# Run on All Rows vs Run on New Rows

> Understand when to trigger full backfills versus incremental processing to control costs and execution scope.

Source: https://cotera.co/docs/guides/columns/run-execution

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When executing [agent columns](https://cotera.co/docs/learn/core-concepts/columns.md) in your [datasets](https://cotera.co/docs/learn/core-concepts/datasets.md), Cotera gives you control over which data gets processed. Understanding the difference between these execution modes helps you manage both costs and processing scope effectively.

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## The Two Execution Modes

### Run on New Rows (Default)

This mode processes only data that hasn't been processed by the current column configuration. When you modify a prompt or change column logic, "Run on new rows" applies those changes going forward without touching previously processed data.

**What happens:**

* Existing rows keep their current outputs
* Only new data rows or [triggers](https://cotera.co/docs/learn/core-concepts/triggers.md) get processed with your updated logic
* Previous results remain unchanged in your [dataset](https://cotera.co/docs/learn/core-concepts/datasets.md)

### Run on All Rows (Backfill)

This mode reprocesses your entire [dataset](https://cotera.co/docs/learn/core-concepts/datasets.md) with the current column configuration. Every row gets processed again, replacing all previous outputs with new results based on your updated logic.

**What happens:**

* All existing rows get reprocessed
* Previous outputs are replaced with new results
* The column applies its current configuration to the complete [dataset](https://cotera.co/docs/learn/core-concepts/datasets.md)

***

## When to Use Each Mode

### Use "Run on New Rows" When:

* **You've made prompt refinements** - You improved your prompt for future processing but don't need to update historical analysis.
* **Your dataset is large** - Processing thousands or millions of rows can be expensive. Incremental updates keep costs manageable.
* **Historical context matters** - You want to preserve what the column understood at the time it processed each row.
* **You're testing changes** - Verify your updates work correctly on new data before committing to a full backfill.

### Use "Run on All Rows" When:

* **You need consistency across all data** - All rows should reflect your current logic, like after fixing a bug or updating classification criteria.
* **You've changed core logic** - Fundamental changes usually require reprocessing everything to maintain data integrity.
* **The dataset is manageable** - Small to medium datasets (hundreds to low thousands of rows) can be reprocessed without significant cost.
* **You're standardizing historical data** - All data needs to follow the same current standards.

***

## Practical Example

### Scenario: Customer Feedback Analysis

You've built an agent column that analyzes customer feedback for sentiment and categorizes issues. Your [dataset](https://cotera.co/docs/learn/core-concepts/datasets.md) contains 25,000 historical reviews plus new ones arriving daily.

**Initial prompt:**

```
Analyze customer feedback for sentiment (positive/negative/neutral)
and identify the main topic discussed.
```

**Improved prompt:**

```
Analyze customer feedback for sentiment (positive/negative/neutral).
Identify the main topic: Product Quality, Customer Service,
Shipping Experience, or Pricing Concerns.
Rate urgency as High, Medium, or Low based on language intensity.
```

**Decision point:**

* **Run on New Rows** - Apply the improved analysis to all future reviews while preserving the simpler analysis on historical data. Keeps processing focused on new information.
* **Run on All Rows** - Reprocess all 25,000 historical reviews to standardize everything with the improved categorization and urgency assessment. Provides complete dataset consistency for historical reporting.

**The choice depends on your needs:**

* Need consistent historical reporting? → Run on all rows
* Only care about improving future analysis? → Run on new rows
* Want to test the new approach first? → Run on new rows initially, then backfill later if needed

***

The execution mode you choose determines the scope of your processing. For large datasets with thousands of rows, consider whether you need historical consistency or if forward-looking improvements are sufficient for your business needs.