DuckDB Node API

An API for using DuckDB in Node.

This is a high-level API meant for applications. It depends on low-level bindings that adhere closely to DuckDB's C API, available separately as @duckdb/duckdb-bindings.

Features

Main differences from duckdb-node

• Native support for Promises; no need for separate duckdb-async wrapper.
• DuckDB-specific API; not based on the SQLite Node API.
• Lossless & efficent support for values of all DuckDB data types.
• Wraps released DuckDB binaries instead of rebuilding DuckDB.
• Built on DuckDB's C API; exposes more functionality.

Roadmap

Some features are not yet complete:

• Binding and appending the MAP and UNION data types
• Appending default values row-by-row
• User-defined types & functions
• Profiling info
• Table description
• APIs for Arrow

See the issues list on GitHub for the most up-to-date roadmap.

Supported Platforms

• Linux arm64
• Linux x64
• Mac OS X (Darwin) arm64 (Apple Silicon)
• Mac OS X (Darwin) x64 (Intel)
• Windows (Win32) x64

Examples

Get Basic Information

import duckdb from '@duckdb/node-api';

console.log(duckdb.version());

console.log(duckdb.configurationOptionDescriptions());

Connect

import { DuckDBConnection } from '@duckdb/node-api';

const connection = await DuckDBConnection.create();

This uses the default instance. For advanced usage, you can create instances explicitly.

Create Instance

import { DuckDBInstance } from '@duckdb/node-api';

Create with an in-memory database:

const instance = await DuckDBInstance.create(':memory:');

Equivalent to the above:

const instance = await DuckDBInstance.create();

Read from and write to a database file, which is created if needed:

const instance = await DuckDBInstance.create('my_duckdb.db');

Set configuration options:

const instance = await DuckDBInstance.create('my_duckdb.db', {
  threads: '4'
});

Instance Cache

Multiple instances in the same process should not attach the same database.

To prevent this, an instance cache can be used:

const instance = await DuckDBInstance.fromCache('my_duckdb.db');

This uses the default instance cache. For advanced usage, you can create instance caches explicitly:

import { DuckDBInstanceCache } from '@duckdb/node-api';

const cache = new DuckDBInstanceCache();
const instance = await cache.getOrCreateInstance('my_duckdb.db');

Connect to Instance

const connection = await instance.connect();

Disconnect

Connections will be disconnected automatically soon after their reference is dropped, but you can also disconnect explicitly if and when you want:

connection.disconnect();

or, equivalently:

connection.close();

Run SQL

const result = await connection.run('from test_all_types()');

Parameterize SQL

const prepared = await connection.prepare('select $1, $2, $3');
prepared.bindVarchar(1, 'duck');
prepared.bindInteger(2, 42);
prepared.bindList(3, listValue([10, 11, 12]), LIST(INTEGER));
const result = await prepared.run();

or:

const prepared = await connection.prepare('select $a, $b, $c');
prepared.bind({
  'a': 'duck',
  'b': 42,
  'c': listValue([10, 11, 12]),
}, {
  'a': VARCHAR,
  'b': INTEGER,
  'c': LIST(INTEGER),
});
const result = await prepared.run();

or even:

const result = await connection.run('select $a, $b, $c', {
  'a': 'duck',
  'b': 42,
  'c': listValue([10, 11, 12]),
}, {
  'a': VARCHAR,
  'b': INTEGER,
  'c': LIST(INTEGER),
});

Unspecified types will be inferred:

const result = await connection.run('select $a, $b, $c', {
  'a': 'duck',
  'b': 42,
  'c': listValue([10, 11, 12]),
});

Specifying Values

Values of many data types are represented using one of the JS primitives boolean, number, bigint, or string. Also, any type can have null values.

Values of some data types need to be constructed using special functions. These are:

| Type | Function | | ---- | -------- | | ARRAY | arrayValue | | BIT | bitValue | | BLOB | blobValue | | DATE | dateValue | | DECIMAL | decimalValue | | INTERVAL | intervalValue | | LIST | listValue | | MAP | mapValue | | STRUCT | structValue | | TIME | timeValue | | TIMETZ | timeTZValue | | TIMESTAMP | timestampValue | | TIMESTAMPTZ | timestampTZValue | | TIMESTAMP_S | timestampSecondsValue | | TIMESTAMP_MS | timestampMillisValue | | TIMESTAMP_NS | timestampNanosValue | | UNION | unionValue | | UUID | uuidValue |

Stream Results

Streaming results evaluate lazily when rows are read.

const result = await connection.stream('from range(10_000)');

Inspect Result Metadata

Get column names and types:

const columnNames = result.columnNames();
const columnTypes = result.columnTypes();

Read Result Data

Run and read all data:

const reader = await connection.runAndReadAll('from test_all_types()');
const rows = reader.getRows();
// OR: const columns = reader.getColumns();

Stream and read up to (at least) some number of rows:

const reader = await connection.streamAndReadUntil(
  'from range(5000)',
  1000
);
const rows = reader.getRows();
// rows.length === 2048. (Rows are read in chunks of 2048.)

Read rows incrementally:

const reader = await connection.streamAndRead('from range(5000)');
reader.readUntil(2000);
// reader.currentRowCount === 2048 (Rows are read in chunks of 2048.)
// reader.done === false
reader.readUntil(4000);
// reader.currentRowCount === 4096
// reader.done === false
reader.readUntil(6000);
// reader.currentRowCount === 5000
// reader.done === true

Get Result Data

Result data can be retrieved in a variety of forms:

const reader = await connection.runAndReadAll(
  'from range(3) select range::int as i, 10 + i as n'
);

const rows = reader.getRows();
// [ [0, 10], [1, 11], [2, 12] ]

const rowObjects = reader.getRowObjects();
// [ { i: 0, n: 10 }, { i: 1, n: 11 }, { i: 2, n: 12 } ]

const columns = reader.getColumns();
// [ [0, 1, 2], [10, 11, 12] ]

const columnsObject = reader.getColumnsObject();
// { i: [0, 1, 2], n: [10, 11, 12] }

Convert Result Data

By default, data values that cannot be represented as JS built-ins are returned as specialized JS objects; see Inspect Data Values below.

To retrieve data in a different form, such as JS built-ins or values that can be losslessly serialized to JSON, use the JS or Json forms of the above result data methods.

Custom converters can be supplied as well. See the implementations of JSDuckDBValueConverter and JsonDuckDBValueConverters for how to do this.

Examples (using the Json forms):

const reader = await connection.runAndReadAll(
  'from test_all_types() select bigint, date, interval limit 2'
);

const rows = reader.getRowsJson();
// [
//   [
//     "-9223372036854775808",
//     "5877642-06-25 (BC)",
//     { "months": 0, "days": 0, "micros": "0" }
//   ],
//   [
//     "9223372036854775807",
//     "5881580-07-10",
//     { "months": 999, "days": 999, "micros": "999999999" }
//   ]
// ]

const rowObjects = reader.getRowObjectsJson();
// [
//   {
//     "bigint": "-9223372036854775808",
//     "date": "5877642-06-25 (BC)",
//     "interval": { "months": 0, "days": 0, "micros": "0" }
//   },
//   {
//     "bigint": "9223372036854775807",
//     "date": "5881580-07-10",
//     "interval": { "months": 999, "days": 999, "micros": "999999999" }
//   }
// ]

const columns = reader.getColumnsJson();
// [
//   [ "-9223372036854775808", "9223372036854775807" ],
//   [ "5877642-06-25 (BC)", "5881580-07-10" ],
//   [
//     { "months": 0, "days": 0, "micros": "0" },
//     { "months": 999, "days": 999, "micros": "999999999" }
//   ]
// ]

const columnsObject = reader.getColumnsObjectJson();
// {
//   "bigint": [ "-9223372036854775808", "9223372036854775807" ],
//   "date": [ "5877642-06-25 (BC)", "5881580-07-10" ],
//   "interval": [
//     { "months": 0, "days": 0, "micros": "0" },
//     { "months": 999, "days": 999, "micros": "999999999" }
//   ]
// }

These methods handle nested types as well:

const reader = await connection.runAndReadAll(
  'from test_all_types() select int_array, struct, map, "union" limit 2'
);

const rows = reader.getRowsJson();
// [
//   [
//     [],
//     { "a": null, "b": null },
//     [],
//     { "tag": "name", "value": "Frank" }
//   ],
//   [
//     [ 42, 999, null, null, -42],
//     { "a": 42, "b": "🦆🦆🦆🦆🦆🦆" },
//     [
//       { "key": "key1", "value": "🦆🦆🦆🦆🦆🦆" },
//       { "key": "key2", "value": "goose" }
//     ],
//     { "tag": "age", "value": 5 }
//   ]
// ]

const rowObjects = reader.getRowObjectsJson();
// [
//   {
//     "int_array": [],
//     "struct": { "a": null, "b": null },
//     "map": [],
//     "union": { "tag": "name", "value": "Frank" }
//   },
//   {
//     "int_array": [ 42, 999, null, null, -42 ],
//     "struct": { "a": 42, "b": "🦆🦆🦆🦆🦆🦆" },
//     "map": [
//       { "key": "key1", "value": "🦆🦆🦆🦆🦆🦆" },
//       { "key": "key2", "value": "goose" }
//     ],
//     "union": { "tag": "age", "value": 5 }
//   }
// ]

const columns = reader.getColumnsJson();
// [
//   [
//     [],
//     [42, 999, null, null, -42]
//   ],
//   [
//     { "a": null, "b": null },
//     { "a": 42, "b": "🦆🦆🦆🦆🦆🦆" }
//   ],
//   [
//     [],
//     [
//       { "key": "key1", "value": "🦆🦆🦆🦆🦆🦆" },
//       { "key": "key2", "value": "goose"}
//     ]
//   ],
//   [
//     { "tag": "name", "value": "Frank" },
//     { "tag": "age", "value": 5 }
//   ]
// ]

const columnsObject = reader.getColumnsObjectJson();
// {
//   "int_array": [
//     [],
//     [42, 999, null, null, -42]
//   ],
//   "struct": [
//     { "a": null, "b": null },
//     { "a": 42, "b": "🦆🦆🦆🦆🦆🦆" }
//   ],
//   "map": [
//     [],
//     [
//       { "key": "key1", "value": "🦆🦆🦆🦆🦆🦆" },
//       { "key": "key2", "value": "goose" }
//     ]
//   ],
//   "union": [
//     { "tag": "name", "value": "Frank" },
//     { "tag": "age", "value": 5 }
//   ]
// }

Column names and types can also be serialized to JSON:

const columnNamesAndTypes = reader.columnNamesAndTypesJson();
// {
//   "columnNames": [
//     "int_array",
//     "struct",
//     "map",
//     "union"
//   ],
//   "columnTypes": [
//     {
//       "typeId": 24,
//       "valueType": {
//         "typeId": 4
//       }
//     },
//     {
//       "typeId": 25,
//       "entryNames": [
//         "a",
//         "b"
//       ],
//       "entryTypes": [
//         {
//           "typeId": 4
//         },
//         {
//           "typeId": 17
//         }
//       ]
//     },
//     {
//       "typeId": 26,
//       "keyType": {
//         "typeId": 17
//       },
//       "valueType": {
//         "typeId": 17
//       }
//     },
//     {
//       "typeId": 28,
//       "memberTags": [
//         "name",
//         "age"
//       ],
//       "memberTypes": [
//         {
//           "typeId": 17
//         },
//         {
//           "typeId": 3
//         }
//       ]
//     }
//   ]
// }

const columnNameAndTypeObjects = reader.columnNameAndTypeObjectsJson();
// [
//   {
//     "columnName": "int_array",
//     "columnType": {
//       "typeId": 24,
//       "valueType": {
//         "typeId": 4
//       }
//     }
//   },
//   {
//     "columnName": "struct",
//     "columnType": {
//       "typeId": 25,
//       "entryNames": [
//         "a",
//         "b"
//       ],
//       "entryTypes": [
//         {
//           "typeId": 4
//         },
//         {
//           "typeId": 17
//         }
//       ]
//     }
//   },
//   {
//     "columnName": "map",
//     "columnType": {
//       "typeId": 26,
//       "keyType": {
//         "typeId": 17
//       },
//       "valueType": {
//         "typeId": 17
//       }
//     }
//   },
//   {
//     "columnName": "union",
//     "columnType": {
//       "typeId": 28,
//       "memberTags": [
//         "name",
//         "age"
//       ],
//       "memberTypes": [
//         {
//           "typeId": 17
//         },
//         {
//           "typeId": 3
//         }
//       ]
//     }
//   }
// ]

Fetch Chunks

Fetch all chunks:

const chunks = await result.fetchAllChunks();

Fetch one chunk at a time:

const chunks = [];
while (true) {
  const chunk = await result.fetchChunk();
  // Last chunk will have zero rows.
  if (chunk.rowCount === 0) {
    break;
  }
  chunks.push(chunk);
}

For materialized (non-streaming) results, chunks can be read by index:

const rowCount = result.rowCount;
const chunkCount = result.chunkCount;
for (let i = 0; i < chunkCount; i++) {
  const chunk = result.getChunk(i);
  // ...
}

Get chunk data:

const rows = chunk.getRows();

const rowObjects = chunk.getRowObjects(result.deduplicatedColumnNames());

const columns = chunk.getColumns();

const columnsObject =
  chunk.getColumnsObject(result.deduplicatedColumnNames());

Get chunk data (one value at a time)

const columns = [];
const columnCount = chunk.columnCount;
for (let columnIndex = 0; columnIndex < columnCount; columnIndex++) {
  const columnValues = [];
  const columnVector = chunk.getColumnVector(columnIndex);
  const itemCount = columnVector.itemCount;
  for (let itemIndex = 0; itemIndex < itemCount; itemIndex++) {
    const value = columnVector.getItem(itemIndex);
    columnValues.push(value);
  }
  columns.push(columnValues);
}

Inspect Data Types

import { DuckDBTypeId } from '@duckdb/node-api';

if (columnType.typeId === DuckDBTypeId.ARRAY) {
  const arrayValueType = columnType.valueType;
  const arrayLength = columnType.length;
}

if (columnType.typeId === DuckDBTypeId.DECIMAL) {
  const decimalWidth = columnType.width;
  const decimalScale = columnType.scale;
}

if (columnType.typeId === DuckDBTypeId.ENUM) {
  const enumValues = columnType.values;
}

if (columnType.typeId === DuckDBTypeId.LIST) {
  const listValueType = columnType.valueType;
}

if (columnType.typeId === DuckDBTypeId.MAP) {
  const mapKeyType = columnType.keyType;
  const mapValueType = columnType.valueType;
}

if (columnType.typeId === DuckDBTypeId.STRUCT) {
  const structEntryNames = columnType.names;
  const structEntryTypes = columnType.valueTypes;
}

if (columnType.typeId === DuckDBTypeId.UNION) {
  const unionMemberTags = columnType.memberTags;
  const unionMemberTypes = columnType.memberTypes;
}

// For the JSON type (https://duckdb.org/docs/data/json/json_type)
if (columnType.alias === 'JSON') {
  const json = JSON.parse(columnValue);
}

Every type implements toString. The result is both human-friendly and readable by DuckDB in an appropriate expression.

const typeString = columnType.toString();

Inspect Data Values

import { DuckDBTypeId } from '@duckdb/node-api';

if (columnType.typeId === DuckDBTypeId.ARRAY) {
  const arrayItems = columnValue.items; // array of values
  const arrayString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.BIT) {
  const bools = columnValue.toBools(); // array of booleans
  const bits = columnValue.toBits(); // arrary of 0s and 1s
  const bitString = columnValue.toString(); // string of '0's and '1's
}

if (columnType.typeId === DuckDBTypeId.BLOB) {
  const blobBytes = columnValue.bytes; // Uint8Array
  const blobString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.DATE) {
  const dateDays = columnValue.days;
  const dateString = columnValue.toString();
  const { year, month, day } = columnValue.toParts();
}

if (columnType.typeId === DuckDBTypeId.DECIMAL) {
  const decimalWidth = columnValue.width;
  const decimalScale = columnValue.scale;
  // Scaled-up value. Represented number is value/(10^scale).
  const decimalValue = columnValue.value; // bigint
  const decimalString = columnValue.toString();
  const decimalDouble = columnValue.toDouble();
}

if (columnType.typeId === DuckDBTypeId.INTERVAL) {
  const intervalMonths = columnValue.months;
  const intervalDays = columnValue.days;
  const intervalMicros = columnValue.micros; // bigint
  const intervalString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.LIST) {
  const listItems = columnValue.items; // array of values
  const listString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.MAP) {
  const mapEntries = columnValue.entries; // array of { key, value }
  const mapString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.STRUCT) {
  // { name1: value1, name2: value2, ... }
  const structEntries = columnValue.entries;
  const structString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.TIMESTAMP_MS) {
  const timestampMillis = columnValue.milliseconds; // bigint
  const timestampMillisString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.TIMESTAMP_NS) {
  const timestampNanos = columnValue.nanoseconds; // bigint
  const timestampNanosString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.TIMESTAMP_S) {
  const timestampSecs = columnValue.seconds; // bigint
  const timestampSecsString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.TIMESTAMP_TZ) {
  const timestampTZMicros = columnValue.micros; // bigint
  const timestampTZString = columnValue.toString();
  const {
    date: { year, month, day },
    time: { hour, min, sec, micros },
  } = columnValue.toParts();
}

if (columnType.typeId === DuckDBTypeId.TIMESTAMP) {
  const timestampMicros = columnValue.micros; // bigint
  const timestampString = columnValue.toString();
  const {
    date: { year, month, day },
    time: { hour, min, sec, micros },
  } = columnValue.toParts();
}

if (columnType.typeId === DuckDBTypeId.TIME_TZ) {
  const timeTZMicros = columnValue.micros; // bigint
  const timeTZOffset = columnValue.offset;
  const timeTZString = columnValue.toString();
  const {
    time: { hour, min, sec, micros },
    offset,
  } = columnValue.toParts();
}

if (columnType.typeId === DuckDBTypeId.TIME) {
  const timeMicros = columnValue.micros; // bigint
  const timeString = columnValue.toString();
  const { hour, min, sec, micros } = columnValue.toParts();
}

if (columnType.typeId === DuckDBTypeId.UNION) {
  const unionTag = columnValue.tag;
  const unionValue = columnValue.value;
  const unionValueString = columnValue.toString();
}

if (columnType.typeId === DuckDBTypeId.UUID) {
  const uuidHugeint = columnValue.hugeint; // bigint
  const uuidString = columnValue.toString();
}

// other possible values are: null, boolean, number, bigint, or string

Displaying Timezones

Converting a TIMESTAMP_TZ value to a string depends on a timezone offset. By default, this is set to the offset for the local timezone when the Node process is started.

To change it, set the timezoneOffsetInMinutes property of DuckDBTimestampTZValue:

DuckDBTimestampTZValue.timezoneOffsetInMinutes = -8 * 60;
const pst = DuckDBTimestampTZValue.Epoch.toString();
// 1969-12-31 16:00:00-08

DuckDBTimestampTZValue.timezoneOffsetInMinutes = +1 * 60;
const cet = DuckDBTimestampTZValue.Epoch.toString();
// 1970-01-01 01:00:00+01

Note that the timezone offset used for this string conversion is distinct from the TimeZone setting of DuckDB.

The following sets this offset to match the TimeZone setting of DuckDB:

const reader = await connection.runAndReadAll(
  `select (timezone(current_timestamp) / 60)::int`
);
DuckDBTimestampTZValue.timezoneOffsetInMinutes =
  reader.getColumns()[0][0];

Append To Table

await connection.run(
  `create or replace table target_table(i integer, v varchar)`
);

const appender = await connection.createAppender('target_table');

appender.appendInteger(42);
appender.appendVarchar('duck');
appender.endRow();

appender.appendInteger(123);
appender.appendVarchar('mallard');
appender.endRow();

appender.flush();

appender.appendInteger(17);
appender.appendVarchar('goose');
appender.endRow();

appender.close(); // also flushes

Append Data Chunk

await connection.run(
  `create or replace table target_table(i integer, v varchar)`
);

const appender = await connection.createAppender('target_table');

const chunk = DuckDBDataChunk.create([INTEGER, VARCHAR]);
chunk.setColumns([
  [42, 123, 17],
  ['duck', 'mallad', 'goose'],
]);
// OR:
// chunk.setRows([
//   [42, 'duck'],
//   [123, 'mallard'],
//   [17, 'goose'],
// ]);

appender.appendDataChunk(chunk);
appender.flush();

See "Specifying Values" above for how to supply values to the appender.

Extract Statements

const extractedStatements = await connection.extractStatements(`
  create or replace table numbers as from range(?);
  from numbers where range < ?;
  drop table numbers;
`);
const parameterValues = [10, 7];
const statementCount = extractedStatements.count;
for (let stmtIndex = 0; stmtIndex < statementCount; stmtIndex++) {
  const prepared = await extractedStatements.prepare(stmtIndex);
  let parameterCount = prepared.parameterCount;
  for (let paramIndex = 1; paramIndex <= parameterCount; paramIndex++) {
    prepared.bindInteger(paramIndex, parameterValues.shift());
  }
  const result = await prepared.run();
  // ...
}

Control Evaluation of Tasks

import { DuckDBPendingResultState } from '@duckdb/node-api';

async function sleep(ms) {
  return new Promise((resolve) => {
    setTimeout(resolve, ms);
  });
}

const prepared = await connection.prepare('from range(10_000_000)');
const pending = prepared.start();
while (pending.runTask() !== DuckDBPendingResultState.RESULT_READY) {
  console.log('not ready');
  await sleep(1);
}
console.log('ready');
const result = await pending.getResult();
// ...

Ways to run SQL

// Run to completion but don't yet retrieve any rows.
// Optionally take values to bind to SQL parameters,
// and (optionally) types of those parameters,
// either as an array (for positional parameters),
// or an object keyed by parameter name.
const result = await connection.run(sql);
const result = await connection.run(sql, values);
const result = await connection.run(sql, values, types);

// Run to completion but don't yet retrieve any rows.
// Wrap in a DuckDBDataReader for convenient data retrieval.
const reader = await connection.runAndRead(sql);
const reader = await connection.runAndRead(sql, values);
const reader = await connection.runAndRead(sql, values, types);

// Run to completion, wrap in a reader, and read all rows.
const reader = await connection.runAndReadAll(sql);
const reader = await connection.runAndReadAll(sql, values);
const reader = await connection.runAndReadAll(sql, values, types);

// Run to completion, wrap in a reader, and read at least
// the given number of rows. (Rows are read in chunks, so more than
// the target may be read.)
const reader = await connection.runAndReadUntil(sql, targetRowCount);
const reader =
  await connection.runAndReadAll(sql, targetRowCount, values);
const reader =
  await connection.runAndReadAll(sql, targetRowCount, values, types);

// Create a streaming result and don't yet retrieve any rows.
const result = await connection.stream(sql);
const result = await connection.stream(sql, values);
const result = await connection.stream(sql, values, types);

// Create a streaming result and don't yet retrieve any rows.
// Wrap in a DuckDBDataReader for convenient data retrieval.
const reader = await connection.streamAndRead(sql);
const reader = await connection.streamAndRead(sql, values);
const reader = await connection.streamAndRead(sql, values, types);

// Create a streaming result, wrap in a reader, and read all rows.
const reader = await connection.streamAndReadAll(sql);
const reader = await connection.streamAndReadAll(sql, values);
const reader = await connection.streamAndReadAll(sql, values, types);

// Create a streaming result, wrap in a reader, and read at least
// the given number of rows.
const reader = await connection.streamAndReadUntil(sql, targetRowCount);
const reader =
  await connection.streamAndReadUntil(sql, targetRowCount, values);
const reader =
  await connection.streamAndReadUntil(sql, targetRowCount, values, types);

// Prepared Statements

// Prepare a possibly-parametered SQL statement to run later.
const prepared = await connection.prepare(sql);

// Bind values to the parameters.
prepared.bind(values);
prepared.bind(values, types);

// Run the prepared statement. These mirror the methods on the connection.
const result = prepared.run();

const reader = prepared.runAndRead();
const reader = prepared.runAndReadAll();
const reader = prepared.runAndReadUntil(targetRowCount);

const result = prepared.stream();

const reader = prepared.streamAndRead();
const reader = prepared.streamAndReadAll();
const reader = prepared.streamAndReadUntil(targetRowCount);

// Pending Results

// Create a pending result.
const pending = await connection.start(sql);
const pending = await connection.start(sql, values);
const pending = await connection.start(sql, values, types);

// Create a pending, streaming result.
const pending = await connection.startStream(sql);
const pending = await connection.startStream(sql, values);
const pending = await connection.startStream(sql, values, types);

// Create a pending result from a prepared statement.
const pending = await prepared.start();
const pending = await prepared.startStream();

while (pending.runTask() !== DuckDBPendingResultState.RESULT_READY) {
  // optionally sleep or do other work between tasks
}

// Retrieve the result. If not yet READY, will run until it is.
const result = await pending.getResult();

const reader = await pending.read();
const reader = await pending.readAll();
const reader = await pending.readUntil(targetRowCount);

Ways to get result data

// From a result

// Asynchronously retrieve data for all rows:
const columns = await result.getColumns();
const columnsJson = await result.getColumnsJson();
const columnsObject = await result.getColumnsObject();
const columnsObjectJson = await result.getColumnsObjectJson();
const rows = await result.getRows();
const rowsJson = await result.getRowsJson();
const rowObjects = await result.getRowObjects();
const rowObjectsJson = await result.getRowObjectsJson();

// From a reader

// First, (asynchronously) read some rows:
await reader.readAll();
// or:
await reader.readUntil(targetRowCount);

// Then, (synchronously) get result data for the rows read:
const columns = reader.getColumns();
const columnsJson = reader.getColumnsJson();
const columnsObject = reader.getColumnsObject();
const columnsObjectJson = reader.getColumnsObjectJson();
const rows = reader.getRows();
const rowsJson = reader.getRowsJson();
const rowObjects = reader.getRowObjects();
const rowObjectsJson = reader.getRowObjectsJson();

// Individual values can also be read directly:
const value = reader.value(columnIndex, rowIndex);

// Using chunks

// If desired, one or more chunks can be fetched from a result:
const chunk = await result.fetchChunk();
const chunks = await result.fetchAllChunks();

// And then data can be retrieved from each chunk:
const columnValues = chunk.getColumnValues(columnIndex);
const columns = chunk.getColumns();
const rowValues = chunk.getRowValues(rowIndex);
const rows = chunk.getRows();

// Or, values can be visited:
chunk.visitColumnValues(columnIndex,
  (value, rowIndex, columnIndex, type) => { /* ... */ }
);
chunk.visitColumns((column, columnIndex, type) => { /* ... */ });
chunk.visitColumnMajor(
  (value, rowIndex, columnIndex, type) => { /* ... */ }
);
chunk.visitRowValues(rowIndex,
  (value, rowIndex, columnIndex, type) => { /* ... */ }
);
chunk.visitRows((row, rowIndex) => { /* ... */ });
chunk.visitRowMajor(
  (value, rowIndex, columnIndex, type) => { /* ... */ }
);

// Or converted:
// The `converter` argument implements `DuckDBValueConverter`,
// which has the single method convertValue(value, type).
const columnValues = chunk.convertColumnValues(columnIndex, converter);
const columns = chunk.convertColumns(converter);
const rowValues = chunk.convertRowValues(rowIndex, converter);
const rows = chunk.convertRows(converter);

// The reader abstracts these low-level chunk manipulations
// and is recommended for most cases.