SQL for JSON Rationalization Part 6: Restriction – General Discussion

After discussing projection, selection is up next in the blog series on SQL for JSON. This first blog on selection focuses on the scalar JSON types Number and String.

Demo Data

As usual, we start with demo data. The collection for this blog is called “selcoll” (for SelectionCollection) and contains the following documents:

select {*} from selcoll



Selection based on Literals

Selection is following the regular Relational SQL syntax and is straightforward. For this discussion only single predicates are shown, not (complex) Boolean expressions of predicates. Boolean expression of predicates follow the usual semantics and do not require a lot of discussion.

select {*} from selcoll where a.b = 25

This JSON SQL statement selects all documents from selcoll that have a property “a” and a property “b” within a sub-document of “a” with the value of 25.

The result is


The following selection has the same semantics and returns the same result:

select {*} from selcoll where 25 = a.b

A selection based on a String literal follows the same syntax:

select {*} from selcoll where c.[1] = 'foobar'

This returns


And the following selection returns the same result:

select {*} from selcoll where 'foobar' = c.[1]

In this context a note is in order. JSON uses double quotes as string delimiter, not single quotes, as SQL does. In order to stay as near as possible to Relational SQL, single quotes are used and transformed into double quotes by the underlying implementation.

Selection based on Value Comparison

It is possible to relate two different values within a document as well (aka, not a self-join that would related values of different documents – this will be discussed in a later blog).

select d.e as de, c.[0] as c0 from selcoll where d.e = c.[0]

This query selects all documents that have the same value in d.e and c.[0]. As added benefit the query projects to those two values as well.

The result is

|de                       |c0                       |
|"foo"                    |"foo"                    |
|"foo2"                   |"foo2"                   |

Any path can be related to any other path without restriction.

While in this blog only numbers and strings are discussed, the above discussed types of restrictions will work for all JSON data types in general, including true, false, null, objects and arrays (discussed in subsequent blogs).


The usual operators are defined: <, >, <>, =, >=, and <=. The semantics of these is defined for Number and String (Relational SQL semantics is taken). For the other JSON types they will have to be defined as the other JSON types do not have a corresponding Relational SQL domain.

Beyond these operators more “interesting” operations are required. For example

select {*} from selcoll where c contains 'foobar'

whereby “c” refers to a JSON array (and possibly a JSON object). This predicate would be true if there is an element in “c” that is of type String and the value of that element is “foobar”. There is a whole set of interesting operations that will be discussed at some point later as well.


As implicitly demonstrated above, a JSON document is only in the result set if (a) the path to the value as specified in the JSON SQL query is present and (b) the value is the value as indicated in the selection clause in JSON SQL.

If the path does not exist or the value does not have a matching value, no result is returned for that document (and not the empty document itself).

There is no implicit type transformation implemented. This means that a Number literal only matches number values, and a String literal only matches string values.

Syntax Twists

Syntax has always a twist, especially if different languages are combined. In this case one of the twists is the single quote. A single quote within a string is represented as two single quotes in Relational SQL. JSON SQL does not have that requirement since strings are delimited by double quotes in JSON and a single quote is treated as regular character. The reverse situation exists also: double quotes have to be escaped within a string in JSON, but not in Relational SQL.

The query (double quote, not escaped in JSON SQL)

select {*} from selcoll where c.[2] = 'ba"r'



And the query (two single quotes, escaped in JSON SQL)

select {*} from selcoll where c.[2] = 'ba''r'




Restriction (or selection) is almost straightforward for the types Number and String in context of JSON SQL. The only twist is the way Relational SQL and JSON SQL differ in denoting String literals as well as encode special characters.

Go [ JSON | Relational ] SQL!


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.

SQL for JSON Rationalization Part 5: Projection – Specific Functionality

The last blog introduced SQL JSON projection and this blog will discuss some of its finer points.

Demo Data

Here are the JSON documents from the collection “tinycoll” used throughout this blog:

select {*} from tinycoll

returns two JSON documents:

{"a": 5,
 "b": {"c": 10,"d": 11},
 "c": [101, 102, {"d": 103}, {"e": 104}]}

{"a": 5,
 "b2": [10, 11],
 "c": [101, 102, {"d": 103}, {"e": 104}]}

AS Clause

In Relational SQL it is possible to rename columns. The AS clause is the means to do this and it contains an alternative column name. Example:

select a as abc from tinycoll

The result contains a column called “abc” instead of “a” and this is standard Relational SQL semantics.

|abc                      |
|5                        |
|5                        |

What does an AS clause mean in context of JSON SQL? In context of JSON SQL an AS clause specifies a path. Example:

select {a as x.y} from tinycoll

The result contains documents with paths “x.y” that contain the value of the corresponding “a” in the original document (if “a” is present).


Fundamentally, it means that the value the original path “a” pointed to is now at a new path “x.y” and that can be seen as relocation that only takes place in the result document. Any valid path is possible in the AS clause.

So far the AS clause supports renaming as well as relocation. Relocation is orthogonal and does not affect the original document. For example, the following relocations are valid:

select {a as b, b as a} from tinycoll

Basically, the values are exchanged between the two paths “a” and “b” (which can be more complex paths, of course).


All AS clauses are applied independently of each other, not in sequence (and therefore “a” and “b” do not contain the same value because of this projection specification).

A final situation is overwriting, meaning, the path in the AS clause can be that of an existing path in a JSON document and that will overwrite the value in the result document. For example:

select {a as c.[0]} from tinycoll

The existing value of “c.[0]” is overwritten and contains the value of “a” in the result document if “a” exists in the original document.


There are a few language constraints that are checked for. These are

  • Path Subsumption. A path in an AS clause must not be a subpath in any other path; otherwise one AS clause might conflict with another one. An example for a violation is: “select {a as c.[2].d, b as c.[2]} from tinycoll”. This is analogous to Relational SQL not allowing the use of the same column name in two different AS clauses.
  • Asterisk Query. An asterisk query cannot have an AS clause; if any change is necessary by means of an AS clause, the paths have to be listed explicitly.
  • Relational Output Path. The path in an AS clause for relation output must be a single value (path of length one) in order to comply to the Relational SQL semantics/model.

Value Non-Existence

The AS clause might create a path in a result document that does not exist in the original document. For example:

select {a as x.[2]} from tinycoll

In this example, the original document does not have an array named “x”. However, the result document is going to have one if “a” is present. The path sets the value of “a” to the third array element, however, the first and second element do not have a value as those elements do not exist. The result of the query is


The JSON standard does not have a notation for an absent value, however, it is needed in order to describe accurately that values are undefined. Therefore, the symbol “<>” is introduced of type String in order to (a) denote that a value is undefined and to (b) represent it as a known data type so that JSON libraries can process it.

“<>” is randomly defined; it can be changed to another symbol as necessary. JSON null cannot be used as JSON null is a valid constant (aka, explicit JSON value) and in contrast to SQL null does not denote “unknown”. The use of JSON null might suggest that there is the value of JSON null, when in reality there is no value at all. Any trailing “<>” are removed and not present in the output JSON documents.

Array Element Replacement

It is possible to replace array elements selectively, for example:

select {a as c.[0], b as c.[1], c.[2]} from tinycoll

will result in


A shortcut syntax like c.[2..9] that refers to the 3rd until 10th elements inclusive is not supported at this point, but could be for convenience. If implemented at some point in time, then this section will be changed.

Likewise for a shortcut syntax like c.[..9], c.[2..] or c.[..] indicating all elements including the 10th, all starting with the 3rd, or all element respectively.

Additional Items

The “select distinct” clause is not specifically discussed as it has the intended semantics based on the JSON document equality definition.

An interesting case on projection is the mixed case, aka, some projection is relational, some other asks for the JSON form. For example,

select a, b, {c, d} from tinycoll

returns relational output, but with certain columns containing JSON objects that can be freely composed from one or more paths. This might be convenient from a final output viewpoint for the client, but would not contribute in major ways to a JSON SQL language definition. Therefore, it is not implemented as of now (and in case this decision changes, this section will be updated in the future).


Projection in context of JSON SQL is not all that straightforward compared to the Relational SQL semantics. This blog highlighted the most important finer points like the AS clause and array processing and outlined some of the additional possible extensions to an implementation.

Go [ JSON | Relational ] SQL!


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.

SQL for JSON Rationalization Part 4: Projection – General Functionality

After the demo in the last blog (Part 3) it is time to discuss some of the assumptions and the projection functionality in more detail – here and in the next blog.

Assumptions: Array Start Index, JSON Literals and JSON Value Equality

The JSON standard does not define the starting index of the first array element. The assumption made here is that the first index is 0 (zero).

The JSON standard requires the literals “null”, “true” and “false” to be lower case. However, the assumption made here is that all lower as well as upper case combinations work, e.g., “True”, for convenience.

Another aspect the JSON standard does not define is equality on JSON values. There are many ways to define when two JSON values are equal. Here equality is defined on the string representation of the JSON values that contain no white space and where the property names in JSON objects are sorted alphabetically.

Definitions: Full and Partial Path

A full path is a sequence of property names as well as array indexes from the root of a JSON document all the way to one of its leaves. The elements of a path are separated by “.”. For example, “c.[3].e” is a full path from the root “c” to the leaf “e” in one of the demo documents of the previous blog. A path must start with a property name and cannot start with an array index. A path cannot be empty and the minimum path consists of a single property name.

Using “.” as separator is a random choice, but made by many systems. Having array indexes enclosed in “[” and “]” is customary also. Denoting an array index as separate path element (aka, enclosed in “.”) is also a convenient choice.

Given a JSON object, a full path might exist within it or not. Given a JSON object and a path, using the path as an access structure identifies a value only if the full path exists in the JSON document. If the path does not exist within the JSON document then no value is identified; especially not the JSON literal “null”.

A partial path is a sequence of property names and array indexes starting at the root, but not necessarily ending at a leaf, but at an intermediary property or array index. This supports “reaching into” a JSON document and identifying a JSON value that is between the root and a leaf.

Like in case of full paths, given a JSON object, a partial path might or might not exist within it. A partial path only identifies a JSON value if the partial path exists within a JSON object. In this case it identifies a composite JSON value.

If a JSON document has only scalar properties, then the root properties are the leaf properties at the same time. Paths in this context are full paths and partial paths cannot exist.


Unlike in the relational model, in context of the JSON model the result of a query can be returned as a relational table, or as a set of JSON documents. The choice is made by the query author.

The projection in a select statement contains one or more (full or partial) paths. If the paths are enclosed by a “{“ and “}” then JSON documents are returned, otherwise a table  (the asterisk projection is discussed below).

For example, the query from the previous blog

select a, b.c, d.[3].e from tinycoll

returns a table with three columns.

Semantically, each path in the projection will be a separate column. Each document from the collection “tinycoll” is taken and a corresponding row is added to the table. For each path of the projection that is found in the document the value is added to the row. If a path does not exist, no value is added in the column corresponding to the path. Therefore, a row can have values in every column, in some columns, or in no column, depending if the paths exist in the document.

As in relational SQL, the order of the paths matters as the corresponding columns will be created in that order.

The column names are created from the paths by replacing “.” in the path representation with “_” as many relational systems do not support “.” as column names.

The query

select {a, b.c, d.[3].e} from tinycoll

returns a set of JSON documents.

Semantically, each document from the collection “tinycoll” is taken and an empty result document is created for it. Each of the paths from the projection are followed within the document from the collection. If a value is found, the path with the corresponding value is added to the result document. It is possible that the document from the collection contains all, some, or none of the paths from the projection. Therefore, the result document might contain all, some, or none of the paths (empty document).

The order of the paths in the projection does not matter as JSON documents are created and order of properties / paths is not defined for JSON objects.

As a note, according to the construction principle of the result JSON documents, the paths in the projection of the select statement and the paths in the result JSON documents are exactly the same (if they exist). No translation is necessary from the viewpoint of the client between the paths in the query and the paths in the result documents.

Asterisk Projection

The asterisk projection is supported. The query

select {*} from tinycoll

returns all documents stored in the collection “tinycoll” as they are without any modification.

The query

select * from tinycoll

Returns a table that has any many columns as there are full and partial paths into all documents of the collection “tinycoll”.

Semantically, each document from the collection “tinycoll” is taken and a row is created for it. For each full as well as partial path in the document the value is retrieved and put into the corresponding column of the row. There is a column for each possible path and the set of columns can be predetermined or dynamically added to the result table as needed. As before, the column names are the path with the “.” replaced by “_”.


This was a first closer look into the details of projection in context of JSON SQL and the next blog will continue the project discussion. The key take away is that JSON SQL can return JSON documents as well as tables based on a well-defined execution semantics centered around JSON paths.

Go [ JSON | Relational ] SQL!


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.


SQL for JSON Rationalization Part 3: Demo

The previous blog outlined an initial glimpse at a JSON SQL query language and how it works when applied to JSON documents. In the following, a demo shows a concrete implementation.

Command Line Interface

The command line interface provides a few basic commands as follows:

JQDR> help
JQDR - JSON Query Done Right
    executequery <JSON SQL query>
    load <table name> <file name>
    deleteload <table name> <file name>
    createtable <table name>
    droptable <table name>
    deletetable <table name>
    existstable <table name>
    <JSON SQL query>

Document Collection

The following very small collection “tinycoll” contains two documents:

{"a": 5,
 "b": {"c": 10, "d": 11},
 "c": [101, 102, {"d": 103}, {"e": 104}]
{"a": 5,
 "b2": [10, 11],
 "c": [101, 102, {"d": 103}, {"e": 104}]

Loading those into the database is accomplished by adding the documents into a file and then load the documents from the file into the database after the table “tinycoll” has been created:

JQDR> load tinycoll src/test/resources/blog/tinycoll.txt

Currently the language does not support an insert statement, however, this is in the plans.

select {*}

A first JSON query is to select all the documents and output those as documents (requested by the { and } in the projection clause):

JQDR> select {*} from tinycoll

select {a, b.c, c.[3].e}

A more interesting query is a projection that reaches into the documents:

JQDR> select {a, b.c, c.[3].e} from tinycoll

A few observations are:

  • Full paths are inserted into the result documents. This allows to access the result documents using the same paths that were used in the projection (aka, a, b.c and c.[3].e).
  • JSON does not have a “value does not exist” representation. Therefore, the JSON query processor inserts “<>” for array values that do not exist, but need to be present in order to provide correct array indexes.

For example, only the 4th array element was projected, so the first three must be part of the result, but represented as “values does not exist” as they were not requested in the projection. In a Relational SQL world SQL NULL would have been used in order to represent “values does not exist” (or is unknown).

select *

This query selects all documents, but the result is in relational table format, not JSON documents (as the { and } are omitted in the projection). Each path into any of the documents of the collection is represented as a separate column. The following shows the resulting 14 columns:

JQDR> select * from tinycoll
|a    |b_c  |b_d  |b               |b2_[0] |b2_[1] |b2      |c_[0] |c_[1] |c_[2]_d |c_[2]     |c_[3]_e |c_[3]     |c                             |
|5    |10   |11   |{"c":10,"d":11} |<>     |<>     |<>      |101   |102   |103     |{"d":103} |104     |{"e":104} |[101,102,{"d":103},{"e":104}] |
|5    |<>   |<>   |<>              |10     |11     |[10,11] |101   |102   |103     |{"d":103} |104     |{"e":104} |[101,102,{"d":103},{"e":104}] |

It is important to note that the column names are default names generated by the JSON SQL query processor and they actually represent the paths (however, instead of a “.”, a “_” is used for the representation).

select a, b.c, c.[3].e

A projection looks as follows

JQDR> select a, b.c, c.[3].e from tinycoll
|a    |b_c  |c_[3]_e |
|5    |10   |104      |
|5    |<>   |104      |

In this case only three columns are returned as the projection projects only three paths.


This demo has provided a first glimpse at a JSON SQL language that supports querying JSON documents and provides the option of returning results as JSON objects or in table form.

The next blog will focus more on projection and the various relevant details of the projection semantics.

Go [ JSON | Relational ] SQL!


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.


SQL for JSON Rationalization Part 2: Is JSON SQL Superset of Relational SQL?

This is not really a precise question as it does not distinguish data model from query language – a distinction that it probably intended to make; so let’s rationalize both.

Table – Row

A table is defined in its basic form by columns and their types (domains), with columns having unique names within a table definition. The contents of a table are zero or more rows, whereby each row has to have for each column either a value (matching the column’s domain) or SQL NULL (indicating “unknown”); no deviation from this structure is possible, neither from the type system.

Collection – Document

In the JSON database world, JSON structures are called documents. Documents are in general grouped together in distinct groups and the grouping mechanism is a named collection. A collection can contain zero or more documents. Each document belongs to exactly one collection.

A collection in general does not impose a constraint on the document structure of its documents, so the whole range from all documents having the same structure to each document having a different structure is possible. That documents in a collection can have different structures is called “schema-free”, or more appropriately “implicit schema-per-document” instead of “schema-per-collection”.

Note: the structure of a document is the set of all paths from the root of the document to the leaves of the document. This definition allows a structural comparison: two documents having the same set of paths are structurally equivalent (or, are of the same schema if there was a schema definition).

Relational SQL

The term “Relational SQL” refers to SQL executing against tables. For example, “select * from abc” selects all rows from the table “abc”, which each row having values for each column of the table.

The query “select a, b from abc” would select all rows from the table “abc”, however, only the columns “a” and “b” within each row (projection).


The term “JSON SQL” refers to SQL executing against collections. For example, in analogy to the Relational SQL query above, “select {*} from def” selects documents (indicated by the “{}”), and within each document all properties (indicated by “*”) from the collection “def”.

The query “select {a, b} from def” would select all objects from the collection “def”, however, only the properties “a” and “b”, in case these are present (more precisely, all paths starting with either “a” or “b” – this is one possible definition of projection in JSON SQL). If none of them is present in a document, then the result is the empty document. If only one property is present, the document only contains the one property.

For example, assume the collection “def” consisting of two documents:

{"a": 1, "b": 2, "c": 3}
{"d": 4, "e": 5, "f": 6}

The query “select {a, b} from def” would return

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

JSON SQL with Table Output

What would the query “select a, b from def” return? This looks like a Relational SQL query, however, querying a collection of documents. The interpretation is to select the value of the property “a” and “b” from each document, however, not return a document, but a structure similar to a row with values only.

For example, “select a, b from def” applied to the above collection it would return

| a | b |
| 1 | 2 |
|   |   |

The first object would contribute the values “1” and “2”, the second object would not contribute values at all, so an empty row is returned (aka, a row where each column is empty and not SQL NULL as SQL NULL is not a valid value in the JSON structure definition).

Of course, the above example has scalar values for “a” and “b”; non-scalar values are supported as well, without restriction. It is therefore possible to have any JSON type in a column as value.

This small example shows that it is possible to not only return documents as a result of a JSON SQL query, but also tables (set of rows).

Collection with Schema Constraint

It is possible, at least conceptually, to enforce constraints on the documents of a collection with the interpretation that each document in the collection must satisfy the constraint. One possible constraint is that all document must have the exact same structure (schema) and only the values can be different.

For example, a constraint in context of collection “xyz” could be that all documents can only have top level properties with names “p” and “q” (paths of length 1), and the type of “p” must be a string, and the type of “q” must be an integer. Basically this constraint enforces a specific document schema on every document in the collection.

The collection “xyz” at a given point in time could have two documents:

{"p": "today", "q": 68}
{"p": "yesterday", "q": 71} 

Basically, this constraint enforces “flat” documents with scalar types, and this can be interpreted as the equivalent to rows of a table with two columns “p” and “q” of type string and integer.


With the appropriate constraint in place (aka, forcing “flat” documents of scalar property type), a collection can be interpreted as table and the documents as rows. Therefore it is possible to provide the equivalent of tables and the equivalent of Relational SQL in context of collections of documents supported by JSON SQL.

However, this is possible because of a constraint. Lifting that constraint allows documents to have any structure, and to have a representation beyond the table model. Therefore, JSON SQL on document collections is a superset of Relational SQL on tables.


In this blog it was shown that a constraint document environment can be interpreted as tables and JSON SQL can be used to accomplish the same functionality as Relational SQL. By lifting the constraint it follows that JSON SQL based on document collections is a superset of Relational SQL based on tables. The answer to the initial question is therefore “YES”.

The intriguing question now is, what will JSON SQL have to provide beyond Relational SQL in order to fully support the JSON document model? Future blogs will make an attempt to address this question in a structured and rationalized approach.

Go [ JSON | Relational ] SQL!


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.

Oracle 12c – SQL for JSON (Part 1): Introduction to Native Support

The Oracle Database 12c Release 1 ( [http://www.oracle.com/technetwork/database/enterprise-edition/overview/index.html] introduces native JSON support and SQL access to JSON. This blog post gives a first introduction.

SQL access to JSON: Native Support

SQL access to JSON is a significant development in itself, but native support in context of a relational database it is actually quite huge and exciting. This blog post series will provide a discussion of the various aspects over several installments, including the mixed use of SQL on JSON with SQL on relational tables.

But first things first.

2-Second Overview

This is a 2-second overview showing how to create a table that can store JSON data, how to insert a row containing a JSON object and how to query it with a simple query.

  supplier_doc CLOB
    CONSTRAINT supplier_doc_ensure_json 
      CHECK (supplier_doc IS JSON));
INSERT INTO supplier
VALUES (125,
  supplierId: 125,
  "supplierName": "FastSupplier"}');
SELECT * FROM supplier;
---------- -------------------------------------------------
       125 {supplierId: 125, "supplierName": "FastSupplier"}

That was easy 🙂

Creating a Table storing JSON

JSON data are stored in columns of regular tables. A constraint placed on a JSON column enforces JSON compliance:

  supplier_doc CLOB
    CONSTRAINT supplier_doc_ensure_json 
      CHECK (supplier_doc IS JSON));

Any attempt to insert invalid JSON data fails because of this constraint. Other columns are regular relational columns and they can be defined and constrained as necessary.

A JSON column stores JSON objects as well as JSON arrays as both are valid top-level JSON structures. Trying to insert scalars will fail. The following two insert statements are valid:

INSERT INTO supplier
VALUES (125,
  supplierId: 125,
  "supplierName": "FastSupplier"}');
INSERT INTO supplier
VALUES (128,

While the JSON [json.org] standard allows duplicate members (aka, keys), many implementations only tolerate or even outright reject it. To avoid storing JSON objects with duplicate keys, the constraint of a JSON column can be extended:

( id number NOT NULL
  supplier_doc CLOB
    CONSTRAINT supplier_doc_ensure_json 
      CHECK (supplier_doc IS JSON (WITH UNIQUE KEYS)));

The following insert will fail with the additional constraint “WITH UNIQUE KEYS” (because of a duplicate keys), but would succeed otherwise.

INSERT INTO supplier
VALUES (126,
  "supplierId": 126,
  "supplierName": "FastSupplier",
  "supplierName": "FS"}');

JSON has a defined syntax, however, many implementations relax it by e.g. allowing to state member names without quotes. To enforce a strict syntax, the constraint on a JSON table can be extended:

( id number NOT NULL
  supplier_doc CLOB
    CONSTRAINT supplier_doc_ensure_json 

The following insert will fail (because one key is not enclosed in quotes), but succeed without the “STRICT” constraint:

INSERT INTO supplier
VALUES (125,
  supplierId: 125,
  "supplierName": "FastSupplier"}');

With the various constraints and their combinations it is possible to restrict the flavor of JSON that is stored in the database. From an architecture perspective this means that the database can be the central point of enforcing conformity.

Since JSON data is stored in colums it is possible to create several columns in a table that contain JSON data. Here is an example of a table with two columns:

( id number NOT NULL
  supplier_doc CLOB
    CONSTRAINT supplier_doc_ensure_json 
  history_doc CLOB
    CONSTRAINT history_doc_ensure_json 

From a data modeling perspective this opens up a whole new dimension. A brief discussion follows later in this blog post.

Inserting JSON into a Table

Inserting JSON data into a table that has at least one JSON column is rather straight forward, as the previous examples have shown.

The insert statement must have a valid JSON object or JSON array in the position of the JSON column(s), or SQL NULL as the value of a JSON column can be unknown.

Querying JSON with SQL

Oracle Database 12c supports a number of functions to query and to manipulate JSON data. In the following only a first impression is given and the full set will be discussed in additional separate blog posts.

The most basic query was already introduced:

SELECT * FROM supplier;

Selecting scalars from JSON:

SELECT s.supplier_doc.supplierName FROM supplier s;

The select clause is what one would expect: the table name followed by the column name followed by the key name using dot notation. The return value is a table with a single column containing strings.

This query selects scalar value across different columns:

SELECT s.id, s.supplier_doc.supplierName FROM supplier s;

In order to be able to construct more interesting queries, a more complex JSON object is used (see the end of this blog post for its details – it is not included here as it is quite large).

Querying a JSON object looks like this:

SELECT s.supplier_doc.businessAddress FROM supplier s;

This returns a table with one column containing a string representing a JSON object. For those objects that do not have a ‘businessAddress’, a SQL null is returned.

Querying a JSON array is done like this:

select s.supplier_doc.shippers from supplier s;

Like previously, the dot-notation path leads to the JSON member that contains an array as value.

The queries included so far give a first impression of how to query JSON data in Oracle 12c. Upcoming blog posts will provide much more details on the query capabilities, amongst other discussions.

Table Design

With the introduction of JSON it is now possible to have a data model design that combines relational and JSON data modeling instead of having just a relational data model or just a JSON data model. A single table supports the combination of relational and JSON data types.

Some important design questions are:

  • Should top level scalar data items be separate columns, or top-level keys in a JSON object, or both?
    • In the above examples, a primary key column ‘id’ was designed, as well as a key ‘supplierId’ in the JSON object. The ‘id’ column enforces a primary key and supports access to the supplier identifier without accessing the JSON document. The JSON document, however, contains the supplier identifier to be self-contained.
  • Should all data be in one JSON document or is it more appropriate to have separate JSON objects in different columns?
    • In one of the above table creation statements two JSON columns where defined, one containing the supplier data, and another one containing history about the supplier. This supports the separation of data that should not be combined, e.g., for security or privacy reasons. An internal supplier history is not part of the operational supplier data and should be kept separate.

More design principles and best practices will emerge over time in this context in addition to the ones mentioned here so far.


The documentation can be found here: [http://docs.oracle.com/database/121/ADXDB/json.htm#ADXDB6246]

Installation Notes

The Oracle Database 12c Release 1  ( is available on Windows as well as Linux and Solaris at the time of this blog post [http://www.oracle.com/technetwork/database/enterprise-edition/downloads/index.html].

In case you want to run the Linux version on Windows, install Virtual Box (version 4.3.15 build 95923 (!) [https://forums.virtualbox.org/viewtopic.php?f=6&t=62615]) and then create a virtual machine with Oracle Linux 6. Once the VM is up and running, install the Oracle Database 12c Release 1 ( in the VM and you are ready to go.

Example Large JSON Object

INSERT INTO supplier
VALUES (123,
  "supplierId": 123",
  "supplierName": "FastSupplier",
  "rating": 5,
  "shippers": [{
    "shipperName": "TopSpeed",
    "address": {
      "street": "Sunrise",
      "streeNumber": 17,
      "city": "Sun City",
      "state": "CA",
      "zip": 12347
    "shipperName": "PerfectPack",
    "address": {
      "street": "Solid Road",
      "streeNumber": 1771,
      "city": "Granite City",
      "state": "CA",
      "zip": 12348
    "shipperName": "EconomicWay",
    "address": {
    "street": "Narrow Bridge",
    "streeNumber": 1999,
    "city": "Central City",
    "state": "CA",
    "zip": 12345
  "businessAddress": {
    "street": "Main Street",
    "streeNumber": 25,
    "city": "Central City",
    "state": "CA",
    "zip": 12345
  "warehouseAddress": {
    "street": "Industrial Street",
    "streeNumber": 2552, 
    "city": "Central City",
    "state": "CA",
    "zip": 12346


The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.

Trending: Multi-Interface and Multi-Data-Model Databases

An interesting development, especially in the NoSQL database space, is the development towards multi-interface and multi-data-model databases, and sometimes both at the same time. While it provides flexibility, it also brings challenges.

Multi-Data-Model Support

In the relational database space, supporting different data models concurrently is not a novelty. Relational databases started off with the relational data model implementation, and later on some of the systems extended the relational model mainly by objects, XML or JSON.

Some databases in the NoSQL space are starting to evolve, too, in this manner and are providing more than one data model concurrently. Some interesting examples are discussed next.

One example in the NoSQL space is Oracle NoSQL [http://www.oracle.com/technetwork/database/database-technologies/nosqldb/]. This system supports a key/value data model whereby the key is used to identify values that are not interpreted by the database itself. In addition, values can be of complex types that are actually interpreted by the database, e.g., in secondary indexes.

Aerospike is another example in the NoSQL space [http://www.aerospike.com/]. Aerospike provides a data model consisting of basic and complex types. In addition, it supports language-native formats as well as large data types that have a specific operational characteristics and data type operations tuned for scale.

Like some relational databases extended data models over time to support specific use cases in a more direct way, some NoSQL databases are also going down that path to more directly support specific application developer needs.

Multi-Interface Support

From an application development perspective a single query API is certainly preferable that provides the complete query expressiveness required. However, especially in the new area of NoSQL databases, it is not clear yet what a good query API actually looks like. This can be observed by different systems providing different query API alternatives.

MongoDB [http://www.mongodb.org/] has a document query interface based on query patterns in form of JSON documents (“Query Documents”). In addition, it provides a map/reduce interface and aggregation pipelines. There are three different APIs that an application developer can choose, and, in addition, they overlap in their functionality. This means that, depending on the query, it can be expressed in all three of them.

Aerospike [http://www.aerospike.com/] provides different language drivers in addition to an Aerospike Query Language.

Cloudant [http://www.cloudant.com], in contrast, supports a REST-api as well as a query interface based on query documents (similar to MongoDB).

Not strictly an external interface, but very important for specific use cases, is the ability to add functionality dynamically to the database in order to move some processing from the application systems into the database itself: user defined functions. For example, MongoDB allows adding functionality through JavaScript functions, whereas Aerospike supports two different types of Lua functions: record user defined functions operate on single records, whereby stream user defined functions support distributed processing.

The Good

Unquestionably, the good part about multi-interface and multi-data-model databases is that an application developer can choose the best combination of data model and access interface for a particular development task. The impedance mismatch between the problem and the solution can be minimized with an appropriate choice.

This also means that developers need to understand the pros and cons of every combination and that requires to go through a learning curve. Going through that learning curve might pay off big time.

In addition, application development teams will have to manage a wider range of implementation variations in terms of application design and engineering, but also in terms of bug fixing and application code maintenance.

The Tricky

The tricky part of multi-interface and multi-data-model databases is that all combinations can be used concurrently, in production as well as post-production (e.g. analytics). Unit and functional tests as well as performance and scale tests become a lot more complicated as they have to test the concurrent execution of various combinations.

Furthermore, many queries can be expressed in different interfaces as those tend to overlap in query expressiveness. So an application developer needs to clearly understand the pros and cons of the query execution that underlies a specific query interface.

Hopefully the query semantics is the same for all combinations (meaning, for example, that predicates are evaluated the same way) and that concurrent use of data models and interfaces does not negatively impact the various clients in terms of concurrency, scale and performance. Any bug introduced through a discrepancy might be very difficult to reproduce and fix.


While multi-interface and multi-data-model databases are a powerful technology, there is considerable impact to the application system engineering activities in terms of knowledge acquisition, development, test and maintenance.

While database vendors certainly strive to have all combinations work in harmony, there might be edge cases where one combination does not give the same result as a different one. From an application development perspective test coverage should ensure semantic equivalence of the used combinations so that misinterpretations or wrong results are avoided.