github.com/porsager/postgres @v3.4.9

• 🚀 Fastest full-featured node & deno client
• 🏷 ES6 Tagged Template Strings at the core
• 🏄‍♀️ Simple surface API
• 🖊️ Dynamic query support
• 💬 Chat and help on Gitter
• 🐦 Follow on Twitter

Getting started

Installation

$ npm install postgres

Usage

Create your sql database instance

// db.js
import postgres from 'postgres'

const sql = postgres({ /* options */ }) // will use psql environment variables

export default sql

Simply import for use elsewhere

// users.js
import sql from './db.js'

async function getUsersOver(age) {
  const users = await sql`
    select
      name,
      age
    from users
    where age > ${ age }
  `
  // users = Result [{ name: "Walter", age: 80 }, { name: 'Murray', age: 68 }, ...]
  return users
}


async function insertUser({ name, age }) {
  const users = await sql`
    insert into users
      (name, age)
    values
      (${ name }, ${ age })
    returning name, age
  `
  // users = Result [{ name: "Murray", age: 68 }]
  return users
}

ESM dynamic imports

The library can be used with ESM dynamic imports as well as shown here.

const { default: postgres } = await import('postgres')

Table of Contents

• Connection
• Queries
• Building queries
• Advanced query methods
• Transactions
• Data Transformation
• Listen & notify
• Realtime subscribe
• Numbers, bigint, numeric
• Result Array
• Connection details
• Custom Types
• Teardown / Cleanup
• Error handling
• TypeScript support
• Reserving connections
• Changelog

Connection

postgres([url], [options])

You can use either a postgres:// url connection string or the options to define your database connection properties. Options in the object will override any present in the url. Options will fall back to the same environment variables as psql.

const sql = postgres('postgres://username:password@host:port/database', {
  host                 : '',            // Postgres ip address[s] or domain name[s]
  port                 : 5432,          // Postgres server port[s]
  database             : '',            // Name of database to connect to
  username             : '',            // Username of database user
  password             : '',            // Password of database user
  ...and more
})

More options can be found in the Connection details section.

Queries

await sql`...` -> Result[]

Postgres.js utilizes Tagged template functions to process query parameters before interpolation. Using tagged template literals benefits developers by:

1. Enforcing safe query generation
2. Giving the sql`` function powerful utility and query building features.

Any generic value will be serialized according to an inferred type, and replaced by a PostgreSQL protocol placeholder $1, $2, .... The parameters are then sent separately to the database which handles escaping & casting.

All queries will return a Result array, with objects mapping column names to each row.

const xs = await sql`
  insert into users (
    name, age
  ) values (
    'Murray', 68
  )

  returning *
`

// xs = [{ user_id: 1, name: 'Murray', age: 68 }]

Please note that queries are first executed when awaited – or instantly by using .execute().

Query parameters

Parameters are automatically extracted and handled by the database so that SQL injection isn't possible. No special handling is necessary, simply use tagged template literals as usual.

const name = 'Mur'
    , age = 60

const users = await sql`
  select
    name,
    age
  from users
  where
    name like ${ name + '%' }
    and age > ${ age }
`
// users = [{ name: 'Murray', age: 68 }]

Be careful with quotation marks here. Because Postgres infers column types, you do not need to wrap your interpolated parameters in quotes like '${name}'. This will cause an error because the tagged template replaces ${name} with $1 in the query string, leaving Postgres to do the interpolation. If you wrap that in a string, Postgres will see '$1' and interpret it as a string as opposed to a parameter.

Dynamic column selection

const columns = ['name', 'age']

await sql`
  select
    ${ sql(columns) }
  from users
`

// Which results in:
select "name", "age" from users

Dynamic inserts

const user = {
  name: 'Murray',
  age: 68
}

await sql`
  insert into users ${
    sql(user, 'name', 'age')
  }
`

// Which results in:
insert into users ("name", "age") values ($1, $2)

// The columns can also be given with an array
const columns = ['name', 'age']

await sql`
  insert into users ${
    sql(user, columns)
  }
`

You can omit column names and simply execute sql(user) to get all the fields from the object as columns. Be careful not to allow users to supply columns that you do not want to be inserted.

Multiple inserts in one query

If you need to insert multiple rows at the same time it's also much faster to do it with a single insert. Simply pass an array of objects to sql().

const users = [{
  name: 'Murray',
  age: 68,
  garbage: 'ignore'
},
{
  name: 'Walter',
  age: 80
}]

await sql`insert into users ${ sql(users, 'name', 'age') }`

// Is translated to:
insert into users ("name", "age") values ($1, $2), ($3, $4)

// Here you can also omit column names which will use object keys as columns
await sql`insert into users ${ sql(users) }`

// Which results in:
insert into users ("name", "age") values ($1, $2), ($3, $4)

Dynamic columns in updates

This is also useful for update queries

const user = {
  id: 1,
  name: 'Murray',
  age: 68
}

await sql`
  update users set ${
    sql(user, 'name', 'age')
  }
  where user_id = ${ user.id }
`

// Which results in:
update users set "name" = $1, "age" = $2 where user_id = $3

// The columns can also be given with an array
const columns = ['name', 'age']

await sql`
  update users set ${
    sql(user, columns)
  }
  where user_id = ${ user.id }
`

Multiple updates in one query

To create multiple updates in a single query, it is necessary to use arrays instead of objects to ensure that the order of the items correspond with the column names.

const users = [
  [1, 'John', 34],
  [2, 'Jane', 27],
]

await sql`
  update users set name = update_data.name, age = (update_data.age)::int
  from (values ${sql(users)}) as update_data (id, name, age)
  where users.id = (update_data.id)::int
  returning users.id, users.name, users.age
`

Dynamic values and where in

Value lists can also be created dynamically, making where in queries simple too.

const users = await sql`
  select
    *
  from users
  where age in ${ sql([68, 75, 23]) }
`

or

const [{ a, b, c }] = await sql`
  select
    *
  from (values ${ sql(['a', 'b', 'c']) }) as x(a, b, c)
`

Building queries

Postgres.js features a simple dynamic query builder by conditionally appending/omitting query fragments. It works by nesting sql`` fragments within other sql`` calls or fragments. This allows you to build dynamic queries safely without risking sql injections through usual string concatenation.

Partial queries

const olderThan = x => sql`and age > ${ x }`

const filterAge = true

await sql`
  select
   *
  from users
  where name is not null ${
    filterAge
      ? olderThan(50)
      : sql``
  }
`
// Which results in:
select * from users where name is not null
// Or
select * from users where name is not null and age > 50

Dynamic filters

await sql`
  select
    *
  from users ${
    id
      ? sql`where user_id = ${ id }`
      : sql``
  }
`

// Which results in:
select * from users
// Or
select * from users where user_id = $1

Dynamic ordering

const id = 1
const order = {
  username: 'asc'
  created_at: 'desc'
}
await sql`
  select 
    * 
  from ticket 
  where account = ${ id }  
  order by ${
    Object.entries(order).flatMap(([column, order], i) =>
      [i ? sql`,` : sql``, sql`${ sql(column) } ${ order === 'desc' ? sql`desc` : sql`asc` }`]
    )
  }
`

SQL functions

Using keywords or calling functions dynamically is also possible by using sql`` fragments.

const date = null

await sql`
  update users set updated_at = ${ date || sql`now()` }
`

// Which results in:
update users set updated_at = now()

Table names

Dynamic identifiers like table names and column names is also supported like so:

const table = 'users'
    , column = 'id'

await sql`
  select ${ sql(column) } from ${ sql(table) }
`

// Which results in:
select "id" from "users"

Quick primer on interpolation

Here's a quick oversight over all the ways to do interpolation in a query template string:

Interpolation syntax	Usage	Example
${ sql`` }	for keywords or sql fragments	await sql`SELECT * FROM users ${sql`order by age desc` }`
${ sql(string) }	for identifiers	await sql`SELECT * FROM ${sql('table_name')`
${ sql([] or {}, ...) }	for helpers	await sql`INSERT INTO users ${sql({ name: 'Peter'})}`
${ 'somevalue' }	for values	await sql`SELECT * FROM users WHERE age = ${42}`

Advanced query methods

Cursors

await sql``.cursor([rows = 1], [fn])

Use cursors if you need to throttle the amount of rows being returned from a query. You can use a cursor either as an async iterable or with a callback function. For a callback function new results won't be requested until the promise / async callback function has resolved.

callback function

await sql`
  select
    *
  from generate_series(1,4) as x
`.cursor(async([row]) => {
  // row = { x: 1 }
  await http.request('https://example.com/wat', { row })
})

for await...of

// for await...of
const cursor = sql`select * from generate_series(1,4) as x`.cursor()

for await (const [row] of cursor) {
  // row = { x: 1 }
  await http.request('https://example.com/wat', { row })
}

A single row will be returned by default, but you can also request batches by setting the number of rows desired in each batch as the first argument to .cursor:

await sql`
  select
    *
  from generate_series(1,1000) as x
`.cursor(10, async rows => {
  // rows = [{ x: 1 }, { x: 2 }, ... ]
  await Promise.all(rows.map(row =>
    http.request('https://example.com/wat', { row })
  ))
})

If an error is thrown inside the callback function no more rows will be requested and the outer promise will reject with the thrown error.

You can close the cursor early either by calling break in the for await...of loop, or by returning the token sql.CLOSE from the callback function.

await sql`
  select * from generate_series(1,1000) as x
`.cursor(row => {
  return Math.random() > 0.9 && sql.CLOSE // or sql.END
})

Instant iteration

await sql``.forEach(fn)

If you want to handle rows returned by a query one by one, you can use .forEach which returns a promise that resolves once there are no more rows.

await sql`
  select created_at, name from events
`.forEach(row => {
  // row = { created_at: '2019-11-22T14:22:00Z', name: 'connected' }
})

// No more rows

Query Descriptions

await sql``.describe() -> Result[]

Rather than executing a given query, .describe will return information utilized in the query process. This information can include the query identifier, column types, etc.

This is useful for debugging and analyzing your Postgres queries. Furthermore, .describe will give you access to the final generated query string that would be executed.

Rows as Array of Values

sql``.values()

Using .values will return rows as an array of values for each column, instead of objects.

This can be useful to receive identically named columns, or for specific performance/transformation reasons. The column definitions are still included on the result array, plus access to parsers for each column.

Rows as Raw Array of Buffers

sql``.raw()

Using .raw will return rows as an array with Buffer values for each column, instead of objects.

This can be useful for specific performance/transformation reasons. The column definitions are still included on the result array, plus access to parsers for each column.

Queries in Files

await sql.file(path, [args], [options]) -> Result[]

Using a file for a query is also supported with optional parameters to use if the file includes $1, $2, etc

const result = await sql.file('query.sql', ['Murray', 68])

Mult