docs.sheetjs.com/docz/docs/03-demos/07-data/10-sql.md

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---
title: SQL Connectors
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pagination_prev: demos/desktop/index
pagination_next: demos/local/index
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sidebar_custom_props:
sql: true
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
### Generating Tables
This example will fetch <https://sheetjs.com/data/cd.xls>, scan the columns of the
first worksheet to determine data types, and generate 6 PostgreSQL statements.
<details><summary><b>Explanation</b> (click to show)</summary>
The relevant `generate_sql` function takes a worksheet name and a table name:
```js
// define mapping between determined types and PostgreSQL types
const PG = { "n": "float8", "s": "text", "b": "boolean" };
function generate_sql(ws, wsname) {
// generate an array of objects from the data
const aoo = XLSX.utils.sheet_to_json(ws);
// types will map column headers to types, while hdr holds headers in order
const types = {}, hdr = [];
// loop across each row object
aoo.forEach(row =>
// Object.entries returns a row of [key, value] pairs. Loop across those
Object.entries(row).forEach(([k,v]) => {
// If this is first time seeing key, mark unknown and append header array
if(!types[k]) { types[k] = "?"; hdr.push(k); }
// skip null and undefined
if(v == null) return;
// check and resolve type
switch(typeof v) {
case "string": // strings are the broadest type
types[k] = "s"; break;
case "number": // if column is not string, number is the broadest type
if(types[k] != "s") types[k] = "n"; break;
case "boolean": // only mark boolean if column is unknown or boolean
if("?b".includes(types[k])) types[k] = "b"; break;
default: types[k] = "s"; break; // default to string type
}
})
);
// The final array consists of the CREATE TABLE query and a series of INSERTs
return [
// generate CREATE TABLE query and return batch
`CREATE TABLE \`${wsname}\` (${hdr.map(h =>
// column name must be wrapped in backticks
`\`${h}\` ${PG[types[h]]}`
).join(", ")});`
].concat(aoo.map(row => { // generate INSERT query for each row
// entries will be an array of [key, value] pairs for the data in the row
const entries = Object.entries(row);
// fields will hold the column names and values will hold the values
const fields = [], values = [];
// check each key/value pair in the row
entries.forEach(([k,v]) => {
// skip null / undefined
if(v == null) return;
// column name must be wrapped in backticks
fields.push(`\`${k}\``);
// when the field type is numeric, `true` -> 1 and `false` -> 0
if(types[k] == "n") values.push(typeof v == "boolean" ? (v ? 1 : 0) : v);
// otherwise,
else values.push(`'${v.toString().replaceAll("'", "''")}'`);
})
if(fields.length) return `INSERT INTO \`${wsname}\` (${fields.join(", ")}) VALUES (${values.join(", ")})`;
})).filter(x => x); // filter out skipped rows
}
```
</details>
```jsx live
function SheetJSQLWriter() {
// define mapping between determined types and PostgreSQL types
const PG = { "n": "float8", "s": "text", "b": "boolean" };
function generate_sql(ws, wsname) {
const aoo = XLSX.utils.sheet_to_json(ws);
const types = {}, hdr = [];
// loop across each key in each column
aoo.forEach(row => Object.entries(row).forEach(([k,v]) => {
// set up type if header hasn't been seen
if(!types[k]) { types[k] = "?"; hdr.push(k); }
// check and resolve type
switch(typeof v) {
case "string": types[k] = "s"; break;
case "number": if(types[k] != "s") types[k] = "n"; break;
case "boolean": if("?b".includes(types[k])) types[k] = "b"; break;
default: types[k] = "s"; break;
}
}));
return [
// generate CREATE TABLE query and return batch
`CREATE TABLE \`${wsname}\` (${hdr.map(h => `\`${h}\` ${PG[types[h]]}`).join(", ")});`
].concat(aoo.map(row => {
const entries = Object.entries(row);
const fields = [], values = [];
entries.forEach(([k,v]) => {
if(v == null) return;
fields.push(`\`${k}\``);
if(types[k] == "n") values.push(typeof v == "boolean" ? (v ? 1 : 0) : v);
else values.push(`'${v.toString().replaceAll("'", "''")}'`);
})
if(fields.length) return `INSERT INTO \`${wsname}\` (${fields.join(", ")}) VALUES (${values.join(", ")})`;
})).filter(x => x).slice(0, 6);
}
const [url, setUrl] = React.useState("https://sheetjs.com/data/cd.xls");
const set_url = React.useCallback((evt) => setUrl(evt.target.value));
const [out, setOut] = React.useState("");
const xport = React.useCallback(async() => {
const ab = await (await fetch(url)).arrayBuffer();
const wb = XLSX.read(ab), wsname = wb.SheetNames[0];
setOut(generate_sql(wb.Sheets[wsname], wsname).join("\n"));
});
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return ( <> {out && ( <><a href={url}>{url}</a><pre>{out}</pre></> )}
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<b>URL: </b><input type="text" value={url} onChange={set_url} size="50"/>
<br/><button onClick={xport}><b>Fetch!</b></button>
</> );
}
```
## Databases
### Query Builders
Query builders are designed to simplify query generation and normalize field
types and other database minutiae.
**Knex**
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**[The exposition has been moved to a separate page.](/docs/demos/data/knex)**
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### Other SQL Databases
The `generate_sql` function from ["Building Schemas from Worksheets"](#building-schemas-from-worksheets)
can be adapted to generate SQL statements for a variety of databases, including:
**PostgreSQL**
The `pg` connector library was tested against the `generate_sql` output as-is.
The `rows` property of a query result is an array of objects that plays nice
with `json_to_sheet`:
```js
const aoa = await connection.query(`SELECT * FROM DataTable`).rows;
const worksheet = XLSX.utils.json_to_sheet(aoa);
```
**MySQL / MariaDB**
The `mysql2` connector library was tested. The differences are shown below,
primarily stemming from the different quoting requirements and field types.
<details><summary><b>Differences</b> (click to show)</summary>
```js
// highlight-start
// define mapping between determined types and MySQL types
const PG = { "n": "REAL", "s": "TEXT", "b": "TINYINT" };
// highlight-end
function generate_sql(ws, wsname) {
// generate an array of objects from the data
const aoo = XLSX.utils.sheet_to_json(ws);
// types will map column headers to types, while hdr holds headers in order
const types = {}, hdr = [];
// loop across each row object
aoo.forEach(row =>
// Object.entries returns a row of [key, value] pairs. Loop across those
Object.entries(row).forEach(([k,v]) => {
// If this is first time seeing key, mark unknown and append header array
if(!types[k]) { types[k] = "?"; hdr.push(k); }
// skip null and undefined
if(v == null) return;
// check and resolve type
switch(typeof v) {
case "string": // strings are the broadest type
types[k] = "s"; break;
case "number": // if column is not string, number is the broadest type
if(types[k] != "s") types[k] = "n"; break;
case "boolean": // only mark boolean if column is unknown or boolean
if("?b".includes(types[k])) types[k] = "b"; break;
default: types[k] = "s"; break; // default to string type
}
})
);
// The final array consists of the CREATE TABLE query and a series of INSERTs
return [
// generate CREATE TABLE query and return batch
// highlight-next-line
`CREATE TABLE ${wsname} (${hdr.map(h =>
// highlight-next-line
`${h} ${PG[types[h]]}`
).join(", ")});`
].concat(aoo.map(row => { // generate INSERT query for each row
// entries will be an array of [key, value] pairs for the data in the row
const entries = Object.entries(row);
// fields will hold the column names and values will hold the values
const fields = [], values = [];
// check each key/value pair in the row
entries.forEach(([k,v]) => {
// skip null / undefined
if(v == null) return;
// highlight-next-line
fields.push(`${k}`);
// when the field type is numeric, `true` -> 1 and `false` -> 0
if(types[k] == "n") values.push(typeof v == "boolean" ? (v ? 1 : 0) : v);
// otherwise,
// highlight-next-line
else values.push(`"${v.toString().replaceAll('"', '""')}"`);
})
if(fields.length) return `INSERT INTO \`${wsname}\` (${fields.join(", ")}) VALUES (${values.join(", ")})`;
})).filter(x => x); // filter out skipped rows
}
```
</details>
The first property of a query result is an array of objects that plays nice
with `json_to_sheet`:
```js
const aoa = await connection.query(`SELECT * FROM DataTable`)[0];
const worksheet = XLSX.utils.json_to_sheet(aoa);
```