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https://github.com/postgrespro/jsquery

JsQuery – json query language with GIN indexing support
https://github.com/postgrespro/jsquery

gin json json-query jsonb query-language

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JsQuery – json query language with GIN indexing support

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JsQuery – json query language with GIN indexing support

=======================================================



Introduction

------------



JsQuery – is a language to query jsonb data type, introduced in PostgreSQL

release 9.4.



It's primary goal is to provide an additional functionality to jsonb

(currently missing in PostgreSQL), such as a simple and effective way

to search in nested objects and arrays, more comparison operators with

indexes support. We hope, that jsquery will be eventually a part of

PostgreSQL.



Jsquery is released as jsquery data type (similar to tsquery) and @@

match operator for jsonb.



Authors

-------



 * Teodor Sigaev , Postgres Professional, Moscow, Russia

 * Alexander Korotkov , Postgres Professional, Moscow, Russia

 * Oleg Bartunov , Postgres Professional, Moscow, Russia



Availability

------------



JsQuery is realized as an extension and not available in default PostgreSQL

installation. It is available from

[github](https://github.com/postgrespro/jsquery)

under the same license as

[PostgreSQL](https://www.postgresql.org/about/licence/)

and supports PostgreSQL 9.4+.



Regards

-------



Development was sponsored by [Wargaming.net](http://wargaming.net).



Installation

------------



JsQuery is PostgreSQL extension which requires PostgreSQL 9.4 or higher.

Before build and install you should ensure following:

    

 * PostgreSQL version is 9.4 or higher.

 * You have development package of PostgreSQL installed or you built

   PostgreSQL from source.

 * You have flex and bison installed on your system. JsQuery was tested on

   flex 2.5.37-2.5.39, bison 2.7.12.

 * Your PATH variable is configured so that pg\_config command available, or set PG_CONFIG variable.

    

Typical installation procedure may look like this:

    

    $ git clone https://github.com/postgrespro/jsquery.git

    $ cd jsquery

    $ make USE_PGXS=1

    $ sudo make USE_PGXS=1 install

    $ make USE_PGXS=1 installcheck

    $ psql DB -c "CREATE EXTENSION jsquery;"



JSON query language

-------------------



JsQuery extension contains `jsquery` datatype which represents whole JSON query

as a single value (like `tsquery` does for fulltext search). The query is an

expression on JSON-document values.



Simple expression is specified as `path binary_operator value` or

`path unary_operator`. See following examples.



 * `x = "abc"` – value of key "x" is equal to "abc";

 * `$ @> [4, 5, "zzz"]` – the JSON document is an array containing values

    4, 5 and "zzz";

 * `"abc xyz" >= 10` – value of key "abc xyz" is greater than or equal to 10;

 * `volume IS NUMERIC` – type of key "volume" is numeric.

 * `$ = true` – the whole JSON document is just a true.

 * `similar_ids.@# > 5` – similar\_ids is an array or object of length greater

   than 5;

 * `similar_product_ids.# = "0684824396"` – array "similar\_product\_ids"

   contains string "0684824396".

 * `*.color = "red"` – there is object somewhere which key "color" has value "red".

 * `foo = *` – key "foo" exists in object.



Path selects a set of JSON values to be checked using given operators. In

the simplest case path is just a key name. In general path is key names and

placeholders combined by dot signs. Path can use the following placeholders:



 * `#` – any index of an array;

 * `#N` – N-th index of an array;

 * `%` – any key of an object;

 * `*` – any sequence of array indexes and object keys;

 * `@#` – length of array or object, may only be used as the last component of

    a path;

 * `$` – the whole JSON document as single value, may only be the whole path.



Expression is true when operator is true against at least one value selected

by path.



Key names could be given either with or without double quotes. Key names

without double quotes may not contain spaces, start with a number or match

a jsquery keyword.



The supported binary operators are:



 * Equality operator: `=`;

 * Numeric comparison operators: `>`, `>=`, `<`, `<=`;

 * Search in the list of scalar values using `IN` operator;

 * Array comparison operators: `&&` (overlap), `@>` (contains),

   `<@` (contained in).



The supported unary operators are:



 * Check for existence operator: `= *`;

 * Check for type operators: `IS ARRAY`, `IS NUMERIC`, `IS OBJECT`, `IS STRING`

   and `IS BOOLEAN`.



Expressions can be complex. Complex expression is a set of expressions

combined by logical operators (`AND`, `OR`, `NOT`) and grouped using braces.



Examples of complex expressions:



 * `a = 1 AND (b = 2 OR c = 3) AND NOT d = 1`

 * `x.% = true OR x.# = true`



Prefix expressions are expressions given in the form `path (subexpression)`.

In this case path selects JSON values to be checked using the given subexpression.

Check results are aggregated in the same way as in simple expressions.



 * `#(a = 1 AND b = 2)` – exists element of array which a key is 1 and b key is 2

 * `%($ >= 10 AND $ <= 20)` – exists object key which values is between 10 and 20



Path can also contain the following special placeholders with "every" semantics:



 * `#:` – every index of an array;

 * `%:` – every key of an object;

 * `*:` – every sequence of array indexes and object keys.



Consider following example.



    %.#:($ >= 0 AND $ <= 1)



This example could be read as following: there is at least one key whose value

is an array of numerics between 0 and 1.



We can rewrite this example in the following form with extra braces:



    %(#:($ >= 0 AND $ <= 1))



The first placeholder `%` checks that the expression in braces is true for at least

one value in the object. The second placeholder `#:` checks if the value is an array

and that all its elements satisfy the expressions in braces.



We can rewrite this example without the `#:` placeholder as follows:



    %(NOT #(NOT ($ >= 0 AND $ <= 1)) AND $ IS ARRAY)



In this example we transform the assertion that every element of array satisfy some

condition to an assertion that there are no elements which don't satisfy the same

condition.



Some examples of using paths:



 * `numbers.#: IS NUMERIC` – every element of "numbers" array is numeric.

 * `*:($ IS OBJECT OR $ IS BOOLEAN)` – JSON is a structure of nested objects

   with booleans as leaf values.

 * `#:.%:($ >= 0 AND $ <= 1)` – each element of array is an object containing

   only numeric values between 0 and 1.

 * `documents.#:.% = *` – "documents" is an array of objects containing at least

   one key.

 * `%.#: ($ IS STRING)` – JSON object contains at least one array of strings.

 * `#.% = true` – at least one array element is an object which contains at least

   one "true" value.



The use of path operators and braces need some further explanation. When the same path

operators are used multiple times, they may refer to different values. If you want them

to always refer to the same value, you must use braces and the `$` operator. For example:



 * `# < 10 AND # > 20` – an element less than 10 exists, and another element

   greater than 20 exists.

 * `#($ < 10 AND $ > 20)` – an element which is both less than 10 and greater

   than 20 exists (impossible).

 * `#($ >= 10 AND $ <= 20)` – an element between 10 and 20 exists.

 * `# >= 10 AND # <= 20` – an element greater or equal to 10 exists, and another

   element less or equal to 20 exists. Please note that this query also can be

   satisfied by an array with no elements between 10 and 20, for instance [0,30].



Same rules apply when searching inside objects and branch structures.



Type checking operators and "every" placeholders are useful for document

schema validation. JsQuery matchig operator `@@` is immutable and can be used

in CHECK constraint. See following example.



```sql

CREATE TABLE js (

    id serial,

    data jsonb,

    CHECK (data @@ '

        name IS STRING AND

        similar_ids.#: IS NUMERIC AND

        points.#:(x IS NUMERIC AND y IS NUMERIC)'::jsquery));

```



In this example the check constraint validates that in the "data" jsonb column

the value of the "name" key is a string, the value of the "similar_ids" key is an array of numerics,

and the value of the "points" key is an array of objects which contain numeric values in

"x" and "y" keys.



See our

[pgconf.eu presentation](http://www.sai.msu.su/~megera/postgres/talks/pgconfeu-2014-jsquery.pdf)

for more examples.



GIN indexes

-----------



JsQuery extension contains two operator classes (opclasses) for GIN which

provide different kinds of query optimization.



 * jsonb\_path\_value\_ops

 * jsonb\_value\_path\_ops



In each of two GIN opclasses jsonb documents are decomposed into entries. Each

entry is associated with a particular value and its path. The difference between

opclasses is in the entry representation, comparison and usage for search

optimization.



For example, the jsonb document

`{"a": [{"b": "xyz", "c": true}, 10], "d": {"e": [7, false]}}`

would be decomposed into following entries:



 * "a".#."b"."xyz"

 * "a".#."c".true

 * "a".#.10

 * "d"."e".#.7

 * "d"."e".#.false



Since JsQuery doesn't support searching in a particular array index, we consider

all array elements to be equivalent. Thus, each array element is marked with

the same `#` sign in its path.



Major problem in the entries representation is its size. In the given example

the key "a" is presented three times. In large branchy documents with long

keys sizes of naive entries, the representation becomes unreasonably large.

Both opclasses address this issue, but in slightly different ways.



### jsonb\_path\_value\_ops



jsonb\_path\_value\_ops represents entry as pair of path hash and value.

Following pseudocode illustrates it:



    (hash(path_item_1.path_item_2. ... .path_item_n); value)



When comparison entries, the path hash is the higher part of entry and the value is

the lower part. This determines the features of this opclass. Since the path

is hashed and it's the higher part of the entry, we need to know the full path to

a value in order to use the it for searching. However, once the path is specified

we can use both exact and range searches very efficiently.



### jsonb\_value\_path\_ops



jsonb\_value\_path\_ops represents entry as pair of the value and a bloom filter

of paths:



    (value; bloom(path_item_1) | bloom(path_item_2) | ... | bloom(path_item_n))



In comparison of entries value is the higher part of entry and bloom filter of

path is its lower part. This determines the features of this opclass. Since

the value is the higher part of an entry, we can only perform exact value search

effectively. A search over a range of values is possible as well, but we have to

filter all the the different paths where matching values occur. The Bloom filter

over path items allows the index to be used for conditions containing `%` and `*` in

their paths.



### Query optimization



JsQuery opclasses perform complex query optimization. It's valuable for a

developer or administrator to see the result of such optimization.

Unfortunately, opclasses aren't allowed to put any custom output in an

EXPLAIN. That's why JsQuery provides these functions to let you see

how particular opclass optimizes given query:



 * gin\_debug\_query\_path\_value(jsquery) – for jsonb\_path\_value\_ops

 * gin\_debug\_query\_value\_path(jsquery) – for jsonb\_value\_path\_ops



The result of these functions is a textual representation of the query tree

where leaves are GIN search entries. Following examples show different results of

query optimization by different opclasses:



    # SELECT gin_debug_query_path_value('x = 1 AND (*.y = 1 OR y = 2)');

     gin_debug_query_path_value

    ----------------------------

     x = 1 , entry 0           +



    # SELECT gin_debug_query_value_path('x = 1 AND (*.y = 1 OR y = 2)');

     gin_debug_query_value_path

    ----------------------------

     AND                       +

       x = 1 , entry 0         +

       OR                      +

         *.y = 1 , entry 1     +

         y = 2 , entry 2       +



Unfortunately, jsonb have no statistics yet. That's why JsQuery optimizer has

to do imperative decision while selecting conditions to be evaluated using

index. This decision is made by assuming that some condition types are less

selective than others. The optimizer divides conditions into following selectivity

classes (listed in descending order of selectivity):



 1. Equality (x = c)

 2. Range (c1 < x < c2)

 3. Inequality (x > c)

 4. Is (x is type)

 5. Any (x = \*)



The optimizer avoids index evaluation of less selective conditions when possible.

For example, in the `x = 1 AND y > 0` query `x = 1` is assumed to be more

selective than `y > 0`. That's why the index isn't used for evaluation of `y > 0`.



    # SELECT gin_debug_query_path_value('x = 1 AND y > 0');

     gin_debug_query_path_value

    ----------------------------

     x = 1 , entry 0           +



With the lack of statistics, decisions made by optimizer can be inaccurate. That's

why JsQuery supports hints. The comments `/*-- index */` or `/*-- noindex */`

placed in the conditions force the optimizer to use or not use an index

correspondingly:



    SELECT gin_debug_query_path_value('x = 1 AND y /*-- index */ > 0');

     gin_debug_query_path_value

    ----------------------------

     AND                       +

       x = 1 , entry 0         +

       y > 0 , entry 1         +



    SELECT gin_debug_query_path_value('x /*-- noindex */ = 1 AND y > 0');

     gin_debug_query_path_value

     ----------------------------

      y > 0 , entry 0           +



Contribution

------------



Please note that JsQuery is still under development. While it's

stable and tested, it may contain some bugs. Don't hesitate to create

[issues at github](https://github.com/postgrespro/jsquery/issues) with your

bug reports.



If there's some functionality you'd like to see added to JsQuery and you feel

like you can implement it, then you're welcome to make pull requests.