[转]在SqlServer 中解析JSON数据

在Sqlserver中可以直接处理Xml格式的数据，但因为项目需要所以要保存JSON格式的数据到Sqlserver中在博客：Consuming JSON Strings in SQL Server中该作者通过自定义类型的方法实现了对JSON的处理,而且Sqlserver可以查询处理后的数据因此可以在项目中放心的使用

来个例子

Select * from parseJSON(‘{
  "联系人":
  {
     "姓名": "huang",
     "网名": "HTL",
     "AGE": 05,
     "男人":true,
      "PhoneNumbers":
     {
        "mobile":"135123100514",
        "phone":"0251-123456789"
     }

  }
}
‘)

条件查询：

Select * from parseJSON(‘{
  "联系人":
  {
     "姓名": "huang",
     "网名": "HTL",
     "AGE": 05,
     "男人":true
  }
}
‘)
 WHERE name=‘姓名‘

排序

Select * from parseJSON(‘{
  "联系人":
  {
     "姓名": "huang",
     "网名": "HTL",
     "AGE": 05,
     "男人":true

  }
}
‘)
ORDER BY name

分组查询

Select valuetype from parseJSON(‘{
  "联系人":
  {
     "姓名": "huang",
     "网名": "HTL",
     "AGE": 05,
     "男人":true
  }
}
‘)
GROUP BY valuetype

原文来源：https://www.simple-talk.com/sql/t-sql-programming/consuming-json-strings-in-sql-server/

It has always seemed strange to Phil that SQL Server has such complete support for XML, yet is completely devoid of any support for JSON. In the end, he was forced, by a website project, into doing something about it. The result is this article, an iconoclastic romp around the representation of hierarchical structures, and some code to get you started.

"The best thing about XML is what it shares with JSON, being human readable. That turns out to be important, not because people should be reading it, because we shouldn‘t, but because it avoids interoperability problems caused by fussy binary encoding issues.

Beyond that, there is not much to like. It is not very good as a data format. And it is not very good as a document format. If it were a good document format, then wikis would use it."

Doug Crockford March 2010

This article describes a TSQL JSON parser and its evil twin, a JSON outputter, and provides the source. It is also designed to illustrate a number of string manipulation techniques in TSQL. With it you can do things like this to extract the data from a JSON document:

Select * from parseJSON(‘{

"Person":

{

"firstName": "John",

"lastName": "Smith",

"age": 25,

"Address":

{

"streetAddress":"21 2nd Street",

"city":"New York",

"state":"NY",

"postalCode":"10021"

},

"PhoneNumbers":

{

"home":"212 555-1234",

"fax":"646 555-4567"

}

}

}

‘)

And get:

...or you can do the round trip:

DECLARE @MyHierarchy Hierarchy

INSERT INTO @myHierarchy

select * from parseJSON(‘{"menu": {

"id": "file",

"value": "File",

"popup": {

"menuitem": [

{"value": "New", "onclick": "CreateNewDoc()"},

{"value": "Open", "onclick": "OpenDoc()"},

{"value": "Close", "onclick": "CloseDoc()"}

]

}

}}‘)

SELECT dbo.ToJSON(@MyHierarchy)

To get:

{

"menu" :   {

"id" : "file",

"value" : "File",

"popup" :   {

"menuitem" :   [

{

"value" : "New",

"onclick" : "CreateNewDoc()"

},

{

"value" : "Open",

"onclick" : "OpenDoc()"

},

{

"value" : "Close",

"onclick" : "CloseDoc()"

}

]

}

}

}

Background

TSQL isn’t really designed for doing complex string parsing, particularly where strings represent nested data structures such as XML, JSON, YAML, or XHTML.

You can do it but it is not a pretty sight; but why would you ever want to do it anyway? Surely, if anything was meant for the ‘application layer‘ in C# or VB.net, then this is it. ‘Oh yes‘, will chime in the application thought police, ‘this is far better done in the application or with a CLR.‘ Not necessarily.

Sometimes, you just need to do something inappropriate in TSQL.

There are a whole lot of reasons why this might happen to you. It could be that your DBA doesn’t allow a CLR, for example, or you lack the necessary skills with procedural code. Sometimes, there isn‘t any application, or you want to run code unobtrusively across databases or servers.

I needed to interpret or ‘shred‘ JSON data. JSON is one of the most popular lightweight markup languages, and is probably the best choice for transfer of object data from a web page. It is, in fact, executable JavaScript that is very quick to code in the browser in order to dump the contents of a JavaScript object, and is lightning-fast to populate the browser object from the database since you are passing it executable code (you need to parse it first for security reasons - passing executable code around is potentially very risky). AJAX can use JSON rather than XML so you have an opportunity to have a much simpler route for data between database and browser, with less opportunity for error.

The conventional way of dealing with data like this is to let a separate business layer parse a JSON ‘document‘ into some tree structure and then update the database by making a series of calls to it. This is fine, but can get more complicated if you need to ensure that the updates to the database are wrapped into one transaction so that if anything goes wrong, then the whole operation can be rolled back. This is why a CLR or TSQL approach has advantages.

"Sometimes, you just 
need to do something 
inappropriate in TSQL..."

I wrote the parser as a prototype because it was the quickest way to determine what was involved in the process, so I could then re-write something as a CLR in a .NET language.  It takes a JSON string and produces a result in the form of an adjacency list representation of that hierarchy. In the end, the code did what I wanted with adequate performance (It reads a json file of  540 name\value pairs and creates the SQL  hierarchy table  in 4 seconds) so I didn‘t bother with the added complexity of maintaining a CLR routine. In order to test more thoroughly what I‘d done, I wrote a JSON generator that used the same Adjacency list, so you can now import and export data via JSON!

These markup languages such as JSON and XML all represent object data as hierarchies. Although it looks very different to the entity-relational model, it isn‘t. It is rather more a different perspective on the same model. The first trick is to represent it as a Adjacency list hierarchy in a table, and then use the contents of this table to update the database. This Adjacency list is really the Database equivalent of any of the nested data structures that are used for the interchange of serialized information with the application, and can be used to create XML, OSX Property lists, Python nested structures or YAML as easily as JSON.

Adjacency list tables have the same structure whatever the data in them. This means that you can define a single Table-Valued  Type and pass data structures around between stored procedures. However, they are best held at arms-length from the data, since they are not relational tables, but something more like the dreaded EAV (Entity-Attribute-Value) tables. Converting the data from its Hierarchical table form will be different for each application, but is easy with a CTE. You can, alternatively, convert the hierarchical table into XML and interrogate that with XQuery.

JSON format.

JSON is designed to be as lightweight as possible and so it has only two structures. The first, delimited by curly brackets, is a collection of name/value pairs, separated by commas. The name is followed by a colon. This structure is generally implemented in the application-level as an object, record, struct, dictionary, hash table, keyed list, or associative array. The other structure is an ordered list of values, separated by commas. This is usually manifested as an array, vector, list, or sequence.

"Using recursion in TSQL is 
like Sumo Wrestlers doing Ballet. 
It is possible but not pretty."

The first snag for TSQL is that the curly or square brackets are not ‘escaped‘ within a string, so that there is no way of shredding a JSON ‘document‘ simply. It is difficult to  differentiate a bracket used as the delimiter of an array or structure, and one that is within a string. Also, interpreting a string into a SQL String isn‘t entirely straightforward since hex codes can be embedded anywhere to represent complex Unicode characters, and all the old C-style escaped characters are used. The second complication is that, unlike YAML, the datatypes of values can‘t be explicitly declared. You have to sniff them out from applying the rules from the JSON Specification.

Obviously, structures can be embedded in structures, so recursion is a natural way of making life easy. Using recursion in TSQL is like Sumo Wrestlers doing Ballet. It is possible but not pretty.

The implementation

Although the code for the JSON Parser/Shredder will run in SQL Server 2005, and even in SQL Server 2000 (with some modifications required), I couldn‘t resist using a TVP (Table Valued Parameter) to pass a hierarchical table to the function, ToJSON, that produces a JSON ‘document‘. Writing a SQL Server 2005 version should not be too hard.

First the function replaces all strings with tokens of the form @Stringxx, where xx is the foreign key of the table variable where the strings are held. This takes them, and their potentially difficult embedded brackets, out of the way. Names are  always strings in JSON as well as  string values.

Then, the routine iteratively finds the next structure that has no structure contained within it, (and is, by definition the leaf structure), and parses it, replacing it with an object token of the form ‘@Objectxxx‘, or ‘@arrayxxx‘, where xxxis the object id assigned to it. The values, or name/value pairs are retrieved from the string table and stored in the hierarchy table. Gradually, the JSON document is eaten until there is just a single root object left.

The JSON outputter is a great deal simpler, since one can be surer of the input, but essentially it does the reverse process, working from the root to the leaves. The only complication is working out the indent of the formatted output string.

In the implementation, you‘ll see a fairly heavy use of PATINDEX. This uses a poor man‘s RegEx, a starving man‘s RegEx. However, it is all we have, and can be pressed into service by chopping the string it is searching (if only it had an optional third parameter like CHARINDEX that specified the index of the start position of the search!). The STUFF function is also a godsend for this sort of string-manipulation work.

CREATE FUNCTION dbo.parseJSON( @JSON NVARCHAR(MAX))

RETURNS @hierarchy TABLE

(

element_id INT IDENTITY(1, 1) NOT NULL, /* internal surrogate primary key gives the order of parsing and the list order */

sequenceNo [int] NULL, /* the place in the sequence for the element */

parent_ID INT,/* if the element has a parent then it is in this column. The document is the ultimate parent, so you can get the structure from recursing from the document */

Object_ID INT,/* each list or object has an object id. This ties all elements to a parent. Lists are treated as objects here */

NAME NVARCHAR(2000),/* the name of the object */

StringValue NVARCHAR(MAX) NOT NULL,/*the string representation of the value of the element. */

ValueType VARCHAR(10) NOT null /* the declared type of the value represented as a string in StringValue*/

)

AS

BEGIN

DECLARE

@FirstObject INT, --the index of the first open bracket found in the JSON string

@OpenDelimiter INT,--the index of the next open bracket found in the JSON string

@NextOpenDelimiter INT,--the index of subsequent open bracket found in the JSON string

@NextCloseDelimiter INT,--the index of subsequent close bracket found in the JSON string

@Type NVARCHAR(10),--whether it denotes an object or an array

@NextCloseDelimiterChar CHAR(1),--either a ‘}‘ or a ‘]‘

@Contents NVARCHAR(MAX), --the unparsed contents of the bracketed expression

@Start INT, --index of the start of the token that you are parsing

@end INT,--index of the end of the token that you are parsing

@param INT,--the parameter at the end of the next Object/Array token

@EndOfName INT,--the index of the start of the parameter at end of Object/Array token

@token NVARCHAR(200),--either a string or object

@value NVARCHAR(MAX), -- the value as a string

@SequenceNo int, -- the sequence number within a list

@name NVARCHAR(200), --the name as a string

@parent_ID INT,--the next parent ID to allocate

@lenJSON INT,--the current length of the JSON String

@characters NCHAR(36),--used to convert hex to decimal

@result BIGINT,--the value of the hex symbol being parsed

@index SMALLINT,--used for parsing the hex value

@Escape INT --the index of the next escape character

DECLARE @Strings TABLE /* in this temporary table we keep all strings, even the names of the elements, since they are ‘escaped‘ in a different way, and may contain, unescaped, brackets denoting objects or lists. These are replaced in the JSON string by tokens representing the string */

(

String_ID INT IDENTITY(1, 1),

StringValue NVARCHAR(MAX)

)

SELECT--initialise the characters to convert hex to ascii

@characters=‘0123456789abcdefghijklmnopqrstuvwxyz‘,

@SequenceNo=0, --set the sequence no. to something sensible.

/* firstly we process all strings. This is done because [{} and ] aren‘t escaped in strings, which complicates an iterative parse. */

@parent_ID=0;

WHILE 1=1 --forever until there is nothing more to do

BEGIN

SELECT

@start=PATINDEX(‘%[^a-zA-Z]["]%‘, @json collate SQL_Latin1_General_CP850_Bin);--next delimited string

IF @start=0 BREAK --no more so drop through the WHILE loop

IF SUBSTRING(@json, @start+1, 1)=‘"‘

BEGIN --Delimited Name

SET @start=@Start+1;

SET @end=PATINDEX(‘%[^\]["]%‘, RIGHT(@json, LEN(@json+‘|‘)-@start) collateSQL_Latin1_General_CP850_Bin);

END

IF @end=0 --no end delimiter to last string

BREAK --no more

SELECT @token=SUBSTRING(@json, @start+1, @end-1)

--now put in the escaped control characters

SELECT @token=REPLACE(@token, FROMString, TOString)

FROM

(SELECT

‘\"‘ AS FromString, ‘"‘ AS ToString

UNION ALL SELECT ‘\\‘, ‘\‘

UNION ALL SELECT ‘\/‘, ‘/‘

UNION ALL SELECT ‘\b‘, CHAR(08)

UNION ALL SELECT ‘\f‘, CHAR(12)

UNION ALL SELECT ‘\n‘, CHAR(10)

UNION ALL SELECT ‘\r‘, CHAR(13)

UNION ALL SELECT ‘\t‘, CHAR(09)

) substitutions

SELECT @result=0, @escape=1

--Begin to take out any hex escape codes

WHILE @escape>0

BEGIN

SELECT @index=0,

--find the next hex escape sequence

@escape=PATINDEX(‘%\x[0-9a-f][0-9a-f][0-9a-f][0-9a-f]%‘, @token collateSQL_Latin1_General_CP850_Bin)

IF @escape>0 --if there is one

BEGIN

WHILE @index<4 --there are always four digits to a \x sequence

BEGIN

SELECT --determine its value

@result=@result+POWER(16, @index)

*(CHARINDEX(SUBSTRING(@token, @escape+2+3-@index, 1),

@characters)-1), @index=@index+1 ;

END

-- and replace the hex sequence by its unicode value

SELECT @token=STUFF(@token, @escape, 6, NCHAR(@result))

END

END

--now store the string away

INSERT INTO @Strings (StringValue) SELECT @token

-- and replace the string with a token

SELECT @JSON=STUFF(@json, @start, @end+1,

‘@string‘+CONVERT(NVARCHAR(5), @@identity))

END

-- all strings are now removed. Now we find the first leaf.

WHILE 1=1  --forever until there is nothing more to do

BEGIN

SELECT @parent_ID=@parent_ID+1

--find the first object or list by looking for the open bracket

SELECT @FirstObject=PATINDEX(‘%[{[[]%‘, @json collate SQL_Latin1_General_CP850_Bin)--object or array

IF @FirstObject = 0 BREAK

IF (SUBSTRING(@json, @FirstObject, 1)=‘{‘)

SELECT @NextCloseDelimiterChar=‘}‘, @type=‘object‘

ELSE

SELECT @NextCloseDelimiterChar=‘]‘, @type=‘array‘

SELECT @OpenDelimiter=@firstObject

WHILE 1=1 --find the innermost object or list...

BEGIN

SELECT

@lenJSON=LEN(@JSON+‘|‘)-1

--find the matching close-delimiter proceeding after the open-delimiter

SELECT

@NextCloseDelimiter=CHARINDEX(@NextCloseDelimiterChar, @json,

@OpenDelimiter+1)

--is there an intervening open-delimiter of either type

SELECT @NextOpenDelimiter=PATINDEX(‘%[{[[]%‘,

RIGHT(@json, @lenJSON-@OpenDelimiter)collate SQL_Latin1_General_CP850_Bin)--object

IF @NextOpenDelimiter=0

BREAK

SELECT @NextOpenDelimiter=@NextOpenDelimiter+@OpenDelimiter

IF @NextCloseDelimiter<@NextOpenDelimiter

BREAK

IF SUBSTRING(@json, @NextOpenDelimiter, 1)=‘{‘

SELECT @NextCloseDelimiterChar=‘}‘, @type=‘object‘

ELSE

SELECT @NextCloseDelimiterChar=‘]‘, @type=‘array‘

SELECT @OpenDelimiter=@NextOpenDelimiter

END

---and parse out the list or name/value pairs

SELECT

@contents=SUBSTRING(@json, @OpenDelimiter+1,

@NextCloseDelimiter-@OpenDelimiter-1)

SELECT

@JSON=STUFF(@json, @OpenDelimiter,

@NextCloseDelimiter-@OpenDelimiter+1,

‘@‘+@type+CONVERT(NVARCHAR(5), @parent_ID))

WHILE (PATINDEX(‘%[[email protected]+.e]%‘, @contents collate SQL_Latin1_General_CP850_Bin))<>0

BEGIN

IF @Type=‘Object‘ --it will be a 0-n list containing a string followed by a string, number,boolean, or null

BEGIN

SELECT

@SequenceNo=0,@end=CHARINDEX(‘:‘, ‘ ‘+@contents)--if there is anything, it will be a string-based name.

SELECT  @start=PATINDEX(‘%[^[email protected]][@]%‘, ‘ ‘+@contents collateSQL_Latin1_General_CP850_Bin)--AAAAAAAA

SELECT @token=SUBSTRING(‘ ‘+@contents, @start+1, @End-@Start-1),

@endofname=PATINDEX(‘%[0-9]%‘, @token collate SQL_Latin1_General_CP850_Bin),

@param=RIGHT(@token, LEN(@token)-@endofname+1)

SELECT

@token=LEFT(@token, @endofname-1),

@Contents=RIGHT(‘ ‘+@contents, LEN(‘ ‘+@contents+‘|‘)-@end-1)

SELECT  @name=stringvalue FROM @strings

WHERE string_id=@param --fetch the name

END

ELSE

SELECT @Name=null,@SequenceNo=@SequenceNo+1

SELECT

@end=CHARINDEX(‘,‘, @contents)-- a string-token, object-token, list-token, number,boolean, or null

IF @end=0

SELECT  @end=PATINDEX(‘%[[email protected]+.e][^[email protected]+.e]%‘, @Contents+‘ ‘ collateSQL_Latin1_General_CP850_Bin)

+1

SELECT

@start=PATINDEX(‘%[^[email protected]+.e][[email protected]+.e]%‘, ‘ ‘+@contents collateSQL_Latin1_General_CP850_Bin)

--select @start,@end, LEN(@contents+‘|‘), @contents

SELECT

@Value=RTRIM(SUBSTRING(@contents, @start, @End-@Start)),

@Contents=RIGHT(@contents+‘ ‘, LEN(@contents+‘|‘)-@end)

IF SUBSTRING(@value, 1, 7)=‘@object‘

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, Object_ID, ValueType)

SELECT @name, @SequenceNo, @parent_ID, SUBSTRING(@value, 8, 5),

SUBSTRING(@value, 8, 5), ‘object‘

ELSE

IF SUBSTRING(@value, 1, 6)=‘@array‘

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, Object_ID, ValueType)

SELECT @name, @SequenceNo, @parent_ID, SUBSTRING(@value, 7, 5),

SUBSTRING(@value, 7, 5), ‘array‘

ELSE

IF SUBSTRING(@value, 1, 7)=‘@string‘

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, ValueType)

SELECT @name, @SequenceNo, @parent_ID, stringvalue, ‘string‘

FROM @strings

WHERE string_id=SUBSTRING(@value, 8, 5)

ELSE

IF @value IN (‘true‘, ‘false‘)

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, ValueType)

SELECT @name, @SequenceNo, @parent_ID, @value, ‘boolean‘

ELSE

IF @value=‘null‘

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, ValueType)

SELECT @name, @SequenceNo, @parent_ID, @value, ‘null‘

ELSE

IF PATINDEX(‘%[^0-9]%‘, @value collate SQL_Latin1_General_CP850_Bin)>0

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, ValueType)

SELECT @name, @SequenceNo, @parent_ID, @value, ‘real‘

ELSE

INSERT INTO @hierarchy

(NAME, SequenceNo, parent_ID, StringValue, ValueType)

SELECT @name, @SequenceNo, @parent_ID, @value, ‘int‘

if @Contents=‘ ‘ Select @SequenceNo=0

END

END

INSERT INTO @hierarchy (NAME, SequenceNo, parent_ID, StringValue, Object_ID, ValueType)

SELECT ‘-‘,1, NULL, ‘‘, @parent_id-1, @type

--

RETURN

END

GO

So once we have a hierarchy, we can pass it to a stored procedure. As the output is an adjacency list, it should be easy to access the data. You might find it handy to create a table type if you are using SQL Server 2008. Here is what I use. (Note that if you drop a Table Valued Parameter type, you will have to drop any dependent functions or procedures first, and re-create them afterwards).

-- Create the data type

IF EXISTS (SELECT * FROM sys.types WHERE name LIKE ‘Hierarchy‘)

DROP TYPE dbo.Hierarchy

go

CREATE TYPE dbo.Hierarchy AS TABLE

(

element_id INT NOT NULL, /* internal surrogate primary key gives the order of parsing and the list order */

sequenceNo [int] NULL, /* the place in the sequence for the element */

parent_ID INT,/* if the element has a parent then it is in this column. The document is the ultimate parent, so you can get the structure from recursing from the document */

[Object_ID] INT,/* each list or object has an object id. This ties all elements to a parent. Lists are treated as objects here */

NAME NVARCHAR(2000),/* the name of the object, null if it hasn‘t got one */

StringValue NVARCHAR(MAX) NOT NULL,/*the string representation of the value of the element. */

ValueType VARCHAR(10) NOT null /* the declared type of the value represented as a string in StringValue*/

PRIMARY KEY (element_id)

)

ToJSON. A function that creates JSON Documents

Firstly, we need a simple utility function:

IF OBJECT_ID (N‘dbo.parseJSON‘) IS NOT NULL

DROP FUNCTION dbo.JSONEscaped

GO

CREATE FUNCTION JSONEscaped ( /* this is a simple utility function that takes a SQL String with all its clobber and outputs it as a sting with all the JSON escape sequences in it.*/

@Unescaped NVARCHAR(MAX) --a string with maybe characters that will break json

)

RETURNS NVARCHAR(MAX)

AS

BEGIN

SELECT  @Unescaped = REPLACE(@Unescaped, FROMString, TOString)

FROM    (SELECT

‘\"‘ AS FromString, ‘"‘ AS ToString

UNION ALL SELECT ‘\‘, ‘\\‘

UNION ALL SELECT ‘/‘, ‘\/‘

UNION ALL SELECT  CHAR(08),‘\b‘

UNION ALL SELECT  CHAR(12),‘\f‘

UNION ALL SELECT  CHAR(10),‘\n‘

UNION ALL SELECT  CHAR(13),‘\r‘

UNION ALL SELECT  CHAR(09),‘\t‘

) substitutions

RETURN @Unescaped

END

And now, the function that takes a JSON Hierarchy table and converts it to a JSON string.

CREATE FUNCTION ToJSON

(

@Hierarchy Hierarchy READONLY

)

/*

the function that takes a Hierarchy table and converts it to a JSON string

Author: Phil Factor

Revision: 1.5

date: 1 May 2014

why: Added a fix to add a name for a list.

example:

Declare @XMLSample XML

Select @XMLSample=‘

<glossary><title>example glossary</title>

<GlossDiv><title>S</title>

<GlossList>

<GlossEntry ID="SGML" SortAs="SGML">

<GlossTerm>Standard Generalized Markup Language</GlossTerm>

<Acronym>SGML</Acronym>

<Abbrev>ISO 8879:1986</Abbrev>

<GlossDef>

<para>A meta-markup language, used to create markup languages such as DocBook.</para>

<GlossSeeAlso OtherTerm="GML" />

<GlossSeeAlso OtherTerm="XML" />

</GlossDef>

<GlossSee OtherTerm="markup" />

</GlossEntry>

</GlossList>

</GlossDiv>

</glossary>‘

DECLARE @MyHierarchy Hierarchy -- to pass the hierarchy table around

insert into @MyHierarchy select * from dbo.ParseXML(@XMLSample)

SELECT dbo.ToJSON(@MyHierarchy)

*/

RETURNS NVARCHAR(MAX)--JSON documents are always unicode.

AS

BEGIN

DECLARE

@JSON NVARCHAR(MAX),

@NewJSON NVARCHAR(MAX),

@Where INT,

@ANumber INT,

@notNumber INT,

@indent INT,

@ii int,

@CrLf CHAR(2)--just a simple utility to save typing!

--firstly get the root token into place

SELECT @CrLf=CHAR(13)+CHAR(10),--just CHAR(10) in UNIX

@JSON = CASE ValueType WHEN ‘array‘ THEN

+COALESCE(‘{‘+@CrLf+‘  "‘+NAME+‘" : ‘,‘‘)+‘[‘

ELSE ‘{‘ END

+@CrLf

+ case when ValueType=‘array‘ and NAME is not null then ‘  ‘ else ‘‘ end

+ ‘@Object‘+CONVERT(VARCHAR(5),OBJECT_ID)

+@CrLf+CASE ValueType WHEN ‘array‘ THEN

case when NAME is null then ‘]‘ else ‘  ]‘+@CrLf+‘}‘+@CrLf end

ELSE ‘}‘ END

FROM @Hierarchy

WHERE parent_id IS NULL AND valueType IN (‘object‘,‘document‘,‘array‘) --get the root element

/* now we simply iterat from the root token growing each branch and leaf in each iteration. This won‘t be enormously quick, but it is simple to do. All values, or name/value pairs withing a structure can be created in one SQL Statement*/

Select @ii=1000

WHILE @ii>0

begin

SELECT @where= PATINDEX(‘%[^[a-zA-Z0-9]@Object%‘,@json)--find NEXT token

if @where=0 BREAK

/* this is slightly painful. we get the indent of the object we‘ve found by looking backwards up the string */

SET@indent=CHARINDEX(char(10)+char(13),Reverse(LEFT(@json,@where))+char(10)+char(13))-1

SET @NotNumber= PATINDEX(‘%[^0-9]%‘, RIGHT(@json,LEN(@JSON+‘|‘)-@Where-8)+‘ ‘)--find NEXT token

SET @NewJSON=NULL --this contains the structure in its JSON form

SELECT

@NewJSON=COALESCE(@NewJSON+‘,‘+@CrLf+SPACE(@indent),‘‘)

+case when parent.ValueType=‘array‘ then ‘‘ else COALESCE(‘"‘+TheRow.NAME+‘" : ‘,‘‘) end

+CASE TheRow.valuetype

WHEN ‘array‘ THEN ‘  [‘+@CrLf+SPACE(@indent+2)

+‘@Object‘+CONVERT(VARCHAR(5),TheRow.[OBJECT_ID])+@CrLf+SPACE(@indent+2)+‘]‘

WHEN ‘object‘ then ‘  {‘+@CrLf+SPACE(@indent+2)

+‘@Object‘+CONVERT(VARCHAR(5),TheRow.[OBJECT_ID])+@CrLf+SPACE(@indent+2)+‘}‘

WHEN ‘string‘ THEN ‘"‘+dbo.JSONEscaped(TheRow.StringValue)+‘"‘

ELSE TheRow.StringValue

END

FROM @Hierarchy TheRow

inner join @hierarchy Parent

on parent.element_ID=TheRow.parent_ID

WHERE TheRow.parent_id= SUBSTRING(@JSON,@where+8, @Notnumber-1)

/* basically, we just lookup the structure based on the ID that is appended to the @Object token. Simple eh? */

--now we replace the token with the structure, maybe with more tokens in it.

Select @JSON=STUFF (@JSON, @where+1, 8+@NotNumber-1, @NewJSON),@ii=@ii-1

end

return @JSON

end

go

ToXML. A function that creates XML

The function that converts a hierarchy  table to XML gives us a JSON to XML converter. It is surprisingly similar to the previous function

IF OBJECT_ID (N‘dbo.ToXML‘) IS NOT NULL

DROP FUNCTION dbo.ToXML

GO

CREATE FUNCTION ToXML

(

/*this function converts a Hierarchy table into an XML document. This uses the same technique as the toJSON function, and uses the ‘entities‘ form of XML syntax to give a compact rendering of the structure */

@Hierarchy Hierarchy READONLY

)

RETURNS NVARCHAR(MAX)--use unicode.

AS

BEGIN

DECLARE

@XMLAsString NVARCHAR(MAX),

@NewXML NVARCHAR(MAX),

@Entities NVARCHAR(MAX),

@Objects NVARCHAR(MAX),

@Name NVARCHAR(200),

@Where INT,

@ANumber INT,

@notNumber INT,

@indent INT,

@CrLf CHAR(2)--just a simple utility to save typing!

--firstly get the root token into place

--firstly get the root token into place

SELECT @CrLf=CHAR(13)+CHAR(10),--just CHAR(10) in UNIX

@XMLasString =‘<?xml version="1.0" ?>

@Object‘+CONVERT(VARCHAR(5),OBJECT_ID)+‘

‘

FROM @hierarchy

WHERE parent_id IS NULL AND valueType IN (‘object‘,‘array‘) --get the root element

/* now we simply iterate from the root token growing each branch and leaf in each iteration. This won‘t be enormously quick, but it is simple to do. All values, or name/value pairs within a structure can be created in one SQL Statement*/

WHILE 1=1

begin

SELECT @where= PATINDEX(‘%[^a-zA-Z0-9]@Object%‘,@XMLAsString)--find NEXT token

if @where=0 BREAK

/* this is slightly painful. we get the indent of the object we‘ve found by looking backwards up the string */

SET@indent=CHARINDEX(char(10)+char(13),Reverse(LEFT(@XMLasString,@where))+char(10)+char(13))-1

SET @NotNumber= PATINDEX(‘%[^0-9]%‘, RIGHT(@XMLasString,LEN(@XMLAsString+‘|‘)-@Where-8)+‘ ‘)--find NEXT token

SET @Entities=NULL --this contains the structure in its XML form

SELECT @Entities=COALESCE(@Entities+‘ ‘,‘ ‘)+NAME+‘="‘

+REPLACE(REPLACE(REPLACE(StringValue, ‘<‘, ‘&lt;‘), ‘&‘, ‘&amp;‘),‘>‘, ‘&gt;‘)

+ ‘"‘

FROM @hierarchy

WHERE parent_id= SUBSTRING(@XMLasString,@where+8, @Notnumber-1)

AND ValueType NOT IN (‘array‘, ‘object‘)

SELECT @Entities=COALESCE(@entities,‘‘),@Objects=‘‘,@name=CASE WHEN Name=‘-‘ THEN‘root‘ ELSE NAME end

FROM @hierarchy

WHERE [Object_id]= SUBSTRING(@XMLasString,@where+8, @Notnumber-1)

SELECT  @Objects=@Objects+@CrLf+SPACE(@indent+2)

+‘@Object‘+CONVERT(VARCHAR(5),OBJECT_ID)

[email protected]+SPACE(@indent+2)+‘‘

FROM @hierarchy

WHERE parent_id= SUBSTRING(@XMLasString,@where+8, @Notnumber-1)

AND ValueType IN (‘array‘, ‘object‘)

IF @Objects=‘‘ --if it is a lef, we can do a more compact rendering

SELECT @NewXML=‘<‘+COALESCE(@name,‘item‘)+@entities+‘ />‘

ELSE

SELECT @NewXML=‘<‘+COALESCE(@name,‘item‘)+@entities+‘>‘

+@Objects+@CrLf++SPACE(@indent)+‘</‘+COALESCE(@name,‘item‘)+‘>‘

/* basically, we just lookup the structure based on the ID that is appended to the @Object token. Simple eh? */

--now we replace the token with the structure, maybe with more tokens in it.

Select @XMLasString=STUFF (@XMLasString, @where+1, 8+@NotNumber-1, @NewXML)

end

return @XMLasString

end

This provides you the means of converting a JSON string into XML

DECLARE @MyHierarchy Hierarchy,@xml XML

INSERT INTO @myHierarchy

select * from parseJSON(‘{"menu": {

"id": "file",

"value": "File",

"popup": {

"menuitem": [

{"value": "New", "onclick": "CreateNewDoc()"},

{"value": "Open", "onclick": "OpenDoc()"},

{"value": "Close", "onclick": "CloseDoc()"}

]

}

}}‘)

SELECT dbo.ToXML(@MyHierarchy)

SELECT @XML=dbo.ToXML(@MyHierarchy)

SELECT @XML

This gives the result...

<?xml version="1.0" ?>

<root>

<menu id="file" value="File">

<popup>

<menuitem>

<item value="New" onclick="CreateNewDoc()" />

<item value="Open" onclick="OpenDoc()" />

<item value="Close" onclick="CloseDoc()" />

</menuitem>

</popup>

</menu>

</root>

(1 row(s) affected)

<root><menu id="file" value="File"><popup><menuitem><item value="New" onclick="CreateNewDoc()" /><item value="Open" onclick="OpenDoc()" /><item value="Close" onclick="CloseDoc()" /></menuitem></popup></menu></root>

(1 row(s) affected)

Wrap-up

The so-called ‘impedence-mismatch‘ between applications and databases is, I reckon, an illusion. The object-oriented nested data-structures that we receive from applications are, if the developer has understood the data correctly,  merely a perspective from a particular entity of the relationships it is involved with. Whereas it is easy to shred XML documents to get the data from it to update the database, it has been trickier with other formats such as JSON. By using techniques like this, it should be possible to liberate the application, or website, programmer from having to do the mapping from the object model to the relational, and spraying the database with ad-hoc TSQL  that uses the base tables or updateable views.  If the database can be provided with the JSON, or the Table-Valued parameter, then there is a better chance of  maintaining full transactional integrity for the more complex updates.

The database developer already has the tools to do the work with XML, but why not the simpler, and more practical JSON? I hope these two routines get you started with experimenting with this.

Interesting JSON-related articles and sites

Since writing this article, Phil has also developed a CSV parser and output and an XML parser (Producing JSON Documents from SQL Server queries via TSQL)

This article has been viewed 123784 times.

来源： <https://www.simple-talk.com/sql/t-sql-programming/consuming-json-strings-in-sql-server/>

来自为知笔记(Wiz)

时间： 2024-10-12 10:26:54

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