Stellar

stellar holds the standardized Stellar tables. Stellar calls its blocks ledgers, and they land in blocks. A transaction here is a wrapper: what it does is carried in a list of operations. So transactions land in transactions, the operations inside them in operations, and what each operation changed in effects.

Tables

Table	Since	Description
`blocks`	2015-09	One row per ledger, with transaction counts, fee pool and total lumens.
`transactions`	2015-09	One row per transaction, with its fees, the account that sent it, and the original encoded form.
`operations`	2015-09	One row per operation, typed by `operation_type`.
`effects`	2015-09	One row per state change an operation produced.

Ledgers are keyed on number, transactions on transaction_hash, operations on operation_id and effects on effect_id. The tables get finer as you go down: a transaction holds one or more operations, and an operation produces any number of effects, including none. For payments and trades, start from operations and filter on operation_type. Use effects when the question is who was credited and who was debited.

Sample queries

Operations by type
Tokenized assets ranked
Priced tokens

Count operations by type on one day.

select
    operation_type,
    count(*) as operations
from `stellar.operations`
where block_timestamp >= timestamp('2026-08-20')
  and block_timestamp < timestamp('2026-08-21')
group by operation_type
order by operations desc
limit 20

Rank the tokenized assets issued on Stellar by circulating market cap.

select
    assets.symbol,
    assets.asset_type,
    daily.market_cap_circulating_total
from `metrics_asset_tokens.market_cap_circulating_total_daily` as daily
join `dimensions.asset_tokens` as deployments
    using (asset_token_id)
join `dimensions.assets` as assets
    using (asset_id)
where daily.timestamp >= timestamp('2026-08-22')
  and daily.timestamp < timestamp('2026-08-23')
  and deployments.chain_id = 'stellar'
order by daily.market_cap_circulating_total desc
limit 20

List the Stellar tokens that carried a daily price over one week.

with priced as (
    select
        token_id,
        avg(price) as avg_price,
        count(*) as price_days
    from `metrics_tokens.price_daily`
    where timestamp >= timestamp('2026-08-16')
      and timestamp < timestamp('2026-08-23')
      and chain_id = 'stellar'
    group by token_id
)

select
    tokens.symbol,
    tokens.name,
    priced.avg_price,
    priced.price_days
from priced
join `dimensions.tokens` as tokens using (token_id)
order by priced.avg_price desc
limit 20

Notes

Above the raw tables sits the rest of the catalog, and Stellar shows up in two parts of it: the tokenized assets issued on it, and the list of tokens we know about with the daily prices they carry. Tokenized funds and stablecoins make up most of the asset side. No app or trading-pool table covers the chain. The market pages go deeper on each of these. Tokenized assets and Stablecoins cover the tokenized assets and their daily tables. Tokens covers the token list and the daily price table.

Overview

Core entities

Core datasets

Market verticals

Chain verticals

Tables

Sample queries

Notes

​Tables

​Sample queries

​Notes

Tables

Sample queries

Notes