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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>8.14. JSON Types</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="datatype-xml.html" title="8.13. XML Type" /><link rel="next" href="arrays.html" title="8.15. Arrays" /></head><body><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">8.14. <acronym xmlns="http://www.w3.org/1999/xhtml" class="acronym">JSON</acronym> Types</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="datatype-xml.html" title="8.13. XML Type">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="datatype.html" title="Chapter 8. Data Types">Up</a></td><th width="60%" align="center">Chapter 8. Data Types</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 11.5 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="arrays.html" title="8.15. Arrays">Next</a></td></tr></table><hr></hr></div><div class="sect1" id="DATATYPE-JSON"><div class="titlepage"><div><div><h2 class="title" style="clear: both">8.14. <acronym class="acronym">JSON</acronym> Types</h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="datatype-json.html#JSON-KEYS-ELEMENTS">8.14.1. JSON Input and Output Syntax</a></span></dt><dt><span class="sect2"><a href="datatype-json.html#JSON-DOC-DESIGN">8.14.2. Designing JSON documents effectively</a></span></dt><dt><span class="sect2"><a href="datatype-json.html#JSON-CONTAINMENT">8.14.3. <code class="type">jsonb</code> Containment and Existence</a></span></dt><dt><span class="sect2"><a href="datatype-json.html#JSON-INDEXING">8.14.4. <code class="type">jsonb</code> Indexing</a></span></dt><dt><span class="sect2"><a href="datatype-json.html#id-1.5.7.22.18">8.14.5. Transforms</a></span></dt></dl></div><a id="id-1.5.7.22.2" class="indexterm"></a><a id="id-1.5.7.22.3" class="indexterm"></a><p>
JSON data types are for storing JSON (JavaScript Object Notation)
data, as specified in <a class="ulink" href="https://tools.ietf.org/html/rfc7159" target="_top">RFC
7159</a>. Such data can also be stored as <code class="type">text</code>, 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 <a class="xref" href="functions-json.html" title="9.15. JSON Functions and Operators">Section 9.15</a>.
</p><p>
There are two JSON data types: <code class="type">json</code> and <code class="type">jsonb</code>.
They accept <span class="emphasis"><em>almost</em></span> identical sets of values as
input. The major practical difference is one of efficiency. The
<code class="type">json</code> data type stores an exact copy of the input text,
which processing functions must reparse on each execution; while
<code class="type">jsonb</code> 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. <code class="type">jsonb</code> also supports indexing, which can be a
significant advantage.
</p><p>
Because the <code class="type">json</code> 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, <code class="type">jsonb</code> 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.
</p><p>
In general, most applications should prefer to store JSON data as
<code class="type">jsonb</code>, unless there are quite specialized needs, such as
legacy assumptions about ordering of object keys.
</p><p>
<span class="productname">PostgreSQL</span> 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.
</p><p>
RFC 7159 permits JSON strings to contain Unicode escape sequences
denoted by <code class="literal">\u<em class="replaceable"><code>XXXX</code></em></code>. In the input
function for the <code class="type">json</code> type, Unicode escapes are allowed
regardless of the database encoding, and are checked only for syntactic
correctness (that is, that four hex digits follow <code class="literal">\u</code>).
However, the input function for <code class="type">jsonb</code> is stricter: it disallows
Unicode escapes for non-ASCII characters (those above <code class="literal">U+007F</code>)
unless the database encoding is UTF8. The <code class="type">jsonb</code> type also
rejects <code class="literal">\u0000</code> (because that cannot be represented in
<span class="productname">PostgreSQL</span>'s <code class="type">text</code> 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.
</p><div class="note"><h3 class="title">Note</h3><p>
Many of the JSON processing functions described
in <a class="xref" href="functions-json.html" title="9.15. JSON Functions and Operators">Section 9.15</a> 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 <code class="type">json</code>
not <code class="type">jsonb</code>. The fact that the <code class="type">json</code> 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.
</p></div><p>
When converting textual JSON input into <code class="type">jsonb</code>, the primitive
types described by <acronym class="acronym">RFC</acronym> 7159 are effectively mapped onto
native <span class="productname">PostgreSQL</span> types, as shown
in <a class="xref" href="datatype-json.html#JSON-TYPE-MAPPING-TABLE" title="Table 8.23. JSON primitive types and corresponding PostgreSQL types">Table 8.23</a>.
Therefore, there are some minor additional constraints on what
constitutes valid <code class="type">jsonb</code> data that do not apply to
the <code class="type">json</code> type, nor to JSON in the abstract, corresponding
to limits on what can be represented by the underlying data type.
Notably, <code class="type">jsonb</code> will reject numbers that are outside the
range of the <span class="productname">PostgreSQL</span> <code class="type">numeric</code> data
type, while <code class="type">json</code> will not. Such implementation-defined
restrictions are permitted by <acronym class="acronym">RFC</acronym> 7159. However, in
practice such problems are far more likely to occur in other
implementations, as it is common to represent JSON's <code class="type">number</code>
primitive type as IEEE 754 double precision floating point
(which <acronym class="acronym">RFC</acronym> 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 <span class="productname">PostgreSQL</span> should be considered.
</p><p>
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 <span class="productname">PostgreSQL</span> types.
</p><div class="table" id="JSON-TYPE-MAPPING-TABLE"><p class="title"><strong>Table 8.23. JSON primitive types and corresponding <span class="productname">PostgreSQL</span> types</strong></p><div class="table-contents"><table class="table" summary="JSON primitive types and corresponding PostgreSQL types" border="1"><colgroup><col /><col /><col /></colgroup><thead><tr><th>JSON primitive type</th><th><span class="productname">PostgreSQL</span> type</th><th>Notes</th></tr></thead><tbody><tr><td><code class="type">string</code></td><td><code class="type">text</code></td><td><code class="literal">\u0000</code> is disallowed, as are non-ASCII Unicode
escapes if database encoding is not UTF8</td></tr><tr><td><code class="type">number</code></td><td><code class="type">numeric</code></td><td><code class="literal">NaN</code> and <code class="literal">infinity</code> values are disallowed</td></tr><tr><td><code class="type">boolean</code></td><td><code class="type">boolean</code></td><td>Only lowercase <code class="literal">true</code> and <code class="literal">false</code> spellings are accepted</td></tr><tr><td><code class="type">null</code></td><td>(none)</td><td>SQL <code class="literal">NULL</code> is a different concept</td></tr></tbody></table></div></div><br class="table-break" /><div class="sect2" id="JSON-KEYS-ELEMENTS"><div class="titlepage"><div><div><h3 class="title">8.14.1. JSON Input and Output Syntax</h3></div></div></div><p>
The input/output syntax for the JSON data types is as specified in
<acronym class="acronym">RFC</acronym> 7159.
</p><p>
The following are all valid <code class="type">json</code> (or <code class="type">jsonb</code>) expressions:
</p><pre class="programlisting">
-- 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;
</pre><p>
</p><p>
As previously stated, when a JSON value is input and then printed without
any additional processing, <code class="type">json</code> outputs the same text that was
input, while <code class="type">jsonb</code> does not preserve semantically-insignificant
details such as whitespace. For example, note the differences here:
</p><pre class="programlisting">
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)
</pre><p>
One semantically-insignificant detail worth noting is that
in <code class="type">jsonb</code>, numbers will be printed according to the behavior of the
underlying <code class="type">numeric</code> type. In practice this means that numbers
entered with <code class="literal">E</code> notation will be printed without it, for
example:
</p><pre class="programlisting">
SELECT '{"reading": 1.230e-5}'::json, '{"reading": 1.230e-5}'::jsonb;
json | jsonb
-----------------------+-------------------------
{"reading": 1.230e-5} | {"reading": 0.00001230}
(1 row)
</pre><p>
However, <code class="type">jsonb</code> will preserve trailing fractional zeroes, as seen
in this example, even though those are semantically insignificant for
purposes such as equality checks.
</p></div><div class="sect2" id="JSON-DOC-DESIGN"><div class="titlepage"><div><div><h3 class="title">8.14.2. Designing JSON documents effectively</h3></div></div></div><p>
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 <span class="quote">“<span class="quote">documents</span>”</span> (datums)
in a table.
</p><p>
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.
</p></div><div class="sect2" id="JSON-CONTAINMENT"><div class="titlepage"><div><div><h3 class="title">8.14.3. <code class="type">jsonb</code> Containment and Existence</h3></div></div></div><a id="id-1.5.7.22.16.2" class="indexterm"></a><a id="id-1.5.7.22.16.3" class="indexterm"></a><p>
Testing <em class="firstterm">containment</em> is an important capability of
<code class="type">jsonb</code>. There is no parallel set of facilities for the
<code class="type">json</code> type. Containment tests whether
one <code class="type">jsonb</code> document has contained within it another one.
These examples return true except as noted:
</p><pre class="programlisting">
-- 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 <span class="emphasis"><strong>not</strong></span> 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;
</pre><p>
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.
</p><p>
As a special exception to the general principle that the structures
must match, an array may contain a primitive value:
</p><pre class="programlisting">
-- 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
</pre><p>
<code class="type">jsonb</code> also has an <em class="firstterm">existence</em> operator, which is
a variation on the theme of containment: it tests whether a string
(given as a <code class="type">text</code> value) appears as an object key or array
element at the top level of the <code class="type">jsonb</code> value.
These examples return true except as noted:
</p><pre class="programlisting">
-- 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';
</pre><p>
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.
</p><div class="tip"><h3 class="title">Tip</h3><p>
Because JSON containment is nested, an appropriate query can skip
explicit selection of sub-objects. As an example, suppose that we have
a <code class="structfield">doc</code> column containing objects at the top level, with
most objects containing <code class="literal">tags</code> fields that contain arrays of
sub-objects. This query finds entries in which sub-objects containing
both <code class="literal">"term":"paris"</code> and <code class="literal">"term":"food"</code> appear,
while ignoring any such keys outside the <code class="literal">tags</code> array:
</p><pre class="programlisting">
SELECT doc->'site_name' FROM websites
WHERE doc @> '{"tags":[{"term":"paris"}, {"term":"food"}]}';
</pre><p>
One could accomplish the same thing with, say,
</p><pre class="programlisting">
SELECT doc->'site_name' FROM websites
WHERE doc->'tags' @> '[{"term":"paris"}, {"term":"food"}]';
</pre><p>
but that approach is less flexible, and often less efficient as well.
</p><p>
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.
</p></div><p>
The various containment and existence operators, along with all other
JSON operators and functions are documented
in <a class="xref" href="functions-json.html" title="9.15. JSON Functions and Operators">Section 9.15</a>.
</p></div><div class="sect2" id="JSON-INDEXING"><div class="titlepage"><div><div><h3 class="title">8.14.4. <code class="type">jsonb</code> Indexing</h3></div></div></div><a id="id-1.5.7.22.17.2" class="indexterm"></a><p>
GIN indexes can be used to efficiently search for
keys or key/value pairs occurring within a large number of
<code class="type">jsonb</code> documents (datums).
Two GIN <span class="quote">“<span class="quote">operator classes</span>”</span> are provided, offering different
performance and flexibility trade-offs.
</p><p>
The default GIN operator class for <code class="type">jsonb</code> supports queries with
top-level key-exists operators <code class="literal">?</code>, <code class="literal">?&</code>
and <code class="literal">?|</code> operators and path/value-exists operator
<code class="literal">@></code>.
(For details of the semantics that these operators
implement, see <a class="xref" href="functions-json.html#FUNCTIONS-JSONB-OP-TABLE" title="Table 9.44. Additional jsonb Operators">Table 9.44</a>.)
An example of creating an index with this operator class is:
</p><pre class="programlisting">
CREATE INDEX idxgin ON api USING GIN (jdoc);
</pre><p>
The non-default GIN operator class <code class="literal">jsonb_path_ops</code>
supports indexing the <code class="literal">@></code> operator only.
An example of creating an index with this operator class is:
</p><pre class="programlisting">
CREATE INDEX idxginp ON api USING GIN (jdoc jsonb_path_ops);
</pre><p>
</p><p>
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:
</p><pre class="programlisting">
{
"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"
]
}
</pre><p>
We store these documents in a table named <code class="structname">api</code>,
in a <code class="type">jsonb</code> column named <code class="structfield">jdoc</code>.
If a GIN index is created on this column,
queries like the following can make use of the index:
</p><pre class="programlisting">
-- Find documents in which the key "company" has value "Magnafone"
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @> '{"company": "Magnafone"}';
</pre><p>
However, the index could not be used for queries like the
following, because though the operator <code class="literal">?</code> is indexable,
it is not applied directly to the indexed column <code class="structfield">jdoc</code>:
</p><pre class="programlisting">
-- Find documents in which the key "tags" contains key or array element "qui"
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc -> 'tags' ? 'qui';
</pre><p>
Still, with appropriate use of expression indexes, the above
query can use an index. If querying for particular items within
the <code class="literal">"tags"</code> key is common, defining an index like this
may be worthwhile:
</p><pre class="programlisting">
CREATE INDEX idxgintags ON api USING GIN ((jdoc -> 'tags'));
</pre><p>
Now, the <code class="literal">WHERE</code> clause <code class="literal">jdoc -> 'tags' ? 'qui'</code>
will be recognized as an application of the indexable
operator <code class="literal">?</code> to the indexed
expression <code class="literal">jdoc -> 'tags'</code>.
(More information on expression indexes can be found in <a class="xref" href="indexes-expressional.html" title="11.7. Indexes on Expressions">Section 11.7</a>.)
</p><p>
Another approach to querying is to exploit containment, for example:
</p><pre class="programlisting">
-- Find documents in which the key "tags" contains array element "qui"
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @> '{"tags": ["qui"]}';
</pre><p>
A simple GIN index on the <code class="structfield">jdoc</code> column can support this
query. But note that such an index will store copies of every key and
value in the <code class="structfield">jdoc</code> column, whereas the expression index
of the previous example stores only data found under
the <code class="literal">tags</code> 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.
</p><p>
Although the <code class="literal">jsonb_path_ops</code> operator class supports
only queries with the <code class="literal">@></code> operator, it has notable
performance advantages over the default operator
class <code class="literal">jsonb_ops</code>. A <code class="literal">jsonb_path_ops</code>
index is usually much smaller than a <code class="literal">jsonb_ops</code>
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.
</p><p>
The technical difference between a <code class="literal">jsonb_ops</code>
and a <code class="literal">jsonb_path_ops</code> 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.
<a href="#ftn.id-1.5.7.22.17.8.3" class="footnote"><sup class="footnote" id="id-1.5.7.22.17.8.3">[6]</sup></a>
Basically, each <code class="literal">jsonb_path_ops</code> index item is
a hash of the value and the key(s) leading to it; for example to index
<code class="literal">{"foo": {"bar": "baz"}}</code>, a single index item would
be created incorporating all three of <code class="literal">foo</code>, <code class="literal">bar</code>,
and <code class="literal">baz</code> 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 <code class="literal">foo</code>
appears as a key. On the other hand, a <code class="literal">jsonb_ops</code>
index would create three index items representing <code class="literal">foo</code>,
<code class="literal">bar</code>, and <code class="literal">baz</code> 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 <code class="literal">jsonb_path_ops</code> search, especially if
there are a very large number of rows containing any single one of the
three index items.
</p><p>
A disadvantage of the <code class="literal">jsonb_path_ops</code> approach is
that it produces no index entries for JSON structures not containing
any values, such as <code class="literal">{"a": {}}</code>. If a search for
documents containing such a structure is requested, it will require a
full-index scan, which is quite slow. <code class="literal">jsonb_path_ops</code> is
therefore ill-suited for applications that often perform such searches.
</p><p>
<code class="type">jsonb</code> also supports <code class="literal">btree</code> and <code class="literal">hash</code>
indexes. These are usually useful only if it's important to check
equality of complete JSON documents.
The <code class="literal">btree</code> ordering for <code class="type">jsonb</code> datums is seldom
of great interest, but for completeness it is:
</p><pre class="synopsis">
<em class="replaceable"><code>Object</code></em> > <em class="replaceable"><code>Array</code></em> > <em class="replaceable"><code>Boolean</code></em> > <em class="replaceable"><code>Number</code></em> > <em class="replaceable"><code>String</code></em> > <em class="replaceable"><code>Null</code></em>
<em class="replaceable"><code>Object with n pairs</code></em> > <em class="replaceable"><code>object with n - 1 pairs</code></em>
<em class="replaceable"><code>Array with n elements</code></em> > <em class="replaceable"><code>array with n - 1 elements</code></em>
</pre><p>
Objects with equal numbers of pairs are compared in the order:
</p><pre class="synopsis">
<em class="replaceable"><code>key-1</code></em>, <em class="replaceable"><code>value-1</code></em>, <em class="replaceable"><code>key-2</code></em> ...
</pre><p>
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:
</p><pre class="programlisting">
{ "aa": 1, "c": 1} > {"b": 1, "d": 1}
</pre><p>
Similarly, arrays with equal numbers of elements are compared in the
order:
</p><pre class="synopsis">
<em class="replaceable"><code>element-1</code></em>, <em class="replaceable"><code>element-2</code></em> ...
</pre><p>
Primitive JSON values are compared using the same
comparison rules as for the underlying
<span class="productname">PostgreSQL</span> data type. Strings are
compared using the default database collation.
</p></div><div class="sect2" id="id-1.5.7.22.18"><div class="titlepage"><div><div><h3 class="title">8.14.5. Transforms</h3></div></div></div><p>
Additional extensions are available that implement transforms for the
<code class="type">jsonb</code> type for different procedural languages.
</p><p>
The extensions for PL/Perl are called <code class="literal">jsonb_plperl</code> and
<code class="literal">jsonb_plperlu</code>. If you use them, <code class="type">jsonb</code>
values are mapped to Perl arrays, hashes, and scalars, as appropriate.
</p><p>
The extensions for PL/Python are called <code class="literal">jsonb_plpythonu</code>,
<code class="literal">jsonb_plpython2u</code>, and
<code class="literal">jsonb_plpython3u</code> (see <a class="xref" href="plpython-python23.html" title="46.1. Python 2 vs. Python 3">Section 46.1</a> for the PL/Python naming convention). If you
use them, <code class="type">jsonb</code> values are mapped to Python dictionaries,
lists, and scalars, as appropriate.
</p></div><div class="footnotes"><br /><hr style="width:100; text-align:left;margin-left: 0" /><div id="ftn.id-1.5.7.22.17.8.3" class="footnote"><p><a href="#id-1.5.7.22.17.8.3" class="para"><sup class="para">[6] </sup></a>
For this purpose, the term <span class="quote">“<span class="quote">value</span>”</span> includes array elements,
though JSON terminology sometimes considers array elements distinct
from values within objects.
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