Always Encrypted feature in SQL Server 2016

With the introduction of SQL Server 2016 in June (Official Final Release), Microsoft had introduced few, new and very useful features in to the SQL Server. One such feature is the ‘Always Encrypted’.

‘Always Encrypted’ is the ability to perform SQL operations (there are restrictions) on your data as it were normal (non encrypted), while keeping them encrypted all the time. This means SQL Server will always get encrypted data to be stored into the tables. This will put an extra layer of protection on to your data making sure that even onsite DBA’s or Developers cannot see the plain text value behind the encrypted data using their level of access. (Users with ‘SysAdmin’ access won’t be able to see these details without the Key). Therefore ‘Always Encryption’ provides a separation between those who own the data (and can view it) and those who manage the data (but should have no access).

 

Why Always Encrypted ?

There are many benefits using Always Encrypted feature:

• It provides a clear separation between the data owners and people who manage it
• Unless proper access is provided via encryption keys, even DBA’s or SysAdmin users cannot access the data in plain text

Ultimately aforementioned points will provide an unparalleled protection against data breaches and help to protect sensitive information such as credit card numbers, personal details etc. Also this will broaden the boundaries where such sensitive information can be kept.

 

How Always Encrypted Works ?

This is a client-side encryption technology which the SQL Server Client Driver plays a key role.

• The data is transparently encrypted inside a client driver
• Client manages the encryption key. SQL Server doesn’t have any information regarding the encryption key.

SQL Server can query and perform certain computations on the encryption data, such as equality comparisson, equality joins, group by etc.

 

Always Encrypted Demonstration

We will see how Always Encrypted can be implemented and used. In order to illustrate, we will use a table which contains employee information.

CREATE TABLE Employee(
	Id			INT
	,FirstName	VARCHAR(100)
	,LastName	VARCHAR(100)
	,DOB		DATE
	,SSN		INT
	,[Address]	VARCHAR(255)
	,PostalCode	INT
)

INSERT INTO Employee (
	[Id],[FirstName],[LastName],[DOB],[SSN],[Address],[PostalCode]) 
VALUES 
	(1,'James','Rubin','20-Jul-1986',173456858,'10585 N 600 E',46310)
	,(2,'Austin','Pyatt','24-Dec-1985',138868248,'100 BENTBROOK CT',27519)
	,(3,'Stacey','Munoz','23-Dec-1988',185682639,'1 WOODSIDE DR',4976)
	,(4,'James','Tweed','03-Jan-1987',133890886,'1 AUNNEK CT',95023)
	,(5,'James','Robles','11-Sep-1989',154135505,'101 FISHTRAP RD',35504)
	,(6,'Ebony','Lewis','17-Jul-1988',120488337,'101 N OAKS DR',35180)
	,(7,'Marian','Caro','20-Nov-1985',115281829,'1017 FISK ST SE',49507)
	,(8,'Lynne','Martinez','22-Apr-1985',157900240,'103 UNITY CT',78214)
	,(9,'Elsa','Cole','25-Apr-1990',150631885,'1001 E FERN AVE APT 201',78501)
	,(10,'Kiley','Caldwell','03-Jan-1988',131368172,'103 NOB HILL LN APT 5',40206)
	,(11,'Michael','Soluri','17-Jun-1985',173245124,'10770 S KILBOURN AVE',60453)
	,(12,'Gregory','Emmons','06-Sep-1988',137693229,'10 LOUISA PL APT 2F',7086)
	,(13,'Jessica','Barr','04-Feb-1989',155895863,'1 FAWNRIDGE DR',94945)
	,(14,'Daniel','Mccabe','06-Sep-1985',148236776,'1 CALLE MARGINAL GARCIA',674)
	,(15,'Sharon','Schwartz','06-Sep-1987',117569460,'1 KRITTER CT',8050)
	,(16,'Dorthy','Wear','13-Dec-1988',170517705,'1 CLARK RD',35747)
	,(17,'Betsy','Blansett','17-Jun-1990',182202498,'10 CALLE 1 DE FLORIDA',612)
	,(18,'Margaret','Payne','25-Jul-1985',157359609,'1003 BLOOMFIELD AVE',7006)
	,(19,'James','Walker','26-Jan-1989',142829150,'100 CONGLETON HOLLOW SPUR RD',40447)
	,(20,'Sarah','Reeves','22-Jun-1990',146171169,'1 BLUEBERRY LN',1832)

 

I have a small MVC Web Application which has a page to list out the aforementioned details from the SQL Server. The MVC Controller will load the details to a list of Employee records and pass it to the Html view which will be displayed as follows.

In the MVC application I have the following data model to load details from the SQL Database Table.

public class Employee {
        public int Id { get; set; }
        public string FirstName { get; set; }
        public string LastName { get; set; }
        public DateTime DOB { get; set; }
        public int SSN { get; set; }
        public string Address { get; set; }
        public int PostalCode { get; set; }

        public Employee() {
            
        }
    }

And I am using the following connection string in order to connect to the SQL Server Database.

 const string zConnectionString =
                @"Server=.\SQL2K16; Network Library=DBMSSOCN;Database=SQLTraining;Trusted_Connection=True;";

 

There are few steps to be followed on both SQL Server and application side (Client Applications) in order to implement and use this feature.

From the SQL Server side, there are few ways to enable the Always Encrypted feature. We will look more details how to use these feature using the wizard.

 

1. Right click the table which you want to encrypt details and select ‘Encrypt Columns’. This will take you the to wizard.

 

2. You will get the introduction screen which contains few details about what ‘Always Encrypted’ is all about. Click next and proceed to the next screen.

This is the column selection screen, which allows you to select which columns you want to encrypt and using which Encryption Type. There are two Encryption Types available in SQL Server 2016.

• Deterministic –> Deterministic encryption always generates the same encrypted value for any given plain text value. Using deterministic encryption allows point lookups, equality joins, grouping and indexing on encrypted columns. However, but may also allow unauthorized users to guess information about encrypted values by examining patterns in the encrypted column, especially if there is a small set of possible encrypted values, such as True/False, or North/South/East/West region. Deterministic encryption must use a column collation with a binary2 sort order for character columns.

 

•  Randomized –> Randomized encryption uses a method that encrypts data in a less predictable manner. Randomized encryption is more secure, but prevents searching, grouping, indexing, and joining on encrypted columns.

This advice has been included in Microsoft Documentation: Use deterministic encryption for columns that will be used as search or grouping parameters, for example a government ID number. Use randomized encryption, for data such as confidential investigation comments, which are not grouped with other records and are not used to join tables.

So in our example we will choose DOB & SSN columns for encryption. For DOB we will choose Randomized and for SSN we will choose Deterministic.

Once the encryption type is chosen the wizard should be similar to the screen shown below.

 

If you look closely, you will be able to see that the Encryption Key combo is disabled. The reason for this is the fact that we haven’t created any Column encryption keys so far. If the keys are created prior to the column selection then you will have the option to choose whether to use an existing key or to generate a new key.

In this illustration, we will use the option which will create a new column encryption key. Click next to proceed to the next step.

3. The next step is the Column Master Key Configuration. A Column Master Key will be used to encrypt and protect the Column Encryption Key, which is used to encrypt the data. We will use the option ‘Auto generated column master key’, which the wizard will generate the key for us. When we are creating a new Master Key, there are two options available, where to store the newly generated key. Clicking on the small info button beside each option will give further details about each option

 

4. Click next to move to the next step. In this step you can decide whether you require a PowerShellscript to be generated for the encryption process or to proceed with the encryption immediately. In this example we will select the second option and click on the next button.

 

In this step you will be presented with the steps which will be followed during the data encryption

Click finish to complete the encryption process. Once process is completed click close button.

 

Now if you check the details on SQL Table you can see that, data in SSN and DOB columns are encrypted.

SELECT * FROM dbo.Employee

If you see the Table creation script for the Employee table now, you could see few changes which has been done by the SQL Server after we enabled the encryption for those two columns.

CREATE TABLE [dbo].[Employee](
	[Id] [INT] NULL,
	[FirstName] [VARCHAR](100) NULL,
	[LastName] [VARCHAR](100) NULL,
	[DOB] [DATE] ENCRYPTED WITH (COLUMN_ENCRYPTION_KEY = [CEK_Auto1], 
		ENCRYPTION_TYPE = RANDOMIZED, 
		ALGORITHM = 'AEAD_AES_256_CBC_HMAC_SHA_256') NULL,
	[SSN] [INT] ENCRYPTED WITH (COLUMN_ENCRYPTION_KEY = [CEK_Auto1], 
		ENCRYPTION_TYPE = DETERMINISTIC, 
		ALGORITHM = 'AEAD_AES_256_CBC_HMAC_SHA_256') NULL,
	[Address] [VARCHAR](255) NULL,
	[PostalCode] [INT] NULL
) ON [PRIMARY]

You can see that it had added the ENCRYPTED WITH clause for those two columns. ENCRYPTED WITH clause consist 3 attributes which are:

• COLUMN_ENCRYPTION_KEY –> CEK_Auto1 since we have chosen the option for SQL to generate a new key.
• ENCRYPTION_TYPE –> Can be either RANDOMIZED or DETERMINISTIC
• ALGORITHM –> This is always AES_256

If you inspect the Always Encrypted keys in the object explorer in SSMS you could see the following meta data for the Master and the Column Encrypted Keys.

 

Column Encrypted Key – CEK_Auto1

• COLUMN_MASTER_KEY –> Name of the column master key protecting the value of the column encryption key.
• ALGORITHM –> Algorithm used to generate the encrypted value of the column encryption key (RSA_OAEP).
• ENCRYPTED_VALUE –> Encrypted value of the column encryption key. The encrypted value is assumed to be produced by encrypting the plaintext of the column encryption key using the specified column master key and the specified algorithm.

For further information please refer to the following url: https://blogs.msdn.microsoft.com/sqlsecurity/2015/07/06/always-encrypted-key-metadata/

 

Column Master Key - CMK_Auto1

• KEY_STORE_PROVIDER_NAME –> Name of a provider for the key store that holds the column master key.
• KEY_PATH –> Key path specifying the location of the column master key in the key store.

 

For further information please refer to the following url: https://blogs.msdn.microsoft.com/sqlsecurity/2015/07/06/always-encrypted-key-metadata/

 

Now if we try to fetch details without doing anything on the sample .Net Application you will get a similar error like shown below.

Now we will look into the things that we required to change on our application side (Business) in order to retrieve the required information.

1. Make sure that the target framework is version 4.6 or higher.

 

2. In the Connection String include ‘Column Encryption Setting=enabled’

And I am using the following connection string in order to connect to the SQL Server Database.

 const string zConnectionString =
@"Server=.\SQL2K16; Network Library=DBMSSOCN;Database=SQLTraining;Trusted_Connection=True;Column Encryption Setting=enabled;";

 

Now if we check the details from our application we can see that DOB and SSN values are fetched as plain text, even though the values are encrypted in the SQL Server.

Hope this will help you to understand the ‘Always Encrypted’ feature in SQL Server 2016 and how to integrate it to an existing application.

Native JSON Support in SQL Server 2016

When it comes to modern web development, JSON is one of the well known technologies when you require to exchange information between different applications. Before JSON, XML was used (and still being used in various applications and technologies) to do this job. But compared to XML, JSON is less verbose (like XML, JSON doesn’t have a closing tag), and it will make the JSON data somewhat smaller in size, ultimately making the data flow much faster. Perhaps the most significant advantage that JSON has over XML is that JSON is a subset of JavaScript, so code to parse and package it fits very naturally into JavaScript code. This seems highly beneficial for JavaScript programs and that happens to be a good reason for JSON to be very popular among web application developers.

However using XML or JSON is up to the personal preference and the requirement.

Prior to SQL Server 2016, there’s wasn’t any support for JSON in the earlier editions. So native JSON support is one of the new features which Microsoft introduced in SQL Server 2016.

Prior to SQL Server 2016 there are other databases which supports JSON.

• MongoDB
• CouchDB
• eXistDB
• Elastisearch
• BaseX
• MarkLogic
• OrientDB
• Oracle Database
• PostgresSQL
• Riak

But my main focus in this post will be the JSON support in SQL Server 2016.

In order to support JSON, in SQL 2016 following in built functions have been introduced:

• ISJSON
• JSON_VALUE
• JSON_QUERY
• JSON_MODIFY
• OPENJSON
• FOR JSON

 

There’s no specific data type in SQL Server to be used for JSON (unlike XML). You have to use NVARCHAR when you interact with JSON in SQL Server.

 

 

This is how we assign JSON data to a variable.

 

DECLARE @varJData AS NVARCHAR(4000)
SET @varJData = 
N'{
	"OrderInfo":{
		"Tag":"#ONLORD_12546_45634",
		"HeaderInfo":{
			"CustomerNo":"CUS0001",
			"OrderDate":"04-Jun-2016",
			"OrderAmount":1200.00,
			"OrderStatus":"1",
			"Contact":["+0000 000 0000000000", "info@abccompany.com", "finance@abccompany.com"]
		},
		"LineInfo":[
			{"ProductNo":"P00025", "Qty":3, "Price":200},
			{"ProductNo":"P12548", "Qty":2, "Price":300}
		]
	}
}'

We will look closely how the aforementioned functions can be used with some sample

 

ISJSON()

As the name implies ISJSON functions is used to validate a given JSON string. The function will return in INT value and If the provided string is properly formatted as JSON it will return 1 else it will return 0.

Eg:

SELECT ISJSON(@varJData)

 

JSON_VALUE()

JSON_VALUE function can be used to return a scalar value from a JSON string.

Eg:

DECLARE @varJData AS NVARCHAR(4000)
SET @varJData = 
N'{
	"OrderInfo":{
		"Tag":"#ONLORD_12546_45634",
		"HeaderInfo":{
			"CustomerNo":"CUS0001",
			"OrderDate":"04-Jun-2016",
			"OrderAmount":1200.00,
			"OrderStatus":"1",
			"Contact":["+0000 000 0000000000", "info@abccompany.com", "finance@abccompany.com"]
		},
		"LineInfo":[
			{"ProductNo":"P00025", "Qty":3, "Price":200},
			{"ProductNo":"P12548", "Qty":2, "Price":300}
		]
	}
}'

SELECT JSON_VALUE(@varJData,'$.OrderInfo.Tag')
SELECT JSON_VALUE(@varJData,'$.OrderInfo.HeaderInfo.CustomerNo')

Please note that the provided key is case sensitive and instead of ‘Tag’ if you pass ‘tag’ it will return a NULL since the function cannot find the key.

SELECT JSON_VALUE(@varJData,'$.OrderInfo.tag') /* This will returns NULL */

In such case if you require to see the exact error or the root cause, you need to specify ‘strict’ prior to the key. Eg:

SELECT JSON_VALUE(@varJData,'strict $.OrderInfo.tag') /* This will thorw an Error */

This will return the following error message instead of returning a NULL value.

Msg 13608, Level 16, State 1, Line 62
Property cannot be found on the specified JSON path.

Also JSON_VALUE can be used to fetch an element from a simple array (not an object array). In our sample JSON there are two arrays, which are ‘Contact’ and ‘LineInfo’, where the first being a simple string array and the latter is an object array.

Suppose if we require to fetch only the phone number from the contact details we can use the following query:

SELECT JSON_VALUE(@varJData,'$.OrderInfo.HeaderInfo.Contact[0]')

Also this can be used when we require to fetch an attribute from an array element as well. Suppose if we require to get the product number from the first element of the ‘LineInfo’ we could use:

SELECT JSON_VALUE(@varJData, '$.OrderInfo.LineInfo[0].ProductNo')

 

JSON_QUERY()

JSON_QUERY function is used when you require to extract an array of data or an object from a JSON.And we can extract the contact details and the line details which are arrays in this scenario as follows

SELECT JSON_QUERY(@varJData, '$.OrderInfo.HeaderInfo.Contact')
SELECT JSON_QUERY(@varJData, '$.OrderInfo.LineInfo')

And this can be used to fetch a certain element from an object array. Suppose if we want to fetch details for the second prodcut in the LineInfo section we can use the following:

SELECT JSON_QUERY(@varJData, '$.OrderInfo.LineInfo[1]')

**Note: If the JSON text contains duplicate properties - for example, two keys with the same name on the same level- the JSON_VALUE and JSON_QUERY functions will return the first value that matches the path.

 

JSON_MODIFY()

JSON_MODIFY function updates the value of a property in a JSON string and return the updated JSON string. The syntax for this function is as follows:

JSON_MODIFY(expression, path, new_value)

Using this function you can either Update, Insert, Delete or Append a value to the JSON string. We will see each of these operations using the above JSON string.

Updating an Exitsing Value

In order to update an existing value you need to provide the exact path followed by the value which should be updated to.

SET @varJData = JSON_MODIFY(@varJData,'$.OrderInfo.Tag','#NEWTAG_00001')
SELECT JSON_VALUE(@varJData,'$.OrderInfo.Tag')

 

Deleting an Existing Value

In order to delete an existing value you need to provide the exact path follwed by the value ‘NULL’.

SET @varJData = JSON_MODIFY(@varJData,'$.OrderInfo.Tag',NULL)
SELECT JSON_VALUE(@varJData,'$.OrderInfo.Tag')
PRINT @varJData

When the value is printed you can see that the ‘Tag’ attribute has been completely removed from the JSON string.

 

Inserting a Value

In order to insert an attribute along with a value you need to provide a path which isn’t currently availble in the JSON followed by the value. If the provided path is already present then the existing value will be replaced by the new value. The new value will always be added at the end of the existing JSON string.

SET @varJData = JSON_MODIFY(@varJData,'$.OrderInfo.Batch','#B_100000')
SELECT JSON_VALUE(@varJData,'$.OrderInfo.Batch')
PRINT @varJData

 

Appending a Value

In order to append an existing array in a JSON, you need to use ‘append’ before the path. Suppose if we require to add another element to the

SET @varJData = JSON_MODIFY(@varJData, 'append $.OrderInfo.HeaderInfo.Contact','+0010 111 1111111111')
SELECT JSON_QUERY(@varJData,'$.OrderInfo.HeaderInfo.Contact')

 

JSON_MODIFY can only manipulate a single value at a time. Therefore if the requirement is to change multiple values within a single query, you need to use JSON_MODIFY function multiple times. Suppose if we require to change the ‘ProductNo’ and the ‘Price’ of the first product in the ‘LineInfo’ we coud use the following syntax.

SET @varJData =
	JSON_MODIFY( 
		JSON_MODIFY(@varJData,'$.OrderInfo.LineInfo[0].ProductNo','P99999')
		,'$.OrderInfo.LineInfo[0].Price'
		,150
	)

 

FOR JSON

FOR JSON functionality is used When we are required to export SQL Tabular data as JSON data. This is very similar to the functionality of ‘FOR XML’. Each row will be formatted as a JSON object and values in cells will be generated as values of those respective JSON objects. Column names (or aliases) will be used as key names. Based on the options provided there are two variations in ‘FOR JSON’ usage.

1. FOR JSON AUTO - This will automatically create nested JSON sub arrays based on the table hierarchy used in the query. (similar to FOR XML AUTO)

2. FOR JSON PATH - This enables you to define the structure of output JSON using the column names/aliases. If you put dot-separated names in the column aliases, JSON properties will follow the naming convention. (This is similar to FOR XML PATH where you can use slash separated paths)

 

In order to illustrate the aforementioned concepts we need to prepare some sample data. Please use the following scripts to generate the necessary data.

--== Generate Required Schemas ==--
CREATE TABLE OrderHeader(
	TAG					VARCHAR(24)
	,ORD_NO				VARCHAR(10)
	,CUST_NO			VARCHAR(8)
	,ORD_DATE			DATE
	,ORD_AMOUNT			MONEY
	,ORD_STATUS			TINYINT
	
)

CREATE TABLE OrderLine(
	ORD_NO				VARCHAR(10)
	,LINE_NO			INT
	,PROD_NO			VARCHAR(8)
	,ORD_QTY			INT
	,ITEM_PRICE			MONEY
)

CREATE TABLE CustomerContact(
	CONT_ID				INT
	,CUST_NO			VARCHAR(8)
	,CONTACT_DETAILS	VARCHAR(24)
)

--== Insert Sample Data ==--
INSERT INTO dbo.OrderHeader(TAG,ORD_NO,CUST_NO,ORD_DATE,ORD_AMOUNT,ORD_STATUS)
VALUES('#ONLORD_12546_45634','ORD_1021','CUS0001','04-Jun-2016',1200.00,1)

INSERT INTO dbo.OrderLine(ORD_NO,LINE_NO,PROD_NO,ORD_QTY,ITEM_PRICE)
VALUES ('ORD_1021',1,'P00025',3,200.00), ('ORD_1021',1,'P12548',2,300.00)

INSERT INTO dbo.CustomerContact(CONT_ID, CUST_NO, CONTACT_DETAILS)
VALUES (1,'CUS0001','+0000 000 0000000000') ,(2,'CUS0001','info@abccompany.com'),(3,'CUS0001','finance@abccompany.com')

 

Extracting data as JSON using FOR JSON AUTO

SELECT 
    H.TAG
   ,H.ORD_NO
   ,H.CUST_NO
   ,H.ORD_DATE
   ,H.ORD_AMOUNT
   ,H.ORD_STATUS
   ,L.ORD_NO
   ,L.LINE_NO
   ,L.PROD_NO
   ,L.ORD_QTY
   ,L.ITEM_PRICE
FROM
	dbo.OrderHeader AS H
	JOIN dbo.OrderLine AS L
		ON L.ORD_NO = H.ORD_NO
WHERE
	H.ORD_NO = 'ORD_1021'
FOR JSON AUTO

 

You will get a similar result which is shown below:

[
    {
        "TAG":"#ONLORD_12546_45634",
        "ORD_NO":"ORD_1021",
        "CUST_NO":"CUS0001",
        "ORD_DATE":"2016-06-04",
        "ORD_AMOUNT":1200.0000,
        "ORD_STATUS":1,
        "L":[
            {"ORD_NO":"ORD_1021","LINE_NO":1,"PROD_NO":"P00025","ORD_QTY":3,"ITEM_PRICE":200.0000},
            {"ORD_NO":"ORD_1021","LINE_NO":1,"PROD_NO":"P12548","ORD_QTY":2,"ITEM_PRICE":300.0000}
        ]
    }
]

As described previously ‘FOR JSON AUTO’ will simply convert the column names or aliases as keys and produce the JSON. Table aliases will be used to create sub arrays.

But we could get a similar resultset like what we had in our previous examples by tweaking the above select statement as follows:

SELECT 
    H.TAG AS Tag
   ,H.ORD_NO AS OrderNo
   ,H.CUST_NO AS CustNo
   ,H.ORD_DATE AS OrderDate
   ,H.ORD_AMOUNT AS OrderAmount
   ,H.ORD_STATUS AS  OrderStatus
   ,LineInfo.ORD_NO AS [OrderNo]
   ,LineInfo.LINE_NO AS [LineNo]
   ,LineInfo.PROD_NO AS [ProdNo]
   ,LineInfo.ORD_QTY AS [Qty]
   ,LineInfo.ITEM_PRICE AS [ItemPrice]
FROM
	dbo.OrderHeader AS H
	JOIN dbo.OrderLine AS LineInfo
		ON LineInfo.ORD_NO = H.ORD_NO
WHERE
	H.ORD_NO = 'ORD_1021'
FOR JSON AUTO, ROOT ('OrderInfo')

Then we will be able to get the following JSON string.

{
    "OrderInfo":[
        {
            "Tag":"#ONLORD_12546_45634",
            "OrderNo":"ORD_1021",
            "CustNo":"CUS0001",
            "OrderDate":"2016-06-04",
            "OrderAmount":1200.0000,
            "OrderStatus":1,
            "LineInfo":[
                {"OrderNo":"ORD_1021","LineNo":1,"ProdNo":"P00025","Qty":3,"ItemPrice":200.0000},
                {"OrderNo":"ORD_1021","LineNo":1,"ProdNo":"P12548","Qty":2,"ItemPrice":300.0000}
            ]
        }
    ]
}

 

Extracting data as JSON using FOR JSON PATH

We can use the FOR JSON PATH functionality to format the output JSON the way we require easily. But there’s a restriction when we use ‘FOR JSON PATH’ to extract data, which is that you cannot have the same column name (or alias) duplicated among multiple columns. This will result in an error.

We will see how the details will be fetched using ‘FOR JSON PATH’

SELECT 
    H.TAG
   ,H.ORD_NO
   ,H.CUST_NO
   ,H.ORD_DATE
   ,H.ORD_AMOUNT
   ,H.ORD_STATUS
   --,L.ORD_NO	--If this line is uncommented it will throw an error	
   ,L.LINE_NO
   ,L.PROD_NO
   ,L.ORD_QTY
   ,L.ITEM_PRICE
FROM
	dbo.OrderHeader AS H
	JOIN dbo.OrderLine AS L
		ON L.ORD_NO = H.ORD_NO
WHERE
	H.ORD_NO = 'ORD_1021'
FOR JSON PATH

 

We will get the following JSON result.

[
    {
        "TAG":"#ONLORD_12546_45634",
        "ORD_NO":"ORD_1021",
        "CUST_NO":"CUS0001",
        "ORD_DATE":"2016-06-04",
        "ORD_AMOUNT":1200.0000,
        "ORD_STATUS":1,
        "LINE_NO":1,
        "PROD_NO":"P00025",
        "ORD_QTY":3,
        "ITEM_PRICE":200.0000
    },
    {
        "TAG":"#ONLORD_12546_45634",
        "ORD_NO":"ORD_1021",
        "CUST_NO":"CUS0001",
        "ORD_DATE":"2016-06-04",
        "ORD_AMOUNT":1200.0000,
        "ORD_STATUS":1,
        "LINE_NO":1,
        "PROD_NO":"P12548",
        "ORD_QTY":2,
        "ITEM_PRICE":300.0000
    }
]

Advantage in using ‘FOR JSON PATH’ is that you have the ability to control the structure using the column names/aliases. When dot seperated aliases are used, JSON properties will follow the naming convention. Please consider the below query and the results.

SELECT 
    H.TAG		AS 'HeaderInfo.Tag'
   ,H.ORD_NO		AS 'HeaderInfo.OrderNo'
   ,H.CUST_NO		AS 'HeaderInfo.CustNo'
   ,H.ORD_DATE		AS 'HeaderInfo.OrderDate'
   ,H.ORD_AMOUNT	AS 'HeaderInfo.OrderAmount'
   ,H.ORD_STATUS	AS 'HeaderInfo.OrderStatus'
   ,L.ORD_NO		AS 'LineInfo.OrderNo'
   ,L.LINE_NO		AS 'LineInfo.LineNo'
   ,L.PROD_NO		AS 'LineInfo.ProdNo'
   ,L.ORD_QTY		AS 'LineInfo.Qty'
   ,L.ITEM_PRICE	AS 'LineInfo.ItemPrice'
FROM
	dbo.OrderHeader AS H
	JOIN dbo.OrderLine AS L
		ON L.ORD_NO = H.ORD_NO
WHERE
	H.ORD_NO = 'ORD_1021'
FOR JSON PATH

You will see the following JSON result.

[
    {
        "HeaderInfo":{
            "Tag":"#ONLORD_12546_45634",
            "OrderNo":"ORD_1021",
            "CustNo":"CUS0001",
            "OrderDate":"2016-06-04",
            "OrderAmount":1200.0000,
            "OrderStatus":1
        },
        "LineInfo":{"OrderNo":"ORD_1021","LineNo":1,"ProdNo":"P00025","Qty":3,"ItemPrice":200.0000}
    },
    {
        "HeaderInfo":{
            "Tag":"#ONLORD_12546_45634",
            "OrderNo":"ORD_1021",
            "CustNo":"CUS0001",
            "OrderDate":"2016-06-04",
            "OrderAmount":1200.0000,
            "OrderStatus":1
        },
        "LineInfo":{"OrderNo":"ORD_1021","LineNo":1,"ProdNo":"P12548","Qty":2,"ItemPrice":300.0000}
    }
]

 

OPENJSON

OPENJSON is a table value function which will go through a given JSON string, and returns a relational table with it’s contents. It’ll iterate through JSON object arrays, elemets and generates a row for each element. There are two variations of this functionality.

• Without a pre-defined schema where the values will be returned as key value pairs including it’s type to identify what sort of value is being returned.
• With a well defined schema. This schema will be provided by us in the OPENJSON statement.

 

OPENJSON without a pre-defined schema

We will use the following JSON data string to find out the types which will be returned based on the data type.

{
    "Null Data":null,
    "String Data":"Some String Data",
    "Numeric Data": 1000.00,
    "Boolean Data": true,
    "Array Data":["A","B","C"],
    "Object Data":{"SomeKey":"Some Value"}
    }

 

DECLARE @vJSON AS NVARCHAR(4000) = N'{
	"Null Data":null,
	"String Data":"Some String Data",
	"Numeric Data": 1000.00,
	"Boolean Data": true,
	"Array Data":["A","B","C"],
	"Object Data":{"SomeKey":"Some Value"}
	}';  
  
SELECT * FROM OPENJSON(@vJSON) 

 

With some realistic set of JSON data.

DECLARE @vJSON AS NVARCHAR(4000) = N'{
	"Tag":"#ONLORD_12546_45634",
	"OrderNo":"ORD_1021",
	"CustNo":"CUS0001",
	"OrderDate":"2016-06-04",
	"OrderAmount":1200.0000,
	"OrderStatus":1
}';  
  
SELECT * FROM OPENJSON(@vJSON)  

 

OPENJSON with a pre-defined schema

We will use the same JSON string which we have used in the previous example and generate the result set with a pre-defined schema.

DECLARE @vJSON AS NVARCHAR(4000) = N'{
	"Tag":"#ONLORD_12546_45634",
	"OrderNo":"ORD_1021",
	"CustNo":"CUS0001",
	"OrderDate":"2016-06-04",
	"OrderAmount":1200.0000,
	"OrderStatus":1
}';  
  
SELECT * FROM OPENJSON(@vJSON) WITH(
	Tag				VARCHAR(24)
	,OrderNo		VARCHAR(8)
	,CustNo			VARCHAR(8)
	,OrderDate		DATE
	,OrderAmount	MONEY
	,OrderStatus	INT
) 
 

This is basically what has  been provided to support with JSON data in SQL 2016 natively. Hope this would be helpful for you.

DROP IF EXISTS in SQL Server 2016 (DIE)

 

Prior to SQL Server 2016, when we need to drop a SQL Object, it's the best practice to check whether the respective object exists or not. Otherwise the operation will return in an error.

DROP TABLE [SomeTable]

If the object is not found it will return the following error.

Msg 3701, Level 11, State 5, Line 11
Cannot drop the table 'SomeTable', because it does not exist or you do not have permission.

Hence we need to change the syntax as:

IF EXISTS(SELECT 'x' FROM sys.objects AS O WHERE O.name = 'SomeTable' AND O.[type] = 'U')
    DROP TABLE [SomeTable]
   
OR

IF OBJECT_ID('dbo.SomeTable','U') IS NOT NULL
    DROP TABLE [SomeTable]
   
   
In SQL Server 2016 there is an easier way to do this using comparatively less amount for coding.

DROP TABLE IF EXISTS [SomeTable];
DROP PROCEDURE IF EXISTS [SomeProcedure];

Even this can be use when dropping columns and constraints from a table.

ALTER TABLE [TableName] DROP CONSTRAINT IF EXISTS [ConstraintName]
ALTER TABLE [TableName] DROP COLUMN IF EXISTS [TableName]

Eg:
CREATE TABLE SomeTable(
    Id        INT
    ,Name    VARCHAR(10)        NOT NULL CONSTRAINT [DF_SomeTable_Name] DEFAULT ('')
)

ALTER TABLE dbo.SomeTable
DROP CONSTRAINT IF EXISTS [DF_SomeTable_Name]

ALTER TABLE dbo.SomeTable
DROP COLUMN IF EXISTS [Name]

The beauty of this functionality is that even the object does not exists, it will not fail and execution will continue.

Currently, the following objects can be dropped with the DIE functionality:

• ASSEMBLY
• VIEW
• DATABASE
• DEFAULT
• FUNCTION
• PROCEDURE
• INDEX
• AGGREGATE
• ROLE
• RULE
• SCHEMA
• SECURITY POLICY
• SEQUENCE
• SYNONYM
• TABLE
• TRIGGER
• TYPE
• USER
• VIEW

Hope this will be useful to you.