8.14. JSON Types
JSON data types are for storing JSON (JavaScript Object Notation) data, as specified in RFC 7159. Such data can also be stored as text
, but the JSON data types have the advantage of enforcing that each stored value is valid according to the JSON rules. There are also assorted JSON-specific functions and operators available for data stored in these data types; see Section 9.15.
Postgres Pro offers two types for storing JSON data: json
and jsonb
. To implement efficient query mechanisms for these data types, Postgres Pro also provides the jsonpath
data type described in Section 8.14.6.
The json
and jsonb
data types accept almost identical sets of values as input. The major practical difference is one of efficiency. The json
data type stores an exact copy of the input text, which processing functions must reparse on each execution; while jsonb
data is stored in a decomposed binary format that makes it slightly slower to input due to added conversion overhead, but significantly faster to process, since no reparsing is needed. jsonb
also supports indexing, which can be a significant advantage.
Because the json
type stores an exact copy of the input text, it will preserve semantically-insignificant white space between tokens, as well as the order of keys within JSON objects. Also, if a JSON object within the value contains the same key more than once, all the key/value pairs are kept. (The processing functions consider the last value as the operative one.) By contrast, jsonb
does not preserve white space, does not preserve the order of object keys, and does not keep duplicate object keys. If duplicate keys are specified in the input, only the last value is kept.
In general, most applications should prefer to store JSON data as jsonb
, unless there are quite specialized needs, such as legacy assumptions about ordering of object keys.
Postgres Pro allows only one character set encoding per database. It is therefore not possible for the JSON types to conform rigidly to the JSON specification unless the database encoding is UTF8. Attempts to directly include characters that cannot be represented in the database encoding will fail; conversely, characters that can be represented in the database encoding but not in UTF8 will be allowed.
RFC 7159 permits JSON strings to contain Unicode escape sequences denoted by \u
. In the input function for the XXXX
json
type, Unicode escapes are allowed regardless of the database encoding, and are checked only for syntactic correctness (that is, that four hex digits follow \u
). However, the input function for jsonb
is stricter: it disallows Unicode escapes for non-ASCII characters (those above U+007F
) unless the database encoding is UTF8. The jsonb
type also rejects \u0000
(because that cannot be represented in Postgres Pro's text
type), and it insists that any use of Unicode surrogate pairs to designate characters outside the Unicode Basic Multilingual Plane be correct. Valid Unicode escapes are converted to the equivalent ASCII or UTF8 character for storage; this includes folding surrogate pairs into a single character.
Note
Many of the JSON processing functions described in Section 9.15 will convert Unicode escapes to regular characters, and will therefore throw the same types of errors just described even if their input is of type json
not jsonb
. The fact that the json
input function does not make these checks may be considered a historical artifact, although it does allow for simple storage (without processing) of JSON Unicode escapes in a non-UTF8 database encoding. In general, it is best to avoid mixing Unicode escapes in JSON with a non-UTF8 database encoding, if possible.
When converting textual JSON input into jsonb
, the primitive types described by RFC 7159 are effectively mapped onto native Postgres Pro types, as shown in Table 8.23. Therefore, there are some minor additional constraints on what constitutes valid jsonb
data that do not apply to the json
type, nor to JSON in the abstract, corresponding to limits on what can be represented by the underlying data type. Notably, jsonb
will reject numbers that are outside the range of the Postgres Pro numeric
data type, while json
will not. Such implementation-defined restrictions are permitted by RFC 7159. However, in practice such problems are far more likely to occur in other implementations, as it is common to represent JSON's number
primitive type as IEEE 754 double precision floating point (which RFC 7159 explicitly anticipates and allows for). When using JSON as an interchange format with such systems, the danger of losing numeric precision compared to data originally stored by Postgres Pro should be considered.
Conversely, as noted in the table there are some minor restrictions on the input format of JSON primitive types that do not apply to the corresponding Postgres Pro types.
Table 8.23. JSON Primitive Types and Corresponding Postgres Pro Types
JSON primitive type | Postgres Pro type | Notes |
---|---|---|
string | text | \u0000 is disallowed, as are non-ASCII Unicode escapes if database encoding is not UTF8 |
number | numeric | NaN and infinity values are disallowed |
boolean | boolean | Only lowercase true and false spellings are accepted |
null | (none) | SQL NULL is a different concept |
8.14.1. JSON Input and Output Syntax
The input/output syntax for the JSON data types is as specified in RFC 7159.
The following are all valid json
(or jsonb
) expressions:
-- Simple scalar/primitive value -- Primitive values can be numbers, quoted strings, true, false, or null SELECT '5'::json; -- Array of zero or more elements (elements need not be of same type) SELECT '[1, 2, "foo", null]'::json; -- Object containing pairs of keys and values -- Note that object keys must always be quoted strings SELECT '{"bar": "baz", "balance": 7.77, "active": false}'::json; -- Arrays and objects can be nested arbitrarily SELECT '{"foo": [true, "bar"], "tags": {"a": 1, "b": null}}'::json;
As previously stated, when a JSON value is input and then printed without any additional processing, json
outputs the same text that was input, while jsonb
does not preserve semantically-insignificant details such as whitespace. For example, note the differences here:
SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::json; json ------------------------------------------------- {"bar": "baz", "balance": 7.77, "active":false} (1 row) SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::jsonb; jsonb -------------------------------------------------- {"bar": "baz", "active": false, "balance": 7.77} (1 row)
One semantically-insignificant detail worth noting is that in jsonb
, numbers will be printed according to the behavior of the underlying numeric
type. In practice this means that numbers entered with E
notation will be printed without it, for example:
SELECT '{"reading": 1.230e-5}'::json, '{"reading": 1.230e-5}'::jsonb; json | jsonb -----------------------+------------------------- {"reading": 1.230e-5} | {"reading": 0.00001230} (1 row)
However, jsonb
will preserve trailing fractional zeroes, as seen in this example, even though those are semantically insignificant for purposes such as equality checks.
For the list of built-in functions and operators available for constructing and processing JSON values, see Section 9.15.
8.14.2. Designing JSON Documents
Representing data as JSON can be considerably more flexible than the traditional relational data model, which is compelling in environments where requirements are fluid. It is quite possible for both approaches to co-exist and complement each other within the same application. However, even for applications where maximal flexibility is desired, it is still recommended that JSON documents have a somewhat fixed structure. The structure is typically unenforced (though enforcing some business rules declaratively is possible), but having a predictable structure makes it easier to write queries that usefully summarize a set of “documents” (datums) in a table.
JSON data is subject to the same concurrency-control considerations as any other data type when stored in a table. Although storing large documents is practicable, keep in mind that any update acquires a row-level lock on the whole row. Consider limiting JSON documents to a manageable size in order to decrease lock contention among updating transactions. Ideally, JSON documents should each represent an atomic datum that business rules dictate cannot reasonably be further subdivided into smaller datums that could be modified independently.
8.14.3. jsonb
Containment and Existence
Testing containment is an important capability of jsonb
. There is no parallel set of facilities for the json
type. Containment tests whether one jsonb
document has contained within it another one. These examples return true except as noted:
-- Simple scalar/primitive values contain only the identical value:
SELECT '"foo"'::jsonb @> '"foo"'::jsonb;
-- The array on the right side is contained within the one on the left:
SELECT '[1, 2, 3]'::jsonb @> '[1, 3]'::jsonb;
-- Order of array elements is not significant, so this is also true:
SELECT '[1, 2, 3]'::jsonb @> '[3, 1]'::jsonb;
-- Duplicate array elements don't matter either:
SELECT '[1, 2, 3]'::jsonb @> '[1, 2, 2]'::jsonb;
-- The object with a single pair on the right side is contained
-- within the object on the left side:
SELECT '{"product": "PostgreSQL", "version": 9.4, "jsonb": true}'::jsonb @> '{"version": 9.4}'::jsonb;
-- The array on the right side is not considered contained within the
-- array on the left, even though a similar array is nested within it:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[1, 3]'::jsonb; -- yields false
-- But with a layer of nesting, it is contained:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[[1, 3]]'::jsonb;
-- Similarly, containment is not reported here:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"bar": "baz"}'::jsonb; -- yields false
-- A top-level key and an empty object is contained:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"foo": {}}'::jsonb;
The general principle is that the contained object must match the containing object as to structure and data contents, possibly after discarding some non-matching array elements or object key/value pairs from the containing object. But remember that the order of array elements is not significant when doing a containment match, and duplicate array elements are effectively considered only once.
As a special exception to the general principle that the structures must match, an array may contain a primitive value:
-- This array contains the primitive string value: SELECT '["foo", "bar"]'::jsonb @> '"bar"'::jsonb; -- This exception is not reciprocal -- non-containment is reported here: SELECT '"bar"'::jsonb @> '["bar"]'::jsonb; -- yields false
jsonb
also has an existence operator, which is a variation on the theme of containment: it tests whether a string (given as a text
value) appears as an object key or array element at the top level of the jsonb
value. These examples return true except as noted:
-- String exists as array element: SELECT '["foo", "bar", "baz"]'::jsonb ? 'bar'; -- String exists as object key: SELECT '{"foo": "bar"}'::jsonb ? 'foo'; -- Object values are not considered: SELECT '{"foo": "bar"}'::jsonb ? 'bar'; -- yields false -- As with containment, existence must match at the top level: SELECT '{"foo": {"bar": "baz"}}'::jsonb ? 'bar'; -- yields false -- A string is considered to exist if it matches a primitive JSON string: SELECT '"foo"'::jsonb ? 'foo';
JSON objects are better suited than arrays for testing containment or existence when there are many keys or elements involved, because unlike arrays they are internally optimized for searching, and do not need to be searched linearly.
Tip
Because JSON containment is nested, an appropriate query can skip explicit selection of sub-objects. As an example, suppose that we have a doc
column containing objects at the top level, with most objects containing tags
fields that contain arrays of sub-objects. This query finds entries in which sub-objects containing both "term":"paris"
and "term":"food"
appear, while ignoring any such keys outside the tags
array:
SELECT doc->'site_name' FROM websites WHERE doc @> '{"tags":[{"term":"paris"}, {"term":"food"}]}';
One could accomplish the same thing with, say,
SELECT doc->'site_name' FROM websites WHERE doc->'tags' @> '[{"term":"paris"}, {"term":"food"}]';
but that approach is less flexible, and often less efficient as well.
On the other hand, the JSON existence operator is not nested: it will only look for the specified key or array element at top level of the JSON value.
The various containment and existence operators, along with all other JSON operators and functions are documented in Section 9.15.
8.14.4. jsonb
Indexing
GIN indexes can be used to efficiently search for keys or key/value pairs occurring within a large number of jsonb
documents (datums). Two GIN “operator classes” are provided, offering different performance and flexibility trade-offs.
The default GIN operator class for jsonb
supports queries with the key-exists operators ?
, ?|
and ?&
, the containment operator @>
, and the jsonpath
match operators @?
and @@
. (For details of the semantics that these operators implement, see Table 9.45.) An example of creating an index with this operator class is:
CREATE INDEX idxgin ON api USING GIN (jdoc);
The non-default GIN operator class jsonb_path_ops
does not support the key-exists operators, but it does support @>
, @?
and @@
. An example of creating an index with this operator class is:
CREATE INDEX idxginp ON api USING GIN (jdoc jsonb_path_ops);
Consider the example of a table that stores JSON documents retrieved from a third-party web service, with a documented schema definition. A typical document is:
{ "guid": "9c36adc1-7fb5-4d5b-83b4-90356a46061a", "name": "Angela Barton", "is_active": true, "company": "Magnafone", "address": "178 Howard Place, Gulf, Washington, 702", "registered": "2009-11-07T08:53:22 +08:00", "latitude": 19.793713, "longitude": 86.513373, "tags": [ "enim", "aliquip", "qui" ] }
We store these documents in a table named api
, in a jsonb
column named jdoc
. If a GIN index is created on this column, queries like the following can make use of the index:
-- Find documents in which the key "company" has value "Magnafone" SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @> '{"company": "Magnafone"}';
However, the index could not be used for queries like the following, because though the operator ?
is indexable, it is not applied directly to the indexed column jdoc
:
-- Find documents in which the key "tags" contains key or array element "qui" SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc -> 'tags' ? 'qui';
Still, with appropriate use of expression indexes, the above query can use an index. If querying for particular items within the "tags"
key is common, defining an index like this may be worthwhile:
CREATE INDEX idxgintags ON api USING GIN ((jdoc -> 'tags'));
Now, the WHERE
clause jdoc -> 'tags' ? 'qui'
will be recognized as an application of the indexable operator ?
to the indexed expression jdoc -> 'tags'
. (More information on expression indexes can be found in Section 11.7.)
Another approach to querying is to exploit containment, for example:
-- Find documents in which the key "tags" contains array element "qui" SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @> '{"tags": ["qui"]}';
A simple GIN index on the jdoc
column can support this query. But note that such an index will store copies of every key and value in the jdoc
column, whereas the expression index of the previous example stores only data found under the tags
key. While the simple-index approach is far more flexible (since it supports queries about any key), targeted expression indexes are likely to be smaller and faster to search than a simple index.
GIN indexes also support the @?
and @@
operators, which perform jsonpath
matching. Examples are
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @? '$.tags[*] ? (@ == "qui")';
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @@ '$.tags[*] == "qui"';
For these operators, a GIN index extracts clauses of the form
out of the accessors_chain
= constant
jsonpath
pattern, and does the index search based on the keys and values mentioned in these clauses. The accessors chain may include .
, key
[*]
, and [
accessors. The index
]jsonb_ops
operator class also supports .*
and .**
accessors, but the jsonb_path_ops
operator class does not.
Although the jsonb_path_ops
operator class supports only queries with the @>
, @?
and @@
operators, it has notable performance advantages over the default operator class jsonb_ops
. A jsonb_path_ops
index is usually much smaller than a jsonb_ops
index over the same data, and the specificity of searches is better, particularly when queries contain keys that appear frequently in the data. Therefore search operations typically perform better than with the default operator class.
The technical difference between a jsonb_ops
and a jsonb_path_ops
GIN index is that the former creates independent index items for each key and value in the data, while the latter creates index items only for each value in the data. [6] Basically, each jsonb_path_ops
index item is a hash of the value and the key(s) leading to it; for example to index {"foo": {"bar": "baz"}}
, a single index item would be created incorporating all three of foo
, bar
, and baz
into the hash value. Thus a containment query looking for this structure would result in an extremely specific index search; but there is no way at all to find out whether foo
appears as a key. On the other hand, a jsonb_ops
index would create three index items representing foo
, bar
, and baz
separately; then to do the containment query, it would look for rows containing all three of these items. While GIN indexes can perform such an AND search fairly efficiently, it will still be less specific and slower than the equivalent jsonb_path_ops
search, especially if there are a very large number of rows containing any single one of the three index items.
A disadvantage of the jsonb_path_ops
approach is that it produces no index entries for JSON structures not containing any values, such as {"a": {}}
. If a search for documents containing such a structure is requested, it will require a full-index scan, which is quite slow. jsonb_path_ops
is therefore ill-suited for applications that often perform such searches.
jsonb
also supports btree
and hash
indexes. These are usually useful only if it's important to check equality of complete JSON documents. The btree
ordering for jsonb
datums is seldom of great interest, but for completeness it is:
Object
>Array
>Boolean
>Number
>String
>Null
Object with n pairs
>object with n - 1 pairs
Array with n elements
>array with n - 1 elements
Objects with equal numbers of pairs are compared in the order:
key-1
,value-1
,key-2
...
Note that object keys are compared in their storage order; in particular, since shorter keys are stored before longer keys, this can lead to results that might be unintuitive, such as:
{ "aa": 1, "c": 1} > {"b": 1, "d": 1}
Similarly, arrays with equal numbers of elements are compared in the order:
element-1
,element-2
...
Primitive JSON values are compared using the same comparison rules as for the underlying Postgres Pro data type. Strings are compared using the default database collation.
8.14.5. Transforms
Additional extensions are available that implement transforms for the jsonb
type for different procedural languages.
The extensions for PL/Perl are called jsonb_plperl
and jsonb_plperlu
. If you use them, jsonb
values are mapped to Perl arrays, hashes, and scalars, as appropriate.
The extensions for PL/Python are called jsonb_plpythonu
, jsonb_plpython2u
, and jsonb_plpython3u
(see Section 46.1 for the PL/Python naming convention). If you use them, jsonb
values are mapped to Python dictionaries, lists, and scalars, as appropriate.
8.14.6. jsonpath Type
The jsonpath
type implements support for the SQL/JSON path language in Postgres Pro to efficiently query JSON data. It provides a binary representation of the parsed SQL/JSON path expression that specifies the items to be retrieved by the path engine from the JSON data for further processing with the SQL/JSON query functions.
The semantics of SQL/JSON path predicates and operators generally follow SQL. At the same time, to provide a most natural way of working with JSON data, SQL/JSON path syntax uses some of the JavaScript conventions:
Dot (
.
) is used for member access.Square brackets (
[]
) are used for array access.SQL/JSON arrays are 0-relative, unlike regular SQL arrays that start from 1.
An SQL/JSON path expression is typically written in an SQL query as an SQL character string literal, so it must be enclosed in single quotes, and any single quotes desired within the value must be doubled (see Section 4.1.2.1). Some forms of path expressions require string literals within them. These embedded string literals follow JavaScript/ECMAScript conventions: they must be surrounded by double quotes, and backslash escapes may be used within them to represent otherwise-hard-to-type characters. In particular, the way to write a double quote within an embedded string literal is \"
, and to write a backslash itself, you must write \\
. Other special backslash sequences include those recognized in JavaScript strings: \b
, \f
, \n
, \r
, \t
, \v
for various ASCII control characters, \x
for a character code written with only two hex digits, NN
\u
for a Unicode character identified by its 4-hex-digit code point, and NNNN
\u{
for a Unicode character code point written with 1 to 6 hex digits. N...
}
A path expression consists of a sequence of path elements, which can be the following:
Path literals of JSON primitive types: Unicode text, numeric, true, false, or null.
Path variables listed in Table 8.24.
Accessor operators listed in Table 8.25.
jsonpath
operators and methods listed in Section 9.15.2.3Parentheses, which can be used to provide filter expressions or define the order of path evaluation.
For details on using jsonpath
expressions with SQL/JSON query functions, see Section 9.15.2.
Table 8.24. jsonpath
Variables
Variable | Description |
---|---|
$ | A variable representing the JSON text to be queried (the context item). |
$varname | A named variable. Its value can be set by the parameter vars of several JSON processing functions. See Table 9.47 and its notes for details. |
@ | A variable representing the result of path evaluation in filter expressions. |
Table 8.25. jsonpath
Accessors
Accessor Operator | Description |
---|---|
| Member accessor that returns an object member with the specified key. If the key name is a named variable starting with |
| Wildcard member accessor that returns the values of all members located at the top level of the current object. |
| Recursive wildcard member accessor that processes all levels of the JSON hierarchy of the current object and returns all the member values, regardless of their nesting level. This is a Postgres Pro extension of the SQL/JSON standard. |
| Same as |
| Array element accessor. The specified |
| Wildcard array element accessor that returns all array elements. |
[6] For this purpose, the term “value” includes array elements, though JSON terminology sometimes considers array elements distinct from values within objects.