Alter a table

Alters a table by doing one of the following:

Adding one or more columns to a table
Dropping one or more columns from a table
Adding automatic embedding generation for one or more vector columns
Removing automatic embedding generation for one or more vector columns

You cannot change a column’s type. Instead, you must drop the column and add a new column.

Similarly, you cannot rename a table. Instead, you must drop and recreate the table.

After you add a column, you should index the column if you want to filter or sort on the column. All indexed column names must use snake case, not camel case. For more information, see Create an index and Create a vector index.

Ready to write code? See the examples for this method to get started. If you are new to the Data API, check out the quickstart.

Result

Python
TypeScript
Java
curl

Adds or drops columns, or adds or removes a vectorize integration for vector columns. Removing a vectorize integration for a column does not remove the vector embeddings stored in the column.

Returns a Table instance that represents the table after the modification.

Although the Table instance that you used to perform the alteration will still work, it will not reflect the updated typing if you added or dropped columns. To reflect the new typing, use the row_type parameter.

Adds or drops columns, or adds or removes a vectorize integration for vector columns. Removing a vectorize integration for a column does not remove the vector embeddings stored in the column.

Returns a promise that resolves to a Table instance that represents the table after the modification.

const newTable = await table.alter<NewSchema>({
    operation: {
      add: {
        columns: {
          venue: 'text',
        },
      },
    },
  });

Adds or drops columns, or adds or removes a vectorize integration for vector columns. Removing a vectorize integration for a column does not remove the vector embeddings stored in the column.

Returns a Table<T> instance that represents the table after the schema change.

Adds or drops columns, or adds or removes a vectorize integration for vector columns. Removing a vectorize integration for a column does not remove the vector embeddings stored in the column.

If the command succeeds, the response indicates the success.

Example response:

{
  "status": {
    "ok": 1
  }
}

Parameters

Python
TypeScript
Java
curl

Use the alter method, which belongs to the astrapy.Table class.

Method signature

alter(
  operation: AlterTableOperation | dict[str, Any],
  *,
  row_type: type[Any] = DefaultRowType,
  table_admin_timeout_ms: int,
  request_timeout_ms: int,
  timeout_ms: int,
) -> Table[NEW_ROW]

Name Type Summary

Name	Type	Summary
`operation`	`AlterTableOperation` \| `dict[str, Any]`	The alter operation to perform. Can be one of the following: An `AlterTableAddColumns` object or a dictionary in the form `{"add": {"columns": COLUMN_DEFINITIONS}}` that specifies the columns to add. Column are defined in the same way as when you create a table. See Add columns to a table and Add a vector column and configure an embedding provider integration for an example. An `AlterTableDropColumns` or a dictionary in the form `{"drop": {"columns": [COLUMN_NAMES]}}` that specifies the names of the columns to remove. See Drop columns from a table for an example. An `AlterTableAddVectorize` object or a dictionary in the form `{"addVectorize": {"columns": VECTORIZE_DEFINITIONS}}` that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table. See Add automatic embedding generation to existing vector columns for an example. An `AlterTableDropVectorize` or a dictionary in the form `{"dropVectorize": {"columns": [COLUMN_NAMES]}}` that specifies the names of the vector columns from which to remove a vectorize integration. See Remove automatic embedding generation from vector columns for an example.
`row_type`	`type`	Optional. A formal specifier for the type checker. If provided, `row_type` must match the type hint specified in the assignment. For more information, see Typing support.
`table_admin_timeout_ms`	`int`	Optional. A timeout, in milliseconds, for the underlying HTTP request. If not provided, the `Database` setting is used. This parameter is aliased as `request_timeout_ms` and `timeout_ms` for convenience.

operation

AlterTableOperation | dict[str, Any]

The alter operation to perform.

Can be one of the following:

An AlterTableAddColumns object or a dictionary in the form {"add": {"columns": COLUMN_DEFINITIONS}} that specifies the columns to add. Column are defined in the same way as when you create a table.

See Add columns to a table and Add a vector column and configure an embedding provider integration for an example.
An AlterTableDropColumns or a dictionary in the form {"drop": {"columns": [COLUMN_NAMES]}} that specifies the names of the columns to remove.

See Drop columns from a table for an example.
An AlterTableAddVectorize object or a dictionary in the form {"addVectorize": {"columns": VECTORIZE_DEFINITIONS}} that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table.

See Add automatic embedding generation to existing vector columns for an example.
An AlterTableDropVectorize or a dictionary in the form {"dropVectorize": {"columns": [COLUMN_NAMES]}} that specifies the names of the vector columns from which to remove a vectorize integration.

See Remove automatic embedding generation from vector columns for an example.

row_type

type

Optional. A formal specifier for the type checker. If provided, row_type must match the type hint specified in the assignment. For more information, see Typing support.

table_admin_timeout_ms

int

Optional. A timeout, in milliseconds, for the underlying HTTP request. If not provided, the Database setting is used. This parameter is aliased as request_timeout_ms and timeout_ms for convenience.

Use the alter method, which belongs to the Table class.

Method signature

async alter(
  options: AlterTableOptions<Schema>
): Table<Schema, PKey>

Name Type Summary

Name	Type	Summary
`operation`	`AlterTableOperations<Schema>`	The alter operation to perform. Can be one of the following: An `{add: AddColumnOperation}` object that specifies the columns to add. Column are defined in the same way as when you create a table. See Add columns to a table and Add a vector column and configure an embedding provider integration for an example. A `{drop: DropColumnOperation}` object that specifies the names of the columns to remove. See Drop columns from a table for an example. An `{addVectorize: AddVectorizeOperation}` object that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table. See Add automatic embedding generation to existing vector columns for an example. A `{dropVectorize: DropVectorizeOperation}` object that specifies the names of the vector columns from which to remove a vectorize integration. See Remove automatic embedding generation from vector columns for an example.
`timeout`	`number` \| `TimeoutDescriptor`	The timeout(s) to apply to HTTP request(s) originating from this method.

operation

AlterTableOperations<Schema>

The alter operation to perform.

Can be one of the following:

An {add: AddColumnOperation} object that specifies the columns to add. Column are defined in the same way as when you create a table.

See Add columns to a table and Add a vector column and configure an embedding provider integration for an example.
A {drop: DropColumnOperation} object that specifies the names of the columns to remove.

See Drop columns from a table for an example.
An {addVectorize: AddVectorizeOperation} object that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table.

See Add automatic embedding generation to existing vector columns for an example.
A {dropVectorize: DropVectorizeOperation} object that specifies the names of the vector columns from which to remove a vectorize integration.

See Remove automatic embedding generation from vector columns for an example.

timeout

number | TimeoutDescriptor

The timeout(s) to apply to HTTP request(s) originating from this method.

Use the alter method, which belongs to the com.datastax.astra.client.tables.Table class.

Method signature

Table<T> alter(AlterTableOperation operation)

Table<T> alter(
  AlterTableOperation operation,
  AlterTableOptions options
)

<R> Table<R> alter(
  AlterTableOperation operation,
  AlterTableOptions options,
  Class<R> clazz
)

Name Type Summary

Name	Type	Summary
`operation`	`AlterTableOperation`	The alter operation to perform. Can be one of the following: An `AlterTableAddColumns` object that specifies the columns to add. Column are defined in the same way as when you create a table. See Add columns to a table and Add a vector column and configure an embedding provider integration for an example. An `AlterTableDropColumns` object that specifies the names of the columns to remove. See Drop columns from a table for an example. An `AlterTableAddVectorize` object that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table. See Add automatic embedding generation to existing vector columns for an example. An `AlterTableDropVectorize` object that specifies the names of the vector columns from which to remove a vectorize integration. See Remove automatic embedding generation from vector columns for an example.
`options`	`AlterTableOptions`	Optional. The options for this operation, including the timeout.
`rowClass`	`Class<?>`	Optional. A specification of the class of the table’s row object. Default: `Row`, which is close to a `Map` object

operation

AlterTableOperation

The alter operation to perform.

Can be one of the following:

An AlterTableAddColumns object that specifies the columns to add. Column are defined in the same way as when you create a table.

See Add columns to a table and Add a vector column and configure an embedding provider integration for an example.
An AlterTableDropColumns object that specifies the names of the columns to remove.

See Drop columns from a table for an example.
An AlterTableAddVectorize object that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table.

See Add automatic embedding generation to existing vector columns for an example.
An AlterTableDropVectorize object that specifies the names of the vector columns from which to remove a vectorize integration.

See Remove automatic embedding generation from vector columns for an example.

options

AlterTableOptions

Optional. The options for this operation, including the timeout.

rowClass

Class<?>

Optional. A specification of the class of the table’s row object.

Default: Row, which is close to a Map object

Use the alterTable command.

Command signature

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "add": {
        "columns": {
          "NEW_COLUMN_NAME": "DATA_TYPE",
          "NEW_COLUMN_NAME": "DATA_TYPE"
        }
      }
    }
  }
}'

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "drop": {
        "columns": [ "COLUMN_NAME", "COLUMN_NAME" ]
      }
    }
  }
}'

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "addVectorize": {
        "columns": {
          "VECTOR_COLUMN_NAME": {
            "provider": "PROVIDER",
            "modelName": "MODEL_NAME",
            "authentication": {
              "providerKey": "API_KEY_NAME"
            },
            "parameters": PARAMETERS
          }
        }
      }
    }
  }
}'

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "dropVectorize": {
        "columns": [ "VECTOR_COLUMN_NAME", "VECTOR_COLUMN_NAME" ]
      }
    }
  }
}'

Name Type Summary

Name	Type	Summary
`operation`	`object`	The alter operation to perform. Can be one of the following: An object in the form `{"add": {"columns": COLUMN_DEFINITIONS}}` that specifies the columns to add. Column are defined in the same way as when you create a table. See Add columns to a table and Add a vector column and configure an embedding provider integration for an example. An object in the form `{"drop": {"columns": [COLUMN_NAMES]}}` that specifies the names of the columns to remove. See Drop columns from a table for an example. An object in the form `{"addVectorize": {"columns": VECTORIZE_DEFINITIONS}}` that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table. See Add automatic embedding generation to existing vector columns for an example. An object in the form `{"dropVectorize": {"columns": [COLUMN_NAMES]}}` that specifies the names of the vector columns from which to remove a vectorize integration. See Remove automatic embedding generation from vector columns for an example.

operation

object

The alter operation to perform.

Can be one of the following:

An object in the form {"add": {"columns": COLUMN_DEFINITIONS}} that specifies the columns to add. Column are defined in the same way as when you create a table.

See Add columns to a table and Add a vector column and configure an embedding provider integration for an example.
An object in the form {"drop": {"columns": [COLUMN_NAMES]}} that specifies the names of the columns to remove.

See Drop columns from a table for an example.
An object in the form {"addVectorize": {"columns": VECTORIZE_DEFINITIONS}} that specifies names of the vector columns for which to add a vectorize integration, and the corresponding vectorize options. Column are defined in the same way as when you create a table.

See Add automatic embedding generation to existing vector columns for an example.
An object in the form {"dropVectorize": {"columns": [COLUMN_NAMES]}} that specifies the names of the vector columns from which to remove a vectorize integration.

See Remove automatic embedding generation from vector columns for an example.

Examples

The following examples demonstrate how to alter a table.

Add columns to a table

When you add columns, the columns are defined in the same way as they are when you create a table.

After you add a column, you should index the column if you want to filter or sort on the column. For more information, see Create an index and Create a vector index.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.info import (
    AlterTableAddColumns,
    ColumnType,
    TableScalarColumnTypeDescriptor,
)

# Get an existing table
client = DataAPIClient("APPLICATION_TOKEN")
database = client.get_database("API_ENDPOINT")
table = database.get_table("TABLE_NAME")

# Add columns
table.alter(
    AlterTableAddColumns(
        columns={
            "is_summer_reading": TableScalarColumnTypeDescriptor(
                column_type=ColumnType.BOOLEAN,
            ),
            "library_branch": TableScalarColumnTypeDescriptor(
                column_type=ColumnType.TEXT,
            ),
        },
    ),
)

import { DataAPIClient } from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient("APPLICATION_TOKEN");
const database = client.db("API_ENDPOINT");
const table = database.table("TABLE_NAME");

// Add columns
(async function () {
  await table.alter({
    operation: {
      add: {
        columns: {
          is_summer_reading: "boolean",
          library_branch: "text",
        },
      },
    },
  });
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.AlterTableAddColumns;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    Table<Row> table =
        new DataAPIClient("APPLICATION_TOKEN")
            .getDatabase("API_ENDPOINT")
            .getTable("TABLE_NAME");

    // Add columns
    AlterTableAddColumns alterOperation =
        new AlterTableAddColumns()
            .addColumnBoolean("is_summer_reading")
            .addColumnText("library_branch");
    table.alter(alterOperation);
  }
}

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "alterTable": {
    "operation": {
      "add": {
        "columns": {
          "is_summer_reading": "boolean",
          "library_branch": "text"
        }
      }
    }
  }
}'

Add a vector column and configure an embedding provider integration

When you add a vector column to a table, you can configure an embedding provider integration for the column. The integration will automatically generate vector embeddings for any data inserted into the column.

The configuration depends on the embedding provider. For the configuration and an example for each provider, see Supported embedding providers.

After you add a column, you should index the column if you want to filter or sort on the column. For more information, see Create an index and Create a vector index.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.info import (
    AlterTableAddColumns,
    TableVectorColumnTypeDescriptor,
    VectorServiceOptions
)

# Get an existing table
client = DataAPIClient("APPLICATION_TOKEN")
database = client.get_database("API_ENDPOINT")
table = database.get_table("TABLE_NAME")

# Add a vector column and configure an embedding provider integration
table.alter(
    AlterTableAddColumns(
        columns={
            "plot_synopsis": TableVectorColumnTypeDescriptor(
              dimension=MODEL_DIMENSIONS,
              service=VectorServiceOptions(
                  provider="PROVIDER",
                  model_name="MODEL_NAME",
                  authentication={
                      "providerKey": "API_KEY_NAME",
                  },
                  parameters=PARAMETERS
              ),
          ),
        },
    ),
)

import { DataAPIClient } from '@datastax/astra-db-ts';

// Get an existing table
const client = new DataAPIClient('APPLICATION_TOKEN');
const database = client.db('API_ENDPOINT');
const table = database.table('TABLE_NAME');

// Add a vector column and configure an embedding provider integration
(async function () {
  await table.alter({
    operation: {
      add: {
        columns: {
          plot_synopsis: {
            type: 'vector',
            dimension: MODEL_DIMENSIONS,
            service: {
              provider: 'PROVIDER',
              modelName: 'MODEL_NAME',
              authentication: {
                providerKey: 'API_KEY_NAME',
              },
              parameters: PARAMETERS,
            },
          },
        },
      },
    },
  });
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.definition.columns.ColumnDefinitionVector;
import com.datastax.astra.client.tables.definition.rows.Row;
import com.datastax.astra.client.core.vector.SimilarityMetric;
import com.datastax.astra.client.tables.commands.AlterTableAddColumns;
import com.datastax.astra.client.core.vectorize.VectorServiceOptions;

public class Example {

    public static void main(String[] args) {
        // Get an existing table
        Table<Row> table = new DataAPIClient("APPLICATION_TOKEN")
            .getDatabase("API_ENDPOINT")
            .getTable("TABLE_NAME");

        // Add a vector column and configure an embedding provider integration
        AlterTableAddColumns alterOperation = new AlterTableAddColumns()
            .addColumnVector(
                "plot_synopsis",
                new ColumnDefinitionVector()
                    .dimension(MODEL_DIMENSIONS)
                    .metric(SimilarityMetric.SIMILARITY_METRIC)
                    .service(
                        new VectorServiceOptions()
                            .provider("PROVIDER")
                            .modelName("MODEL_NAME")
                            .authentication(Map.of("providerKey", "API_KEY_NAME"))
                            .parameters(PARAMETERS)
                    )
            );
        table.alter(alterOperation);
    }
}

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "add": {
        "columns": {
          "plot_synopsis": {
            "type": "vector",
            "dimension": MODEL_DIMENSIONS,
            "service": {
              "provider": "PROVIDER",
              "modelName": "MODEL_NAME",
              "authentication": {
                "providerKey": "API_KEY_NAME"
              },
              "parameters": PARAMETERS
            }
          }
        }
      }
    }
  }
}'

Drop columns from a table

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.info import AlterTableDropColumns

# Get an existing table
client = DataAPIClient("APPLICATION_TOKEN")
database = client.get_database("API_ENDPOINT")
table = database.get_table("TABLE_NAME")

# Drop columns
table.alter(
    AlterTableDropColumns(
        columns=["is_summer_reading", "library_branch"],
    ),
)

import { DataAPIClient } from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient("APPLICATION_TOKEN");
const database = client.db("API_ENDPOINT");
const table = database.table("TABLE_NAME");

// Drop columns
(async function () {
  await table.alter({
    operation: {
      drop: {
        columns: ["is_summer_reading", "library_branch"],
      },
    },
  });
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.AlterTableDropColumns;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    Table<Row> table =
        new DataAPIClient("APPLICATION_TOKEN")
            .getDatabase("API_ENDPOINT")
            .getTable("TABLE_NAME");

    // Drop columns
    AlterTableDropColumns alterOperation =
        new AlterTableDropColumns("is_summer_reading", "library_branch");
    table.alter(alterOperation);
  }
}

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "alterTable": {
    "operation": {
      "drop": {
        "columns": ["is_summer_reading", "library_branch"]
      }
    }
  }
}'

Add automatic embedding generation to existing vector columns

You can configure an embedding provider integration for an existing vector column. The integration will automatically generate vector embeddings for any data inserted into the column.

The configuration depends on the embedding provider. For the configuration and an example for each provider, see Supported embedding providers.

If your vector column already includes vector data, make sure the service options are compatible with the existing embeddings. This ensures accurate vector search results.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.info import (
    AlterTableAddVectorize,
    VectorServiceOptions,
)

# Get an existing table
client = DataAPIClient("APPLICATION_TOKEN")
database = client.get_database("API_ENDPOINT")
table = database.get_table("TABLE_NAME")

# Configure an embedding provider integration for a vector column
table.alter(
    AlterTableAddVectorize(
        columns={
            "plot_synopsis": VectorServiceOptions(
                provider="PROVIDER",
                model_name="MODEL_NAME",
                authentication={
                    "providerKey": "API_KEY_NAME",
                },
                parameters=PARAMETERS
            ),
        },
    ),
)

import { DataAPIClient } from '@datastax/astra-db-ts';

// Get an existing table
const client = new DataAPIClient('APPLICATION_TOKEN');
const database = client.db('API_ENDPOINT');
const table = database.table('TABLE_NAME');

// Configure an embedding provider integration for a vector column
(async function () {
  await table.alter({
    operation: {
      addVectorize: {
        columns: {
          plot_synopsis: {
            provider: 'PROVIDER',
            modelName: 'MODEL_NAME',
            authentication: {
              providerKey: 'API_KEY_NAME',
            },
            parameters: PARAMETERS,
          },
        },
      },
    },
  });
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.tables.definition.rows.Row;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.AlterTableAddVectorize;
import com.datastax.astra.client.core.vectorize.VectorServiceOptions;

public class Example {

    public static void main(String[] args) {
        // Get an existing table
        Table<Row> table = new DataAPIClient("APPLICATION_TOKEN")
            .getDatabase("API_ENDPOINT")
            .getTable("TABLE_NAME");

        // Configure an embedding provider integration for a vector column
        AlterTableAddVectorize alterOperation =
            new AlterTableAddVectorize()
                .columns(
                    Map.of(
                        "plot_synopsis",
                        new VectorServiceOptions()
                            .provider("PROVIDER")
                            .modelName("MODEL_NAME")
                            .authentication(Map.of("providerKey", "API_KEY_NAME"))
                            .parameters(PARAMETERS)
                            )
                            );
        table.alter(alterOperation);
    }
}

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "alterTable": {
    "operation": {
      "addVectorize": {
        "columns": {
          "plot_synopsis": {
            "provider": "PROVIDER",
            "modelName": "MODEL_NAME",
            "authentication": {
              "providerKey": "API_KEY_NAME"
            },
            "parameters": PARAMETERS
          }
        }
      }
    }
  }
}'

Remove automatic embedding generation from vector columns

You can remove automatic embedding generation for one or more vector columns. Removing a vectorize integration from a column does not remove the vector embeddings stored in the column.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.info import AlterTableDropVectorize

# Get an existing table
client = DataAPIClient("APPLICATION_TOKEN")
database = client.get_database("API_ENDPOINT")
table = database.get_table("TABLE_NAME")

# Remove automatic embedding generation
table.alter(
    AlterTableDropVectorize(
        columns=["plot_synopsis"],
    ),
)

import { DataAPIClient } from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient("APPLICATION_TOKEN");
const database = client.db("API_ENDPOINT");
const table = database.table("TABLE_NAME");

// Remove automatic embedding generation
(async function () {
  await table.alter({
    operation: {
      dropVectorize: {
        columns: ["plot_synopsis"],
      },
    },
  });
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.AlterTableDropVectorize;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    Table<Row> table =
        new DataAPIClient("APPLICATION_TOKEN")
            .getDatabase("API_ENDPOINT")
            .getTable("TABLE_NAME");

    // Remove automatic embedding generation
    AlterTableDropVectorize alterOperation = new AlterTableDropVectorize("plot_synopsis");
    table.alter(alterOperation);
  }
}

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "alterTable": {
    "operation": {
      "dropVectorize": {
        "columns": ["plot_synopsis"]
      }
    }
  }
}'

Client reference

Python
TypeScript
Java
curl

For more information, see the client reference.

Client reference documentation is not applicable for HTTP.

Alter a table

Result

Parameters

Examples

Add columns to a table

Add a vector column and configure an embedding provider integration

Drop columns from a table

Add automatic embedding generation to existing vector columns

Remove automatic embedding generation from vector columns

Client reference

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