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types](https://duckdb.org/docs/sql/data_types/overview).\n* Wraps [released DuckDB binaries](https://github.com/duckdb/duckdb/releases) instead of rebuilding DuckDB.\n* Built on [DuckDB's C API](https://duckdb.org/docs/api/c/overview); exposes more functionality.\n\n### Roadmap\n\nSome features are not yet complete. See the [issues list on GitHub](https://github.com/duckdb/duckdb-node-neo/issues)\nfor the most up-to-date roadmap.\n\n### Supported Platforms\n\n- Linux arm64 glibc\n- Linux arm64 musl\n- Linux x64 glibc\n- Linux x64 musl\n- Mac OS X (Darwin) arm64 (Apple Silicon)\n- Mac OS X (Darwin) x64 (Intel)\n- Windows (Win32) arm64\n- Windows (Win32) x64\n\n## Examples\n\n### Get Basic Information\n\n```ts\nimport duckdb from '@duckdb/node-api';\n\nconsole.log(duckdb.version());\n\nconsole.log(duckdb.configurationOptionDescriptions());\n```\n\n### Connect\n\n```ts\nimport { DuckDBConnection } from '@duckdb/node-api';\n\nconst connection = await DuckDBConnection.create();\n```\n\nThis uses the default instance.\nFor advanced usage, you can create instances explicitly.\n\n### Create Instance\n\n```ts\nimport { DuckDBInstance } from '@duckdb/node-api';\n```\n\nCreate with an in-memory database:\n```ts\nconst instance = await DuckDBInstance.create(':memory:');\n```\n\nEquivalent to the above:\n```ts\nconst instance = await DuckDBInstance.create();\n```\n\nRead from and write to a database file, which is created if needed:\n```ts\nconst instance = await DuckDBInstance.create('my_duckdb.db');\n```\n\nSet [configuration options](https://duckdb.org/docs/stable/configuration/overview.html#configuration-reference):\n```ts\nconst instance = await DuckDBInstance.create('my_duckdb.db', {\n  threads: '4'\n});\n```\n\n### Instance Cache\n\nMultiple instances in the same process should not\nattach the same database.\n\nTo prevent this, an instance cache can be used:\n```ts\nconst instance = await DuckDBInstance.fromCache('my_duckdb.db');\n```\n\nThis uses the default instance cache. For advanced usage, you can create\ninstance caches explicitly:\n```ts\nimport { DuckDBInstanceCache } from '@duckdb/node-api';\n\nconst cache = new DuckDBInstanceCache();\nconst instance = await cache.getOrCreateInstance('my_duckdb.db');\n```\n\n### Connect to Instance\n\n```ts\nconst connection = await instance.connect();\n```\n\n### Disconnect\n\nConnections will be disconnected automatically soon after their reference\nis dropped, but you can also disconnect explicitly if and when you want:\n\n```ts\nconnection.disconnectSync();\n```\n\nor, equivalently:\n\n```ts\nconnection.closeSync();\n```\n\n### Run SQL\n\n```ts\nconst result = await connection.run('from test_all_types()');\n```\n\n### Parameterize SQL\n\n```ts\nconst prepared = await connection.prepare('select $1, $2, $3');\nprepared.bindVarchar(1, 'duck');\nprepared.bindInteger(2, 42);\nprepared.bindList(3, listValue([10, 11, 12]), LIST(INTEGER));\n// OR, with type inference: prepared.bindList(3, [10, 11, 12]);\nconst result = await prepared.run();\n```\n\nor:\n\n```ts\nconst prepared = await connection.prepare('select $a, $b, $c');\nprepared.bind({\n  'a': 'duck',\n  'b': 42,\n  'c': listValue([10, 11, 12]),\n}, {\n  'a': VARCHAR,\n  'b': INTEGER,\n  'c': LIST(INTEGER),\n});\nconst result = await prepared.run();\n```\n\nor even:\n\n```ts\nconst result = await connection.run('select $a, $b, $c', {\n  'a': 'duck',\n  'b': 42,\n  'c': listValue([10, 11, 12]),\n}, {\n  'a': VARCHAR,\n  'b': INTEGER,\n  'c': LIST(INTEGER),\n});\n```\n\nUnspecified types will be inferred:\n\n```ts\nconst result = await connection.run('select $a, $b, $c', {\n  'a': 'duck',\n  'b': 42,\n  'c': listValue([10, 11, 12]),\n});\n```\n\n### Specifying Values\n\nValues of many data types are represented using one of the JS primitives\n`boolean`, `number`, `bigint`, or `string`.\nAlso, any type can have `null` values.\n\nValues of some data types need to be constructed using special functions.\nThese are:\n\n| Type | Function |\n| ---- | -------- |\n| `ARRAY` | `arrayValue` |\n| `BIT` | `bitValue` |\n| `BLOB` | `blobValue` |\n| `DATE` | `dateValue` |\n| `DECIMAL` | `decimalValue` |\n| `INTERVAL` | `intervalValue` |\n| `LIST` | `listValue` |\n| `MAP` | `mapValue` |\n| `STRUCT` | `structValue` |\n| `STRUCT` (unnamed, a tuple) | `tupleValue` |\n| `TIME` | `timeValue` |\n| `TIMETZ` | `timeTZValue` |\n| `TIMESTAMP` | `timestampValue` |\n| `TIMESTAMPTZ` | `timestampTZValue` |\n| `TIMESTAMPTZ_NS` | `timestampTZNanosValue` |\n| `TIMESTAMP_S` | `timestampSecondsValue` |\n| `TIMESTAMP_MS` | `timestampMillisValue` |\n| `TIMESTAMP_NS` | `timestampNanosValue` |\n| `UNION` | `unionValue` |\n| `UUID` | `uuidValue` |\n\nAlternatively, `jsToDuckDBValue` converts a plain JS value to the form\na given DuckDB type expects, so these functions need not be called\ndirectly:\n\n```ts\nimport { jsToDuckDBValue, DATE, TIMESTAMP_MS } from '@duckdb/node-api';\n\njsToDuckDBValue(new Date('2024-01-15T12:34:56.789Z'), TIMESTAMP_MS);\n// same as timestampMillisValue(1705322096789n)\n\njsToDuckDBValue(new Date('2024-01-15T00:00:00.000Z'), DATE);\n// same as dateValue(19737)\n\njsToDuckDBValue(null, DATE); // null, for any type\n```\n\nIt is generic over the type, so a statically known one checks its input:\n\n```ts\njsToDuckDBValue(new Date(), TIMESTAMP); // ok\njsToDuckDBValue(new Date(), INTEGER);   // rejected at compile time\n```\n\nWhat each type accepts is given by `JSInputTypeForTypeId`, and by\n`JSInputTypeFor` when keyed by a `DuckDBType`. These are wider than\nwhat is read back where a second spelling is unambiguous: `BIGINT` and\nthe wider integers accept `number` as well as `bigint`, and `DECIMAL`\naccepts either a double or a raw scaled `bigint`.\n\nThe result is a `DuckDBValue`, which is what `appendValue`,\n`bindValue`, and `setRows` all accept.\n\n### Stream Results\n\nStreaming results evaluate lazily when rows are read.\n\n```ts\nconst result = await connection.stream('from range(10_000)');\n```\n\n### Close Results\n\nResults are destroyed automatically soon after their reference is\ndropped, but you can also close them explicitly to free their memory:\n\n```ts\nresult.closeSync();\n```\n\nFrom DuckDB 2.0, a streaming result that hasn't been read to the end, or\na pending result that hasn't been executed, blocks its connection: the\nnext statement on it fails until the result is closed. A result reader\nor pending result can be closed the same way:\n\n```ts\nconst reader = await connection.streamAndReadUntil(sql, 100);\nreader.closeSync();\nconst rows = reader.getRows(); // rows already read stay available\n```\n\nColumn names and types also stay available after closing; reading more\nrows throws.\n\nThe same applies to appenders: from DuckDB 2.0, an appender can't flush\nwhile its connection has such a result. Appenders flush on `flushSync()`\nand `closeSync()`, and automatically about every 204,800 rows, so with a\nstreaming result open, appending fails at the next flush, and the rows\nappended since the last successful flush are lost. Read the result to the\nend or close it before appending, or append on a separate connection.\nClose appenders explicitly too: one that is flushed when it's garbage\ncollected can't report a failure.\n\n### Inspect Result Metadata\n\nGet column names and types:\n```ts\nconst columnNames = result.columnNames();\nconst columnTypes = result.columnTypes();\n```\n\n### Read Result Data\n\nRun and read all data:\n```ts\nconst reader = await connection.runAndReadAll('from test_all_types()');\nconst rows = reader.getRows();\n// OR: const columns = reader.getColumns();\n```\n\nStream and read up to (at least) some number of rows:\n```ts\nconst reader = await connection.streamAndReadUntil(\n  'from range(5000)',\n  1000\n);\nconst rows = reader.getRows();\n// rows.length === 2048. (Rows are read in chunks of 2048.)\n```\n\nRead rows incrementally:\n```ts\nconst reader = await connection.streamAndRead('from range(5000)');\nreader.readUntil(2000);\n// reader.currentRowCount === 2048 (Rows are read in chunks of 2048.)\n// reader.done === false\nreader.readUntil(4000);\n// reader.currentRowCount === 4096\n// reader.done === false\nreader.readUntil(6000);\n// reader.currentRowCount === 5000\n// reader.done === true\n```\n\n### Get Result Data\n\nResult data can be retrieved in a variety of forms:\n\n```ts\nconst reader = await connection.runAndReadAll(\n  'from range(3) select range::int as i, 10 + i as n'\n);\n\nconst rows = reader.getRows();\n// [ [0, 10], [1, 11], [2, 12] ]\n\nconst rowObjects = reader.getRowObjects();\n// [ { i: 0, n: 10 }, { i: 1, n: 11 }, { i: 2, n: 12 } ]\n\nconst columns = reader.getColumns();\n// [ [0, 1, 2], [10, 11, 12] ]\n\nconst columnsObject = reader.getColumnsObject();\n// { i: [0, 1, 2], n: [10, 11, 12] }\n```\n\n### Convert Result Data\n\nBy default, data values that cannot be represented as JS built-ins\nare returned as specialized JS objects; see `Inspect Data Values` below.\n\nTo retrieve data in a different form, such as JS built-ins or values that\ncan be losslessly serialized to JSON, use the `JS` or `Json` forms of the\nabove result data methods.\n\nCustom converters can be supplied as well. See the implementations of\n[JSDuckDBValueConverter](https://github.com/duckdb/duckdb-node-neo/blob/main/api/src/JSDuckDBValueConverter.ts)\nand [JsonDuckDBValueConverters](https://github.com/duckdb/duckdb-node-neo/blob/main/api/src/JsonDuckDBValueConverter.ts)\nfor how to do this.\n\nExamples (using the `Json` forms):\n\n```ts\nconst reader = await connection.runAndReadAll(\n  'from test_all_types() select bigint, date, interval limit 2'\n);\n\nconst rows = reader.getRowsJson();\n// [\n//   [\n//     \"-9223372036854775808\",\n//     \"5877642-06-25 (BC)\",\n//     { \"months\": 0, \"days\": 0, \"micros\": \"0\" }\n//   ],\n//   [\n//     \"9223372036854775807\",\n//     \"5881580-07-10\",\n//     { \"months\": 999, \"days\": 999, \"micros\": \"999999999\" }\n//   ]\n// ]\n\nconst rowObjects = reader.getRowObjectsJson();\n// [\n//   {\n//     \"bigint\": \"-9223372036854775808\",\n//     \"date\": \"5877642-06-25 (BC)\",\n//     \"interval\": { \"months\": 0, \"days\": 0, \"micros\": \"0\" }\n//   },\n//   {\n//     \"bigint\": \"9223372036854775807\",\n//     \"date\": \"5881580-07-10\",\n//     \"interval\": { \"months\": 999, \"days\": 999, \"micros\": \"999999999\" }\n//   }\n// ]\n\nconst columns = reader.getColumnsJson();\n// [\n//   [ \"-9223372036854775808\", \"9223372036854775807\" ],\n//   [ \"5877642-06-25 (BC)\", \"5881580-07-10\" ],\n//   [\n//     { \"months\": 0, \"days\": 0, \"micros\": \"0\" },\n//     { \"months\": 999, \"days\": 999, \"micros\": \"999999999\" }\n//   ]\n// ]\n\nconst columnsObject = reader.getColumnsObjectJson();\n// {\n//   \"bigint\": [ \"-9223372036854775808\", \"9223372036854775807\" ],\n//   \"date\": [ \"5877642-06-25 (BC)\", \"5881580-07-10\" ],\n//   \"interval\": [\n//     { \"months\": 0, \"days\": 0, \"micros\": \"0\" },\n//     { \"months\": 999, \"days\": 999, \"micros\": \"999999999\" }\n//   ]\n// }\n```\n\nThese methods handle nested types as well:\n\n```ts\nconst reader = await connection.runAndReadAll(\n  'from test_all_types() select int_array, struct, map, \"union\" limit 2'\n);\n\nconst rows = reader.getRowsJson();\n// [\n//   [\n//     [],\n//     { \"a\": null, \"b\": null },\n//     [],\n//     { \"tag\": \"name\", \"value\": \"Frank\" }\n//   ],\n//   [\n//     [ 42, 999, null, null, -42],\n//     { \"a\": 42, \"b\": \"🦆🦆🦆🦆🦆🦆\" },\n//     [\n//       { \"key\": \"key1\", \"value\": \"🦆🦆🦆🦆🦆🦆\" },\n//       { \"key\": \"key2\", \"value\": \"goose\" }\n//     ],\n//     { \"tag\": \"age\", \"value\": 5 }\n//   ]\n// ]\n\nconst rowObjects = reader.getRowObjectsJson();\n// [\n//   {\n//     \"int_array\": [],\n//     \"struct\": { \"a\": null, \"b\": null },\n//     \"map\": [],\n//     \"union\": { \"tag\": \"name\", \"value\": \"Frank\" }\n//   },\n//   {\n//     \"int_array\": [ 42, 999, null, null, -42 ],\n//     \"struct\": { \"a\": 42, \"b\": \"🦆🦆🦆🦆🦆🦆\" },\n//     \"map\": [\n//       { \"key\": \"key1\", \"value\": \"🦆🦆🦆🦆🦆🦆\" },\n//       { \"key\": \"key2\", \"value\": \"goose\" }\n//     ],\n//     \"union\": { \"tag\": \"age\", \"value\": 5 }\n//   }\n// ]\n\nconst columns = reader.getColumnsJson();\n// [\n//   [\n//     [],\n//     [42, 999, null, null, -42]\n//   ],\n//   [\n//     { \"a\": null, \"b\": null },\n//     { \"a\": 42, \"b\": \"🦆🦆🦆🦆🦆🦆\" }\n//   ],\n//   [\n//     [],\n//     [\n//       { \"key\": \"key1\", \"value\": \"🦆🦆🦆🦆🦆🦆\" },\n//       { \"key\": \"key2\", \"value\": \"goose\"}\n//     ]\n//   ],\n//   [\n//     { \"tag\": \"name\", \"value\": \"Frank\" },\n//     { \"tag\": \"age\", \"value\": 5 }\n//   ]\n// ]\n\nconst columnsObject = reader.getColumnsObjectJson();\n// {\n//   \"int_array\": [\n//     [],\n//     [42, 999, null, null, -42]\n//   ],\n//   \"struct\": [\n//     { \"a\": null, \"b\": null },\n//     { \"a\": 42, \"b\": \"🦆🦆🦆🦆🦆🦆\" }\n//   ],\n//   \"map\": [\n//     [],\n//     [\n//       { \"key\": \"key1\", \"value\": \"🦆🦆🦆🦆🦆🦆\" },\n//       { \"key\": \"key2\", \"value\": \"goose\" }\n//     ]\n//   ],\n//   \"union\": [\n//     { \"tag\": \"name\", \"value\": \"Frank\" },\n//     { \"tag\": \"age\", \"value\": 5 }\n//   ]\n// }\n```\n\nColumn names and types can also be serialized to JSON:\n```ts\nconst columnNamesAndTypes = reader.columnNamesAndTypesJson();\n// {\n//   \"columnNames\": [\n//     \"int_array\",\n//     \"struct\",\n//     \"map\",\n//     \"union\"\n//   ],\n//   \"columnTypes\": [\n//     {\n//       \"typeId\": 24,\n//       \"valueType\": {\n//         \"typeId\": 4\n//       }\n//     },\n//     {\n//       \"typeId\": 25,\n//       \"entryNames\": [\n//         \"a\",\n//         \"b\"\n//       ],\n//       \"entryTypes\": [\n//         {\n//           \"typeId\": 4\n//         },\n//         {\n//           \"typeId\": 17\n//         }\n//       ]\n//     },\n//     {\n//       \"typeId\": 26,\n//       \"keyType\": {\n//         \"typeId\": 17\n//       },\n//       \"valueType\": {\n//         \"typeId\": 17\n//       }\n//     },\n//     {\n//       \"typeId\": 28,\n//       \"memberTags\": [\n//         \"name\",\n//         \"age\"\n//       ],\n//       \"memberTypes\": [\n//         {\n//           \"typeId\": 17\n//         },\n//         {\n//           \"typeId\": 3\n//         }\n//       ]\n//     }\n//   ]\n// }\n\nconst columnNameAndTypeObjects = reader.columnNameAndTypeObjectsJson();\n// [\n//   {\n//     \"columnName\": \"int_array\",\n//     \"columnType\": {\n//       \"typeId\": 24,\n//       \"valueType\": {\n//         \"typeId\": 4\n//       }\n//     }\n//   },\n//   {\n//     \"columnName\": \"struct\",\n//     \"columnType\": {\n//       \"typeId\": 25,\n//       \"entryNames\": [\n//         \"a\",\n//         \"b\"\n//       ],\n//       \"entryTypes\": [\n//         {\n//           \"typeId\": 4\n//         },\n//         {\n//           \"typeId\": 17\n//         }\n//       ]\n//     }\n//   },\n//   {\n//     \"columnName\": \"map\",\n//     \"columnType\": {\n//       \"typeId\": 26,\n//       \"keyType\": {\n//         \"typeId\": 17\n//       },\n//       \"valueType\": {\n//         \"typeId\": 17\n//       }\n//     }\n//   },\n//   {\n//     \"columnName\": \"union\",\n//     \"columnType\": {\n//       \"typeId\": 28,\n//       \"memberTags\": [\n//         \"name\",\n//         \"age\"\n//       ],\n//       \"memberTypes\": [\n//         {\n//           \"typeId\": 17\n//         },\n//         {\n//           \"typeId\": 3\n//         }\n//       ]\n//     }\n//   }\n// ]\n```\n\n### Infer JS Types from DuckDB Types\n\nThe mapping each converter applies is also available at the type level.\n`JSTypeFor` gives the JS type that `getRowsJS` and the other `JS` methods\nproduce for a given DuckDB type; `JsonTypeFor` does the same for the `Json`\nmethods.\n\n```ts\nimport {\n  JSTypeFor,\n  JsonTypeFor,\n  DuckDBBigIntType,\n  DuckDBIntegerType,\n  DuckDBTimestampTZType,\n} from '@duckdb/node-api';\n\ntype A = JSTypeFor<DuckDBIntegerType>;     // number\ntype B = JSTypeFor<DuckDBBigIntType>;      // bigint\ntype C = JSTypeFor<DuckDBTimestampTZType>; // Date\n\ntype D = JsonTypeFor<DuckDBBigIntType>;      // string\ntype E = JsonTypeFor<DuckDBTimestampTZType>; // string\n```\n\nThe two differ wherever a value has no lossless JSON form: 64-bit and\nwider integers, temporal types, `DECIMAL`, `BLOB`, `BIT` and `GEOMETRY`\nrender as strings, and `FLOAT` and `DOUBLE` widen to `number | string`\nso that Infinity and NaN survive.\n\nBoth are also available keyed by type id, as `JSTypeForTypeId` and\n`JsonTypeForTypeId`, which is the form to use when the id is what you have:\n\n```ts\ntype F = JSTypeForTypeId[DuckDBTypeId.INTEGER]; // number\n```\n\nThese describe non-NULL values. Since any column can be NULL, a value read\nfrom one is `JSTypeFor<T> | null`.\n\n### Fetch Chunks\n\nFetch all chunks:\n```ts\nconst chunks = await result.fetchAllChunks();\n```\n\nFetch one chunk at a time:\n```ts\nconst chunks = [];\nwhile (true) {\n  const chunk = await result.fetchChunk();\n  // Last chunk will have zero rows.\n  if (chunk.rowCount === 0) {\n    break;\n  }\n  chunks.push(chunk);\n}\n```\n\nFor materialized (non-streaming) results, chunks can be read by index:\n```ts\nconst rowCount = result.rowCount;\nconst chunkCount = result.chunkCount;\nfor (let i = 0; i < chunkCount; i++) {\n  const chunk = result.getChunk(i);\n  // ...\n}\n```\n\nGet chunk data:\n```ts\nconst rows = chunk.getRows();\n\nconst rowObjects =\n  chunk.getRowObjects(result.deduplicatedColumnNames());\n\nconst columns = chunk.getColumns();\n\nconst columnsObject =\n  chunk.getColumnsObject(result.deduplicatedColumnNames());\n```\n\nGet chunk data (one value at a time)\n```ts\nconst columns = [];\nconst columnCount = chunk.columnCount;\nfor (let columnIndex = 0; columnIndex < columnCount; columnIndex++) {\n  const columnValues = [];\n  const columnVector = chunk.getColumnVector(columnIndex);\n  const itemCount = columnVector.itemCount;\n  for (let itemIndex = 0; itemIndex < itemCount; itemIndex++) {\n    const value = columnVector.getItem(itemIndex);\n    columnValues.push(value);\n  }\n  columns.push(columnValues);\n}\n```\n\n### Inspect Data Types\n\n```ts\nimport { DuckDBTypeId } from '@duckdb/node-api';\n\nif (columnType.typeId === DuckDBTypeId.ARRAY) {\n  const arrayValueType = columnType.valueType;\n  const arrayLength = columnType.length;\n}\n\nif (columnType.typeId === DuckDBTypeId.DECIMAL) {\n  const decimalWidth = columnType.width;\n  const decimalScale = columnType.scale;\n}\n\nif (columnType.typeId === DuckDBTypeId.ENUM) {\n  const enumValues = columnType.values;\n}\n\nif (columnType.typeId === DuckDBTypeId.LIST) {\n  const listValueType = columnType.valueType;\n}\n\nif (columnType.typeId === DuckDBTypeId.MAP) {\n  const mapKeyType = columnType.keyType;\n  const mapValueType = columnType.valueType;\n}\n\nif (columnType.typeId === DuckDBTypeId.STRUCT) {\n  const structEntryNames = columnType.names;\n  const structEntryTypes = columnType.valueTypes;\n}\n\nif (columnType.typeId === DuckDBTypeId.UNION) {\n  const unionMemberTags = columnType.memberTags;\n  const unionMemberTypes = columnType.memberTypes;\n}\n\n// For the JSON type (https://duckdb.org/docs/data/json/json_type)\nif (columnType.alias === 'JSON') {\n  const json = JSON.parse(columnValue);\n}\n```\n\nEvery type implements toString.\nThe result is both human-friendly and readable by DuckDB in an appropriate expression.\n\n```ts\nconst typeString = columnType.toString();\n```\n\n### Inspect Data Values\n\n```ts\nimport { DuckDBTypeId } from '@duckdb/node-api';\n\nif (columnType.typeId === DuckDBTypeId.ARRAY) {\n  const arrayItems = columnValue.items; // array of values\n  const arrayString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.BIT) {\n  const bools = columnValue.toBools(); // array of booleans\n  const bits = columnValue.toBits(); // arrary of 0s and 1s\n  const bitString = columnValue.toString(); // string of '0's and '1's\n}\n\nif (columnType.typeId === DuckDBTypeId.BLOB) {\n  const blobBytes = columnValue.bytes; // Uint8Array\n  const blobString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.DATE) {\n  const dateDays = columnValue.days;\n  const dateString = columnValue.toString();\n  const { year, month, day } = columnValue.toParts();\n}\n\nif (columnType.typeId === DuckDBTypeId.DECIMAL) {\n  const decimalWidth = columnValue.width;\n  const decimalScale = columnValue.scale;\n  // Scaled-up value. Represented number is value/(10^scale).\n  const decimalValue = columnValue.value; // bigint\n  const decimalString = columnValue.toString();\n  const decimalDouble = columnValue.toDouble();\n}\n\nif (columnType.typeId === DuckDBTypeId.INTERVAL) {\n  const intervalMonths = columnValue.months;\n  const intervalDays = columnValue.days;\n  const intervalMicros = columnValue.micros; // bigint\n  const intervalString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.LIST) {\n  const listItems = columnValue.items; // array of values\n  const listString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.MAP) {\n  const mapEntries = columnValue.entries; // array of { key, value }\n  const mapString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.STRUCT) {\n  if (columnType.isTuple) {\n    // An unnamed struct, such as (1, 'a'): items in entry order\n    const tupleItems = columnValue.items; // array of values\n  } else {\n    // { name1: value1, name2: value2, ... }\n    const structEntries = columnValue.entries;\n  }\n  const structString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIMESTAMP_MS) {\n  const timestampMillis = columnValue.milliseconds; // bigint\n  const timestampMillisString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIMESTAMP_NS) {\n  const timestampNanos = columnValue.nanoseconds; // bigint\n  const timestampNanosString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIMESTAMP_S) {\n  const timestampSecs = columnValue.seconds; // bigint\n  const timestampSecsString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIMESTAMP_TZ) {\n  const timestampTZMicros = columnValue.micros; // bigint\n  const timestampTZString = columnValue.toString();\n  const {\n    date: { year, month, day },\n    time: { hour, min, sec, micros },\n  } = columnValue.toParts();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIMESTAMP) {\n  const timestampMicros = columnValue.micros; // bigint\n  const timestampString = columnValue.toString();\n  const {\n    date: { year, month, day },\n    time: { hour, min, sec, micros },\n  } = columnValue.toParts();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIME_TZ) {\n  const timeTZMicros = columnValue.micros; // bigint\n  const timeTZOffset = columnValue.offset;\n  const timeTZString = columnValue.toString();\n  const {\n    time: { hour, min, sec, micros },\n    offset,\n  } = columnValue.toParts();\n}\n\nif (columnType.typeId === DuckDBTypeId.TIME) {\n  const timeMicros = columnValue.micros; // bigint\n  const timeString = columnValue.toString();\n  const { hour, min, sec, micros } = columnValue.toParts();\n}\n\nif (columnType.typeId === DuckDBTypeId.UNION) {\n  const unionTag = columnValue.tag;\n  const unionValue = columnValue.value;\n  const unionValueString = columnValue.toString();\n}\n\nif (columnType.typeId === DuckDBTypeId.UUID) {\n  const uuidHugeint = columnValue.hugeint; // bigint\n  const uuidString = columnValue.toString();\n}\n\n// other possible values are: null, boolean, number, bigint, or string\n```\n\n### Displaying Timezones\n\nConverting a TIMESTAMP_TZ or TIMESTAMPTZ_NS value to a string depends on\na timezone offset. By default, this is set to the offset for the local\ntimezone when the Node process is started.\n\nTo change it, set the `timezoneOffsetInMinutes`\nproperty of `DuckDBTimestampTZValue`:\n\n```ts\nDuckDBTimestampTZValue.timezoneOffsetInMinutes = -8 * 60;\nconst pst = DuckDBTimestampTZValue.Epoch.toString();\n// 1969-12-31 16:00:00-08\n\nDuckDBTimestampTZValue.timezoneOffsetInMinutes = +1 * 60;\nconst cet = DuckDBTimestampTZValue.Epoch.toString();\n// 1970-01-01 01:00:00+01\n```\n\n`DuckDBTimestampTZNanosecondsValue` (TIMESTAMPTZ_NS) uses the same\n`DuckDBTimestampTZValue.timezoneOffsetInMinutes` setting.\n\nNote that the timezone offset used for this string\nconversion is distinct from the `TimeZone` setting of DuckDB.\n\nThe following sets this offset to match the `TimeZone` setting of DuckDB:\n\n```ts\nconst reader = await connection.runAndReadAll(\n  `select (timezone(current_timestamp) / 60)::int`\n);\nDuckDBTimestampTZValue.timezoneOffsetInMinutes =\n  reader.getColumns()[0][0];\n```\n\n### Append To Table\n\n```ts\nawait connection.run(\n  `create or replace table target_table(i integer, v varchar)`\n);\n\nconst appender = await connection.createAppender('target_table');\n\nappender.appendInteger(42);\nappender.appendVarchar('duck');\nappender.endRow();\n\nappender.flushSync();\n\nappender.appendInteger(123);\nappender.appendVarchar('mallard');\nappender.endRow();\n\nappender.clear(); // discards the unflushed 'mallard' row\n\nappender.appendInteger(17);\nappender.appendVarchar('goose');\nappender.endRow();\n\nappender.closeSync(); // also flushes\n```\n\n### Append Data Chunk\n\n```ts\nawait connection.run(\n  `create or replace table target_table(i integer, v varchar)`\n);\n\nconst appender = await connection.createAppender('target_table');\n\nconst chunk = DuckDBDataChunk.create([INTEGER, VARCHAR]);\nchunk.setColumns([\n  [42, 123, 17],\n  ['duck', 'mallad', 'goose'],\n]);\n// OR:\n// chunk.setRows([\n//   [42, 'duck'],\n//   [123, 'mallard'],\n//   [17, 'goose'],\n// ]);\n\nappender.appendDataChunk(chunk);\nappender.flushSync();\n```\n\nSee \"Specifying Values\" above for how to supply values to the appender.\n\n### Append Data Chunk of JS Values\n\n`setRowsConverted` and `setColumnsConverted` fill a data chunk from\nvalues in another representation, converting on the way in.\n`JSToDuckDBValueConverter` converts from plain JS, so a `Date` can be\nwritten to a `TIMESTAMP` column without constructing a\n`DuckDBTimestampValue` first:\n\n```ts\nimport {\n  DuckDBDataChunk,\n  JSToDuckDBValueConverter,\n  INTEGER,\n  TIMESTAMP,\n  VARCHAR,\n} from '@duckdb/node-api';\n\nawait connection.run(\n  `create or replace table target_table(\n    i integer, v varchar, t timestamp\n  )`\n);\n\nconst appender = await connection.createAppender('target_table');\n\nconst chunk = DuckDBDataChunk.create([INTEGER, VARCHAR, TIMESTAMP]);\nchunk.setRowsConverted(\n  [\n    [42, 'duck', new Date('2024-01-15T12:34:56.000Z')],\n    [123, 'mallard', null],\n  ],\n  JSToDuckDBValueConverter\n);\n\nappender.appendDataChunk(chunk);\nappender.flushSync();\n```\n\nThe column types come from the chunk, so no types are passed to the\nconverter. `setColumnValuesConverted` does the same for a single\ncolumn.\n\n### Buffer Rows Into Data Chunks\n\n`DuckDBDataChunkWriter` accumulates rows and emits them as filled data\nchunks, for callers producing a row at a time. `forAppender` takes the\ncolumn types from the appender:\n\n```ts\nimport {\n  DuckDBDataChunkWriter,\n  JSToDuckDBValueConverter,\n} from '@duckdb/node-api';\n\nawait connection.run(\n  `create or replace table target_table(i integer, v varchar)`\n);\n\nconst appender = await connection.createAppender('target_table');\n\nconst writer = DuckDBDataChunkWriter.forAppender(appender, {\n  converter: JSToDuckDBValueConverter,\n});\n\nwriter.appendRow([42, 'duck']);\nwriter.appendRow([123, 'mallard']);\nwriter.appendRow([17, 'goose']);\n\n// Emits the buffered rows. The last chunk is usually a partial one.\nwriter.flush();\nappender.closeSync();\n```\n\nA chunk is emitted every `rowsPerDataChunk` rows, which defaults to the\nDuckDB vector size. Omit the converter to write `DuckDBValue`s instead.\nRows are copied as they are appended, so the same array can be reused\nacross calls.\n\nCallers who already have rows in chunk-sized batches do not need a writer;\nfilling a data chunk directly, as above, avoids the buffering.\n\n### Scalar Functions\n\n```ts\nconnection.registerScalarFunction(\n  DuckDBScalarFunction.create({\n    name: 'my_add',\n    mainFunction: (info, input, output) => {\n      const v0 = input.getColumnVector(0);\n      const v1 = input.getColumnVector(1);\n      for (let rowIndex = 0; rowIndex < input.rowCount; rowIndex++) {\n        output.setItem(\n          rowIndex,\n          v0.getItem(rowIndex) + v1.getItem(rowIndex)\n        );\n      }\n      output.flush();\n    },\n    returnType: INTEGER,\n    parameterTypes: [INTEGER, INTEGER],\n  })\n);\nconst reader = await connection.runAndReadAll('select my_add(2, 3)');\nconst rows = reader.getRows();\n// [ [ 5 ] ]\n```\n\nAn optional `initFunction` runs once per DuckDB worker thread, giving each thread\nits own state:\n\n```ts\nconnection.registerScalarFunction(\n  DuckDBScalarFunction.create({\n    name: 'my_counter',\n    initFunction: (initInfo) => {\n      initInfo.setState({ next: 0 });\n    },\n    mainFunction: (functionInfo, input, output) => {\n      const state = functionInfo.state as { next: number };\n      for (let rowIndex = 0; rowIndex < input.rowCount; rowIndex++) {\n        output.setItem(rowIndex, state.next++);\n      }\n      output.flush();\n    },\n    returnType: INTEGER,\n    volatile: true,\n  })\n);\nconst reader = await connection.runAndReadAll(\n  'select my_counter() from range(3)'\n);\nconst rows = reader.getRows();\n// [ [ 0 ], [ 1 ], [ 2 ] ]\n```\n\nMarking the function `volatile` is required whenever its result depends on init\nstate rather than only on its arguments: without it, DuckDB may treat the call as\nconstant and evaluate it just once.\n\nNote that this is per-thread state, unlike a table function's `initData`, which is\nshared across the scan. Init info also exposes the client context, bind data, and\nextra info, and reports errors with `setError`. Callbacks are always run on the JS\nthread, so they are serialized even when DuckDB evaluates in parallel.\n\n### Table Functions\n\n```ts\nconnection.registerTableFunction(\n  DuckDBTableFunction.create({\n    name: 'my_range',\n    parameterTypes: [INTEGER],\n    bindFunction: (info) => {\n      const count = info.getParameter(0) as number;\n      info.addResultColumn('n', INTEGER);\n      info.setBindData({ count });\n    },\n    initFunction: (info) => {\n      info.setInitData({ nextRow: 0 });\n    },\n    mainFunction: (info, output) => {\n      const { count } = info.bindData as { count: number };\n      const initData = info.initData as { nextRow: number };\n      const chunkSize = Math.min(2048, count - initData.nextRow);\n      // Set the row count before writing: that is what sizes the\n      // vectors.\n      // A row count of zero reports that the scan is finished.\n      output.rowCount = chunkSize;\n      if (chunkSize > 0) {\n        const column = output.getColumnVector(0);\n        for (let i = 0; i < chunkSize; i++) {\n          column.setItem(i, initData.nextRow + i);\n        }\n        column.flush();\n        initData.nextRow += chunkSize;\n      }\n    },\n  })\n);\nconst reader =\n  await connection.runAndReadAll('select * from my_range(3)');\nconst rows = reader.getRows();\n// [ [ 0 ], [ 1 ], [ 2 ] ]\n```\n\nThe main function is called repeatedly until it reports a row count of zero.\nBind data is read-only during the scan; use init data to track progress. Setting\n`supportsProjectionPushdown: true` makes DuckDB report the columns actually\nselected, which init can read with `getColumnIndexes`.\n\nFor a scan that runs on several threads, set `maxThreads` from the init function\nand give each thread its own state with `localInitFunction`; the main function\nreads it with `localInitData`. Callbacks are always run on the JS thread, so they\nare serialized even when DuckDB scans in parallel.\n\n### Extract Statements\n\n```ts\nconst extractedStatements = await connection.extractStatements(`\n  create or replace table numbers as from range(?);\n  from numbers where range < ?;\n  drop table numbers;\n`);\nconst parameterValues = [10, 7];\nconst statementCount = extractedStatements.count;\nfor (let stmtIndex = 0; stmtIndex < statementCount; stmtIndex++) {\n  const prepared = await extractedStatements.prepare(stmtIndex);\n  let parameterCount = prepared.parameterCount;\n  for (let paramIndex = 1; paramIndex <= parameterCount; paramIndex++) {\n    prepared.bindInteger(paramIndex, parameterValues.shift());\n  }\n  const result = await prepared.run();\n  // ...\n}\n```\n\n### Control Evaluation of Tasks\n\nDuckDB splits work into relatively short tasks. By controlling the\nevaluation of these tasks explicitly, better cooperative multithreading\ncan be accomplished. This is especially important for maximum throughput\nin the Node environment, which has a small, fixed number of threads for\nrunning asynchronous work.\n(See https://docs.libuv.org/en/stable/threadpool.html)\n\nBelow is a low-level way of controlling the evaluation of processing.\nSee the \"startThenRead\" and \"startStreamThenRead\" methods for helpers\nthat do much of this for you.\n\n```ts\nimport { DuckDBPendingResultState } from '@duckdb/node-api';\n\nasync function sleep(ms) {\n  return new Promise((resolve) => {\n    setTimeout(resolve, ms);\n  });\n}\n\nconst prepared = await connection.prepare('from range(10_000_000)');\nconst pending = prepared.startStream();\nwhile (pending.runTask() !== DuckDBPendingResultState.RESULT_READY) {\n  console.log('not ready');\n  await sleep(1);\n}\nconsole.log('ready');\nconst result = await pending.getResult();\n// ...\n```\n\nEquivalently, using `startStreamThenRead`:\n\n```ts\nconst reader = await connection.startStreamThenRead(sql);\n```\n\n### Ways to run SQL\n\n```ts\n// Run to completion but don't yet retrieve any rows.\n// Optionally take values to bind to SQL parameters,\n// and (optionally) types of those parameters,\n// either as an array (for positional parameters),\n// or an object keyed by parameter name.\nconst result = await connection.run(sql);\nconst result = await connection.run(sql, values);\nconst result = await connection.run(sql, values, types);\n\n// Run to completion but don't yet retrieve any rows.\n// Wrap in a DuckDBDataReader for convenient data retrieval.\nconst reader = await connection.runAndRead(sql);\nconst reader = await connection.runAndRead(sql, values);\nconst reader = await connection.runAndRead(sql, values, types);\n\n// Run to completion, wrap in a reader, and read all rows.\nconst reader = await connection.runAndReadAll(sql);\nconst reader = await connection.runAndReadAll(sql, values);\nconst reader = await connection.runAndReadAll(sql, values, types);\n\n// Run to completion, wrap in a reader, and read at least\n// the given number of rows. (Rows are read in chunks, so more than\n// the target may be read.)\nconst reader = await connection.runAndReadUntil(sql, targetRowCount);\nconst reader =\n  await connection.runAndReadAll(sql, targetRowCount, values);\nconst reader =\n  await connection.runAndReadAll(sql, targetRowCount, values, types);\n\n// Create a streaming result and don't yet retrieve any rows.\nconst result = await connection.stream(sql);\nconst result = await connection.stream(sql, values);\nconst result = await connection.stream(sql, values, types);\n\n// Create a streaming result and don't yet retrieve any rows.\n// Wrap in a DuckDBDataReader for convenient data retrieval.\nconst reader = await connection.streamAndRead(sql);\nconst reader = await connection.streamAndRead(sql, values);\nconst reader = await connection.streamAndRead(sql, values, types);\n\n// Create a streaming result, wrap in a reader, and read all rows.\nconst reader = await connection.streamAndReadAll(sql);\nconst reader = await connection.streamAndReadAll(sql, values);\nconst reader = await connection.streamAndReadAll(sql, values, types);\n\n// Create a streaming result, wrap in a reader, and read at least\n// the given number of rows.\nconst reader = await connection.streamAndReadUntil(sql, targetRowCount);\nconst reader =\n  await connection.streamAndReadUntil(sql, targetRowCount, values);\nconst reader = await connection.streamAndReadUntil(\n  sql, targetRowCount, values, types\n);\n\n// Prepared Statements\n\n// Prepare a possibly-parametered SQL statement to run later.\nconst prepared = await connection.prepare(sql);\n\n// Bind values to the parameters.\nprepared.bind(values);\nprepared.bind(values, types);\n\n// Run the prepared statement. These mirror the methods on the\n// connection.\nconst result = prepared.run();\n\nconst reader = prepared.runAndRead();\nconst reader = prepared.runAndReadAll();\nconst reader = prepared.runAndReadUntil(targetRowCount);\n\nconst result = prepared.stream();\n\nconst reader = prepared.streamAndRead();\nconst reader = prepared.streamAndReadAll();\nconst reader = prepared.streamAndReadUntil(targetRowCount);\n\n// Pending Results\n\n// Create a pending result.\nconst pending = await connection.start(sql);\nconst pending = await connection.start(sql, values);\nconst pending = await connection.start(sql, values, types);\n\n// The methods beginning with \"startThenRead\" provide some, but not\n// full, cooperative multithreading. They use pending results to split\n// processing into short tasks, but they fully materialize the result,\n// which can take some time (and memory). For full cooperative\n// multithreading, see the \"startStreamThenRead\" methods below.\nconst reader = await connection.startThenRead(sql);\nconst reader = await connection.startThenRead(sql, values);\nconst reader = await connection.startThenRead(sql, values, types);\n\nconst reader = await connection.startThenReadAll(sql);\nconst reader = await connection.startThenReadAll(sql, values);\nconst reader = await connection.startThenReadAll(sql, values, types);\n\nconst reader = await connection.startThenReadUntil(sql, targetRowCount);\nconst reader =\n  await connection.startThenReadUntil(sql, targetRowCount, values);\nconst reader = await connection.startThenReadUntil(\n  sql, targetRowCount, values, types\n);\n\n// Create a pending, streaming result.\nconst pending = await connection.startStream(sql);\nconst pending = await connection.startStream(sql, values);\nconst pending = await connection.startStream(sql, values, types);\n\n// The methods beginning with \"startStreamThenRead\" are the best options\n// for cooperative multithreading. By creating a streaming result, they\n// prevent the result from being fully materialized. By using a pending\n// result, they split processing into short tasks, preventing any single\n// task from occupying a thread for too long.\nconst reader = await connection.startStreamThenRead(sql);\nconst reader = await connection.startStreamThenRead(sql, values);\nconst reader = await connection.startStreamThenRead(sql, values, types);\n\nconst reader = await connection.startStreamThenReadAll(sql);\nconst reader = await connection.startStreamThenReadAll(sql, values);\nconst reader =\n  await connection.startStreamThenReadAll(sql, values, types);\n\nconst reader =\n  await connection.startStreamThenReadUntil(sql, targetRowCount);\nconst reader = await connection.startStreamThenReadUntil(\n  sql, targetRowCount, values\n);\nconst reader = await connection.startStreamThenReadUntil(\n  sql, targetRowCount, values, types);\n\n// Create a pending result from a prepared statement.\nconst pending = await prepared.start();\nconst pending = await prepared.startStream();\n\nwhile (pending.runTask() !== DuckDBPendingResultState.RESULT_READY) {\n  // optionally sleep or do other work between tasks\n}\n\n// Or, run tasks (cooperatively) until the result is ready.\nawait pending.runAllTasks();\n\n// Retrieve the result. If not yet READY, will run until it is.\nconst result = await pending.getResult();\n\nconst reader = await pending.read();\nconst reader = await pending.readAll();\nconst reader = await pending.readUntil(targetRowCount);\n```\n\n### Ways to get result data\n\n```ts\n// From a result\n\n// Asynchronously retrieve data for all rows:\nconst columns = await result.getColumns();\nconst columnsJS = await result.getColumnsJS();\nconst columnsJson = await result.getColumnsJson();\nconst columnsObject = await result.getColumnsObject();\nconst columnsObjectJS = await result.getColumnsObjectJS();\nconst columnsObjectJson = await result.getColumnsObjectJson();\nconst rows = await result.getRows();\nconst rowsJS = await result.getRowsJS();\nconst rowsJson = await result.getRowsJson();\nconst rowObjects = await result.getRowObjects();\nconst rowObjectsJS = await result.getRowObjectsJS();\nconst rowObjectsJson = await result.getRowObjectsJson();\n\n// Asynchronous iterators can be used to retrieve partial data:\nfor await (const chunk of result) {\n  // ...\n}\n\n// Each chunk can be converted to rows:\nfor await (const rows of result.yieldRows()) {\n  // ...\n}\n// See also variations of \"yieldRow\" for returning row objects\n// (instead of row arrays), and with JS, JSON, or custom conversion.\n\n// From a reader\n\n// First, (asynchronously) read some rows:\nawait reader.readAll();\n// or:\nawait reader.readUntil(targetRowCount);\n\n// Then, (synchronously) get result data for the rows read:\nconst columns = reader.getColumns();\nconst columnsJS = reader.getColumnsJS();\nconst columnsJson = reader.getColumnsJson();\nconst columnsObject = reader.getColumnsObject();\nconst columnsObjectJS = reader.getColumnsObjectJS();\nconst columnsObjectJson = reader.getColumnsObjectJson();\nconst rows = reader.getRows();\nconst rowsJS = reader.getRowsJS();\nconst rowsJson = reader.getRowsJson();\nconst rowObjects = reader.getRowObjects();\nconst rowObjectsJS = reader.getRowObjectsJS();\nconst rowObjectsJson = reader.getRowObjectsJson();\n\n// Individual values can also be read directly:\nconst value = reader.value(columnIndex, rowIndex);\n\n// Using chunks\n\n// If desired, one or more chunks can be fetched from a result:\nconst chunk = await result.fetchChunk();\nconst chunks = await result.fetchAllChunks();\n\n// And then data can be retrieved from each chunk:\nconst columnValues = chunk.getColumnValues(columnIndex);\nconst columns = chunk.getColumns();\nconst rowValues = chunk.getRowValues(rowIndex);\nconst rows = chunk.getRows();\n\n// Or, values can be visited:\nchunk.visitColumnValues(columnIndex,\n  (value, rowIndex, columnIndex, type) => { /* ... */ }\n);\nchunk.visitColumns((column, columnIndex, type) => { /* ... */ });\nchunk.visitColumnMajor(\n  (value, rowIndex, columnIndex, type) => { /* ... */ }\n);\nchunk.visitRowValues(rowIndex,\n  (value, rowIndex, columnIndex, type) => { /* ... */ }\n);\nchunk.visitRows((row, rowIndex) => { /* ... */ });\nchunk.visitRowMajor(\n  (value, rowIndex, columnIndex, type) => { /* ... */ }\n);\n\n// Or converted:\n// The `converter` argument implements `DuckDBValueConverter`,\n// which has the single method convertValue(value, type).\nconst columnValues = chunk.convertColumnValues(columnIndex, converter);\nconst columns = chunk.convertColumns(converter);\nconst rowValues = chunk.convertRowValues(rowIndex, converter);\nconst rows = chunk.convertRows(converter);\n\n// The reader abstracts these low-level chunk manipulations\n// and is recommended for most cases.\n```\n","readmeFilename":"README.md"}