> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindsdb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Update a Table

## Description

MindsDB provides two ways of using the `UPDATE` statement:

1. The regular `UPDATE` statement updates specific column values in an existing table.

2. The `UPDATE FROM SELECT` statement updates data in an existing table from a subselect query. It can be used as an alternative to `CREATE TABLE` or `INSERT INTO` to store predictions.

## Syntax

Here is an example of the regular `UPDATE` statement:

```sql theme={null}
UPDATE integration_name.table_name
SET column_name = new_value
WHERE column_name = old_value
```

<Info>
  Please replace the placeholders as follows:

  * `integration_name` is the name of the connected data source.
  * `table_name` is the table name within that data source.
  * `column_name` is the column name within that table.
</Info>

And here is an example of the `UPDATE FROM SELECT` statement that updates a table with predictions made within MindsDB:

```sql theme={null}
UPDATE
    integration_to_be_updated.table_to_be_updated
SET
    column_to_be_updated = prediction_data.predicted_value_column,
FROM 
    (
        SELECT p.predicted_value_column, p.column1, p.column2
        FROM integration_name.table_name as t
        JOIN model_name as p
    ) AS prediction_data
WHERE
    column1 = prediction_data.column1
    AND column2 = prediction_data.column2
```

Below is an alternative for the `UPDATE FROM SELECT` statement that updates a table with predictions. This syntax is easier to write.

```sql theme={null}
UPDATE
    integration_to_be_updated.table_to_be_updated
ON 
    column1, column2
FROM 
    (
        SELECT p.predicted_value_column as column_to_be_updated, p.column1, p.column2
        FROM integration_name.table_name as t
        JOIN model_name as p
    ) 
```

<Info>
  The steps followed by the syntax:

  * It executes query from the `FROM` clause to get the output data. In our example, we query for predictions, but it could be a simple select from another table. Please note that it is aliased as `prediction_data`.
  * It updates all rows from the `table_to_be_updated` table (that belongs to the `integration_to_be_updated` integration) that match the `WHERE` clause criteria. The rows are updated with values as defined in the `SET` clause.
</Info>

<Tip>
  It is recommended to use the primary key column(s) in the WHERE clause (here, `column1` and `column2`), as the primary key column(s) uniquely identify each row. Otherwise, the `UPDATE` statement may lead to unexpected results by altering rows that you didn't want to affect.
</Tip>