> ## Documentation Index > Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt > Use this file to discover all available pages before exploring further. # Schema and Data Evolution > Learn how to manage table schemas in LanceDB, including adding, altering, and dropping columns. export const RsAlterVectorColumn = "let old_dim = 384;\nlet new_dim = 1024;\nlet vector_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\n \"embedding\",\n DataType::FixedSizeList(\n Arc::new(Field::new(\"item\", DataType::Float32, true)),\n old_dim,\n ),\n true,\n ),\n]));\nlet vector_batch = RecordBatch::try_new(\n vector_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(0.1_f32); old_dim as usize])],\n old_dim,\n ),\n ),\n ],\n)\n.unwrap();\nlet vector_reader =\n RecordBatchIterator::new(vec![Ok(vector_batch)].into_iter(), vector_schema.clone());\nlet vector_table = db\n .create_table(\"vector_alter_example\", vector_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alter_columns.\n// Use add_columns + drop_columns + alter_columns(rename) to replace the column.\nvector_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"embedding_v2\".to_string(),\n format!(\"arrow_cast(NULL, 'FixedSizeList({}, Float32)')\", new_dim),\n )]),\n None,\n )\n .await\n .unwrap();\nvector_table.drop_columns(&[\"embedding\"]).await.unwrap();\nvector_table\n .alter_columns(&[ColumnAlteration::new(\"embedding_v2\".to_string())\n .rename(\"embedding\".to_string())])\n .await\n .unwrap();\n"; export const TsAlterVectorColumn = "const oldDim = 384;\nconst newDim = 1024;\nconst vectorSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\n \"embedding\",\n new arrow.FixedSizeList(\n oldDim,\n new arrow.Field(\"item\", new arrow.Float16(), true),\n ),\n true,\n ),\n]);\nconst vectorData = lancedb.makeArrowTable(\n [{ id: 1, embedding: Array.from({ length: oldDim }, () => Math.random()) }],\n { schema: vectorSchema },\n);\nconst vectorTable = await db.createTable(\"vector_alter_example\", vectorData, {\n mode: \"overwrite\",\n});\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alterColumns.\n// Use addColumns + dropColumns + alterColumns(rename) to replace the column.\nawait vectorTable.addColumns([\n {\n name: \"embedding_v2\",\n valueSql: `arrow_cast(NULL, 'FixedSizeList(${newDim}, Float16)')`,\n },\n]);\nawait vectorTable.dropColumns([\"embedding\"]);\nawait vectorTable.alterColumns([{ path: \"embedding_v2\", rename: \"embedding\" }]);\n"; export const AlterVectorColumn = "vector_dim = 768 # Your embedding dimension\ntable_name = \"vector_alter_example\"\ndb = tmp_db\ndata = [\n {\n \"id\": 1,\n \"embedding\": np.random.random(vector_dim).tolist(),\n },\n]\ntable = db.create_table(table_name, data, mode=\"overwrite\")\n\ntable.alter_columns(\n dict(path=\"embedding\", data_type=pa.list_(pa.float32(), vector_dim))\n)\n"; export const RsDropColumnsMultiple = "// Remove the second temporary column\nschema_drop_table.drop_columns(&[\"temp_col2\"]).await.unwrap();\n"; export const TsDropColumnsMultiple = "// Remove the second temporary column\nawait schemaDropTable.dropColumns([\"temp_col2\"]);\n"; export const DropColumnsMultiple = "# Remove the second temporary column\ntable.drop_columns([\"temp_col2\"])\n"; export const RsDropColumnsSingle = "// Remove the first temporary column\nschema_drop_table.drop_columns(&[\"temp_col1\"]).await.unwrap();\n"; export const TsDropColumnsSingle = "// Remove the first temporary column\nawait schemaDropTable.dropColumns([\"temp_col1\"]);\n"; export const DropColumnsSingle = "# Remove the first temporary column\ntable.drop_columns([\"temp_col1\"])\n"; export const RsSchemaDropSetup = "let schema_drop_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float64, false),\n Field::new(\"temp_col1\", DataType::Utf8, false),\n Field::new(\"temp_col2\", DataType::Int32, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_drop_batch = RecordBatch::try_new(\n schema_drop_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2, 3])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n Arc::new(StringArray::from(vec![\"X\", \"Y\", \"Z\"])),\n Arc::new(Int32Array::from(vec![100, 200, 300])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(0.1_f32); 128]),\n Some(vec![Some(0.2_f32); 128]),\n Some(vec![Some(0.3_f32); 128]),\n ],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_drop_reader = RecordBatchIterator::new(\n vec![Ok(schema_drop_batch)].into_iter(),\n schema_drop_schema.clone(),\n);\nlet schema_drop_table = db\n .create_table(\"schema_evolution_drop_example\", schema_drop_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; export const TsSchemaDropSetup = "const schemaDropData = [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200.0,\n temp_col1: \"X\",\n temp_col2: 100,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800.0,\n temp_col1: \"Y\",\n temp_col2: 200,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 3,\n name: \"Headphones\",\n price: 150.0,\n temp_col1: \"Z\",\n temp_col2: 300,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n];\nconst schemaDropTable = await db.createTable(\n \"schema_evolution_drop_example\",\n schemaDropData,\n { mode: \"overwrite\" },\n);\n"; export const SchemaDropSetup = "if data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200.00,\n \"temp_col1\": \"X\",\n \"temp_col2\": 100,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800.00,\n \"temp_col1\": \"Y\",\n \"temp_col2\": 200,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 3,\n \"name\": \"Headphones\",\n \"price\": 150.00,\n \"temp_col1\": \"Z\",\n \"temp_col2\": 300,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\ntable = tmp_db.create_table(\"schema_evolution_drop_example\", data, mode=\"overwrite\")\n"; export const RsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nlet expression_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"price_text\", DataType::Utf8, false),\n]));\nlet expression_batch = RecordBatch::try_new(\n expression_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1])),\n Arc::new(StringArray::from(vec![\"$100\"])),\n ],\n)\n.unwrap();\nlet expression_reader = RecordBatchIterator::new(\n vec![Ok(expression_batch)].into_iter(),\n expression_schema.clone(),\n);\nlet expression_table = db\n .create_table(\"schema_evolution_expression_example\", expression_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nexpression_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"price_numeric\".to_string(),\n \"cast(replace(price_text, '$', '') as int)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\nexpression_table.drop_columns(&[\"price_text\"]).await.unwrap();\nexpression_table\n .alter_columns(&[ColumnAlteration::new(\"price_numeric\".to_string())\n .rename(\"price\".to_string())])\n .await\n .unwrap();\n"; export const RsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"sale_price\".to_string())\n .rename(\"final_price\".to_string())\n .cast_to(DataType::Float64)\n .set_nullable(true)])\n .await\n .unwrap();\n"; export const TsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nconst expressionTable = await db.createTable(\n \"schema_evolution_expression_example\",\n [{ id: 1, price_text: \"$100\" }],\n { mode: \"overwrite\" },\n);\n\nawait expressionTable.addColumns([\n {\n name: \"price_numeric\",\n valueSql: \"cast(replace(price_text, '$', '') as int)\",\n },\n]);\nawait expressionTable.dropColumns([\"price_text\"]);\nawait expressionTable.alterColumns([\n { path: \"price_numeric\", rename: \"price\" },\n]);\n"; export const AlterColumnsWithExpression = "# For custom transforms, create a new column from a SQL expression.\nexpression_table = tmp_db.create_table(\n \"schema_evolution_expression_example\",\n [{\"id\": 1, \"price_text\": \"$100\"}],\n mode=\"overwrite\",\n)\n\nexpression_table.add_columns(\n {\"price_numeric\": \"cast(replace(price_text, '$', '') as int)\"}\n)\nexpression_table.drop_columns([\"price_text\"])\nexpression_table.alter_columns({\"path\": \"price_numeric\", \"rename\": \"price\"})\n"; export const TsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nawait schemaAlterTable.alterColumns([\n {\n path: \"sale_price\",\n rename: \"final_price\",\n dataType: new arrow.Float64(),\n nullable: true,\n },\n]);\n"; export const AlterColumnsMultiple = "# Rename, change type, and make nullable in one operation\ntable.alter_columns(\n {\n \"path\": \"sale_price\",\n \"rename\": \"final_price\",\n \"data_type\": pa.float64(),\n \"nullable\": True,\n }\n)\n"; export const RsAlterColumnsNullable = "// Make the name column nullable\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"name\".to_string()).set_nullable(true)])\n .await\n .unwrap();\n"; export const TsAlterColumnsNullable = "// Make the name column nullable\nawait schemaAlterTable.alterColumns([{ path: \"name\", nullable: true }]);\n"; export const AlterColumnsNullable = "# Make the name column nullable\ntable.alter_columns({\"path\": \"name\", \"nullable\": True})\n"; export const RsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"price\".to_string()).cast_to(DataType::Int64)])\n .await\n .unwrap();\n"; export const TsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nawait schemaAlterTable.alterColumns([\n { path: \"price\", dataType: new arrow.Int64() },\n]);\n"; export const AlterColumnsDataType = "# Change price from int32 to int64 for larger numbers\ntable.alter_columns({\"path\": \"price\", \"data_type\": pa.int64()})\n"; export const RsAlterColumnsRename = "// Rename discount_price to sale_price\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"discount_price\".to_string())\n .rename(\"sale_price\".to_string())])\n .await\n .unwrap();\n"; export const TsAlterColumnsRename = "// Rename discount_price to sale_price\nawait schemaAlterTable.alterColumns([\n { path: \"discount_price\", rename: \"sale_price\" },\n]);\n"; export const AlterColumnsRename = "# Rename discount_price to sale_price\ntable.alter_columns({\"path\": \"discount_price\", \"rename\": \"sale_price\"})\n"; export const RsSchemaAlterSetup = "let schema_alter_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Int32, false),\n Field::new(\"discount_price\", DataType::Float64, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_alter_batch = RecordBatch::try_new(\n schema_alter_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\"])),\n Arc::new(Int32Array::from(vec![1200, 800])),\n Arc::new(Float64Array::from(vec![1080.0, 720.0])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(0.1_f32); 128]), Some(vec![Some(0.2_f32); 128])],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_alter_reader = RecordBatchIterator::new(\n vec![Ok(schema_alter_batch)].into_iter(),\n schema_alter_schema.clone(),\n);\nlet schema_alter_table = db\n .create_table(\"schema_evolution_alter_example\", schema_alter_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; export const TsSchemaAlterSetup = "const schemaAlter = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\"name\", new arrow.Utf8()),\n new arrow.Field(\"price\", new arrow.Int32()),\n new arrow.Field(\"discount_price\", new arrow.Float64()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 128,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n]);\nconst schemaAlterData = lancedb.makeArrowTable(\n [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200,\n discount_price: 1080.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800,\n discount_price: 720.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n ],\n { schema: schemaAlter },\n);\nconst schemaAlterTable = await db.createTable(\n \"schema_evolution_alter_example\",\n schemaAlterData,\n { mode: \"overwrite\" },\n);\n"; export const SchemaAlterSetup = "table_name = \"schema_evolution_alter_example\"\nif data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200,\n \"discount_price\": 1080.0,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800,\n \"discount_price\": 720.0,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\nschema = pa.schema(\n {\n \"id\": pa.int64(),\n \"name\": pa.string(),\n \"price\": pa.int32(),\n \"discount_price\": pa.float64(),\n \"vector\": pa.list_(pa.float32(), 128),\n }\n)\ntable = tmp_db.create_table(table_name, data, schema=schema, mode=\"overwrite\")\n"; export const RsAddColumnsNullable = "// Add a nullable timestamp column\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"last_ordered\".to_string(),\n \"cast(NULL as timestamp)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; export const TsAddColumnsNullable = "// Add a nullable timestamp column\nawait schemaAddTable.addColumns([\n {\n name: \"last_ordered\",\n valueSql: \"cast(NULL as timestamp)\",\n },\n]);\n"; export const AddColumnsNullable = "# Add a nullable timestamp column\ntable.add_columns({\"last_ordered\": \"cast(NULL as timestamp)\"})\n"; export const RsAddColumnsDefaultValues = "// Add a stock status column with default value\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"in_stock\".to_string(),\n \"cast(true as boolean)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; export const TsAddColumnsDefaultValues = "// Add a stock status column with default value\nawait schemaAddTable.addColumns([\n {\n name: \"in_stock\",\n valueSql: \"cast(true as boolean)\",\n },\n]);\n"; export const AddColumnsDefaultValues = "# Add a stock status column with default value\ntable.add_columns({\"in_stock\": \"cast(true as boolean)\"})\n"; export const RsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"discounted_price\".to_string(),\n \"cast((price * 0.9) as float)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; export const TsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nawait schemaAddTable.addColumns([\n {\n name: \"discounted_price\",\n valueSql: \"cast((price * 0.9) as float)\",\n },\n]);\n"; export const AddColumnsCalculated = "# Add a discounted price column (10% discount)\ntable.add_columns({\"discounted_price\": \"cast((price * 0.9) as float)\"})\n"; export const RsSchemaAddSetup = "let schema_add_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float64, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_add_batch = RecordBatch::try_new(\n schema_add_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2, 3])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(0.1_f32); 128]),\n Some(vec![Some(0.2_f32); 128]),\n Some(vec![Some(0.3_f32); 128]),\n ],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_add_reader = RecordBatchIterator::new(\n vec![Ok(schema_add_batch)].into_iter(),\n schema_add_schema.clone(),\n);\nlet schema_add_table = db\n .create_table(\"schema_evolution_add_example\", schema_add_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; export const TsSchemaAddSetup = "const schemaAddData = [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 3,\n name: \"Headphones\",\n price: 150.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n];\nconst schemaAddTable = await db.createTable(\n \"schema_evolution_add_example\",\n schemaAddData,\n { mode: \"overwrite\" },\n);\n"; export const SchemaAddSetup = "table_name = \"schema_evolution_add_example\"\nif data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200.00,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800.00,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 3,\n \"name\": \"Headphones\",\n \"price\": 150.00,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\ntable = tmp_db.create_table(table_name, data, mode=\"overwrite\")\n"; Schema evolution enables non-breaking modifications to a database table's structure — such as adding columns, altering data types, or dropping fields — to adapt to evolving data requirements without service interruptions. LanceDB supports ACID-compliant schema evolution through granular operations (add/alter/drop columns), allowing you to: * Iterate Safely: Modify schemas in production with versioned datasets and backward compatibility * Scale Seamlessly: Handle ML model iterations, regulatory changes, or feature additions * Optimize Continuously: Remove unused fields or enforce new constraints without downtime ## Schema evolution operations LanceDB supports three primary schema evolution operations: 1. **Adding new columns**: Extend your table with additional attributes 2. **Altering existing columns**: Change column names, data types, or nullability 3. **Dropping columns**: Remove unnecessary columns from your schema Schema evolution operations are applied immediately but do not typically require rewriting all data. However, data type changes may involve more substantial operations. ## Add new columns You can add new columns to a table with the [`add_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.add_columns) method in Python, [`addColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#addcolumns) in TypeScript/JavaScript, or `add_columns` in Rust. New columns are populated based on SQL expressions you provide. ### Set up the example table First, let's create a sample table with product data to demonstrate schema evolution: {SchemaAddSetup} {TsSchemaAddSetup} {RsSchemaAddSetup} ### Add derived columns You can add new columns that are derived from existing data using SQL expressions: {AddColumnsCalculated} {TsAddColumnsCalculated} {RsAddColumnsCalculated} ### Add columns with default values Add boolean columns with default values for status tracking: {AddColumnsDefaultValues} {TsAddColumnsDefaultValues} {RsAddColumnsDefaultValues} ### Add nullable columns Add timestamp columns that can contain NULL values: {AddColumnsNullable} {TsAddColumnsNullable} {RsAddColumnsNullable} When adding columns that should contain NULL values, be sure to cast the NULL to the appropriate type, e.g., `cast(NULL as timestamp)`. ## Alter existing columns You can alter columns using the [`alter_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.alter_columns) method in Python, [`alterColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#altercolumns) in TypeScript/JavaScript, or `alter_columns` in Rust. This allows you to: * Rename a column * Change a column's data type * Modify nullability (whether a column can contain NULL values) ### Set up the example table Create a table with a custom schema to demonstrate column alterations: {SchemaAlterSetup} {TsSchemaAlterSetup} {RsSchemaAlterSetup} ### Rename columns Change column names to better reflect their purpose: {AlterColumnsRename} {TsAlterColumnsRename} {RsAlterColumnsRename} ### Change data types Convert column data types for better performance or compatibility: {AlterColumnsDataType} {TsAlterColumnsDataType} {RsAlterColumnsDataType} ### Make columns nullable You can alter columns to contain NULL values: {AlterColumnsNullable} {TsAlterColumnsNullable} {RsAlterColumnsNullable} ### Multiple changes at once Apply several alterations in a single operation: {AlterColumnsMultiple} {TsAlterColumnsMultiple} {RsAlterColumnsMultiple} ### Expression-based type changes For transformations that are not simple casts (for example, converting `"$100"` to an integer), use a SQL-expression column add, then drop and rename: {AlterColumnsWithExpression} {TsAlterColumnsWithExpression} {RsAlterColumnsWithExpression} ### Alter embedding types and dimensions It's quite common to need to change an embedding column's schema, in case a new model becomes available with a different embedding dimension. * In Python, the example shows an in-place type update when the cast is compatible. * In TypeScript and Rust, the example shows a dimension change (`384 -> 1024`), which cannot be cast in-place. For dimension changes, use this 3-step pattern: add a new column with the target type, drop the old column, then rename the new column to the original name. {AlterVectorColumn} {TsAlterVectorColumn} {RsAlterVectorColumn} **`FixedSizeList` Dimension Changes in TypeScript and Rust** `alterColumns` / `alter_columns` can cast between compatible types, but changing `FixedSizeList` dimensions (for example `384 -> 1024`) is not a compatible cast. For such cases, use `addColumns` / `add_columns` (with `arrow_cast`), then `dropColumns` / `drop_columns`, then rename the replacement column. Changing data types requires rewriting the column data and may be resource-intensive for large tables. Renaming columns or changing nullability is more efficient as it only updates metadata. ## Drop columns You can remove columns using the [`drop_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.drop_columns) method in Python, [`dropColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#dropcolumns) in TypeScript/JavaScript, or `drop_columns` in Rust. ### Set Up the example table Create a table with temporary columns that we'll remove: {SchemaDropSetup} {TsSchemaDropSetup} {RsSchemaDropSetup} ### Drop single columns Remove individual columns that are no longer needed: {DropColumnsSingle} {TsDropColumnsSingle} {RsDropColumnsSingle} ### Drop multiple columns Remove several columns at once for efficiency: {DropColumnsMultiple} {TsDropColumnsMultiple} {RsDropColumnsMultiple} Dropping columns cannot be undone. Make sure you have backups or are certain before removing columns.