SqlJuxt – Using partial function application to improve the API

After I got all of the tests passing for creating and comparing primary keys on a table I looked back at the code and decided that it had a bit of a smell to it.  Consider the following code snippet:

let private withPrimaryKey columns table isClustered =
    let cs = getColumnsByNames columns table
    {table with primaryKey = Some {name = sprintf "PK_%s" table.name; columns = cs; isClustered = isClustered}}
let WithClusteredPrimaryKey columns table =
    withPrimaryKey columns table true

let WithNonClusteredPrimaryKey columns table =
    withPrimaryKey columns table false

The isClustered parameter is of type bool. This in itself does not feel quite right. You can see what I mean when you look at the implementation of WithClusteredPrimaryKey or WithNonClusteredPrimaryKey. The line reads “withPrimaryKey columns table false”. It is obvious what all of those parameters are except the bool at the end. What does false mean?

Clearly this would be much better if it was self describing which we can easily do in F# using a discriminate union. By defining one like so:


The other part that was causing the code to smell but was perhaps less obvious was the order of the parameters. As described by the excellent post on F# for fun and profit on partial function application the order of your parameters is very important. In the builder functions shown above table is always placed last this means you do not need to mention it when using the script builder API, for example:

let rightTable = CreateTable "DifferentKeyTable"
                        |> WithInt "Column1"
                        |> WithInt "Column2" 
                        |> WithInt "Column3" 
                        |> WithClusteredPrimaryKey [("Column1", ASC); ("Column2", ASC)]
                        |> Build 

Notice how the table parameter does not need to be explicitly passed around. You would have to pass this if it was not the last parameter.

So we know that the order is important if we look at the with primary key functions we can see that the isClustered parameter being last stops us from using partial application so when we define the two methods to create a non clustered and clustered primary key we have to explicitly pass all of the parameters.

Here is the redesigned code of the WithPrimaryKey methods on the TableBuilder API:

let WithPrimaryKey clustering columns table =
    let cs = getColumnsByNames columns table
    {table with primaryKey = Some {name = sprintf "PK_%s" table.name; columns = cs; Clustering = clustering}}
let WithClusteredPrimaryKey = WithPrimaryKey CLUSTERED
let WithNonClusteredPrimaryKey = WithPrimaryKey NONCLUSTERED

Notice how much cleaner these three methods are now. By moving the clustering parameter to the start and using a discriminate union instead of a bool we have achieved two things. Firstly, the code is now self documenting as we are not passing true or false but instead passing CLUSTERED or NONCLUSTERED. Secondly because the clustering parameter is now first we can use the magic of partial function application to define WithClusteredPrimaryKey and WithNonClusteredPrimaryKey. Those two methods simply bake in whether the key is clustered or not and then leave you to fill in the rest of the parameters.

I really love how F# allows you to write beautiful code like this. I’m still learning to write functional code and am really enjoying the experience. Any feedback comments are welcome so please keep them coming.

If you want to check out the full source code and delve deeper feel free to check out the SqlJuxt GitHub repository.

SqlJuxt – Building primary keys on a table

There were a few interesting design decisions I had to make when designing the code to script a primary key on a table. Before I dive into them I think it is good to see the finished code, here is a test that uses the TableBuilder to create a clustered primary key on a table:

let ``should be able to build a table with a clustered primary key on a mulitple columns``() =
    CreateTable "RandomTableName"
        |> WithInt "MyKeyColumn"
        |> WithInt "SecondKeyColumn"
        |> WithVarchar "ThirdCol" 50
        |> WithVarchar "ForthCol" 10
        |> WithClusteredPrimaryKey [("MyKeyColumn", ASC); ("SecondKeyColumn", DESC); ("ThirdCol", DESC)]
        |> Build
        |> should equal @"CREATE TABLE [dbo].[RandomTableName]( [MyKeyColumn] [int] NOT NULL, [SecondKeyColumn] [int] NOT NULL, [ThirdCol] [varchar](50) NOT NULL, [ForthCol] [varchar](10) NOT NULL )

ALTER TABLE [dbo].[RandomTableName] ADD CONSTRAINT [PK_RandomTableName] PRIMARY KEY CLUSTERED ([MyKeyColumn] ASC, [SecondKeyColumn] DESC, [ThirdCol] DESC)

What I like about this code is that just from reading it, it is obvious what the code will do. It will create a create table script with 4 columns with a clustered primary key on 3 of those cloumns.

I decided to go with the approach to take the minimal amount of arguments possible in order to define the primary key. Those being the list of column names and sort order of the columns that you want as a primary key. Originally I thought about allowing the user to specify the primary key name but actually this just adds noise to the code. One can be generated using the convention “PK_” + tableName. Also why make the user think about the name for the primary key when all we really care about is that there is a primary key there and that it has a unique name.

I love the way that in f# you can use discriminate unions to represent states to make the code easy to read and work with. In the above example I could have taken many approaches to specify the column sort order such as using a bool to say whether or not the column is ascending. However, if I had gone with that approach then when calling the code you would have ended up with “columnName true” or “columnName false”. This already feels horrible as just from reading the code you do not know what the true or false means. By defining a discriminate union of ASC/DESC you can immediately tell what the parameter is and what it is doing.

The primary key is defined as a constraint as the following type:

type Constraint = {name: string; columns: (Column * SortDirection)  list; isClustered: bool}

Then the table type has been extended to add a Constraint as a primary key using the option type. As a table may or may not have a primary key. It is nice that we can use Option to represent this rather than having to rely on null like we would in an imperative language.

The hardest part to making this work is taking the list of (String * SortDirection) that the WithClusteredPrimaryKey function takes and turning that in to a list of (Column * SortDirection). This is done using the following function:

let private getColumnsByNames (columnNames: (string * SortDirection) list) table =
            columnNames |> List.map(fun (c,d) -> let column = table.columns |> List.tryFind(fun col ->  match col with
                                                                                            | IntColumn i when i.name = c -> true
                                                                                            | VarColumn v when v.name = c -> true
                                                                                            | _ -> false)
                                                 match column with
                                                    | Some col -> (col, d)
                                                    | None -> failwithf "no column named %s exists on table %s" c table.name )

It is great in F# how we can let the types guide us. If you look at the signature of the function above then we can see that it is:

getColumnsByNames (columnNames: (string * SortDirection) list) -> (table:Table) -> (Column * SortDirection) list

When you look at the types you can see that there are not too many ways this function could be implemented. Using what is in the room as Erik Meijer would say we go through the columns on the table and match them up with the column names that were passed in (throwing an exception if a name is passed in that is not on the table) and then return the actual column along with the sort direction.

F# is proving to be an interesting choice in writing the database comparison library. It is a totally different way of thinking but I feel that I’m starting to come to terms with thinking functionally.

If you want to check out the full source code and delve deeper feel free to check out the SqlJuxt GitHub repository.

SqlJuxt – Using disposable in F# to drop a test database

The integration tests in SqlJuxt need to do the following:

  • Create two databases
  • Set the databases up in a certain state (create tables, views etc)
  • Compare them
  • Assert the result of the comparison
  • Clean up the databases at the end (drop them)

Before I dive in to how I went about making this work I think its good to take a look at how the finished test looks:

let ``should return identical when two tables are the same``() =
    use left = createDatabase()
    use right = createDatabase()
    let table = CreateTable "TestTable"
                    |> WithNullableInt "Column1"
                    |> Build 

    runScript left table
    runScript right table
    loadSchema left.ConnectionString
            |> compareWith right.ConnectionString
            |> should equal IsMatch

I think that test reads really well. In fact you can tell exactly what is going on in the test from the code which is one of the key ingredients of a good test. The test is creating two databases, then creating the same table on both of the databases. It then compares them using the SqlJuxt library and expects the databases to match. The result of the comparison is a discriminate union which I will talk about more in an upcoming post. For now you can read that it says “should equal IsMatch” which is really clear.

The astute reader will notice that nowhere in the test does it mention dropping the databases so you might wonder how this is done. The secret behind this is IDisposable. I got the idea from reading this page on let, use and do on the F# for fun and profit site. Scott talks about using the dispose to stop a timer so I thought it would be neat to use it to drop the database.

To make this work the createDatabase function returns a class that implements IDisposable. Notice that the variables left and right are declared using the ‘use’ keyword and not the ‘let’ keyword. This means that when the variables go out of scope Dispose is automatically called.

This is how the disposable database type is defined:

type DisposableDatabase(name, connectionString) =

    member this.ConnectionString = connectionString
    member this.Name = name

    interface System.IDisposable with 
        member this.Dispose() = 
            dropDatabase this.Name

The code is a little bit clunky as we have to declare a class to implement IDisposable but the cool part is that we do not have to explicitly drop the database in our test it just happens for us.

If you want to check out the full source code and delve deeper feel free to check out the SqlJuxt GitHub repository.

SqlJuxt – Implementing the builder pattern in F#

As an imperative programmer by trade a pattern that I often like to use is the builder pattern for building objects. This pattern is especially useful for test code.

The reasons the builder pattern is so useful is because:

  • It means you only have to “new” the object up in a single place meaning your test code effectively goes through an API (the builder API) to create the object
  • It makes you code really readable so you can understand exactly what it is doing without having to look into how the code works

In my SqlJuxt project I started by coding it in C#. I wanted a builder to make a table create script. This would mean that in my integration tests I could fluently create a script that would create a table and then I could run it in to a real database and then run the comparison. This makes the tests very readable and easy to write. In C# using the table builder looks like:

    var table = Sql.BuildScript()
                   .WithTableNamed("MyTable", t => t.WithColumns(c => c.NullableVarchar("First", 23)

I think that is pretty nice code. You can easily read that code and tell that it will create a table named “MyTable” with a nullable varchar column and a nullable int column.

I wanted to achieve the same thing in F# but the catch is I did not just want to translate the C# in to F# (which is possible) I wanted to write proper functional code. Which means you should not really create classes or mutable types! The whole way the builder pattern works is you store state on the builder and then with each method call you change the state of the builder and then return yourself. When the build method is called at the end you use all of the state to build the object (note an implicit call to the Build method is not needed in the above code as I have overridden the implicit conversion operator).

I hunted around for inspiration and found it in the form of how the TopShelf guys had written their fluent API.

This is how using the table builder looks in F#:

    let table = CreateTable "TestTable"
                    |> WithNullableInt "Column1"
                    |> Build 

I think that is pretty sweet! The trick to making this work is to have a type that represents a database table. Obviously the type is immutable. The CreateTable function takes a name and then returns a new instance of the table type with the name set:

    let CreateTable name =
        {name = name; columns = []}

Then each of the functions to create a column take the table and a name in the case of a nullable int column and then return a new immutable table instance with the column appended to the list of columns. The trick is to take the table type as the last parameter to the function. This means you do not have to implicitly pass it around. As you can see from the CreateTable code above. The Build function then takes the table and translates it in to a sql script ready to be run in to the database.

Here is an example of a complete test to create a table:

    let ``should be able to build a table with a mixture of columns``() =
       CreateTable "MultiColumnTable"
           |> WithVarchar "MyVarchar" 10
           |> WithInt "MyInt"
           |> WithNullableVarchar "NullVarchar" 55
           |> WithNullableInt "NullInt"
           |> Build 
           |> should equal @"CREATE TABLE [dbo].[MultiColumnTable]( [MyVarchar] [varchar](10) NOT NULL, [MyInt] [int] NOT NULL, [NullVarchar] [varchar](55) NULL, [NullInt] [int] NULL )

I think that test is really readable and explains exactly what it is doing. Which is exactly what a test should do.

If you want to follow along with the project then check out the SqlJuxt GitHub repository.

SqlJuxt – A database comparison tool written in F#

As part of a new project I have decided to write a Sql database comparison tool in F#. I wanted a project that I could learn F# with (having dabbled a bit in the past). I have just finished re-reading the excellent Thinking Functionally series on the F# for fun and profit site. Scott has done a fantastic job on there of explaining F# concepts from the perspective of an imperative programmer. Kudos to Scott for that.

So what is the database comparison tool going to be? Well it is initially going to be an API library written in F# that allows you to compare two Sql databases to find the differences. Then from there I might extend it to allow you to script the differences to make the databases the same and then possibly write a front end for it. The other idea I have in mind is to write a C# shim to allow you to use the library nicely from F#. Although the shim library is not strictly necessary using F# from C# or vice versa can be a bit cumbersome unless you put a bit of effort in to making it work well.

That’s it for now stay tuned as I blog about my foray into the world of functional programming. If you want to keep up with how the project is going then you can check out the Sql Juxt GitHub repository.

Converting decimal numbers to Roman Numerals in C#

I decided to create a little project to implement converting decimal numbers to Roman Numerals in C#. You can solve this problem in quite a few different ways, I wanted to talk through the pattern based approach that I went for.

The Roman Numerals from 0 to 9 are as follows:

  • 0 = “” (empty string)
  • 1 = I
  • 2 = II
  • 3 = III
  • 4 = IV
  • 5 = V
  • 6 = VI
  • 7 = VII
  • 8 = VIII
  • 9 = IX

To start the project I wrote a set of tests that checked these first 10 cases. I like taking this approach as it allows you to solve for the simple base case, then you can refactor your solution to be more generic. Our first implementation that solves for the first ten numbers is:

public static string ToRomanNumeral(this int integer)
     var mapping = new Dictionary<int, string>
         {0, ""},
         {1, "I"},
         {2, "II"},
         {3, "III"},
         {4, "IV"},
         {5, "V"},
         {6, "VI"},
         {7, "VII"},
         {8, "VIII"},
         {9, "IX"},

     return mapping[integer];

Obviously there is no error checking etc we are just solving for the 10 first cases. I decided to implement the method as an extension method on int as it makes the code look neat as it will allow you to write:

    var romanNumeral = 9.ToRomanNumeral();

The next step is to look at the Roman Numerals up to 100 and see if we can spot any patterns. We don’t want to have to manually type out a dictionary for every Roman Numeral! If we look at the Roman Numeral representation for the tens column from 0 to 100 we find they are:

  • 0 = “” (empty string)
  • 10 = X
  • 20 = XX
  • 30 = XXX
  • 40 = XL
  • 50 = L
  • 60 = LX
  • 70 = LXX
  • 80 = LXXX
  • 90 = XC

We can straight away see that it is exactly the same as the numbers from 0 to 9 except you replace I with X, V with L and X with C. So lets pull that pattern out into something that can create a mapping dictionary given the 3 symbols. Doing this gives you the following method:

private static Dictionary<int, string> CreateMapping(string baseSymbol, string midSymbol, string upperSymbol)
    return new Dictionary<int, string>
        {0, ""},
        {1, baseSymbol},
        {2, baseSymbol + baseSymbol},
        {3, baseSymbol + baseSymbol + baseSymbol},
        {4, baseSymbol + midSymbol},
        {5, midSymbol},
        {6, midSymbol + baseSymbol},
        {7, midSymbol + baseSymbol + baseSymbol},
        {8, midSymbol + baseSymbol + baseSymbol + baseSymbol},
        {9, baseSymbol + upperSymbol},

We can now call the above method with the symbols for the column we want to calculate passing in the correct symbols. So for the units column we would write:

    var unitMapping = CreateMapping("I", "V", "X");

Now we have this we it is straight forward to create the mapping for the hundreds column. To complete our implementation we want to add some error checks as we are only going to support Roman Numerals from 0 to 4000. The full solution is now quite trivial. We simply check the input is in our valid range (between 0 and 4000). Then we loop through each column looking up the symbol for that column in our mapping dictionary that we generate using our function above. Then we simply concatenate the symbols together using a StringBuilder and return the result.

The full solution with all of the tests is available on my GitHub repository: https://github.com/kevholditch/RomanNumerals.

A neat query pattern written in C#

By using nuget packages such as the brilliant Dapper it is possible to create a very concise way to access your database without using very much code. I particularly like the query pattern I’m going to go through today, it’s lightweight, simple and encourages composition!

Lets work from the outside in. Clone the query pattern github repository so you can follow along. I have written the program in the repository to work against the Northwind example database. If you haven’t got the Northwind database installed you can find information on that over on msdn.

Take a look at the program.cs file the essence of which is captured in the code snippet below:

Console.WriteLine("Enter category to search for:");
var name = Console.ReadLine();

var categories = queryExector.Execute<GetCategoriesMatchingNameCriteria, GetCategoriesMatchingNameResult>(new GetCategoriesMatchingNameCriteria { Name = name }).Categories;

Console.WriteLine("categories found matching name: {0}", name);

foreach (var category in categories)

The program above takes an input from the user and then does a like match with any category from the Northwind database that matches the user’s input. You can see how few liens of code this has taken to achieve. Note nowhere do we have reams of ADO .net code cluttering up the joint.

We are modelling a query as something that takes TCriteria and returns TResult. The interface for a query is shown below:

public interface IQuery<in TCriteria, out TResult>
    TResult Execute(TCriteria criteria);

By representing a query in this way and using Dapper the implementation is very short and to the point:

public class GetCategoriesMatchingNameQuery : IQuery<GetCategoriesMatchingNameCriteria, GetCategoriesMatchingNameResult>
    private readonly IDbConnection _dbConnection;

    public GetCategoriesMatchingNameQuery(IDbConnection dbConnection)
        _dbConnection = dbConnection;

    public GetCategoriesMatchingNameResult Execute(GetCategoriesMatchingNameCriteria matchingNameCriteria)
        string term = "%" + matchingNameCriteria.Name.Replace("%", "[%]").Replace("[", "[[]").Replace("]", "[]]") + "%";

        var result = new GetCategoriesMatchingNameResult
            Categories = _dbConnection.Query<CategoryEntity>(@"select CategoryID, CategoryName, Description from categories where categoryName like @term", new { term })                

        return result;

Note that is the class above the actual query is 3 lines of code! It can be done on one line if you wanted but that would make the code harder to read. As the queries are created automatically using an abstract factory in Castle Windsor we can do things like decorate them to apply caching or logging across the board or even both. Any cross cutting concern you can think of can be done easily. Queries can also be composed together easily you just have a query take a dependency on another query or multiple queries, then simply chain them together.

I really love how clean and concise this code is. By letting Dapper do the heavy lifting we aren’t bogged down with lots of ADO .net code that isn’t part of the IP of your business application.