@sqlrooms/duckdb / useDuckDbQuery

Function: useDuckDbQuery()

Deprecated

Use useSql instead

Call Signature

useDuckDbQuery<Row>(options): object

A React hook for executing SQL queries with automatic state management. Provides two ways to ensure type safety:

1. Using TypeScript types (compile-time safety only)
2. Using Zod schemas (both compile-time and runtime validation)

Type Parameters

Type Parameter	Description
Row	The TypeScript type for each row in the result

Parameters

Parameter	Type	Description
options	{ query: string; enabled: boolean; }	Configuration object containing the query and execution control
options.query	string	-
options.enabled?	boolean	-

Returns

object

Object containing the query result, loading state, and any error

Object containing the validated query result, loading state, and any error

Name	Type
data	undefined | UseSqlQueryResult<Row>
error	null | Error
isLoading	boolean

Deprecated

Use useSql instead

Example

// Option 1: Using TypeScript types (faster, no runtime validation)
interface User {
  id: number;
  name: string;
  email: string;
}

const {data, isLoading, error} = useSql<User>({
  query: 'SELECT id, name, email FROM users'
});

// Option 2: Using Zod schema (slower but with runtime validation)
const userSchema = z.object({
  id: z.number(),
  name: z.string(),
  email: z.string().email(),
  createdAt: z.string().transform(str => new Date(str)) // Transform string to Date
});

const {data: validatedData, isLoading, error} = useSql(
  userSchema,
  {query: 'SELECT id, name, email, created_at as createdAt FROM users'}
);

Error Handling

if (isLoading) return <div>Loading...</div>;
if (error) {
  // With Zod, you can catch validation errors specifically
  if (error instanceof z.ZodError) {
    return <div>Validation Error: {error.errors[0].message}</div>;
  }
  return <div>Error: {error.message}</div>;
}
if (!data) return null;

Data Access Methods

There are several ways to access data with different performance characteristics:

1. Typed Row Access (getRow, rows(), toArray())

• Provides type safety and validation
• Converts data to JavaScript objects
• Slower for large datasets due to object creation and validation

// Iterate through rows using the rows() iterator (recommended)
for (const user of data.rows()) {
  console.log(user.name, user.email);
}

// Traditional for loop with index access
for (let i = 0; i < data.length; i++) {
  const user = data.getRow(i);
  console.log(`User ${i}: ${user.name} (${user.email})`);
}

// Get all rows as an array
const allUsers = data.toArray();

// With Zod schema, transformed fields are available
for (const user of validatedData.rows()) {
  console.log(`Created: ${user.createdAt.toISOString()}`); // createdAt is a Date object
}

2. Direct Arrow Table Access

• Much faster for large datasets
• Columnar access is more efficient for analytics
• No type safety or validation

// For performance-critical operations with large datasets:
const nameColumn = data.arrowTable.getChild('name');
const emailColumn = data.arrowTable.getChild('email');

// Fast columnar iteration (no object creation)
for (let i = 0; i < data.length; i++) {
  console.log(nameColumn.get(i), emailColumn.get(i));
}

// Note: For filtering data, it's most efficient to use SQL in your query
const { data } = useSql<User>({
  query: "SELECT * FROM users WHERE age > 30"
});

3. Using Flechette for Advanced Operations

For more advanced Arrow operations, consider using Flechette, a faster and lighter alternative to the standard Arrow JS implementation.

// Example using Flechette with SQL query results
import { tableFromIPC } from '@uwdata/flechette';

// Convert Arrow table to Flechette table
const serializedData = data.arrowTable.serialize();
const flechetteTable = tableFromIPC(serializedData);

// Extract all columns into a { name: array, ... } object
const columns = flechetteTable.toColumns();

// Create a new table with a selected subset of columns
const subtable = flechetteTable.select(['name', 'email']);

// Convert to array of objects with customization options
const objects = flechetteTable.toArray({
  useDate: true,  // Convert timestamps to Date objects
  useMap: true    // Create Map objects for key-value pairs
});

// For large datasets, consider memory management
serializedData = null; // Allow garbage collection of the serialized data

Flechette provides several advantages:

• Better performance (1.3-1.6x faster value iteration, 7-11x faster row object extraction)
• Smaller footprint (~43k minified vs 163k for Arrow JS)
• Support for additional data types (including decimal-to-number conversion)
• More flexible data value conversion options

Call Signature

useDuckDbQuery<Schema>(schema, options): object

A React hook for executing SQL queries with automatic state management. Provides two ways to ensure type safety:

1. Using TypeScript types (compile-time safety only)
2. Using Zod schemas (both compile-time and runtime validation)

Type Parameters

Type Parameter	Description
Schema extends ZodType<any, ZodTypeDef, any>	The Zod schema type that defines the shape and validation of each row

Parameters

Parameter	Type	Description
schema	Schema	A Zod schema that defines the expected shape and validation rules for each row
options	{ query: string; enabled: boolean; }	Configuration object containing the query and execution control
options.query	string	-
options.enabled?	boolean	-

Returns

object

Object containing the query result, loading state, and any error

Object containing the validated query result, loading state, and any error

Name	Type
data	undefined | UseSqlQueryResult<TypeOf<Schema>>
error	null | Error
isLoading	boolean

Deprecated

Use useSql instead

Example

// Option 1: Using TypeScript types (faster, no runtime validation)
interface User {
  id: number;
  name: string;
  email: string;
}

const {data, isLoading, error} = useSql<User>({
  query: 'SELECT id, name, email FROM users'
});

// Option 2: Using Zod schema (slower but with runtime validation)
const userSchema = z.object({
  id: z.number(),
  name: z.string(),
  email: z.string().email(),
  createdAt: z.string().transform(str => new Date(str)) // Transform string to Date
});

const {data: validatedData, isLoading, error} = useSql(
  userSchema,
  {query: 'SELECT id, name, email, created_at as createdAt FROM users'}
);

Error Handling

if (isLoading) return <div>Loading...</div>;
if (error) {
  // With Zod, you can catch validation errors specifically
  if (error instanceof z.ZodError) {
    return <div>Validation Error: {error.errors[0].message}</div>;
  }
  return <div>Error: {error.message}</div>;
}
if (!data) return null;

Data Access Methods

There are several ways to access data with different performance characteristics:

1. Typed Row Access (getRow, rows(), toArray())

• Provides type safety and validation
• Converts data to JavaScript objects
• Slower for large datasets due to object creation and validation

// Iterate through rows using the rows() iterator (recommended)
for (const user of data.rows()) {
  console.log(user.name, user.email);
}

// Traditional for loop with index access
for (let i = 0; i < data.length; i++) {
  const user = data.getRow(i);
  console.log(`User ${i}: ${user.name} (${user.email})`);
}

// Get all rows as an array
const allUsers = data.toArray();

// With Zod schema, transformed fields are available
for (const user of validatedData.rows()) {
  console.log(`Created: ${user.createdAt.toISOString()}`); // createdAt is a Date object
}

2. Direct Arrow Table Access

• Much faster for large datasets
• Columnar access is more efficient for analytics
• No type safety or validation

// For performance-critical operations with large datasets:
const nameColumn = data.arrowTable.getChild('name');
const emailColumn = data.arrowTable.getChild('email');

// Fast columnar iteration (no object creation)
for (let i = 0; i < data.length; i++) {
  console.log(nameColumn.get(i), emailColumn.get(i));
}

// Note: For filtering data, it's most efficient to use SQL in your query
const { data } = useSql<User>({
  query: "SELECT * FROM users WHERE age > 30"
});

3. Using Flechette for Advanced Operations

For more advanced Arrow operations, consider using Flechette, a faster and lighter alternative to the standard Arrow JS implementation.

// Example using Flechette with SQL query results
import { tableFromIPC } from '@uwdata/flechette';

// Convert Arrow table to Flechette table
const serializedData = data.arrowTable.serialize();
const flechetteTable = tableFromIPC(serializedData);

// Extract all columns into a { name: array, ... } object
const columns = flechetteTable.toColumns();

// Create a new table with a selected subset of columns
const subtable = flechetteTable.select(['name', 'email']);

// Convert to array of objects with customization options
const objects = flechetteTable.toArray({
  useDate: true,  // Convert timestamps to Date objects
  useMap: true    // Create Map objects for key-value pairs
});

// For large datasets, consider memory management
serializedData = null; // Allow garbage collection of the serialized data

Flechette provides several advantages:

• Better performance (1.3-1.6x faster value iteration, 7-11x faster row object extraction)
• Smaller footprint (~43k minified vs 163k for Arrow JS)
• Support for additional data types (including decimal-to-number conversion)
• More flexible data value conversion options