Hashing values in SQL Server

TL;DR

  • SQL Server can hash values using some of the common hashing algorithms like MD or SHA.
  • It is possible to use XQuery in addition to XPath in XML value() function to do things T-SQL cannot do on its own.

The Details

Hash values or (hash codes) is what we typically use to store_passwords in databases. We use salt values too. Definitely, we don’t store clear text passwords. Right?

Typically during a login to an application, a password is combined with a salt value stored somewhere in a database and a hash value is calculated which is then compared with the hash value previously stored in a database. But there are other options. It is possible to calculate hash values directly in a database using T-SQL. It could be useful if a bulk update needs to be performed if you want to generate a lot of test users with predefined passwords during a database migration, but it is also possible to overwrite a password hash and gain access to the application as any user.

SQL Server (starting with 2008) has hashbytes function which can be used to calculate hashes using a number of different algorithms. The algorithms supported differ from version to version. The latest 2017 supports MD2, MD4, MD5, SHA, SHA1, SHA2_256, SHA2_512.

Let’s have a look at how to use it

declare @password nvarchar(32) = 'Secret1234'

select 'MD5' Algorithm, hashbytes('md5', @password) Hash
union all select 'SHA' ,hashbytes('sha', @password)
union all select 'SHA2_256' ,hashbytes('sha2_256', @password)

which produces something like this

Hashing results

You can see that the results are of varbinary type. That’s OK if your application is storing the hash in this format, but from my experiance, most developers will not know that a byte array can be stored in a database and they will convert it into a Base64 string.

SQL Server does not do Base64 encoding (not as far as I know) but it does support XML and XQuery, and they do encoding. So let’s use it.

declare @password nvarchar(32) = 'Secret1234'

select Algorithm
,convert(xml, N'').value('
xs:base64Binary(xs:hexBinary(sql:column("Hash")))', 'varchar(max)'
) Base64Hash
from (
select 'MD5' Algorithm, hashbytes('md5', @password) Hash
union all select 'SHA' ,hashbytes('sha', @password)
union all select 'SHA2_256' ,hashbytes('sha2_256', @password)
) t

Here I’m converting an empty string N'' to an XML type which creates an empty XML object which allows me to use it’s value() method to execute XQuery which is a functional language. In most examples of XML in SQL Server the only thing you will see is XPath in value() but XQuery can be used too.

The results are more what you’d expect:

Hashes to Base64

One thing to note. The hashing algorithms operator on bytes so are not only case sensitive but type sensitive too.

declare @password nvarchar(32) = 'Secret1234'

select
'varchar' Type
,hashbytes('md5', convert(varchar(32), @password)) Hash
union all select
'nvarchar'
,hashbytes('md5', convert(nvarchar(32), @password))

Have a look at the results. Only because type used is different, the hash is different too.

Hash values using different types

If you want to make it work with .Net make sure to use nvarchar as in .Net strings are Unicode.

How can it be useful?

  • It is possible to push hash calculation and comparison to database which means the correct hash and salt value are never loaded to the application memory.
  • It is possible to generate hashes for test users in database seeding scripts avoiding application doing row by row processing.
  • It is possible to generate hashes of objects to detect changes (although the checksum function can good enough and faster too).
  • It is possible to use XQuery to do things T-SQL cannot do.

But it brings some risk too
* It is possible to gain access to an account by updating values in a database even when using hashing with salt.

Backing SQL on Linux to Windows Share

How to back up a database from SQL Server on Linux (perhaps in a Docker container) to a Windows Share already on the network?

If you want to know how to run SQL Server on Linux in a Docker container read my earlier post.

Container run-time privileges and Linux capabilities

Before we start it is important to note that by default the containers are run with very limited privileges and cannot do much. That’s by design, to make them more secure. However, that prevents them from being able to use CIFS protocol to mount to an SMB share, exactly what we are trying to do. If you just follow the steps in the next sections when you try to mount the share you will get

Unable to apply new capability set.

To avoid this problem the container needs to be created with two extra capabilities: SYS_ADMIN and DAC_READ_SEARCH. You can read more about it on the here. But to make the long story short you need to add two --cap-add parameters to your docker run command.

The full command from my earlier post becomes:

docker run -d `
   -e ACCEPT_EULA=Y -e SA_PASSWORD=Secret1234 `
   -p 14333:1433 --name sql1 `
   --cap-add SYS_ADMIN --cap-add DAC_READ_SEARCH ` 
   mcr.microsoft.com/mssql/server:latest

Create a container with those capabilities before continuing.

The Naive Approach

Let’s assume you have a network share already available and it is accessible using UNC \\FileShare1\SqlBackups\. Being used to Windows networking one would expect to simply take a backup like so, for example:

backup database [TestDb] 
   to disk='\\FileShare1\SqlBackups\TestDB.bak'

The command completes with no errors and yet there is no TestDB.bak file in \\FileShare1\SqlBackups. Stranger still it is possible to restore from that file which doesn’t seem to exist. Try:

restore filelistonly 
   from disk='\\FileShare1\SqlBackups\TestDB.bak'

It works, because Linux translates the UNC it knows nothing about (after all it’s a Windows thing) to a local file /FileShare/SqlBackups/TestDB.bak. If you remote to the container (or execute bash on it with docker exec -it sql bash) you will find /FileShare1/SqlBackups/TestDB.bak file.

Interesting, but not what we expected.

Getting into SMB and CIFS

To solve this problem a network share needs to be mounted to a node in the Linux file system. There are two ways to do it, one is temporary using the mount command only, or a more permanent involving editing the fstab file and then using mount. But before we can do it, one problem has to be solved first. The Windows file server shares the folder using the SMB protocol which is gibberish to Linux. Luckily Linux can be taught to speak CIFS protocol which is compatible with SMB. The way to do it is to install the cifs-utils package. On Ubuntu (SQL Server vNext in a container runs on Ubuntu so I use it as an example) is to install the package with apt-get. Other flavours of the OS will use their own package managers, but overall the process is the same.

First, you will have to Connect to SQL Server on Linux.

Then the CIFS protocol utilities have to be installed using apt-get on Ubuntu.

apt-get update
apt-get install cifs-utils

You will be asked if you want to really do it as it will take an extra 41 MB of disk space. Press Y to agree.

After this operation, 41.2 MB of additional disk space will be used.
Do you want to continue? [Y/n]

Now a directory where the backups will be mounted needs to be created. Typically the external mounts are all in /mnt. So let’s create a backups directory there.

mkdir /mnt/backups

Now it is time to mount.

mount -t cifs //FileShare/SqlBackups /mnt/backups \
   -o username=yourusername,domain=yourdomain,file_mode=0777,dir_mode=0777,rw,sec=ntlm

You will be asked for a password and if everything is correct you will get prompt again which means everything went well. It is important to note the change from \ to /.

It should not be possible to navigate to that directory and create a test text file.

cd /mnt/backups
touch test.txt

With that, the test.txt file should be now visible on \\FileShare1\Backups.

And to take the backup to that share becomes no different to taking any other backup to disk:

backup database [TestDb] 
   to disk='/mnt/backups/testdb.bak'

Using Shares to Interact with the Host

It is also possible to use this approach instead of using -v or --mount to share the file system between a container and its host. It is arguably a bit more effort but it allows to easily share backup and restore location between multiple containers without read/write issues associated with folder binding.

Connect to SQL Server on Linux

How to connect to SQL Server on Linux?
It really depends on what one means by ‘connect’.

Query SQL Server on Linux from Windows

To connect to the SQL Server instance running on Linux directly or in a container to run a query it is no different from connecting to one running on Windows. Just connect using the DNS name or an IP and a port number if it is not the default 1433. The only difference is that only SQL Authentication is supported so you will not be able to use Windows Authentication and your domain credentials.

Query SQL Server from Linux

Azure Data Studio (formerly known as SQL Server Operations Studio) can run on Linux offering the same user experience as on Windows. It is also possible to use the sqlcmd which works exactly the same as the one on windows. To install it on Ubuntu or any Linux flavour using apt-get package manager you can simply do

apt-get install mssql-tools

End then just use it the same way as you would normally do

sqlcmd -S localhost -U sa -P mysecretpassword

In the default SQL Server Docker image the mssql-tools are already installer but they are not added to the $PATH variable. You can either add it, or use the full path to execute it which changes the above command to

/opt/mssql-tools/bin/sqlcmd -S localhost -U sa -P mysecretpassword

Remote to Linux remote server

When managing a SQL Server instance it is sometimes necessary to connect to the operating system on which it runs. This is very different on Linux to running SQL Server on Windows. There is no remote desktop, and PowerShell doesn’t really work either. That’s where the typical Linux admin tools become necessary.

In a way the Secure Shell or SSH is the Linux equivalent of the Remote Desktop. There are many ways to do it, especially from another Linux box. Most of us, SQL DBAs, will be typically on a Windows machine. In that case a common approach is to use PuTTy. But if you use PowerShell and keep up with updates it is now possible to SSH directly for a PowerShell terminal too.

ssh mysqlonlinux.mydomain.com

or

ssh 10.11.12.13

Simple as that. You will connect with a specific user and now most of the admin commands will have to start with sudo which allows to execute them with elevated permissions. It’s similar to Windows’ Run as Administrator.

Connect to Docker container from the host

If you want to connect to a container running your SQL Server on Linux from the host use docker exec to start a bash shell.

docker exec -it sql1 bash

where sql1 is the name of the container hosting SQL Server.

You will connect with superuser permissions and sudo will not be necessary. The prompt will look something like this:

root@2ff21ad83800:/#

SSH into a container running SQL Server on Linux

If you cannot or don’t want to remote onto the host first to connect to the container running your SQL Server instance SSH is the way to go, again. By default, the SQL Server image does not have SSH server so you will have to install it or a custom image will have to be created. Once that is done it is no different from connecting with the ssh (or PuTTy) to any other Linux server. It doesn’t matter that it runs in a container.

Using SQL Server on Docker (1)

It is yesterday’s news. SQL Server runs on Linux, and on Docker too. There are plenty of blog posts showing how to install, start and connect to it too.

During last Data Relay, I have seen Mark Pryce-Maher‘s talk on SQL Server on Linux (twice). It was a good talk, but it followed the same pattern so not surprisingly a common question from the audience appeared to be ‘why would you want to do it?’.

I mean, the biggest claim to fame appears to be that SQL Server actually works on Linux. It doesn’t bring any new features. In fact, some features are missing. A good part of docker demos appears to be an exercise in futility too. One starts a container with SQL Server, creates a database, inserts some data then the container restarts and as if by magic the data is gone, the database never existed. So, indeed, why would you want to run an SQL Server on Docker?

This post is my attempt to address this question by showing how I use SQL Server on Docker and how I deal with the persistence problem.

First: Get it up and running!

If you want to know how to run and connect to SQL Server on Docker read the Microsoft’s Quickstart Document, or Andrew Pruski’s blog post about running vNext on Docker.

Making the long story short, assuming you are running Windows, have Docker installed, configured to run Linux containers and that you don’t have an SQL Server instance locally installed:

docker pull mcr.microsoft.com/mssql/server:latest
docker run -d -e ACCEPT_EULA=Y -e SA_PASSWORD=Secret1234 -p 1433:1433 --name sql mcr.microsoft.com/mssql/server:latest

That’s all you need to do to have the latest production SQL Server (Developer Edition) instance running on localhost on port 1433.

Creating a database that survives container restarts

If you have the container from the previous example running let’s stop and remove it first.

docker stop sql
docker rm sql

There is a number of ways to persist container data. For my purposes the simples appears to be docker volumes. You can read more about docker persistence on the official documentation site but for now let’s just create a local volume called sqlvolume

docker volume create sqlvolume

and mount that volume on the container

docker run -d -e ACCEPT_EULA=Y -e SA_PASSWORD=Secret1234 -p 1433:1433 --mount source=sqlvolume,target=/data --name sql mcr.microsoft.com/mssql/server:latest

Now, if you create a database with files on the mounted volume like so:

create database [TestDb]
on primary (name = N'TestDb', filename = N'/Data/TestDb.mdf')
log on (name = N'TestDb_log', filename = N'/Data/TestDb.ldf')

and you stop and start the container

docker stop sql
docker start sql

as if by magic, the TestDb database survived the restart.

Why I find it useful

So with some considerable effort, I was able to create a database that survives a restart of a container or that of a host system. Nothing a standard SQL Server instance running on windows couldn’t do! So why do I do it?

I write my blog posts on my laptop and I don’t want to commit to installing SQL Server on it. I want to be able to try out vNext while still having the ability to run demos against 2017 and I’m definitely not installing two instances on my laptop. Luckily I don’t have to. With the above setup all I have to do is run docker start sql and within seconds I have a local instance of SQL Server ready to serve my queries. When I’m done I do docker stop sql and it is as if the SQL Server was never there.

Performance of JSON in SQL Server

Have you heard claims that in SQL Server the new JSON data support performs 10 times better than the old XML storage? And all that despite using nvarchar type for storage instead of the specialised xml type. Claims like this one on blogs.msdn.microsoft.com. I used to do a lot of XML in SQL Server and now I use JSON too so I decided to check how they compare when it comes to performance in SQL Server (2017).

Setting the Scene

XML Data Type has been supported by SQL Server since version 2005. It was implemented as a new built-in data type. It is possible to define a column as an XML type with, or without schema and then query the non-relational data together with relational data. It came with for xml clause and three main methods: modify, query and value defined on the XML type.

Let’s see an example of retrieving a value of a node from an XML variable:

declare @xml xml = N'<Test>123</Test>';
select @xml.value(N'(//Test/text())[1]', N'int');

JSON support was introduced in SQL Server 2016. That’s right, JSON support but not a JSON type. The data is stored as plain text, something that was possible before of course, but there is for json clause and 4 methods: isjson, json_modify, json_query and json_value.

An example of retrieving value from a JSON document can look something like this:

declare @json nvarchar(max) = N'{ "Test": "123" }';
select json_value(@json, '$.Test');

It would be difficult to compare performance of every single usecase so I have decided to focus on comparing single value retrieval from a document using .value and json_value for XML and JSON respectively.

Sample XML document

<test>
   <row key="ValueA" value="123" />
   <row key="ValueB" value="-432" />
   ...
   <row key="ValueZ" value="849" />
</test>

Sample JSON document

{
   "ValueA": "123",
   "ValueB": "-432",
   ...
   "ValueZ": 849"
}

I have tables created where there are only two columns, an integer primary key Id and a Data column of the test type. The tables are populated with 100,000 random records. The test consists of simply calculating a sum of a specific property from the document across all the rows in the table. To put things in perspective I will compare performance on the document types to aggregate on an int and an varchar type columns.

Test XML query

select sum(
   [Data].value('(//test/row[@key="ValueA"]/@value)[1]', 'int')
) 
from dbo.XmlData

Test JSON query

select sum(
   convert(int, json_value([Data], '$.ValueA'))
) 
from dbo.JsonData

There are no indexes or computed columns to help with the performance of the queries as the objective is to simply measure performance of parsing and document querying.

First Comparison Results

Json in SQL Server Performance Comparison

That can’t be right!? Can it? JSON query took on average of 0.43 seconds which is 36 times faster than the 16.79 seconds it took to sum the values out of the XML documents. Better (or worse) still when compared to an XML with schema which took 40.47 seconds the JSON type seems to be 94 times faster. So what happened to the 10 times faster claim? Well, the native VARCHAR column performed 10 times faster at 0.04 seconds than the JSON one. the INT column was still faster at 0.03 seconds.

It is also worth looking at the logical reads. Native INT and VARCHAR columns required just over 200 logical reads while both JSON and schema-less XML took around 12,000 logical reads, while the XML with schema required over 34,000.

Playing with storage types

The above were rather unexpected results. Both in terms of CPU and the logical reads. And that got me thinking, what if I could reduce the number of reads? The simplest way to do so would be to reduce the size of data by changing the types. I have created additional tables with the exact copy of the JSON test records but using VARCHAR(max), NVARCHAR(512) and VARCHAR(512) types in addition to the standard (as in most common in all the examples I have seen) NVARCHAR(max).

Performance of JSON data with different storage types

Another surprise! As expected using VARCHAR instead of unicode NVARCHAR halved the number of logical reads but that didn’t translate to improved CPU times. Using json_value on VARCHAR(MAX) was significantly (and consistently) worse than the same method on NVARCHAR(MAX). Specific maximum length types performed better than the the types with the max length but there too unicode required less CPU time despite having twice as many page reads.

At the beginning or at the end

So is it really possible that the old XML type is 36 times worse than on-the-fly JSON parsing? Perhaps there are some benefits to XML storage? I’d imagine the JSON parser is clever enough to stop parsing after the first matching node is found which probably means that the performance will depend on weather we are looking for a value of a property close to the beginning or close to the end of the document? And, perhaps, that’s where XML will perform better being pre-parsed type? I have modified the tests to look at the first and last values from the documents.

Performance depending on value position in a document

Here the results were more as expected. When using json_value on a NVARCHAR(max) data ValueZ being at the end of the document structure performed worse than ValueA which is at the beginning. In fact ValueZ queries took 10 times longer than ValueA. XML still has shown differences but ValueZ queries took only 30% longer than the ValueA ones. Still worst case JSON query was 4 times faster than the best case XML query!

Storage

So that shows that JSON on unicode types uses less CPU time but requires more logical reads which suggests more pressure on RAM and potentially more I/O. How does the storage requirements of all those types compare?

Data storage comparison

XML with Schema is the most expensive in terms of storage, by quite some margin. Despite being visually more verbose the XML type actually takes slightly less disk space than the unicode json which is twice as much as non-unicode json. As one would expect the standard INT columns require least.

Conclusions

JSON data in SQL Server 2017 really is faster than the old XML type. How much faster will depend on the position of the specific value in the document. That’s important, the structure and ordering of properties in the document matters. If you are in control of the JSON document structure consider putting properties more likely to be queried at the beginning of the document and those you know you will index at the end so that you don’t have to parse through them when working with properties which are not indexed. Types matter too. NVARCHAR is faster than VARCHAR but will use twice logical reads (and therefor RAM). Specific lengths perform better than using (max).

Command Line Tools for SQL Server

TLDR: To efficiently manage multiple SQL Server instances learn PowerShell and check out the dbatools. There is also the mssql-cli to look at too.


Since its introduction in 2005 the SQL Server Management Studio (SSMS) was the tool used for development and administration of SQL Server databases. Most of the common, day to day tasks one can perform by pointing and clicking without the need to remember (or at least type in) all those confusing commands. But I never liked mice much and the main reason I kept using SSMS was that the only real alternative was the sqlcmd command line utility which is a command line tool, but doesn’t make you any more productive than SSMS.

But the times are changing. I attended ~SQL~Data Relay earlier this week and it struck me that a lot of SQL Server demos where done without the SSMS. And as I started thinking about it I realised that in fact I don’t use it that frequently in my job either. With the power of PowerShell and the dbatools and dbachecks module I can no longer imagine managing SQL Servers with SSMS.

Examples

Imagine you’ve got 3 servers (just to make the example simple, but it could be 30 or 300, it doesn’t really matter). All we have to do is define a variable holding a list of those servers like so:

$s = server1,server2,server3

And now we can get to work. Here are a few example of tasks which in SSMS would involve too much clicking for my liking, and task I can perform with a single PowerShell command.

Find a Database

I’ve got hundreds of databases on each instance but I need to find the specific one. All I have to do is

Find-DbaDatabase -SqlInstance $s -Pattern "PartOfMyDatabaseName"

And it really can be just a part of the name, so I don’t have to remember the whole name, it doesn’t matter if I remember the beginning or the end of the name.

Check server logs

Let’s say I need to check all my servers error logs for a specific five minutes when our system had a wobble. With SSMS it’s hours of mouse abuse. With PowerShell it’s a single command again

Get-DbaSqlLog -SqlInstance $s -Afater '2018-10-10 10:00' -Before '2018-10-10 10:05`

It will collate all error logs for the time period between 10:00 and 10:05 on 10/10/2018. If the output is too long to read in the PowerShell console you can pipe it to Out-GridView. In fact you can do that with any powershell command returning data. Try adding | Out-GridView at the end of any command like so in the log example

Get-DbaSqlLog -SqlInstance $s -Afater '2018-10-10 10:00' -Before '2018-10-10 10:05` | Out-GridView

Find DB Growth Events

How to find when my databases' files have grown recently? It is possible with SSMS but why would you do it if you can do:

Find-DbaDbGrowthEvent -SqlInstance $s | Out-GridView

That’s it. That is how simple it is. The above command returns all the growth events from all of the servers previously defined in the $s variable.

Conclusions

I still use SSMS, especially when it comes to managing Availability Groups and the AG Dashboards. It is useful for report writing and some ad hoc querying although for those sort of tasks I started using ~SQL Server Operations Studio~ recently renamed to Azure Data Studio. It is based on VS Code and runs on Windows, Linux and macOS, and although it doesn’t have all the features of SQL Server Management Studio, it does have some gems of its own which are not available in SSMS. Another reason could be performance tuning and looking at execution plans, but for those reasons I have long switched to the free Sentry One product Plan Explorer.

For anything else I use PowerShell with dbatools. Looking at the content of SQL Relay demos I’m not alone on that trend.

Parsing Database Object Names

Problem: Parse database object identifiers from single name to 4 part name and extract individual elements. So for example Server1.MyDb.dbo.TableA is TableA in schema dbo in MyDb database on a linked server Server1.

I’m not looking for a solution that will validate the name is valid, that it meets all the requirements, or even if it is well formatted. I’m assuming that it is a reasonable name, I just want to extract all the components provided: Linked Server, Database, Schema and Object names.

Solution 1 – PowerShell and String Splitting

Define a function that splits the string by ‘.’ and assigns values based on number of elements in the array.

function Parse-SqlName {
    param([string]$name)
    process {
        $output = [pscustomobject]@{ Server = ""; Database = ""; Schema = ""; Object = ""; }

        $names = $name.Split('.')
        if ($names.Length -ge 4) { $output.Server = $names[-4] }
        if ($names.Length -ge 3) { $output.Database = $names[-3] }
        if ($names.Length -ge 2) { $output.Schema = $names[-2] }
        if ($names.Length -ge 1) { $output.Object = $names[-1] }

        return $output 
    }
}

To use it call

Parse-SqlName "Server1.MyDb.dbo.TableA"

This returns

Server Database Schema Object
Server1 MyDb dbo TableA

#Solution 2 – Regular Expression

Regular Expression is another way to parse strings so I set myself a challange to create one that will do the same as the above function and would work regardless how many parts of the identifier are present. Here is the result

[\[]?(?:(?:(?:(?<Server>[\w_&@$ -]+)[\.\[\]]+)?(?<Database>[\w_&@$ -]+)[\.\[\]]+)?(?<Schema>[\w_&@$ -]+)[\.\[\]]+)?(?<Object>[\w_&@$ -]+)\]?

After processing a string with it up to four groups will be available. It can be used in any language that supports regex, but here is an example using PowerShell again

function Parse-SqlName2 {
    param([string]$name)
    process {
        if ($name -match '[\[]?(?:(?:(?:(?<Server>[\w_&@$ -]+)[\.\[\]]+)?(?<Database>[\w_&@$ -]+)[\.\[\]]+)?(?<Schema>[\w_&@$ -]+)[\.\[\]]+)?(?<Object>[\w_&@$ -]+)\]?') {
            return [pscustomobject]@{
                Server = $Matches.Server 
                Database = $Matches.Database 
                Schema = $Matches.Schema 
                Object = $Matches.Object 
            }
        }
    }
}