You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


<?xml version="1.0" encoding="UTF-8" ?>
<!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>
<link rel="stylesheet" type="text/css" href="../mysqludf.css"/>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<title>lib_mysql_json - A library of MySQL UDFs for working with JSON</title>
</head>
<body>
	<div>
		<a href="../index.html">Top</a>
	|	<a href="../mysql_udf_repository_libraries.html">Up</a>
	</div>
	<h1>lib_mysqludf_json</h1>
	<div>
		<a href="lib_mysqludf_json.html">Documentation</a>
	|	<a href="lib_mysqludf_json.so">Binary</a>
	|	<a href="lib_mysqludf_json.sql">Installation</a>
	|	<a href="lib_mysqludf_json.c">Source</a>
	</div>
	<p>
		<a href="http://www.json.org/" target="_json">JSON</a> is an abbreviation of JavaScript Object Notation.
		JSON uses a subset of the <a href="http://www.ecma-international.org/publications/standards/Ecma-262.htm" target="_ecma">ecmascript</a> 
		(javascript) language to denote javascript data structures (see also <a href="http://tools.ietf.org/html/rfc4627" target="_ietf">RFC 4627</a>).
		As such, JSON is a text-oriented data format. 
	</p>
	<p>
		JSON is particularly useful as a format for data exchange. 
		It is mainly used in AJAX web applications as an alternative or in addition to XML. 
		As such, JSON offers a few advantages over XML:		
	</p>
	<ul>
		<li>less verbosity, resulting in a smaller amount of data to transfer</li>
		<li>directly deserializable to yield the corresponding object structure using the javascript built-in <code>eval()</code> function.</li>
	</ul>
	<p>
		Usually, database data is mapped to the JSON format downstream of the database.
		For example, PHP offers an extension to convert PHP data structures to JSON.
		The disadvantage of that approach is that it usually requires one to iterate through 
		the resultset to create an object structure out of it before that object structure
		can be encoded into the JSON format.
		Also, JSON encoding using an UDF is much faster. 
	</p>
	<h2>General notes of usage</h2>
	<p>
		There are a few things to keep in mind when using these UDF's.
		The JSON UDFs are designed for ease of use. 
		They were designed with the assumption in mind that in many cases,
		only a simple and straightforward mapping is required.
		So, the UDFs try to make simple mappings easy and with as little overhead as possible.
		At the same time the UDFs will still be useful to generate more complicated mappings.
	</p>
	<p>
		Here's a general overview of general functionality offered by <code>lib_mysqludf_json</code>:	
	</p>
	<dl>
		<dt>Variable-length parameterlists allow straightforward mapping</dt>
			<dd>
				In many cases, a mapping is needed to map row-level data 
				to either a JSON array or a JSON object. 
				Variable argument lists allow a single method invocation 
				to accept a whole list of items that are 
				subsequently mapped to JSON structures.
			</dd>
		<dt>Proper escaping of string data</dt>
			<dd>
				String values are mapped to JSON strings. 
				These strings are automatically escaped to conform to the JSON syntax. 
				At the same time it is very straightforward 
				to bypass the automatic escaping behaviour when necessary.
			</dd>
		<dt>Consistent <code>NULL</code> handling</dt>
			<dd>
				Database <code>NULL</code> values are handled according to the datatype.
				String <code>NULL</code> values map to javascript <code>null</code>, 
				and numeric	<code>NULL</code> values map to javascript <code>NaN</code>.
			</dd>
		<dt>Automatic mapping of expressions to JSON object members</dt>
			<dd>
				Often, when creating JSON objects from relational data,
				it is perfectly fine to use column names as object member names.
				In those cases, one need not provide member names explicitly.
				The functions simple reuse the literal text of the argument expressions.
				At the same time, it is easy to map members to a custom name.
			</dd>
		<dt>Basic validity checking of JSON member names</dt>
			<dd>
				Constant object member names are checked to see if they are valid 
				JSON member names. 
			</dd>
		<dt>Support for easy nesting of JSON structures</dt>
			<dd>
				Occassionally, there is a need for nesting JSON objects and/or arrays.
				Nesting of JSON UDF functions is intuitive and straigtforward.
				Normally, a string result returned by an inner function would be escaped
				by the outer function, but this is not the case when nesting the json UDF's.
			</dd>
	</dl>
	<h3>Escaping and quoting of string data</h3>
	<p>
		Argument string values are normally automatically mapped to JSON double quoted strings. 
		The string value is escaped to for a valid JSON value.
		The following characters are escaped by prefixing them with a <code>\</code> or slash character:
	</p>
	<ul>
		<li><code>"</code> - The double quote character is mapped as <code>\"</code></li>
		<li>linefeed - The linefeed character is mapped to <code>\n</code></li>
		<li>carriage return - The carriage return character is mapped to <code>\r</code></li>
		<li><code>\</code> - The backslash character is mapped to <code>\\</code></li>
	</ul>
	<p>
		Quoting and escaping can be avoided if the expression that returns the string 
		starts with the prefix <code>json_</code> or has an alias starting with that prefix.
	</p>
	<h3>Mapping expressions to Object members</h3>
	<p>
		To make it as easy as possible to map rows to JSON objects, the <code>json_object</code> function
		maps each argument to one member. 
		The expression text is used as member name, and the value as member value.
		This allows a lot of common cases to be handled without effort.
	</p>
	<p>
		However, that is not to say that the <code>json_object</code> function blindly uses the expression text.
		With a few exceptions, the expressions are checked to see if they make a valid JSON member name.
		In some cases, a modification of the expression text is used.
		Currently modication occurs for qualified names. 
		A qualified column expression like <code>aTableAlias.columnName</code> is modified to read <code>columnName</code>.		
	</p>
	<p>
		It is always possible to override the automatic member name mapping.
		This is done by including an alias to the expression inside the function call.
	</p>
	<h1>Functions</h1>
	<p>
		This library <code>lib_mysqludf_json</code> aims to offer a complete set of functions to map relational data into JSON format.
		The following functions are currently supported:
	</p>
	<ol>
		<li>
			<a href="#json_array"><code>json_array</code></a> 
			- maps a variable number of arguments to a JSON array.
		</li>
		<li>
			<a href="#json_members"><code>json_members</code></a> 
			- maps a variable number of name-value pairs to a list of JSON object members.
		</li>
		<li>
			<a href="#json_object"><code>json_object</code></a> 
			- maps a variable number of arguments to a JSON object.
		</li>
		<li>
			<a href="#json_values"><code>json_values</code></a> 
			- maps a variable number of arguments to JSON values.
		</li>
	</ol>
	<p>
		Use <a href="#lib_mysqludf_json_info"><code>lib_mysqludf_json_info()</code></a> to obtain information about the currently installed version of <code>lib_mysqludf_json</code>.
	</p>
	<a name="json_array"></a><h2>json_array</h2>
	<p>
		<code>json_array</code> takes a variable number of arguments and returns a string that denotes a javascript array with the arguments as members.
	</p>
	<h3>Syntax</h3>
<pre>json_array([<b>arg1</b>,..,<b>argN</b>])</pre>
	<h3>Parameters and Return Values</h3>
	<dl>
		<dt><code>[<b>arg1</b>,..,<b>argN</b>]</code></dt>
		<dd>
			A (possibly empty) list of values of any type. 			
			<ul>
				<li>Numeric types are mapped to decimal numbers.</li>
				<li>Strings, bit and temporal types are mapped to double quoted strings.</li>
			</ul>
			<code>NULL</code> values are handled as follows:
			<ul>
				<li>For numeric types, <code>NULL</code> is mapped to <code>NaN</code>.</li>
				<li>For string types, <code>NULL</code> is mapped to <code>null</code>.</li>
			</ul>
			The argumentlist maybe empty, which case an string is returned that denotes an empty array.			
		</dd>
		<dt>returns</dt>
		<dd>
			A javascript expression that evaluates to an array.
		</dd>
	</dl>
	<h3>Installation</h3>
	<p>
		Place the shared library binary in an appropriate location. 
		Log in to mysql as root or as another user with sufficient privileges, and select any database.		
		Then, create the function using the following DDL statement:
	</p>
	<pre>
CREATE FUNCTION json_array RETURNS STRING SONAME 'lib_mysqludf_json.so';	
	</pre>
	<p>
		The function will be globally available in all databases.
	</p>
	<p>
		The deinstall the function, run the following statement:
	</p>
	<pre>
DROP FUNCTION json_array;
	</pre>
	<h3>Examples</h3>
	<p>
		Create an array of user data:
	</p>
	<pre>
select json_array(
           customer_id
       ,   first_name
       ,   last_name
       ,   last_update
       ) as customer
from   customer 
where  customer_id =1;
	</pre>
	<p>yields this result:</p>
	<pre>
+------------------------------------------+
| customer                                 |
+------------------------------------------+
| [1,"MARY","SMITH","2006-02-15 04:57:20"] |
+------------------------------------------+
	</pre>	
	<p>
	To obtain nested arrays, simply nest the function calls:	
	</p>
	<pre>
select json_array(
           customer_id
       ,   json_array(
               first_name
           ,   last_name
	       )
       ,   last_update
       ) as customer
from   customer 
where  customer_id =1;
	</pre>
	<p>yields this result:</p>
	<pre>
+--------------------------------------------+
| customer                                   |
+--------------------------------------------+
| [1,["MARY","SMITH"],"2006-02-15 04:57:20"] |
+--------------------------------------------+
	</pre>	
	<p>
		Note that arrays can be nested freely as described.
		The string returned by the inner <code>json_</code> function is not
		escaped by the outer <code>json_</code> function.
	</p>
	<p>
		Note that string values are automatically mapped to double-quoted json strings.
		If need be, special characters are escaped. 
		For example:
	</p>
	<pre>
select json_array('\"\"');
	</pre>
	<p>
		returns:
	</p>
	<pre>
+--------------------+
| json_array('\"\"') |
+--------------------+
| ["\"\""]           |
+--------------------+	
	</pre>
	<p>
		Sometimes one needs to avoid this automatic escaping.
		This would be the case for example when storing JSON strings directly in the database.		
		If you want to avoid escaping to map a string value as is, use an alias with the prefix <code>json_</code>.
	</p>
	<pre>
select json_array('\"\"' json_);
	</pre>
	<p>
		returns:
	</p>
	<pre>
+--------------------------+
| json_array('\"\"' json_) |
+--------------------------+
| [""]                     |
+--------------------------+
	</pre>
	<p>
		The alias is not required when the expression itself already has a name that starts with the <code>json_</code> prefix.
		For example, if you have a table with a column called <code>json_data</code>, then the value for that column will not be escaped.
		If that is actually an undesired effect, and you realy want to escape the value after all, 
		simply use an alias with a prefix not equal to <code>json_</code>.
	</p>
	<h3>See Also</h3>
	<ul>
		<li><a href="#json_object"><code>json_object</code></a></li>
	</ul>
	<a name="json_members"></a><h2>json_members</h2>
	<p>
		This function can be used to turn an arbitrary list 
		of name-value pairs into a list of JSON object members.
		Normally, one does not need this function directy, 
		as the automatic mapping offered by <code>json_object</code>
		is usually sufficient.
	</p>
	<p>
		One of the things the automatic mapping does not handle well
		is nesting objects. The <code>json_members</code> function was
		designed especially for the purpose of easy nesting of objects,
		and also to work with array-type object memebers.
	</p>
	<h3>Syntax</h3>
<pre>json_members(<b>name1</b>,<b>value1</b>[,..,..,<b>nameN</b>,<b>valueN</b>])</pre>
	<h3>Parameters and Return Values</h3>
	<dl>
		<dt><code><b>name1</b>,<b>value1</b>[,..,..,<b>nameN</b>,<b>valueN</b>]</dt>
		<dd>
			A list of name-value pairs.
			The name is used as a member JSON object member name.
			The value is used as the value of the member.
		</dd>
		<dt>returns</dt>
		<dd>A comma separated list of JSON member names with the associated JSON value.</dd>
	</dl>
	<p>
		If the name is a constant expression, a check is performed to see if it yields a valid json member name.
		In that case, a modification is applied to qualified column names in that the qualifier is stripped from the member name.
		If the name is not a constant expression, no checking is performed at all. 
		The value of the name argument is simply used as-is as member name, which may lead to syntactically invalid JSON expressions.
	</p>
	<h3>Installation</h3>
	<p>
		Place the shared library binary in an appropriate location. 
		Log in to mysql as root or as another user with sufficient privileges, and select any database.		
		Then, create the function using the following DDL statement:
	</p>
	<pre>
CREATE FUNCTION json_members RETURNS STRING SONAME 'lib_mysqludf_json.so';	
	</pre>
	<p>
		The function will be globally available in all databases.
	</p>
	<p>
		The deinstall the function, run the following statement:
	</p>
	<pre>
DROP FUNCTION json_members;
	</pre>
	<h3>Examples</h3>
	<p>
	To obtain nested objects, use this function as an argument for <code>json_object</code>
	</p>
	<pre>
select      json_object(
                f.last_update
            ,   json_members(
                    'film'
                ,   json_object(
                        f.film_id
                    ,   f.title
                    ,   f.last_update
                    )
                ,   'category'
                ,   json_object(
                        c.category_id
                    ,   c.name
                    ,   c.last_update
                    )
                )
            ) as film_category
from        film_category fc 
inner join  film f
on          fc.film_id = f.film_id
inner join  category c
on          fc.category_id = c.category_id
where       f.film_id =1;
	</pre>
	<p>yields a string representing the following JSON object (indentation added for readability):</p>	
	<pre>
{
    last_update:"2006-02-15 05:03:42"
,   film:{
        "film_id":1
    ,   "title":"ACADEMY DINOSAUR"
    ,   "last_update":"2006-02-15 05:03:42"
    }
,   category:{
        "category_id":6
    ,   "name":"Documentary"
    ,   "last_update":"2006-02-15 04:46:27"
    }
}	
	</pre>
	<p>
		Note that the result of the inner <code>json_object</code> calls is not escaped.
		That's because <code>json_members</code> will not automatically escape values from expressions where the literal expression text has the prefix <code>json_</code>.
		This can pose a problem. 
		If you want to use <code>json_members</code> to map a string in column, no escaping is applied if the column happens to have a name that starts with <code>json_</code>.
		In those cases, you can use an alias in the function call:
	</p>
	<pre>
select      json_object(
                json_members(
                    'data'
                ,   json_data data
                )
            )
from        json_data_table
	</pre>
	<h3>See Also</h3>
	<ul>
		<li><a href="#json_object"><code>json_object</code></a></li>
	</ul>
	<a name="json_object"></a><h2>json_object</h2>
	<p>
		<code>json_values</code> takes a variable number of arguments, maps them to JSON values and returns a concatenation of these.
	</p>
	<h3>Syntax</h3>
<pre>json_object([<b>arg1</b>,..,<b>argN</b>])</pre>
	<h3>Parameters and Return Values</h3>
	<dl>
		<dt><code>[<b>arg1</b>,..,<b>argN</b>]</code></dt>
		<dd>
			A (possibly empty) list of values of any type. 
			<p>
			The expression text passed as argument is used as member name.
			This is by design: 
			it makes it very easy to render plain table data as json objects.
			The expression is checked to verify that it yields a valid javascript identifier.
			</p>
			<p>
			Sometimes, it is not appropriate to use the expression as member name.
			In those cases, one can use an alias inside the function call (see examples).			
			</p>
			<p>
			The argument value is the value of the corresponing member.
			</p>
			<ul>
				<li>Numeric types are mapped to decimal numbers.</li>
				<li>Strings, bit and temporal types are mapped to double quoted strings.</li>
			</ul>
			<p>
			<code>NULL</code> values are handled as follows:
			</p>
			<ul>
				<li>For numeric types, <code>NULL</code> is mapped to <code>NaN</code>.</li>
				<li>For string types, <code>NULL</code> is mapped to <code>null</code>.</li>
			</ul>
			<p>
				The argumentlist maybe empty, which case an empty string is returned.
			</p>
		</dd>
		<dt>returns</dt>
		<dd>
			A javascript expression that evaluates to an javascript value.
		</dd>
	</dl>
	<h3>Installation</h3>
	<p>
		Place the shared library binary in an appropriate location. 
		Log in to mysql as root or as another user with sufficient privileges, and select any database.		
		Then, create the function using the following DDL statement:
	</p>
	<pre>
CREATE FUNCTION json_object RETURNS STRING SONAME 'lib_mysqludf_json.so';	
	</pre>
	<p>
		The function will be globally available in all databases.
	</p>
	<p>
		The deinstall the function, run the following statement:
	</p>
	<pre>
DROP FUNCTION json_object;
	</pre>
	<h3>Examples</h3>
	<p>
		Create an object of user data:
	</p>
	<pre>
select json_object(
           customer_id
       ,   first_name
       ,   last_name
       ,   last_update
       ) as customer
from   customer 
where  customer_id =1;
	</pre>
	<p>yields this result:</p>
	<pre>
+---------------------------------------------------------------------------------------+
| customer                                                                              |
+---------------------------------------------------------------------------------------+
| {customer_id:1,first_name:"MARY",last_name:"SMITH",last_update:"2006-02-15 04:57:20"} |
+---------------------------------------------------------------------------------------+
	</pre>	
	<p>
		Use the <a href="#json_member"><code>json_member</code></a> function to nest objects:
	</p>
	<pre>
select      json_object(
                f.last_update
            ,   json_members(
                    'film'
                ,   json_object(
                        f.film_id
                    ,   f.title
                    ,   f.last_update
                    )
                ,   'category'
                ,   json_object(
                        c.category_id
                    ,   c.name
                    ,   c.last_update
                    )
                )
            ) as film_category
from        film_category fc 
inner join  film f
on          fc.film_id = f.film_id
inner join  category c
on          fc.category_id = c.category_id
where       f.film_id =1;
	</pre>
	<h3>See Also</h3>
	<ul>
		<li><a href="#json_members"><code>json_members</code></a></li>
	</ul>
	<a name="json_values"></a><h2>json_values</h2>
	<p>
		<code>json_values</code> takes a variable number of arguments and returns a string that denotes a concatenation of javascript objects
	</p>
	<h3>Syntax</h3>
<pre>json_values([<b>arg1</b>,..,<b>argN</b>])</pre>
	<h3>Parameters and Return Values</h3>
	<dl>
		<dt><code>[<b>arg1</b>,..,<b>argN</b>]</code></dt>
		<dd>
			A (possibly empty) list of values of any type. 
			<ul>
				<li>Numeric types are mapped to decimal numbers.</li>
				<li>Strings, bit and temporal types are mapped to double quoted strings.</li>
			</ul>
			<p>
			<code>NULL</code> values are handled as follows:
			</p>
			<ul>
				<li>For numeric types, <code>NULL</code> is mapped to <code>NaN</code>.</li>
				<li>For string types, <code>NULL</code> is mapped to <code>null</code>.</li>
			</ul>
			<p>
				The argumentlist maybe empty, in which case an empty string is returned.
			</p>
		</dd>
		<dt>returns</dt>
		<dd>
			A javascript value.
		</dd>
	</dl>
	<h3>Examples</h3>
	<p>
		Create an javascript value:
	</p>
	<pre>
select json_values('json');
	</pre>	
	Returns:
	<pre>
+---------------------+
| json_values('json') |
+---------------------+
| "json"              |
+---------------------+
1 row in set (0.00 sec)
	</pre>	
</body>
</html>