Skip to main content

​
What Are Metrics?

Metrics are aggregation functions available as fields on Cube Record types. They let you compute statistics directly in your GraphQL query without post-processing. When you select a metric field alongside dimension fields, the query groups by the selected dimensions and computes the metric for each group. Supported metrics:
MetricSQL EquivalentDescription
countCOUNT(*) or COUNT(column)Count rows or distinct values
sumSUM(column)Sum of numeric values
avgAVG(column)Average of numeric values
minMIN(column)Minimum value
maxMAX(column)Maximum value
uniqCOUNT(DISTINCT column)Count of unique values

​
Metric Fields on Record Types

Metrics appear as top-level fields on each Cube’s Record type. Not all Cubes support all metrics — it depends on the Cube definition. 
type DEXTradesRecord {
  # Dimension fields...
  Block { ... }
  Trade { ... }

  # Metric fields
  count: Int
  sum(of: DEXTradesSumOf!): Float
}
To use a metric, simply include it in your field selection: 
query {
  DEXTrades(
    network: sol
    tokenAddress: "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v"
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    count
    Trade { Dex { ProtocolName } }
  }
}
This query groups DEX trades by protocol name and returns the count for each group.

​
The of Parameter

Metrics like sum, avg, min, max, and uniq require an of parameter that specifies which dimension to aggregate. The of value is an enum generated per Cube, following the dimension path naming convention. 
sum(of: Trade_Buy_Amount)
avg(of: Trade_Buy_PriceInUSD)
min(of: Block_Time)
max(of: Trade_Sell_Amount)
uniq(of: Trade_Buy_Account_Owner)

​
Example: Total Buy Volume by DEX

query {
  DEXTrades(
    network: sol
    tokenAddress: "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v"
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    count
    sum(of: Trade_Buy_Amount)
    Trade { Dex { ProtocolName } }
  }
}
Response: 
{
  "data": {
    "DEXTrades": [
      {
        "count": 1842,
        "sum": 2847291.45,
        "Trade": { "Dex": { "ProtocolName": "Raydium" } }
      },
      {
        "count": 923,
        "sum": 1293847.12,
        "Trade": { "Dex": { "ProtocolName": "Orca" } }
      }
    ]
  }
}

​
count — Counting Rows

count without an of parameter counts the total rows in each group (equivalent to COUNT(*)): 
query {
  DEXTrades(
    network: sol
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    count
  }
}
When used with dimension fields, it returns the count per group: 
query {
  DEXTrades(
    network: sol
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    count
    Trade { Dex { ProtocolName } }
  }
}

​
uniq — Count Distinct

uniq maps to COUNT(DISTINCT column) in SQL. Use it to count unique values of a dimension: 
query {
  DEXTrades(
    network: sol
    tokenAddress: "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v"
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    uniq(of: Trade_Buy_Account_Owner)
  }
}
This returns the number of unique buyer wallets that traded USDC today on Solana.

​
selectWhere — HAVING-Style Filtering

selectWhere lets you filter on aggregation results, similar to SQL’s HAVING clause. It is applied after grouping and aggregation, allowing you to filter groups based on their metric values. 
query {
  DEXTrades(
    network: sol
    where: { Block: { Time: { after: "2025-03-27T00:00:00Z" } } }
  ) {
    count(selectWhere: { gt: 100 })
    Trade { Dex { ProtocolName } }
  }
}
This returns only DEX protocols that had more than 100 trades today — protocols with fewer trades are excluded from the result. selectWhere supports the same numeric operators as IntFilter/FloatFilter:
OperatorDescription
gtGreater than
gteGreater than or equal
ltLess than
lteLess than or equal
eqEqual to
neNot equal to

​
Practical Example: Top Traders

Find the top 10 wallets by trade count for a token today, showing their total buy volume and unique trade count: 
query TopTraders {
  DEXTrades(
    network: sol
    tokenAddress: "DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263"
    where: {
      Block: { Time: { after: "2025-03-27T00:00:00Z" } }
    }
    orderBy: Block_Time_DESC
    limit: { count: 10 }
  ) {
    count
    sum(of: Trade_Buy_Amount)
    Trade {
      Buy {
        Account { Owner }
      }
    }
  }
}
Response: 
{
  "data": {
    "DEXTrades": [
      {
        "count": 47,
        "sum": 892341023.5,
        "Trade": {
          "Buy": {
            "Account": { "Owner": "7xKXtg2CW87d97TXJSDpbD5jBkheTqA83TZRuJosgAsU" }
          }
        }
      },
      {
        "count": 31,
        "sum": 451203847.2,
        "Trade": {
          "Buy": {
            "Account": { "Owner": "3kMq5RezM9XBbBGRNxP9vXkJHAfG8S7gn5WfBsHFQr7T" }
          }
        }
      }
    ]
  }
}

​
Combining Multiple Metrics

You can select multiple metric fields in a single query: 
query {
  DEXTrades(
    network: sol
    tokenAddress: "So11111111111111111111111111111111111111112"
    where: {
      Block: { Time: { after: "2025-03-27T00:00:00Z" } }
    }
  ) {
    count
    sum(of: Trade_Buy_Amount)
    min(of: Trade_Buy_PriceInUSD)
    max(of: Trade_Buy_PriceInUSD)
    uniq(of: Trade_Buy_Account_Owner)
    Trade { Dex { ProtocolName } }
  }
}
This returns per-DEX statistics: trade count, total volume, price range, and unique traders — all in one query.

​
Metrics vs Pre-Aggregated Cubes

A common question: should I use metrics on DWD Cubes or query DWM/DWS Cubes directly?
ApproachWhen to UsePerformance
Metrics on DWD (e.g., DEXTrades.count)Custom aggregations, ad-hoc groupings, flexible time windowsSlower — aggregates raw events at query time
DWM Cubes (e.g., Pairs, Tokens)Standard time-series analysis, OHLC charts, volume over timeFast — reads pre-computed minute-level rollups
DWS Cubes (e.g., TokenHolders, WalletTokenPnL)Current snapshots, cumulative totals, leaderboardsFastest — reads pre-aggregated summary data
Rule of thumb: Use DWM/DWS Cubes when they cover your use case — they’re pre-aggregated and significantly faster. Fall back to DWD Cubes with metrics when you need custom groupings or aggregation logic that pre-built Cubes don’t support.

​
Decision Guide

Use Pairs (DWM). It already has pre-computed open/high/low/close/volume per minute. No need to aggregate DEXTrades yourself.
Use Tokens (DWM). It pre-aggregates trade counts, volumes, and unique traders per minute.
Use TokenHolders (DWS). Pre-computed latest balances per holder — much faster than aggregating BalanceUpdates.
Use DEXTrades with count + sum(of: Trade_Buy_Amount) grouped by Trade.Dex.ProtocolName. This isn’t available as a pre-built Cube, so DWD metrics are the right choice.
Use WalletTokenPnL (DWS). Pre-computed buy/sell volumes and trade counts per wallet-token pair.

​
Next Steps

Data Cubes

Explore all 25 Cubes and their field structures.

Query Examples

See real-world query examples with metrics and aggregation.