Tables and Views

All views and tables of the exposed schema and accessible by the active database role are available for querying. They are exposed in one-level deep routes.

Read

For instance the full contents of a table people is returned at

GET /people HTTP/1.1

There are no deeply/nested/routes. Each route provides OPTIONS, GET, HEAD, POST, PATCH, and DELETE verbs depending entirely on database permissions.

Note

Why not provide nested routes? Many APIs allow nesting to retrieve related information, such as /films/1/director. We offer a more flexible mechanism (inspired by GraphQL) to embed related information. It can handle one-to-many and many-to-many relationships. This is covered in the section about Resource Embedding.

Horizontal Filtering (Rows)

You can filter result rows by adding conditions on columns. For instance, to return people aged under 13 years old:

GET /people?age=lt.13 HTTP/1.1

You can evaluate multiple conditions on columns by adding more query string parameters. For instance, to return people who are 18 or older and are students:

GET /people?age=gte.18&student=is.true HTTP/1.1

Operators

These operators are available:

Abbreviation

In PostgreSQL

Meaning

eq

=

equals

gt

>

greater than

gte

>=

greater than or equal

lt

<

less than

lte

<=

less than or equal

neq

<> or !=

not equal

like

LIKE

LIKE operator (to avoid URL encoding you can use * as an alias of the percent sign % for the pattern)

ilike

ILIKE

ILIKE operator (to avoid URL encoding you can use * as an alias of the percent sign % for the pattern)

match

~

~ operator, see Pattern Matching

imatch

~*

~* operator, see Pattern Matching

in

IN

one of a list of values, e.g. ?a=in.(1,2,3) – also supports commas in quoted strings like ?a=in.("hi,there","yes,you")

is

IS

checking for exact equality (null,true,false,unknown)

isdistinct

IS DISTINCT FROM

not equal, treating NULL as a comparable value

fts

@@

Full-Text Search using to_tsquery

plfts

@@

Full-Text Search using plainto_tsquery

phfts

@@

Full-Text Search using phraseto_tsquery

wfts

@@

Full-Text Search using websearch_to_tsquery

cs

@>

contains e.g. ?tags=cs.{example, new}

cd

<@

contained in e.g. ?values=cd.{1,2,3}

ov

&&

overlap (have points in common), e.g. ?period=ov.[2017-01-01,2017-06-30] – also supports array types, use curly braces instead of square brackets e.g. :code: ?arr=ov.{1,3}

sl

<<

strictly left of, e.g. ?range=sl.(1,10)

sr

>>

strictly right of

nxr

&<

does not extend to the right of, e.g. ?range=nxr.(1,10)

nxl

&>

does not extend to the left of

adj

-|-

is adjacent to, e.g. ?range=adj.(1,10)

not

NOT

negates another operator, see Logical operators

or

OR

logical OR, see Logical operators

and

AND

logical AND, see Logical operators

all

ALL

comparison matches all the values in the list, see Logical operators

any

ANY

comparison matches any value in the list, see Logical operators

For more complicated filters you will have to create a new view in the database, or use a stored procedure. For instance, here’s a view to show “today’s stories” including possibly older pinned stories:

CREATE VIEW fresh_stories AS
SELECT *
  FROM stories
 WHERE pinned = true
    OR published > now() - interval '1 day'
ORDER BY pinned DESC, published DESC;

The view will provide a new endpoint:

GET /fresh_stories HTTP/1.1

Logical operators

Multiple conditions on columns are evaluated using AND by default, but you can combine them using OR with the or operator. For example, to return people under 18 or over 21:

GET /people?or=(age.lt.18,age.gt.21) HTTP/1.1

To negate any operator, you can prefix it with not like ?a=not.eq.2 or ?not.and=(a.gte.0,a.lte.100) .

You can also apply complex logic to the conditions:

GET /people?grade=gte.90&student=is.true&or=(age.eq.14,not.and(age.gte.11,age.lte.17)) HTTP/1.1

Operator Modifiers

You may further simplify the logic using the any/all modifiers of eq,like,ilike,gt,gte,lt,lte,match,imatch.

For instance, to avoid repeating the same column for or, use any to get people with last names that start with O or P:

GET /people?last_name=like(any).{O*,P*} HTTP/1.1

In a similar way, you can use all to avoid repeating the same column for and. To get the people with last names that start with O and end with n:

GET /people?last_name=like(all).{O*,*n} HTTP/1.1

Pattern Matching

The pattern-matching operators (like, ilike, match, imatch) exist to support filtering data using patterns instead of concrete strings, as described in the PostgreSQL docs.

To ensure best performance on larger data sets, an appropriate index should be used and even then, it depends on the pattern value and actual data statistics whether an existing index will be used by the query planner or not.

Full-Text Search

The fts filter mentioned above has a number of options to support flexible textual queries, namely the choice of plain vs phrase search and the language used for stemming. Suppose that tsearch is a table with column my_tsv, of type tsvector. The following examples illustrate the possibilities.

GET /tsearch?my_tsv=fts(french).amusant HTTP/1.1

GET /tsearch?my_tsv=plfts.The%20Fat%20Cats HTTP/1.1

GET /tsearch?my_tsv=not.phfts(english).The%20Fat%20Cats HTTP/1.1

GET /tsearch?my_tsv=not.wfts(french).amusant HTTP/1.1

Using websearch_to_tsquery requires PostgreSQL of version at least 11.0 and will raise an error in earlier versions of the database.

Vertical Filtering (Columns)

When certain columns are wide (such as those holding binary data), it is more efficient for the server to withhold them in a response. The client can specify which columns are required using the sql:select parameter.

GET /people?select=first_name,age HTTP/1.1

[
  {"first_name": "John", "age": 30},
  {"first_name": "Jane", "age": 20}
]

The default is *, meaning all columns. This value will become more important below in Resource Embedding.

Renaming Columns

You can rename the columns by prefixing them with an alias followed by the colon : operator.

GET /people?select=fullName:full_name,birthDate:birth_date HTTP/1.1

[
  {"fullName": "John Doe", "birthDate": "04/25/1988"},
  {"fullName": "Jane Doe", "birthDate": "01/12/1998"}
]

Casting Columns

Casting the columns is possible by suffixing them with the double colon :: plus the desired type.

GET /people?select=full_name,salary::text HTTP/1.1

[
  {"full_name": "John Doe", "salary": "90000.00"},
  {"full_name": "Jane Doe", "salary": "120000.00"}
]

JSON Columns

You can specify a path for a json or jsonb column using the arrow operators(-> or ->>) as per the PostgreSQL docs.

CREATE TABLE people (
  id int,
  json_data json
);

GET /people?select=id,json_data->>blood_type,json_data->phones HTTP/1.1

[
  { "id": 1, "blood_type": "A-", "phones": [{"country_code": "61", "number": "917-929-5745"}] },
  { "id": 2, "blood_type": "O+", "phones": [{"country_code": "43", "number": "512-446-4988"}, {"country_code": "43", "number": "213-891-5979"}] }
]

GET /people?select=id,json_data->phones->0->>number HTTP/1.1

[
  { "id": 1, "number": "917-929-5745"},
  { "id": 2, "number": "512-446-4988"}
]

This also works with filters:

GET /people?select=id,json_data->blood_type&json_data->>blood_type=eq.A- HTTP/1.1

[
  { "id": 1, "blood_type": "A-" },
  { "id": 3, "blood_type": "A-" },
  { "id": 7, "blood_type": "A-" }
]

Note that ->> is used to compare blood_type as text. To compare with an integer value use ->:

GET /people?select=id,json_data->age&json_data->age=gt.20 HTTP/1.1

[
  { "id": 11, "age": 25 },
  { "id": 12, "age": 30 },
  { "id": 15, "age": 35 }
]

Composite / Array Columns

The arrow operators(->, ->>) can also be used for accessing composite fields and array elements.

CREATE TYPE coordinates (
  lat decimal(8,6),
  long decimal(9,6)
);

CREATE TABLE countries (
  id int,
  location coordinates,
  languages text[]
);

GET /countries?select=id,location->>lat,location->>long,primary_language:languages->0&location->lat=gte.19 HTTP/1.1

[
  {
    "id": 5,
    "lat": "19.741755",
    "long": "-155.844437",
    "primary_language": "en"
  }
]

Important

When using the -> and ->> operators, PostgREST uses a query like to_jsonb(<col>)->'field'. To make filtering and ordering on those nested fields use an index, the index needs to be created on the same expression, including the to_jsonb(...) call:

CREATE INDEX ON mytable ((to_jsonb(data) -> 'identification' ->> 'registration_number'));

Computed / Virtual Columns

Filters may be applied to computed columns(a.k.a. virtual columns) as well as actual table/view columns, even though the computed columns will not appear in the output. For example, to search first and last names at once we can create a computed column that will not appear in the output but can be used in a filter:

CREATE TABLE people (
  fname text,
  lname text
);

CREATE FUNCTION full_name(people) RETURNS text AS $$
  SELECT $1.fname || ' ' || $1.lname;
$$ LANGUAGE SQL;

-- (optional) add an index to speed up anticipated query
CREATE INDEX people_full_name_idx ON people
  USING GIN (to_tsvector('english', full_name(people)));

A full-text search on the computed column:

GET /people?full_name=fts.Beckett HTTP/1.1

As mentioned, computed columns do not appear in the output by default. However you can include them by listing them in the vertical filtering select parameter:

GET /people?select=*,full_name HTTP/1.1

Important

Computed columns must be created in the exposed schema or in a schema in the extra search path to be used in this way. When placing the computed column in the exposed schema you can use an unnamed argument, as in the example above, to prevent it from being exposed as an RPC under /rpc.

Ordering

The reserved word order reorders the response rows. It uses a comma-separated list of columns and directions:

GET /people?order=age.desc,height.asc HTTP/1.1

If no direction is specified it defaults to ascending order:

GET /people?order=age HTTP/1.1

If you care where nulls are sorted, add nullsfirst or nullslast:

GET /people?order=age.nullsfirst HTTP/1.1

GET /people?order=age.desc.nullslast HTTP/1.1

You can also use Computed / Virtual Columns to order the results, even though the computed columns will not appear in the output. You can sort by nested fields of JSON Columns with the JSON operators.

Limits and Pagination

PostgREST uses HTTP range headers to describe the size of results. Every response contains the current range and, if requested, the total number of results:

HTTP/1.1 200 OK
Range-Unit: items
Content-Range: 0-14/*

Here items zero through fourteen are returned. This information is available in every response and can help you render pagination controls on the client. This is an RFC7233-compliant solution that keeps the response JSON cleaner.

There are two ways to apply a limit and offset rows: through request headers or query parameters. When using headers you specify the range of rows desired. This request gets the first twenty people.

GET /people HTTP/1.1
Range-Unit: items
Range: 0-19

Note that the server may respond with fewer if unable to meet your request:

HTTP/1.1 200 OK
Range-Unit: items
Content-Range: 0-17/*

You may also request open-ended ranges for an offset with no limit, e.g. Range: 10-.

The other way to request a limit or offset is with query parameters. For example

GET /people?limit=15&offset=30 HTTP/1.1

This method is also useful for embedded resources, which we will cover in another section. The server always responds with range headers even if you use query parameters to limit the query.

Exact Count

In order to obtain the total size of the table or view (such as when rendering the last page link in a pagination control), specify Prefer: count=exact as a request header:

HEAD /bigtable HTTP/1.1
Range-Unit: items
Range: 0-24
Prefer: count=exact

Note that the larger the table the slower this query runs in the database. The server will respond with the selected range and total

HTTP/1.1 206 Partial Content
Range-Unit: items
Content-Range: 0-24/3573458

Planned Count

To avoid the shortcomings of exact count, PostgREST can leverage PostgreSQL statistics and get a fairly accurate and fast count. To do this, specify the Prefer: count=planned header.

HEAD /bigtable?limit=25 HTTP/1.1
Prefer: count=planned

HTTP/1.1 206 Partial Content
Content-Range: 0-24/3572000

Note that the accuracy of this count depends on how up-to-date are the PostgreSQL statistics tables. For example in this case, to increase the accuracy of the count you can do ANALYZE bigtable. See ANALYZE for more details.

Estimated Count

When you are interested in the count, the relative error is important. If you have a planned count of 1000000 and the exact count is 1001000, the error is small enough to be ignored. But with a planned count of 7, an exact count of 28 would be a huge misprediction.

In general, when having smaller row-counts, the estimated count should be as close to the exact count as possible.

To help with these cases, PostgREST can get the exact count up until a threshold and get the planned count when that threshold is surpassed. To use this behavior, you can specify the Prefer: count=estimated header. The threshold is defined by db-max-rows.

Here’s an example. Suppose we set db-max-rows=1000 and smalltable has 321 rows, then we’ll get the exact count:

HEAD /smalltable?limit=25 HTTP/1.1
Prefer: count=estimated

HTTP/1.1 206 Partial Content
Content-Range: 0-24/321

If we make a similar request on bigtable, which has 3573458 rows, we would get the planned count:

HEAD /bigtable?limit=25 HTTP/1.1
Prefer: count=estimated

HTTP/1.1 206 Partial Content
Content-Range: 0-24/3572000

Update

To update a row or rows in a table, use the PATCH verb. Use Horizontal Filtering (Rows) to specify which record(s) to update. Here is an example query setting the category column to child for all people below a certain age.

PATCH /people?age=lt.13 HTTP/1.1

{ "category": "child" }

Updates also support Prefer: return=representation plus Vertical Filtering (Columns).

Warning

Beware of accidentally updating every row in a table. To learn to prevent that see Block Full-Table Operations.

Insert

All tables and auto-updatable views can be modified through the API, subject to permissions of the requester’s database role.

To create a row in a database table post a JSON object whose keys are the names of the columns you would like to create. Missing properties will be set to default values when applicable.

POST /table_name HTTP/1.1

{ "col1": "value1", "col2": "value2" }

If the table has a primary key, the response can contain a Location header describing where to find the new object by including the header Prefer: return=headers-only in the request. Make sure that the table is not write-only, otherwise constructing the Location header will cause a permissions error.

On the other end of the spectrum you can get the full created object back in the response to your request by including the header Prefer: return=representation. That way you won’t have to make another HTTP call to discover properties that may have been filled in on the server side. You can also apply the standard Vertical Filtering (Columns) to these results.

URL encoded payloads can be posted with Content-Type: application/x-www-form-urlencoded.

POST /people HTTP/1.1
Content-Type: application/x-www-form-urlencoded

name=John+Doe&age=50&weight=80

Note

When inserting a row you must post a JSON object, not quoted JSON.

Yes
{ "a": 1, "b": 2 }

No
"{ \"a\": 1, \"b\": 2 }"

Some JavaScript libraries will post the data incorrectly if you’re not careful. For best results try one of the Client-Side Libraries built for PostgREST.

Important

It’s recommended that you use triggers instead of rules. Insertion on views with complex rules might not work out of the box with PostgREST due to its usage of CTEs. If you want to keep using rules, a workaround is to wrap the view insertion in a stored procedure and call it through the Stored Procedures interface. For more details, see this github issue.

Bulk Insert

Bulk insert works exactly like single row insert except that you provide either a JSON array of objects having uniform keys, or lines in CSV format. This not only minimizes the HTTP requests required but uses a single INSERT statement on the back-end for efficiency.

To bulk insert CSV simply post to a table route with Content-Type: text/csv and include the names of the columns as the first row. For instance

POST /people HTTP/1.1
Content-Type: text/csv

name,age,height
J Doe,62,70
Jonas,10,55

An empty field (,,) is coerced to an empty string and the reserved word NULL is mapped to the SQL null value. Note that there should be no spaces between the column names and commas.

To bulk insert JSON post an array of objects having all-matching keys

POST /people HTTP/1.1
Content-Type: application/json

[
  { "name": "J Doe", "age": 62, "height": 70 },
  { "name": "Janus", "age": 10, "height": 55 }
]

Bulk Insert with Default Values

Any missing columns in the payload will be inserted as null values. To use the DEFAULT column value instead, use the Prefer: missing=default header.

Having:

create table foo (
  id bigint generated by default as identity primary key
, bar text
, baz int default 100
);

A request:

POST /foo?columns=id,bar,baz HTTP/1.1
Content-Type: application/json
Prefer: return=representation

[
  { "bar": "val1"
  }
, { "bar": "val2"
  , "baz": 15
  }
]

Will result in:

[
  { "id":  1
  , "bar": "val1"
  , "baz": 100
  }
, { "id":  2
  , "bar": "val2"
  , "baz": 15
  }
]

Specifying Columns

By using the columns query parameter it’s possible to specify the payload keys that will be inserted and ignore the rest of the payload.

POST /datasets?columns=source,publication_date,figure HTTP/1.1
Content-Type: application/json

{
  "source": "Natural Disaster Prevention and Control",
  "publication_date": "2015-09-11",
  "figure": 1100,
  "location": "...",
  "comment": "...",
  "extra": "...",
  "stuff": "..."
}

In this case, only source, publication_date and figure will be inserted. The rest of the JSON keys will be ignored.

Using this also has the side-effect of being more efficient for Bulk Insert since PostgREST will not process the JSON and it’ll send it directly to PostgreSQL.

Upsert

You can make an upsert with POST and the Prefer: resolution=merge-duplicates header:

POST /employees HTTP/1.1
Prefer: resolution=merge-duplicates

[
  { "id": 1, "name": "Old employee 1", "salary": 30000 },
  { "id": 2, "name": "Old employee 2", "salary": 42000 },
  { "id": 3, "name": "New employee 3", "salary": 50000 }
]

By default, upsert operates based on the primary key columns, you must specify all of them. You can also choose to ignore the duplicates with Prefer: resolution=ignore-duplicates. This works best when the primary key is natural, but it’s also possible to use it if the primary key is surrogate (example: “id serial primary key”). For more details read this issue.

Important

After creating a table or changing its primary key, you must refresh PostgREST schema cache for upsert to work properly. To learn how to refresh the cache see Schema Cache Reloading.

On Conflict

By specifying the on_conflict query parameter, you can make upsert work on a column(s) that has a UNIQUE constraint.

POST /employees?on_conflict=name HTTP/1.1
Prefer: resolution=merge-duplicates

[
  { "name": "Old employee 1", "salary": 40000 },
  { "name": "Old employee 2", "salary": 52000 },
  { "name": "New employee 3", "salary": 60000 }
]

PUT

A single row upsert can be done by using PUT and filtering the primary key columns with eq:

PUT /employees?id=eq.4 HTTP/1.1

{ "id": 4, "name": "Sara B.", "salary": 60000 }

All the columns must be specified in the request body, including the primary key columns.

Delete

To delete rows in a table, use the DELETE verb plus Horizontal Filtering (Rows). For instance deleting inactive users:

DELETE /user?active=is.false HTTP/1.1

Deletions also support Prefer: return=representation plus Vertical Filtering (Columns).

DELETE /user?id=eq.1 HTTP/1.1
Prefer: return=representation

{"id": 1, "email": "johndoe@email.com"}

Warning

Beware of accidentally deleting all rows in a table. To learn to prevent that see Block Full-Table Operations.

Limited Update/Delete

You can limit the amount of affected rows by Update or Delete with the limit query parameter. For this, you must add an explicit order on a unique column(s).

PATCH /users?limit=10&order=id&last_login=lt.2017-01-01 HTTP/1.1

{ "status": "inactive" }

DELETE /users?limit=10&order=id&status=eq.inactive HTTP/1.1

If your table has no unique columns, you can use the ctid system column.

Using offset to target a different subset of rows is also possible.

Note

There is no native UPDATE...LIMIT or DELETE...LIMIT support in PostgreSQL; the generated query simulates that behavior and is based on this Crunchy Data blog post.