Working with JSON Data

LightDB-A Database supports JSON as specified in the RFC 7159 document and enforces data validity according to the JSON rules. There are also JSON-specific functions and operators available for the json and jsonb data types. See JSON Functions and Operators.

LightDB-A Database offers two types for storing JSON (JavaScript Object Notation) data: json and jsonb. To implement efficient query mechanisms for these data types, LightDB-A Database also provides the jsonpath data type described in jsonpath Type later in this topic.

This section contains the following topics:

• About JSON Data
• JSON Input and Output Syntax
• Designing JSON documents
• jsonb Containment and Existence
• jsonb Indexing
• Transforms
• jsonpath Type

Parent topic: Querying Data

About JSON Data

The json and jsonb data types accept almost identical sets of values as input. The major difference is one of efficiency.

• The json data type stores an exact copy of the input text. This requires JSON processing functions to reparse json data on each execution. The json data type does not alter the input text.
  • Semantically-insignificant white space between tokens is preserved, as well as the order of keys within JSON objects.
  • All key/value pairs are kept even if a JSON object contains duplicate keys. For duplicate keys, JSON processing functions consider the last value as the operative one.

• The jsonb data type stores a decomposed binary format of the input text. The conversion overhead makes data input slightly slower than the json data type. However, The JSON processing functions are significantly faster because reparsing jsonb data is not required. The jsonb data type alters the input text.

  • White space is not preserved.
  • The order of object keys is not preserved.
  • Duplicate object keys are not kept. If the input includes duplicate keys, only the last value is kept.

• The jsonb data type supports indexing. See jsonb Indexing.

In general, JSON data should be stored as the jsonb data type unless there are specialized needs, such as legacy assumptions about ordering of object keys.

LightDB-A Database 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 are allowed.

About Unicode Characters in JSON Data

The RFC 7159 document permits JSON strings to contain Unicode escape sequences denoted by \uXXXX.

• The LightDB-A Database input function for the json data type allows Unicode escapes regardless of the database encoding and checks Unicode escapes only for syntactic correctness (a \u followed by four hex digits).
• The LightDB-A Database input function for the jsonb data type is more strict. It does not allow Unicode escapes for non-ASCII characters (those above U+007F) unless the database encoding is UTF8. The jsonb type also rejects \u0000, which cannot be represented in the LightDB-A Database text type, and it requires that any use of Unicode surrogate pairs to designate characters outside the Unicode Basic Multilingual Plane be correct. Valid Unicode escapes, except for \u0000, 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 JSON Functions and Operators 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.

Mapping JSON Data Types to LightDB-A Data Types

When converting JSON text input into jsonb data, the primitive data types described by RFC 7159 are effectively mapped onto native LightDB-A Database data types, as shown in the following table.

There are some minor constraints on what constitutes valid jsonb data that do not apply to the json data type, nor to JSON in the abstract, corresponding to limits on what can be represented by the underlying data type. Notably, when converting data to the jsonb data type, numbers that are outside the range of the LightDB-A Database numeric data type are rejected, while the json data type does not reject such numbers.

Such implementation-defined restrictions are permitted by RFC 7159. However, in practice such problems might occur in other implementations, as it is common to represent the JSON 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 other systems, be aware of the possibility of losing numeric precision compared to data originally stored by LightDB-A Database.

Also, as noted in the previous table, there are some minor restrictions on the input format of JSON primitive types that do not apply to the corresponding LightDB-A Database data types.

JSON Input and Output Syntax

The input and output syntax for the json data type 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, the json data type outputs the same text that was input, while the jsonb data type 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 with the jsonb data type, 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, the jsonb data type preserves trailing fractional zeroes, as seen in previous example, even though those are semantically insignificant for purposes such as equality checks.

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 JSON 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.

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": "LightDB-A", "version": "7.0.0", "jsonb":true}'::jsonb @> '{"version":"7.0.0"}'::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

-- But with a layer of nesting, it 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. For containment, the order of array elements is not significant when doing a containment match, and duplicate array elements are effectively considered only once.

As an 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.

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"}]}';

The query with this predicate could accomplish the same thing:

SELECT doc->'site_name' FROM websites
  WHERE doc->'tags' @> '[{"term":"paris"}, {"term":"food"}]';

However, the second approach is less flexible and is 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 JSON Functions and Operators.

jsonb Indexing

The LightDB-A Database jsonb data type, supports GIN, btree, and hash indexes.

• GIN Indexes on jsonb Data
• Btree and Hash Indexes on jsonb Data

GIN Indexes on jsonb Data

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 @>, ?, ?& and ?| operators. (For details of the semantics that these operators implement, see JSON Operators.) 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. This is a typical document:

{
    "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"
    ]
}

The JSON documents are stored 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. The operator ? is indexable, however, the comparison 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';

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 might be worthwhile:

CREATE INDEX idxgintags ON api USING GIN ((jdoc -> 'tags'));

Now, the WHERE clause jdoc -> 'tags' ? 'qui' is recognized as an application of the indexable operator ? to the indexed expression jdoc -> 'tags'. For information about expression indexes, see Indexes on Expressions.

Another approach to querying JSON documents 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. However, the 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 accessors_chain = constant out of the jsonpath pattern, and does the index search based on the keys and values mentioned in these clauses. The accessors chain may include .key, [*], and [index] accessors. The 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.

Note For this discussion, the term value includes array elements, though JSON terminology sometimes considers array elements distinct from values within objects.

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 ill-suited for applications that often perform such searches.

Btree and Hash Indexes on jsonb Data

jsonb also supports btree and hash indexes. These are usually useful only when it is important to check the equality of complete JSON documents.

For completeness, the btree ordering for jsonb datums 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 ...

Object keys are compared in their storage order. In particular, since shorter keys are stored before longer keys, this can lead to orderings that might not be intuitive, 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 LightDB-A Database data type. Strings are compared using the default database collation.

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. If you use them, jsonb values are mapped to Python dictionaries, lists, and scalars, as appropriate.

jsonpath Type

The jsonpath type implements support for the SQL/JSON path language in LightDB-A Database 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. 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 JSON strings: \b, \f, \n, \r, \t, \v for various ASCII control characters, and \uNNNN for a Unicode character identified by its 4-hex-digit code point. The backslash syntax also includes two cases not allowed by JSON: \xNN for a character code written with only two hex digits, and \u{N...} for a character code written with 1 to 6 hex digits.

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 the jsonpath Variables table below.

• Accessor operators listed in the jsonpath Accessors table below.

• jsonpath operators and methods listed in SQL/JSON Path Operators and Methods.

• Parentheses, 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 SQL/JSON Filter Expression Elements.

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