# Find heavy DataPrime queries

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### TL;DR[​](#tldr "Direct link to TL;DR")

This recipe helps you **find and rank queries that scan large volumes of data** in the system dataset, aiding in query optimization and cost analysis.

### Problem / Use case[​](#problem--use-case "Direct link to Problem / Use case")

You want to monitor DataPrime usage and detect which queries consume the most storage, to identify costly or inefficient patterns.

### Query[​](#query "Direct link to Query")

```
source system/engine.queries

| filter queryInfo.queryOutcome.status == 'Completed'

| filter queryInfo.queryOutcome.storage.bytesScanned:number > 1000000

| groupby queryInfo.queryOutcome.storage.bytesScanned:number as avg_bytes_scanned

| sortby avg_bytes_scanned desc
```

### Explanation[​](#explanation "Direct link to Explanation")

1. `source system/engine.queries` — Reads from the system dataset containing execution metadata for all DataPrime queries.
2. `filter queryInfo.queryOutcome.status == 'Completed'` — Keeps only successfully completed queries.
3. `filter queryInfo.queryOutcome.storage.bytesScanned:number > 1000000` — Filters queries that scanned more than 1 MB of data.
4. `groupby queryInfo.queryOutcome.storage.bytesScanned:number as avg_bytes_scanned` — Groups by the amount of data scanned and calculates the average.
5. `sortby avg_bytes_scanned desc` — Lists results from the most data-intensive queries to the least.

### Expected output[​](#expected-output "Direct link to Expected output")

| avg\_bytes\_scanned |
| ------------------- |
| 1666196703          |
| 1647101739          |
| 1249189292          |
| 1071874573          |
| 1046265388          |

### Variations[​](#variations "Direct link to Variations")

* Replace the threshold (`> 1000000`) with another value to catch lighter or heavier scans.
* Add `groupby queryInfo.queryText` to identify specific query patterns responsible for large scans.
* Use `count()` to see how frequently large-scan queries occur per user or time window.