initiated transactions on Celestia
- **Logic**: Processes raw Celestia message events where `event_type` equals 'message'. Uses a JavaScript function to parse the `attributes` array containing JSON-formatted data. Extracts the `sender` field from parsed JSON objects to identify transaction originators. Each record represents a user address that sent a transaction, along with the block timestamp and transaction hash.
- **Key fields**: `user_address` (extracted sender), `block_timestamp`, `transaction_hash`, `chain` (set to 'celestia')

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active addresses
- **Logic**: Groups transaction sender data by day using timestamp truncation to daily boundaries. Takes distinct `user_address` values per day to eliminate duplicate counting of addresses that sent multiple transactions on the same day. Creates the foundation dataset for rolling window calculations.
- **Key fields**: `timestamp` (truncated to day), `user_dau` (distinct daily active addresses)

### Component C: HyperLogLog sketch creation
- **Purpose**: Enables efficient distinct counting across rolling windows
- **Logic**: Converts daily active user lists into HyperLogLog sketches using `hll_count.init()` with precision 15. HLL sketches allow approximate distinct counting that can be merged across time periods without losing accuracy. Groups by timestamp, chain, user type, and product dimensions to create sketches for each combination.
- **Key fields**: `hll` (HyperLogLog sketch), dimension breakdowns

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each date
- **Logic**: Generates a datespine covering all dates from the earliest data through yesterday. For each date in the datespine, creates a join table that includes that date plus the previous 6 days (7 days total). This ensures consistent window sizes even when some days have no activity. Joins the datespine with HLL sketches to create window data.
- **Key fields**: `timestamp` (window end date), `joinday` (dates within the window)

### Component E: Weekly aggregation with dimensional breakdowns
- **Purpose**: Calculates weekly active addresses across multiple dimensions
- **Logic**: Merges HLL sketches within each 7-day window using `hll_count.merge_partial()` to combine sketches from different days without double-counting addresses that were active on multiple days. Creates separate aggregations for different dimensional breakdowns: project level (all data combined), chain level (by blockchain), user type level, and product level. Uses appropriate null values for dimensions not being aggregated.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (weekly active count from merged HLL)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project, chain, user type, product) into a single result set. Filters to only include 'chain' and 'project_dbt_tag' dimensions for the final active addresses metric.
- **Currency conversion**: Not applicable - this is a count metric
- **Time aggregation**: Uses 7-day rolling windows where each day's value represents unique addresses active in that day plus the previous 6 days
- **Final calculation**: Extracts the final count from merged HyperLogLog sketches using `hll_count.extract()`, then relabels `user_wau` as `active_addresses_weekly` to align with standard blockchain analytics terminology where "users" and "active addresses" are equivalent concepts

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

Unique active addresses over a rolling 7-day window.

CelestiaTIA

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape