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 addresses (monthly) Methodology

## 1. Overview

The Celestia active addresses monthly metric measures the number of unique addresses that have been active on the Celestia blockchain within any 30-day rolling window, aggregated monthly. This metric provides insight into sustained user engagement and network adoption by counting addresses that have sent transactions during the measurement period. The calculation uses HyperLogLog probabilistic counting for efficient aggregation of unique addresses across different dimensional breakdowns.

## 2. Data sources

- **Celestia blockchain events**: Message events from Celestia blockchain transactions containing sender address information and event attributes
- **Transaction metadata**: Block timestamps and transaction hashes from Celestia blockchain operations  
- **Chain configuration data**: Chain availability filters and processing parameters
- **Processing metadata**: Table modification timestamps for incremental data processing

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Extract unique user addresses from Celestia blockchain message events
- **Logic**: Filter blockchain events to include only message events where `event_type` equals `message`. Use a JavaScript function to parse JSON-formatted event attributes and extract the `sender` field containing the user address. Each transaction sender is classified as a blockchain user with user type `Transaction sender`. Block timestamps are preserved for time-based aggregations.
- **Key fields**: `block_timestamp` for temporal grouping, `transaction_hash` for transaction identification, `user_address` extracted from event attributes, `chain` set to celestia, `market_sector_id` set to blockchains_l1, `business_segment_id` set to blockchain

### Component B: Daily active user aggregation  
- **Purpose**: Create daily unique address counts from the extracted user data
- **Logic**: Group user addresses by day using timestamp truncation to daily granularity. Remove duplicate addresses within each day to count unique daily active users. Preserve dimensional attributes including chain, user type, market sector, business segment, and product identifiers for downstream breakdowns.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing the unique user address, `timestamp_grain` set to day, dimensional grouping fields

### Component C: Monthly active user calculation with HyperLogLog
- **Purpose**: Calculate 30-day rolling monthly active users using probabilistic counting
- **Logic**: Create HyperLogLog sketches for each daily unique address set to enable efficient approximate counting. Generate a complete datespine covering all days from the earliest data through yesterday. For each day, create a 30-day lookback window by joining with the previous 29 days. Aggregate HyperLogLog sketches across the 30-day window and extract the approximate unique count. This produces a rolling 30-day unique user count for each day. Create dimensional breakdowns by grouping and merging HyperLogLog sketches across different dimensions: project level (`project_dbt_tag`), chain level (`chain`), user type level (`user_type`), and product level (`product_id`).
- **Key fields**: `timestamp` for the window end date, `user_mau` containing the 30-day unique address count, `dimensions` indicating the breakdown level, `chain_id` for chain-specific breakdowns

### Final aggregation
- **Combining components**: The monthly active user data is directly used as the active addresses metric since each user is identified by their unique blockchain address
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Monthly aggregation uses the 30-day rolling window approach where each day represents the unique addresses active in the trailing 30 days, then partitioned by month for final storage
- **Final calculation**: The `user_mau` field is renamed to `active_addresses_monthly` to reflect that in blockchain contexts, users are identified by their addresses. The metric represents the count of unique addresses that have sent at least one transaction in any 30-day period, measured at monthly intervals

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

Unique addresses active over a rolling 30-day window.

CelestiaTIA

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape