EF Core – Combining SQL and NoSQL databases for better performance

This is the second article in my series on Entity Framework Core (EF Core) performance tuning. In this article I take that performance even further than part 1 did, by changing the applications to a two-database CQRS (Command Query Responsibility Segregation) architecture, where one database is SQL and the other is NoSQL (I explain what that architecture looks like later in the article).

  1. Entity Framework Core performance tuning – a worked example.
  2. EF Core – Combining SQL and NoSQL for better performance (this article).

Note: This article is from chapter 14 (which will be available from the end of November 2017) in the book “Entity Framework in Action” that I am writing for Manning publications. Chapter 14 contains more on the EF Core parts on the application mentioned in this article. The book is on early access, which means you can buy the book now, and you get the chapters as I write them – plus the finished book when the book is complete.

NOTE: Manning Publications have offered the coupon to get 37% off my book, Entity Framework in Action” – use fccefcoresmith

Executive summary for those in a hurry

I have an example ASP.NET Core application that uses EF Core to display a set of books – called my book selling site. In this article, I look at the performance book selling site which I have converted to a CQRS (explained below) architecture. In this I write data to a primary SQL Server database, but also build a read-side NoSQL database which the application queries use to show the list of books.

I run various queries to test the differences in performance, but the main one is sorting the books by their book reviews and then showing the top 100. I do this for three sizes of database

  • 100,000 books, which has ½ million book reviews.
  • 250,000 books, which has 1.4 million book reviews.
  • 500,000 books, which has 2.7 million book reviews.

The “sort by average review votes” is a particularly difficult query for a SQL-only database, as it must dynamically calculate the average votes value of all the nook reviews in the database – and as you can see my test data has a lot of reviews!

The figure below shows the difference in performance of the “best-SQL” (hand-tuned SQL run by Dapper) against what I call the SQL+NoSQL CQRS version of my application, for the three sizes of databases.

You can try the SQL+NoSQL CQRS at http://cqrsravendb.efcoreinaction.com/. It has 250,000 books in its database.

Note: The site cqrsravnedb.efcoreinaction.com isn’t running on some massively powerful cloud system. The NoSQL database is running courtesy of www.RavenHQ.com (thanks to them for doing that) and is running on the lowest level of server, so you won’t get the same performance as shown in the chart above.

Setting the scene – my example application

Note: It you read the first article then you know this – skip to the next section called “Explaining the new CQRS architecture and the SQL+NoSQL databases”.

Right from the second chapter of my book I introduce an example book selling site, and as the book progresses I add more attributes to the application. Admittedly the site only has a few features, but I purposely included some sorting and filtering features that can be a challenge when the site has a lot of books to show. The diagram below shows you the features of the site.

What makes the book list a challenge?

I tried to create an example application which was at least credible, and included some complicated queries. The diagram below shows you the listing of a book, and some information on how easy or complex it is to get that data from the database.

I ran a series of tests checking on sorting, filtering and paging, but with this design they all had the same performance (unlike the previous article). But I did run tests will different numbers of books and related data in the database.

Note: I did all my testing on my development PC using the ASP.NET Core example book selling site running in debug mode. The timings are from Chrome, and are the total time it takes for the page to return (including downloading the content). For comparison, accessing the About page, which has no database accesses in it, typically takes 11 ms to load, so the database accesses are always going to be slower than that.

Explaining the new CQRS architecture and the SQL+NoSQL databases

This article is about me upgrading my example book selling site to what is known as a CQRS (Command Query Responsibility Segregation) architecture using a polyglot database structure. Those terms might not that useful if you haven’t heard them before, so let me give you a diagram and then explain those terms in a bit more detail.

The CQRS architecture acknowledges that the read-side of an application is different from the write-side. Reads are often complicated, drawing in data from multiple places, while the write side is often much simpler. Separating the code for each part can help you focus on the specific features of each part.

A “polyglot database structure” is one which uses a combination of different storage types, for instance, relational databases, NoSQL databases, flat files, and so on. The idea is that each database type has its strengths and weaknesses, and by used two or more different database types you can obtain a better overall system.

I think a polyglot CQRS architecture, with SQL for the write-side database and NoSQL for the read-side database makes a lot of sense for business applications where there are more reads than writes, for instance, eCommerce applications, job sites, calender/todo etc. etc.

The SQL write-side database makes sense because business application often use relational. Think about a real book selling site: it would have a LOT of complex, linked data, to handle all the parts of the system, such as suppliers, inventory, pricing, orders, payment, delivery, tracking, audits and so on. I think a relational (e.g. SQL) database with its high level of data integrity for relational data, is the right choice for many business problems.

But those relationships and some aspect of database can make a SQL database slow at retrieval of data. My “sort by average votes” is particularly hard on a SQL database because of how many book reviews it needs to look at to sort by average votes. But I have seen equally complex queries in other systems I have built that don’t scale well either.

One answer is to create a read-side database that contains the exact data that the user wants to see, or sort/filter on. This could be another SQL database (I did that in Part3 of the previous article), but it makes a lot more sense for that database to be a NoSQL database. Why? Because:

  1. A NoSQL database is simpler to read, as it each entry is normally self-contained, with no links outside it. This should make it quicker at reading than a SQL database.
  2. NoSQL databases are normally easier to scale out, that is, have multiple databases containing the “same” data. This is because a write to a NoSQL database is dealing in writing one complete entry. (This is a big topic, and I’m not going to discuss this – see this Stack Overflow answer for part of the reason).
  3. I think a CQRS + NoSQL read-side database does a better job of holding “cached values” than can be done with a SQL/relational database does – I cover this later in the section about the complexity and robustness of the software.

As you will see in the rest of this article these features make my SQL+NoSQL CQRS architecture perform very well, and still have a high level of data integrity.

Why I chose RavenDB as my NoSQL database

I’m not an expert of NoSQL databases but when my first article went out I got a tweet from Oren Eini (Twitter @ayende), who is the main force behind the RavenDb NoSQL database. He quite rightly felt that his RavenDB database would be faster than the SQL equivalent at sorting/searching this data.

His tweet gave me an opportunity to get some help with my lack of NoSQL knowledge, so I sent him the tweet below, and the choice of NoSQL database was made. And it’s turned out to be a very good choice indeed, as you will see.

RavenDB has a .NET library that supports LINQ queries, which meant I could use the LINQ queries I had developed for EF Core with almost no changes. RavenDB also allowed me to create indexes on certain parameters, and would then automatically generate indexes for the various combinations. Indexes are vital to fast performance and most NoSQL systems have them.

I want to thank Oren Eini (see https://ayende.com/blog/) for his help and support, and Jonathan Matheus of RavenHQ , who has kindly provided me with free hosting of the RavenDB database that I use in my live site – http://cqrsravendb.efcoreinaction.com/.

How I added the CQRS architecture to my example book selling site

I only wanted to move the book list view part to the NoSQL read-side database, and not for the order processing part, because it’s only the book list view that has a performance issue. It turns out that while adding CQRS does require a fair amount of work, it is very simple to only apply the CQRS architecture to part of my application. The figure below shows the design of my changed example book selling site application, with the book list implemented as a two-database CQRS part.

My example book selling site is an ASP.NET Core application with EF Core used for the database access code. It is a layered application, as shown in the diagram above. The book selling site isn’t that complicated but I still needed to be careful as I modified the application over to a CQRS read-side database. I therefore wanted a design that minimized the changes, and isolated the new parts.

I came up with a design that keeps all the NoSQL/RavenDB parts separate. In my final design, the EF Core doesn’t know, or care, what database is being used for the read-side part of the CQRS system. This made the update simpler, plus offered the possibility of changing the NoSQL database I used – I really like RavenDB, with its support of LINQ, but EF Core version 2.1.0 will support Azure’s NoSQL database, Cosmos, so that might be an interesting alternative.

By keeping as much of the new database code in the NoSQLData layer, and using interfaces, kept the impact of my changes to a minimum. Figure 14.3 shows how I hid the NoSQL code behind interfaces to keep the NoSQL code isolated. I used dependency injection to provide both the DataLayer and the ServiceLayer with methods that allowed access to the database.

Here is a typical entry in the NoSQL database. As you can see I have pre-calculated the number of Reviews on this book, ReviewsCount, and the average of the six review votes, ReviewsAverageVotes. These two properties, plus the Id (not shown – RavenDB keeps that separate), the PublishedOn, and the ActualPrice all have indexes.

{
    "Title": "Adobe AIR in Action",
    "PublishedOn": "2008-07-01T00:00:00.0000000",
    "Price": 379,
    "ActualPrice": 189.5,
    "PromotionPromotionalText": "today only - 50% off! ",
    "AuthorsOrdered": "Joey Lott, Kathryn Rotondo, Sam Ahn, Ashley Atkins",
    "ReviewsCount": 6,
    "ReviewsAverageVotes": 3.5
}

Is changing my application to a SQL+NoSQL CQRS design worth the effort?

Implementing this two-database CQRS architecture wasn’t simple and took over a week to develop, which is long for me. Admittedly the main part was learning how to handle the RavenDB database approach, but there were also some complex EF Core parts to write to. So, was it worth the effort? To answer that question, I’m going to look at three distinct aspects.

  1. Was book list display, with its various sorts and filters, quicker?
  2. What was the impact writing to the database performance?
  3. What effect did it have on software, both in effort, complexity, and robustness?

1. Was book list display quicker?

Well, you have already seen the graph, but I’ll show it again. It beats the “best-SQL” version on sort by votes hands-down.

Now, this isn’t the whole story as in the first article I covered four levels of performance-tuning, and there was a faster version after the “best-SQL” version. In that version, referred to Part 3 (SQL cached values), I created “cached values” very like the values you see in the NoSQL database. In fact, it was the similarity of the Part 3 (SQL cached values) book query to getting json data that made me think of using a NoSQL database with CQRS.

If we compare the Part 3 (SQL cached-values) solution with the SQL+NoSQL CQRS version, then the difference is very much smaller, as shown by the graph below.

Note: You can access a live site with the Part 3 (SQL cached values) at http://efcoreinaction.com/. This site has 100,000 books in it.

This second graph shows that it’s the cached values, with their indexes, that makes the difference. RavenDB still has the edge of the Part 3 (SQL cached-values) version, but there is another aspect that I didn’t measure here, that is scalability.

Scalability is about how many simultaneous users an application can handle while still providing a ‘good’ performance. Because NoSQL databases such as RavenDB are dealing with a single entry containing all the information then a read or write is simpler than the SQL case. In general, this makes NoSQL databases easier to duplicate, that is, have multiple databases all containing the same data. The effect of having multiple databases on scalability can be significant. Not only can you spread database access across multiple databases, but you can locate databases geographically around the world to provide shorter access times. This final point clinches the performance issue for me – the SQL+NoSQL CQRS solution wins on performance.

SQL+NoSQL CQRS read performance summary: Excellent

2. What was the impact writing to the database performance?

I said earlier on that a SQL+NoSQL CQRS architecture is going to be slower on writes, as it must write to two databases. I have measured this in my solution and there is an effect, but it’s pretty small.

Taking a common case, which is a user adding a review to a book the results are as follows:

Solution type Total time Notes
Part 3 (SQL cached-values) 13 ms Simple addition of Review entity to the database and a recalculate of the cached values.
SQL+NoSQL CQRS 35 ms The extra time is mainly around writing to the NoSQL database. I measured the RavenDB update taking 25ms, which is quite long compared to a SQL write.

For a function that takes less than 50ms to return to the user, it’s not worth performance tuning. But in applications with more complex updates this time might get too long. But there are plenty of ways to handle this – one way is to pass the update a background task to be executed so that the application returns immediately to the user. The only down side of that approach is the user may be shown out-of-date data, which could be confusing – these are the trade-offs you must think through.

SQL+NoSQL CQRS write performance summary: Good, and could be made better if needed.

3. What effect did it have on software, both in effort, complexity and robustness?

The table below compares the development effort, complexity and robustness of the two designs: The Part 3 (SQL cached-values) solution and the SQL+NoSQL CQRS.

Solution type Effort Complexity Robustness
Part 3 (SQL cached-values) ~ 3 days Complex concurrency Good
SQL+NoSQL CQRS ~ 8 days Complex structure Very good (see below)

The effort was bigger for the SQL+NoSQL CQRS solution, but still in a very sensible time. Remember too, that is the time to performance tune an existing, SQL-only, EF Core application – 8 days to improve performance isn’t that bad in my opinion.

On the added complexity, the two solutions added to the original application was about the same –the added complexity was just in different areas in the two solutions.

The big plus for me was on the on robustness of the application. Keeping cached values correct is really difficult, and I think the SQL+NoSQL CQRS did a much better job of that than the Part 3 (SQL cached-values) did. This is because the SQL+NoSQL CQRS’s architecture “designs-out” any concurrency issues. The diagram compares Part 3 (SQL cached-values) handling of concurrency issues that could make the cached values out of date, with the same problem in the SQL+NoSQL CQRS solution.

Unlike the Part 3 (SQL cached values) solution, where I had to consider each cached value separately, and devise a different solution for each of them, the CQRS design handles all potential problems in one go – it effectively designs them out.

NOTE One other cache-invalidating error I can envisage is if the two updates to the NoSQL database arrive in the wrong order, say due to a retry. But that problem is simpler to fix, say by adding timestamps to each update and never allowing an older update to be applied.

SQL+NoSQL CQRS software summary: it took a bit more develop time, but it’s worth the effort.

Conclusion

I have altered my example book selling site from a SQL-only solution to one that uses a CQRS (Command Query Responsibility Segregation) architecture with a separate read-side and write-side database.  The write-side database I kept as a SQL/relational database because of its ability to track relationships with a high level of data integrity. On the read-side database I used a NoSQL database, RavenDB. I called this my SQL+NoSQL CQRS solution.

The SQL+NoSQL CQRS solution won by a large factor the on read-performance against the “best-SQL” database. Even against a SQL solution that pre-calculated cached values then SQL+NoSQL CQRS solution was only slightly faster, but has a much better chance to scale horizontally (i.e. multiple duplicate databases) than its SQL counterpart. This means the SQL+NoSQL CQRS won on all fronts for read-side queries.

The SQL+NoSQL CQRS solution did introduce a drop in performance for writes to the database, as two databases had to be updated. In my application that was small enough to ignore, but if it did become a problem there are ways around that.

The SQL+NoSQL CQRS solution took the most development time of any of the performance tuning I did – taking ~8 days to implement compared to ~3 days for the Part 3 (SQL cached values) solution took. However, I think the performance gain was impressive and the overall solution is more robust.

I hope you have found this article and the previous article interesting. Please do add comments and suggestions. I’m always learning.

Further reading

Happy coding!

Entity Framework Core performance tuning – a worked example

This is the first in a series of articles on performance tuning EF Core. In this article I take an example book selling site that I have built for my book and see how fast I can get it to perform. I have got some really excellent performance out of Entity Framework Core (EF Core) and I wanted to share it with you all.

  1. Entity Framework Core performance tuning – a worked example (this article).
  2. EF Core – Combining SQL and NoSQL for better performance.

Note: This article is from chapter 13 of my book, which  will be available from the end of October 2017, and has lot more detail (its 33 pages long). The book is on early access, which means you can buy the book now, and you get the chapters as I write them – plus the finished book when the book is complete.

NOTE: Manning Publications have offered the coupon to get 37% off my book, Entity Framework in Action” – use fccefcoresmith

Executive summary for those in a hurry

I build an example book selling web site using EF Core version 2.0.0 and filled it with 100,000 books and ½ million reviews. I then went through four staged of performance tuning. The figure below shows you the results through these stages for two different, but demanding areas of performance:

  • Displaying 100 books out of 100,000 possible books.
  • Displaying 100 books out of 100,000 possible books, sorted by average votes
    (timings are how long the web request took to finish, as measured by Chrome on my development PC).

The four stages on the graph are:

  1. Part 1: Working with EF Core built queries
    1. I get my LINQ code in a form that EF Core can turn it into good SQL
    2. I performance tune the existing EF Core query by adding a DbFunction.
  2. Part 2: I build a SQL query by copying EF Core’s SQL and ‘tweaked’ one bit to get some extra performance
  3. Part 3: I added cached values to the database that held hard-to-calculate values, such as the average votes for a book.

You can try the final, Part 3, “tuned up” example book selling site at  http://efcoreinaction.com/.

Note: The site www.efcoreinaction.com isn’t running on some massively powerful cloud system, but on shared hosting site that costs £3.50/month. (see Web Wiz, who are a great hosting site for .NET. They have always been early supporters of new .NET features.)


Setting the scene – my example application

Right from the second chapter of my book I introduce an example book selling site, and as the book progresses I add more feature to the application. Admittedly the site only has a few features, but I purposely included some sorting and filtering features that can be a challenge when the site has a lot of books to show. The diagram below shows you the features of the site.

What makes the book list a challenge?

I tried to create an example application which was at least credible, and included some complicated queries. The diagram below shows you the listing of a book, and some information on how easy or complex it is to get that data from the database.

On top of that the sorting, filtering and paging commands adds another level of complexity. I came up with four sort and filter tests that got progressively difficult, each done with page size of 10 and 100 to see how much showing more books effected performance. Here are the four tests.

  1. Easy – just show the page, with default everything.
  2. Moderate – sort the books by their publication date.
  3. Hard – sort by average votes, which is calculated from all the reviews linked to a book.
  4. Double hard – filter by average votes and the sort by price, both of which are calculated values.

Note: I did all my testing on my development PC using the ASP.NET Core example book selling site running in debug mode. The timings are from Chrome, and are the total time it takes for the page to return (including downloading the content). For comparison, accessing the About page, which has no database accesses in it, typically takes 11 ms to load, so the database accesses are always going to be slower than that.


My four steps in performance tuning

I went through four iterations of tuning of the application, and I will cover each one in this article. The stages are:

  1. Tuning up my EF Core query
    1. Making sure EF Core could translate all parts of my LINQ query to SQL
    2. Tweaking my query by adding a user defined function
  2. Going SQL – moving the query to Dapper
  3. Modifying the database – adding cached values (this is running on efcoreinaction.com)

Part 1a – Getting my query to translate into SQL

I started this performance tuning process by looking at what EF Core can do with well written code. Throughout my book, I have recommended the LINQ Select query, as this creates the most efficient database access. The Select query works well for the book display as it needs to get data from other tables, this the Reviews, and it doesn’t need some of the columns in the Books table, such as the book’s Description column, which could be very long. Here is my Select query object.

public static IQueryable<BookListDto>           
    MapBookToDto(this IQueryable<Book> books)   
{
    return books.Select(p => new BookListDto
    {
        BookId = p.BookId,                      
        Title = p.Title,                        
        Price = p.Price,                        
        PublishedOn = p.PublishedOn,            
        ActualPrice = p.Promotion == null       
                ? p.Price                       
                : p.Promotion.NewPrice,         
        PromotionPromotionalText =              
                p.Promotion == null             
                  ? null                        
                  : p.Promotion.PromotionalText,
        AuthorNamesOrdered = p.AuthorsLink
                .OrderBy(q => q.Order)
                .Select(q => q.Author.Name),
        ReviewsCount = p.Reviews.Count,        
        ReviewsAverageVotes =                  
            p.Reviews.Select(y => (double?)y.NumStars).Average() 
    });
}

Note the calculation at the end of the MapBookToDto code, which calculates the average votes from all the reviews linked to this book. I took me a bit of time to get the LINQ command in the right format – finally solved it by asking a question of the EF Core issues page. But the result is that average is done in the database using the SQL command AVG. This was critical in getting the performance I needed, not only in calculating the value to so in each book info, but also to make the “sort by average votes” as quick as it was.

Here are the results for 100,000 books (same data as on the www.efcoreinaction.com site).

Test 10 books 100 books Comment
All books 45 ms 230 ms Sorts on primary key
Sort by publication dates 45 ms 230 ms Sorts on PublishedOn, with index
Sort by votes 500 ms 650 ms Must average ½ million reviews!
Sort by price, filter by 4+ votes 450 ms 600 ms Less books, so quicker

The “sort on votes” performance, at 500ms sounds slow, but it was a lot quicker than I expected. EF Core produced the ideal SQL, using the SQL AVG command, but it had to be called 100,000 times, and had a total of over ½ million Reviews to process. For some applications, this level of performance would be acceptable, but for this example I deem that this is too slow, and I will be looking again at the “sort on votes” in Part 2 and Part 3.

The one item that did seem very slow was the display of 100 books. I analysed that and, because the Authors names came back as a collection, EF Core ran a separate query for each book – so, instead of 2 database access it had 12 database accesses for 10 books, and 102 database accesses for the 100 books.

I know that every separate database access costs, so I wanted to remove those extra database accesses. And because I knew something that EF Core didn’t know, then I could fix it, as you will see in the next Part.

Part 1b – tweak the query with the DbFunction feature

So, in my first performance tweak, I wanted to see if I keep all the good SQL that EF Core has produced, but inject a bit of my own SQL to improve the performance of the AuthorNamesOrdered part. What I knew was I only wanted the names to concatenate them into a comma-delimited string, and I could do that in SQL very easily.

EF Core 2.0.0, released in August 2017, has a great new feature contribution by Paul Middleton. This allows you to register a SQL user defined function (UDF) with EF Core and then use it in the query. This is a really useful tool, and I expect to be using DbFunctions a lot in the future.

So, with help from Stack Overflow I produced a UDF, as shown below.

CREATE FUNCTION AuthorsStringUdf (@bookId int)
RETURNS NVARCHAR(4000)
AS
BEGIN
-- Thanks to https://stackoverflow.com/a/194887/1434764
DECLARE @Names AS NVARCHAR(4000)
SELECT @Names = COALESCE(@Names + ', ', '') + a.Name
FROM Authors AS a, Books AS b, BookAuthor AS ba 
WHERE ba.BookId = @bookId
      AND ba.AuthorId = a.AuthorId 
      AND ba.BookId = b.BookId
ORDER BY ba.[Order]
RETURN @Names
END
GO

I could then replace the extraction of a list of author’s names with a call to my UDF, as shown below.

public static IQueryable<BookListDto>            
    MapBookToDto(this IQueryable<Book> books)    
{
    return books.Select(p => new BookListDto
    {
        //… other code removed for clarity
        AuthorsOrdered = UdfDefinitions.AuthorsStringUdf(p.BookId),
        //… etc.
    });
}

The UDF returns a single string, so there is no need for extra database accessed. After this change, there were only two databases accessed, a count for paging and then the real query, no matter how many books in a page.

Note: I have not covered how to register the UDF with EF Core, nor the best way to add the UDF to the EF Core created database. There just isn’t room in this article. There is some information on this in the this GitHub documentation. I do have more information on DbFunctions in  chapter 8 of my book, and how to add UDFs and other SQL code to an EF Core database in chapter 11.

Before I show you the improved performance figures let me first show you the new SQL that EF Core produces. EF Core now produces very readable SQL, almost like how a human would lay it out (I did a couple of minor changes to the layout to make the diagram work, but is mostly as EF Core created it).

The only change in the SQL from the Part 1a version is the call to the UDF, but the effect on performance is great, especially on the 100-book display, is good. See the figures below

Here are the results for using the 100,000 book test data, with the ‘Improvement’ columns showing how much faster the Part 1b implementation is compared to Part 1a’s performance.

Test 10 books Improvement 100 books Improvement
All books 30 ms 150% 80 ms 250%
Sort by publication dates 30 ms 150% 80 ms 250%
Sort by votes 480 ms minor 530 ms minor
Sort by price, filter by 4+ votes 420 ms minor 470 ms minor

Part 2 – converting the query to hand-written SQL

When I studied the SQL from part 1b, I noticed that EF Core repeated any calculation in the SQL ORDER BY section, rather than referring to the SELECT’s alias name of the calculated value. I did some tests and they showed using the SELECT’s alias name, ReviewsAverageVote, which holds the calculated average vote, in the ORDER BY section would improve performance.

Based on this I decided to replace the EF Core query with a fully, hand-code SQL command. That meant I had to write the SQL code, but because EF Core’s SQL is so great (and readable), I just copied it (!), and replaced the one area, the ORDER BY part, with some tweaked SQL as shown below – see the ReviewsAverageVotes value used in the ORDER BY at the end of the SQL

SELECT TOP(@pageSize) [b].[BookId], [b].[Title], 
     [b].[Price], [b].[PublishedOn], 
CASE
    WHEN [p.Promotion].[PriceOfferId] IS NULL
    THEN [b].[Price] ELSE [p.Promotion].[NewPrice]
END AS [ActualPrice], 
[p.Promotion].[PromotionalText] AS [PromotionPromotionalText], 
[dbo].AuthorsStringUdf([b].[BookId]) AS [AuthorsOrdered], 
(
    SELECT COUNT(*)
    FROM [Review] AS [r]
    WHERE [b].[BookId] = [r].[BookId]
) AS [ReviewsCount], (
    SELECT AVG(CAST([y].[NumStars] AS float))
    FROM [Review] AS [y]
    WHERE [b].[BookId] = [y].[BookId]
) AS [ReviewsAverageVotes]
FROM [Books] AS [b]
LEFT JOIN [PriceOffers] AS [p.Promotion] 
    ON [b].[BookId] = [p.Promotion].[BookId]
WHERE ([b].[SoftDeleted] = 0) 
ORDER BY [ReviewsAverageVotes] DESC

The next problem was how I would execute the SQL. I could have used ADO.NET but the well-respected mini-OR/M called Dapper. Dapper has a feature that maps the data returned from the SQL to a .NET class by matching the class property names to the column names returned. EF Core also has a feature that can provide the correct type of connection for Dapper to access the database, which makes using Dapper pretty easy.

The unit test code below shows how to use Dapper with an EF Core’s application DbContext – the Dapper part is the line “Query<Book>(“SELECT * FROM Books”)” in the code below.

[Fact]
public void DapperReadBooksEfCoreSqlServer()
{
    //SETUP
    //… I removed the options and database setup 
    //so that it’s easier to see the Dapper part 
    using (var context = new EfCoreContext(options))
    {
        //ATTEMPT
        var books = context.Database 
            .GetDbConnection() 
            .Query<Book>("SELECT * FROM Books"); 

        //VERIFY
        books.Count().ShouldEqual(4);
    }
}

The book list query I implemented in chapter 2 was split up into four separate query objects: MapBookToDto, OrderBooksBy, FilterBooksBy, and a generic paging method. That meant each part was easy to understand and could be separately tested. Duplicating this query in Dapper requires me to sacrifice the elegance of query objects and move to a design that combines snippets of SQL to create the query. But that’s what I need to do to squeeze out the best performance.

The results of swapping over to this improved SQL is shown in the table below, with the big change in bold (Sort on Average votes).

Test scenario Part 1b – EF Core Part 2 – SQL Comment
Show books  10 =30 ms
100 = 80 ms
10 =30 ms
100 = 85 ms
No real change – within normal variations
Sort on PublishedOn 10 = 30 ms
100 = 80 ms
10 = 30 ms
100 = 90 ms
No real change – within normal variations
Sort by average votes 10 = 500 ms
100 = 530 ms
10 = 325 ms
100 = 390 ms
The improved SQL is about 40% faster than EF Core
Filter by average votes (4 or more), sort by price 10 = 440 ms
100 = 490 ms
10 = 455 ms
100 = 520 ms
No real change – within normal variations

As you can see, the only part that improved was the “sort by average votes” situation. What I found interesting is that none of the other more mundane queries had improved. I kind of expected them to because Dapper’s current site states its ten times faster or more than EF (most likely EF6.x), which didn’t show up in these results.

I therefore tried a simple “read one book row” test, and yes, in that case Dapper is about seven times faster than EF Core. That makes sense, as EF Core has several things to do on loading, like relational fixup, while Dapper just executes the SQL. But it turns out, when it comes to large or complex queries where the database execution is the limiting factor, then the performance is down to the quality SQL produced, and not the library you use. The fact is, small accesses aren’t normally the ones that need performance tuning, as they are quick anyway, using Dapper or EF Core only has a difference if the SQL is different, which in the ”sort by average votes” case it was.

But the question is, “was all that effort to swap to SQL worth it?” Before you decide that you should read Part 3, where I use another approach that take more work, but improves performance much more than my hand-tuned SQL does.

Part 3 –  Modifying the database to increase performance

The final stage I decided to try modifying the database and adding cached values for the calculations that take a lot of time. These are:

  1. The Price, which varies depending on whether there is a PriceOffer promotion linked to the book.
  2. The average votes and number of reviews for this book
  3. The AuthorsString, containing the comma-delimited list of authors of a book.

Caching is a great tool for improving performance, but ensuring the cached value is up to date needs very careful handing. This is especially true in a database, where you may have a simultaneous update of the same row, which could make the cached value incorrect.

In chapter 13 in my book I cover how I handled the three types of caching, which need different techniques. But for this article I’m going to focus on the most difficult one of the three, that is the caching of the average votes and number of reviews for this book. This is difficult because I still need to keep the actual Review entities so that users can look at the Review’s comments on a book. This means I have two versions of the average votes value: one that is found by averaging the reviews dynamically, as I have done in Part 1a to 2, or the cached value held in the AverageVotes in my modified Book entity class. Keeping these in set is a challenge.

To keep my cached values up to date I need to make some fairly large changes to how things work, as summarised below:

  1. I still need the Reviews property, which sets up the one-to-many relationship between a Book entity and its Review But I must ensure that Review entities cannot be added or removed from the Reviews property, that is, all adds or removes must go through my AddReview and RemoveReview methods.I also I need to “lock down” the Review entity class by making its properties have private setters, and stop any methods outside of the DataLayer from being able to create a Review entity instance.
  2. Because the AverageVotes and ReviewsCount properties are cached values then there is the possibility of a concurrent adding or removing of a Review, which could invalidate the cached values.

I will deal with each of these two stages separately

Lock down access to Reviews collection property

EF Core allows navigational properties to be set up which EF Core will use them when doing certain read and writes to the database. My initial Book entity class had a property called Reviews that contained a list of Review entities for this book instance. I would then add or remove a Review instance from the list to change what reviews where linked to the Book.

For my caching of the values related to the Reviews to be kept up to date I need intercept all changes to the Book’s Reviews. To do that I use a feature in EF Core called backing fields. This allow me to ‘hide’ the Reviews list, and provide a IEnumerable<Review> Reviews property, which a developer cannot add or remove from. Here is what the updated Book entity class looks like

public class Book
{
    private readonly List<Review> _reviews 
        = new List<Review>(); 

    //… other properties removed for clarity

    public IEnumerable<Review> Reviews => 
         _reviews.ToList();
…

Now I can add two methods, AddReview and RemoveReview, to the Book class that update its backing field _reviews and at the same time recalculate the AverageVotes and ReviewCount cache properties.

The other back door to changing the Reviews is if the develop loaded a Review entity and altered its values, say changing the reviews vote from 1 to 5. To stop the developer from do that I set all the Review class property setters to private, and I make the constructor internal, so only my DataLayer can create a new instance of the Review class.

The figure below shows the backing field in the Book class and its AddReview method in blue, with the “locked down” Review class in purple.

Handling simultaneous additions of a Reviews to the same book

The other problem is multiple users adding Reviews at the same time. There is a small window of time between the loading of the current Reviews linked to a Book and the saving of the new review collection and the associated cached values. In that time window, another Review could be added/removed from the same book by another user, which would make the cached values out of step with the database.

The solution to this problem is to use EF Core’s concurrency conflict feature. What I do is add the attribute [ConcurrencyCheck] to the AverageVotes and ReviewsCount properties. This means that EF Core will check they haven’t changed since the Book instance was loaded, which is window where other updates could make my calculations invalid. If they have been changed then I can write some code that recalculates the cached values using the current state of the database.  I’m not going to show the code for that because the article is already very long. You can find my concurrency code here.

The performance results from Part 3 – cached values

The results from all this is impressive, as you can see in the table below

Test 10 books Improvement over Part 2 (SQL) 100 books Improvement over Part 2 (SQL)
All books 30 ms no improvement 80 ms no improvement
Sort by publication dates 30 ms no improvement 80 ms no improvement
Sort by votes 30 ms 12 times better 80 ms 5 times better
Sort by price, filter by 4+ votes 30 ms 12 times better 80 ms 5 times better

Extra notes on performance:

  • I tried ½ million books: the 10-book time was 85 ms and the 100-book time was 150 ms. For this amount of books the database server is starting to be a limiting factor.
  • In Parts 1a, 1b and 2 if the sort by price, filtered by +1 votes, then the timings were all over a second in length. But this Part 3 implementation stills comes in at 30ms.
  • I took the SQL produced by EF Core in Part 3 and used Dapper to execute it, but there was no further performance improvement so it wasn’t worth doing.

Obviously, the standout improvement are the last two items, which were the ones I was targeting. You can see this code in action, with 100,000 books, at http://efcoreinaction.com/

Summary of the whole performance tuning work

Below is a repeat of the chart I had at the beginning that shows you the results for Part 1a to 3 for the 100-book display and 100-book display with sort on average votes.

However, what this graph does convey is that the Part 3 performance improvements make all the 10-book displays, or any sort or filter, come in at around 30 ms. As the 10-book display is the default then the site is now really fast. Also, the 30 ms is the time for the ASP.NET page to display, which includes other overheads outside the database access – in actual fact, the database accesses are between 10 ms to 15 ms.

The other questions I asked myself was how much effort did each stage take and where there any problem or adverse effects to the application? The table below gives my answers to these questions.

Part Effort Comments
1a. Straight EF Core Few days Finding the correct format for the LINQ average wasn’t easy. I had to ask the EF Core team via EF Core’s GitHub issue page. But once I got this right EF Core produced great SQL code.
1b. +DbFunction ½ day Very easy. I like the DbFunction a lot, and in this case, it made a significant improvement to the performance.
2. Better SQL One day Fairly easy, as I simply copied the SQL code that EF Core produces and tweaked the bit that my experiments said would help. But only useful if you can come up with some better SQL, i.e. the difference in speed of Dapper over EF Core only helps on small, very quick database accesses, which you don’t need to performance tune anyway!
3. Cached values Three days Definitely hard work, but also a fantastic result. I think the performance changes to the code don’t hinder future development of the application at all, and in some cases improves it.

Overall I am very pleased with the process. EF Core produces great SQL code from the get go, but only because I made sure my LINQ queries in a way such that EF Core can translate every part into SQL. I had to persevere with the LINQ Average method to get EF Core to translate that to SQL.

Note I think plenty of developer will fall foul of this problem and think EF Core is slow – but before you blame EF Core check your logs for QueryClientEvaluationWarning warnings, which tell you part of your LINQ query didn’t translate to SQL and is being run in software. See the section “Understanding the limitations of Client vs. Server evaluation” in my  article about EF Core’s Client vs. Server evaluation feature.

But when I had to go beyond straight EF Core I found there was plenty I could do. And I didn’t go as far as I wanted to due to time and limits on the chapter length – my next step was to change the application’s architecture (I give a few ideas at the end of chapter 13).

Conclusion

I took a query that was designed around a fictitious, but believable example, which wasn’t a walk over to implement. I got the best out of EF Core, and I think that performance was quite credible. I then pushed through two more stages, ending with a complex, but eminently implementable solution where the slow parts of the query were between eight and fifteen times faster from where I started. But more importantly all the queries for a 10-book display are at 30 ms.

You most likely selected EF Core for your database access library because it allowed you to develop your application rapidly (good choice!). That means you should have a working application quickly, and can then you can performance tune the parts that need it.

When you start performance tuning I would recommend persevering with conventional EF Core (see my note about QueryClientEvaluationWarning). But at some point you might decide that the standard EF Core approach won’t produce the performance you need. That’s when this article could be useful to you, so bookmark this URL for that day (or buy my book 🙂 ).


If you are interested in .NET in general I recommend the ASP.NET weekly newsletter, curated by Jerrie Pelser. This contains a list of articles on ASP.NET, Entity Framework and .NET in general (mostly .NET Core versions)  – sign up at https://www.getrevue.co/profile/aspnetweekly

Talking at Nottingham, UK .NET Meetup – July 2017

For anyone in the UK, I am talking at a .NET Meetup group in Nottingham UK on Monday, July 31st 2017. I was contacted by Marvin Rounce who co-ordinates the group as a result of the book in I writing for Manning Publishing,  “Entity Framework Core in Action“.

The meeting is open to anyone (but there may be a limit on numbers) and is free – you just need to sign up to the Meetup via this link.

Note: Entity Framework Core is an open-source library, built by a Microsoft and other contributors, that provides a O/RM (Object-Relational Mapper) that allows you to access a database as in an Object-Oriented way. It runs on a .NET platform, including the new .NET Core, which can run on multiple platforms such as Windows, Linux and Apple.

The topics I will be covering are:

1. Building Better Entity Framework Applications

This talk cover some of the topics that I cover in my article “Six ways to build better Entity Framework (Core and EF6) applications“, but going into more detail on the overall design, and a number of useful packages I have found for improving the speed of development.

The aim is to help people who have not used Entity Framework (EF) before, or people who what to explore different ways to build applications with EF quickly and robustly. I also will talk about how to structure your code so that it is easier to performance tune.

2. DevOps and Entity Framework – migrating your production database

This talk looks at what feature EF provides for changing the structure of a database, that is, altering what tables, columns, foreign keys, constraints etc. that make up the database schema.

EF provides a number of approaches to changing a database schema, the best known is EF’s migration feature, but that approach does have some limitations.  I will describe some other options for handling database migrations, including a method I have used successfully on some a few of my projects.

I will also look at the issues around updating a production database, and what you need to think about so that you don’t lose any of your live data!

Entity Framework Core: Client vs. Server evaluation

Entity Framework Core (EF Core) is a new, ground-up rewrite of the highly successful Entity Framework 6 (EF6.x) database access library for the .NET platform.  I have been commissioned by Manning Publishing to write a book on EF Core called Entity Framework Core in Action. In writing this book I have looked at any and all aspects of EF Core, and I am really impressed with the topic I am going to cover in this article, that is, Client vs. Server evaluation.

Client vs. Server evaluation is a feature that makes it easier to write queries to the database, because it allows you to include methods in your query that cannot be run on the database, that is, methods that EF Core cannot convert to SQL commands.

In my Entity Framework Core in Action book I am writing I use a database containing various technical books as an example (seemed appropriate) – you can see a live site at http://efcoreinaction.com/. The diagram below shows you just the the Book table with a many-to-many link to each Book’s Author(s).

Now I want to list a book, with its primary key, title and the Author(s) as a comma delimited string. The code below will do this, in this case I take the first book in the database:

var book = context.Books
    .Select(p => new
    {
        p.BookId,
        p.Title,
        AuthorsString = string.Join(", ",
            p.AuthorsLink
            .OrderBy(q => q.Order)
            .Select(q => q.Author.Name)),
    }
    ).First();

A typical output would be

BookId Title AuthorsString
1 Beginning Scala Vishal Layka, David Pollak

What happens is that EF Core converts the query into SQL to run on the database, but one method, string.Join, isn’t supported as a SQL command by EF Core’s SQL database provider, and at this point Client vs. Server evaluation comes in.

What happens is that EF Core can sort the commands into two groups:

  1. Commands that can be converted into SQL commands and run on the database server
  2. Commands that cannot be converted into SQL, so therefore must be run in software in the application, known as the client.

The figure below gives you a better view of this process. This shows the effect of running the EF Core query I just listed on a book that had two authors, Jack and Jill. As you can see Authors property will be set to “Jack, Jill”, while the BookId and Title would be set to the value of the corresponding columns in the Books Table.

Stage 1, the translation of the LINQ query, is where (with the help of the database provider) EF Core splits up the LINQ commands and other methods into either “run on database” (Server) or “run in software” (Client).

Stage 2 is where the SQL commands are run on the database server and the values are read back. The SQL that EF Core creates is given below.

SELECT TOP(1) [p].[BookId], [p].[Title]
FROM [Books] AS [p]

SELECT [q.Author].[Name]
FROM [BookAuthor] AS [q]
INNER JOIN [Authors] AS [q.Author] ON 
    [q].[AuthorId] = [q.Author].[AuthorId]
WHERE @_outer_BookId = [q].[BookId]
ORDER BY [q].[Order]

Note that EF Core always reads collections using a separate SQL command (EF6.x would have output one commands to read it all, but that can be inefficient in cases where the collection is large).

Stage 3 is run because EF Core detected commands that had to be run on the Client. It gets the data it needs from the SQL data and runs the software-only methods on the Client.

In stage 4 you see that EF Core combines the Server data and the Client data and provides the composite data back to the caller.

The Client vs. Server evaluation feature gives you as a developer the ability to create complex queries, and EF Core will optimize the query to run as much it can on the database Server. But if there is some method in your query that cannot be run on the database server then the query won’t fail, but EF Core will apply that method after SQL Server has done its part.

The example I showed is simple, but the possibilities are endless. But there are a few things to watch out for.

What about Entity Framework 6.x (EF6.x)?

The Client vs. Server evaluation feature is new in EF Core, and is not available in EF6.x. This means that I have to add extra stages in my query if I want to use methods such as string.Join. This makes it harder work in those cases. Obviously in EF Core this gets easier.

If you are still on EF6.x then I can recommend Alexander Zaytsev’s DelegateDecompiler (DD for short) which provides a similar feature (see this article, where I use the DD). While I have used DD, it does require me to add a command at the end of every query. Also DD is not (currently) available on .NET Core.

Understanding the limitations of Client vs. Server evaluation

I think the Client vs. Server evaluation feature is a very useful addition to EF. But, like all powerful features, it is best to understand what is going on so that you can use it in the right way.

Firstly, the obvious thing is the method you provide is run on every entity (row) you read from the database. If you have 10,000 rows in the database and don’t filter/limit what is loaded then, as well as a SQL command that takes a long time, your processor will spend a long time running your method 10,000 times.

The second point is subtler: The Client vs. Server evaluation feature blurs the lines between what is run in the database and what is run in the client. It would be possible to create a query that worked, but was slower than it could be because it had to use client-side evaluation. To give you some context, in EF6.x this form of mixed client/server query would have failed because it did not support that. That meant in EF6.x you had to do something about it – often by changing the query to better suit the database. Now your query may work, but could perform worse than one you write such that EF Core can convert it directly to SQL commands.

One extreme example of the problem is that Client vs. Server evaluation allows you to sort on a client-side evaluated property, which means the sorting is done in the client rather than in the database server. I tried this by replacing the .First() command with .Sort(p => p. AuthorsString) in listing 2.9 and returning a list of books. In that case EF Core produces SQL code that reads all the books, then read each row individually, twice, which is definitely not optimal.

Tips on performance tuning Client vs. Server evaluation

EF Core’s Client vs. Server evaluation feature has some very useful features you can use to make sure you aren’t producing poor-performing.

Firstly, EF Core will log a warning the first time a Client vs. Server evaluation will adversely affect performance, and this logging is intelligent. In the case of my book query that used string.Join then no warning is given because the string.Join does not affect the performance. However if I added the .Sort(p => p. AuthorsString) command then I get a warning.

Secondly, if you want to stop any poorly performing Client vs. Server evaluation queries from happening then you can configure EF Core to throw an exception if a poorly performing query is found.  See code below:

protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
{
    optionsBuilder .UseSqlServer(
       @"Server=(localdb)\mssqllocaldb;Database=MyDatabase;Trusted_Connection=True;")
       .ConfigureWarnings(warnings
            warnings.Throw(RelationalEventId.QueryClientEvaluationWarning));
}

Note: The Microsoft documentation for Client vs. Server evaluation can be found at https://docs.microsoft.com/en-us/ef/core/querying/client-eval

Conclusion

EF Core has a load of nice features and, at the moment, misses some EF6.x features that that I rather liked. But I think Client vs. Server evaluation is one of those great features that will help me to build queries quickly, and often at no reduction of performance. For that I am very grateful.

Happy coding

Six ways to build better Entity Framework (Core and EF6) applications

In this article I describe six different approaches to building applications that use Microsoft’s database access framework, Entity Framework (EF). All six approaches are based on software principles and patterns that many of you will be familiar with. This article shows how I applied these principles and patterns in real-world applications that use EF. The six principles and patterns are:

  1. Separation of concerns – building the right architecture.
  2. The Service Layer – separating data actions from presentation action.
  3. Repositories – picking the right sort of database access pattern.
  4. Dependency injection – turning your database code into services.
  5. Building business logic – using Domain-Driven Design with EF.
  6. Performance tuning EF – get it working, but be ready to make it faster if you need to.

I have an interesting perspective when it comes to software principles and patterns. I started out as a programmer, but then moved into technical management 1988. I came back to programming in 2009, a 21-year gap, and the whole software world had changed. Thankfully over those years some seriously clever people had been thinking about the science of programming, and I avidly read their articles and books.

While it was great to learn things from these software giants, I found it took some time to find the right ways to apply their ideas in the applications I developed. This article brings together the combination of ideas from some of the great software thinkers with my years of learning as I have applied and refined their approaches in building applications that used EF for database access.

All the software and figures in this article come from a book I am writing for Manning Publication called Entity Framework Core in Action that is now on early-access release i.e. if you buy now you will get the chapters as I write them, and the full book when it’s finished. The code is therefore based on the new .NET Core frameworks: EF Core and ASP.NET Core. However, these software principles and patterns apply equally well to the older Entity Framework, version 6 (EF6.x) framework and ASP.NET MVC5.

Note: I’m going to assume you know about Entity Framework already. If you don’t then I recommend you read the first chapter of my book, which is free, or look at Microsoft’s EF Core documentation which includes a number of example applications.

Before I start I need to say that the descriptions I show are my way of implementing the principles and patterns I talk about. There are plenty of other ways to implement each of six topics, mine are just one of them. Have fun yourself developing your own techniques and libraries that will improve and accelerate your development.

1. Principle: Separation of concerns – building on the right architecture

There is a software principal called Separation of Concerns (SoC), which says that you should:

  • Put code that has a similar, or strongly-related functions should be grouped together – in .NET terms put in separate projects. This called cohesion.
  • Make each group/project as self-contained as possible. Each piece of code should have a clear interface and scope of work that is unlikely to change to because of other callers changing what they do. This is called low coupling.

NOTE: Here is a more in-depth article on Separation of Concerns if you want to look into SoC some more.

For simplicity most examples of EF code on the web tend to show EF Core database commands being called directly from whatever application type they are using. This doesn’t follow SoC and nor does it really represent how real applications are written. I took the decision in my book to use a more representative software architecture for my example code, and I used a layered architecture. Using this does make it a little more difficult for the reader, but I build up the application structure over a number of chapter. Here is the final architecture of my example book selling site.

I could have used a number of different architectures, see this link for a good list, but the layered approach works well for small to medium applications. A layered architecture is also a good fit for cloud hosting, where cloud providers can spin up more instances of the web application if it is under a heavy load, that is, it will run multiple copies of a web application and place a load balancer to spread the load over all the copies. This known as scale out on Microsoft’s Azure and auto scaling on Amazon’s AWS.

The figure below shows how I apply SoC to my database access code. It shows the same software architecture, but with all my EF database access code highlighted in bubbles. The size of the bubbles relates to the amount of EF code you will find in each layer. Notice that the ASP.NET Core project and the pure business logic (BizLogic) project have no EF Core query/update code in them at all.

As I go through this article I will refer back to SoC, as it is an integral part of how I approach database applications.

2. Pattern: The Service Layer – separating data actions from presentation actions

One of the influential books I read when I came back to programming was Dino Esposito and Andrea Saltarello’s book Microsoft .NET: Architecting Applications for the Enterprise, published in 2009. Some of the technology he covered then is now superseded, but the patterns are still relevant today (Dino has written a number of newer books, see this link). This book introduced me to the use of Service Layer, which Martin Fowler previously described in his classic book in 2002.

Dino Esposito says in his book that the Service Layer “sets a boundary between two interfacing layers” and Martin Fowler’s site link says the Service Layer “Defines an application’s boundary with a layer of services that establishes a set of available operations and coordinates the application’s response in each operation”. That all sounds great, but how does that help my applications? Quite a bit actually.

I’m going to describe how the Service Layer acts as an adapter in this section. Later, in the section of business logic, I will cover the second way the Service Layer can serve me, by being in command of running my business code.

The Service Layer as an adapter

In a layered architecture there is often a data mismatch between the database/business logic and the presentation layer. The Domain-Driven Design (DDD) approach, which I describe later, says that the database and the business logic should be focused on the business rules, while the Presentation Layer is about giving the user a great user experience, or in the case of a web service, providing a standard and simple API.

For this reason, the Service Layer becomes a crucial layer, as it can be the layer that understands both sides and can transform the data between the two worlds. This keeps the business logic and the database uncluttered by the presentation needs, like drop down list and json AJAX calls. Similarly, by having the Service Layer deliver pre-formatted data in exactly the form the presentation layer needs then it makes it much simpler for the presentation layer to show that data.

When dealing with database accesses via EF the Service Layers uses an adapter pattern to transform from the data layer/business logic layers to/from the presentation layer. Databases tend to minimise duplication of data and maximises the relational links between data, while the presentation layer is about showing the data in a form that the user finds useful.

The figure below shows an example of this difference in approach. The image comes from the list of books produced by the example book selling site that I create in my book. You can see how I have to pick data from lots of different tables in the database, and do some calculations, to form a summary of a book in my book list display.

Note: You can see this book list in action on the live site that hosts the example book selling site, at http://efcoreinaction.com/

EF provides a way of building queries, called select loading, that can ‘pick out’ the relevant columns from each table and combine them into a DTO/ViewModel class that exactly fits the user view. I apply this transform in the Service Layer, alone with other sorting, filtering and paging features. The listing below is the select query using EF Core to build the book summary you just saw in the figure above.

public static IQueryable<BookListDto> 
    MapBookToDto(this IQueryable<Book> books)   
{
    return books.Select(p => new BookListDto
    {
        BookId = p.BookId,                      
        Title = p.Title,                        
        Price = p.Price,                        
        PublishedOn = p.PublishedOn,            
        ActualPrice = p.Promotion == null       
                ? p.Price : p.Promotion.NewPrice,         
        PromotionPromotionalText =              
                p.Promotion == null             
                  ? null : p.Promotion.PromotionalText,
        AuthorsOrdered = string.Join(", ",      
                p.AuthorsLink                   
                .OrderBy(q => q.Order)          
                .Select(q => q.Author.Name)),   
        ReviewsCount = p.Reviews.Count,         
        ReviewsAverageVotes =                   
                p.Reviews.Count == 0            
                ? null                          
                : (decimal?)p.Reviews           
                    .Select(q => q.NumStars).Average()
    });
} 

Note: The code above wouldn’t work with EF6.x because it includes the command string.Join that cannot be converted into SQL but EF6.x. EF Core has a called Client vs. Server Evaluation, which allows methods that cannot be translated to SQL to be included. They are run after the data has been returned from the database.

Yes, this code is complex, but to build the summary we need to pull data from lots of different places and do some calculations at the same time, so that’s what you get. I have built a library called GenericServices (currently only available for EF6.x) with automates the building of EF select loading commands like this by using a LINQ Mapper called AutoMapper. This significantly improves the speed of development of these complex queries.

3. Pattern: Picking the right sort of database access pattern

There are a number of different ways we can form your EF database access inside an application, with different levels of hiding the EF access code from the rest of the application. In the figure below I show four different data access patterns.

The four types of database access patterns are:

  1. Repository + Unit of Work (Repo+UOW). This hides all the EF Core behind code that provides a different interface to EF. The idea being you could replace EF with another database access framework with no change to the methods that call the Repo+UOW.
  2. EF repository. This is a repository patterns that doesn’t try and hide the EF code like the Repo+UOW pattern does. EF repositories assume that you as developer know the rules of EF, such as using tracked entities and calling SaveChanges for updates, and you will abide by them.
  3. Query Object. Query objects encapsulate the code for a database query, that is a database read. They hold the whole code for a query or for complex queries it might hold part of a query. Query objects are normally built as extension methods with IQueryable<T> inputs and outputs so that they can be chained together to build more complex queries.
  4. Direct calls to EF. This represents the case where you simply place the EF code you need in the method that needs it. For instance, all the EF code to build a list of books would be in the ASP.NET action method that shows that list.

NOTE: AS I said earlier I have created a library called GenericServices for EF6.x (and EF Core in the future). This is a form of EF repository.

I used the Repo+UOW pattern, which was the recommended approach at the time, in a big project in 2014 – and I found it was really hard work. I and many others realised Repo+UOW wasn’t the way to go – see my article ‘Is the Repository pattern useful with Entity Framework?’. The Repo+UOW can be a valid pattern in some cases where hiding of the certain part of the data is needed, but I think there are better ways to do this with some of the new EF Core features, such as backing fields.

At the other end of the spectrum is the direct calls to EF in the method that needs it. This fails the separation of concerns principal because the database code is mixed in with other code not directly involved in database issues.

So, having ruled out the two extremes I would recommend:

  • Query Objects for building queries, often breaking down large queries into a series of query objects. The previous listing in this article of the method called MapBookToDto, which is a query object. I cover query objects in chapter 2 of my book.
  • For Create, Update and Delete (and business logic which I cover later) I use a EF repository pattern, that is, I create a method that encapsulates the EF database access. This isolates the EF code and makes it easier to refactor or performance tune that code.

The listing below shows a class with two EF repository methods for changing the publication date of a book in my example book selling site. I cover this in chapter 3 of my book.

public class ChangePubDateService : IChangePubDateService
{
    private readonly EfCoreContext _context;

    public ChangePubDateService(EfCoreContext context)
    {
        _context = context;
    }

    public ChangePubDateDto GetOriginal(int id)    
    {
        return _context.Books
            .Select(p => new ChangePubDateDto      
            {                                      
                BookId = p.BookId,                 
                Title = p.Title,                   
                PublishedOn = p.PublishedOn        
            })                                     
            .Single(k => k.BookId == id);          
    }

    public Book UpdateBook(ChangePubDateDto dto)   
    {
        var book = _context.Books.Find(dto.BookId);
        book.PublishedOn = dto.PublishedOn;        
        _context.SaveChanges();                    
        return book;                               
    }
}

4. Pattern: Turning your database code into services

I have used dependency injection (DI) for years and I think it’s really useful approach. I want to show you a way you can inject your database access code into an ASP.NET Core application.

Note: If you haven’t used DI before have a look at this article for an introduction, or this longer article from another of the great thinker, Martin Fowler.

The benefits of doing this are twofold. Firstly, DI will dynamically link together your database access to into the parts of the presentation/web API code that need it. Secondly, because I am using interfaces, it is very easy to replace the calls to the database access code with mocks for unit testing.

I haven’t got the space in this article to give you all the details (I takes five pages in chapter 5 of my book to cover this), but here are the main steps, with links to online documentation if you want to follow it up. Here are the steps:

  1. You need to make each of your database access code into thin repositories. That is a class containing a method, or methods, that the front-end code needs to call. See the ChangePubDateService class listed above.
  2. You need to add an interface to each EF repository class. You can see the IChangePubDateService interface applied to the ChangePubDateService class listed above.
  3. You need to register your EF repository class against its interface in the DI provider. This will depend on your application. For ASP.NET Core see this article.
  4. Then you need to inject it into the front-end method that needs it. In ASP.NET Core you can inject into an action method using the [FromServices] Note: I use this DI parameter injection rather than the more normal constructor injection because it means I only create the class when I really need it, i.e. it is more efficient this way.

Note: I realise that is a lot to take in. If you need more information can look at the GitHub repo associated with my book. Here are some useful links:

At the end of this you have a method you can call to access the database. The listing below shows an ASP.NET Core action method that calls the UpdateBook method of the ChangePubDateService class that I listed previously. Line 4 has the [FromServices] attribute that tells the DI provider to inject the ChangePubDateService class into the parameter called service.


[HttpPost]
[ValidateAntiForgeryToken]
public IActionResult ChangePubDate(ChangePubDateDto dto,
   [FromServices]IChangePubDateService service)
   {
      service.UpdateBook(dto);
      return View("BookUpdated",
         "Successfully changed publication date");
}

NOTE: There is a way to do parameter injecting into an ASP.NET MVC action method, but it involves you having to override the default Binder. See The section “How DI is used in SampleMvcWebApp” at the bottom of this page, and my DiModelBinder in the associated GitHub repo.

The result of all this is that database code is nicely separated into its own class/method and your front-end code just has to call the method, not knowing what it contains. And unit testing is easy, as you can check the database access code on its own, and replace the same code in your front-end call with a mocking class that implements the interface.

5. Pattern: Building business logic – using Domain-Driven Design

Real-world applications are built to supply some sort of service, ranging from holding a simple list of things on your computer through to managing a nuclear reactor. Every different real-world problem has a set of rules, often referred to as business rules, or by the more generic name, domain rules.

Another book that I read some time ago that had a big impact on me was “Domain-Driven Design” by Eric Evans. The Domain-Driven Design (DDD) approach says that the business problem you are trying to solve must drive the whole of the development. Eric then goes on to explain how the business logic should be isolated from everything else other that the database classes so that you can give all your attention to what Eric Evans calls the “difficult task” of writing business logic.

There are lots of debates about whether EF Core is suitable for a DDD approach, because the business logic code is normally separate from the EF entity classes which it maps to the database. However, Eric Evans is pragmatic on this point and says in the section entitled “Working within your (database access) Frameworks” that, and I quote:

“In general, don’t fight your framework. Seek ways to keep the fundamentals of domain-driven design and let go of the specifics when your framework is antagonistic”
Page 157, Domain-Driven Design, by Eric Evans, 2004.

Note: I had to look up the word antagonistic: it means “showing or feeling active opposition or hostility towards someone or something”.

Over the years I have developed a DDD approach that works with EF and I have dedicated the whole of chapter 4 of my book to the topic of business logic because it is so important to get right. Here is a summary of the guidelines in that chapter:

  1. The business logic has first call on how the database structure is defined

Because the problem I am trying to solve, called the “Domain Model” by Eric Evans, is the heart of the problem then it should define the way the whole application is designed. Therefore, I try to make the database structure, and the entity classes, match my business logic data needs as much as I can.

  1. The business logic should have no distractions

Writing the business logic is difficult enough in itself, so I isolate it from all the other application layers, other than the entity classes. That means when I write the business logic I only have to think about the business problem I am trying to fix. I leave the task of adapting the data for presentation to the Service Layer in my application.

  1. Business logic should think it is working on in-memory data

This is something Eric Evans taught me – write your business logic as if the data was in-memory. Of course there needs to be some a ‘load’ and ‘save’ parts, but for the core of my business logic I treat, as much as is practical, the data as if it is a normal, in-memory class or collection.

  1. Isolate the database access code into a separate project

This fairly new rule came out of writing an e-commerce application with some complex pricing and deliver rules. Before this I used EF directly in my business logic, but I found that it was hard to maintain, and difficult to performance tune. Now I have another project, which is a companion to the business logic, and holds all the database access code.

  1. The business logic should not call EF Core’s SaveChanges directly

The business logic does not call EF Core’s SaveChanges method directly. I have a class in the Service Layer whose job it is to run the business logic – this is a case of the Service Layer implementing the command pattern. and, if there are no errors, it calls SaveChanges. The main reason is to have control of whether to write the data out, but there are other benefits that I describe in the book.

The figure below shows the original software architecture, but with the focus on how the business logic is handled. The five numbers, with comments, match the numbered guidelines above.

In my book I use the processing of an order for books as an example of a piece of business logic. You can see this business logic in action by going to the companion live site, http://efcoreinaction.com/, where you can ‘buy’ a book. The site uses an HTTP cookie to hold your basket and your identity (saves you having to log in). No money needed – as the terms and conditions says, you aren’t actually going to buy a book.

The code is too long to add to this article, but I have written another article called Architecture of Business Layer working with Entity Framework (Core and v6) which covers the same area in more detail and contains plenty of code examples.

6. Principle: Get your EF code working, but be ready make it faster if you need to.

The recommended approach to developing software is to get it to work, and then worry about making it faster. Another more nuanced approach, attributed to Kent Beck, is Make it Work. Make it Right. Make it Fast. Either way, these principle says we should leave performance tuning to the end. I would add a second part: you should only performance tune if you need to.

In this article I am talking about database accesses via EF. I can develop pretty complex database accesses in EF really quickly – at least five times faster than using ADO.NET or Dapper. That covers the “get it working part”. The down side is that EF doesn’t always produce the best performing SQL commands: sometimes it’s because EF didn’t come up with a good SQL translation, and sometimes it’s because the LINQ code I wrote isn’t as efficient as I thought it was. The question is: does it matter?

For example, I developed a small e-commerce site (the code took me 10 months) which had a little over a 100 difference database accesses and about 20 tables. More than 60% of the database accessed were on the admin side, with maybe 10% of accesses that really mattered to the paying user.

To show this graphically I have picked out three features from my example book selling site and then graded them by two scales:

  • vertically, what the user expects in terms of performance.
  • Horizontally, how difficult is the database access.

This gives you the figure below, with top right highlighted as area where we really need to think about performance tuning.

My analysis says that only the book search query needs work to improve it. The user is used to fast searches thanks to Google etc. and will get frustrated if my application is too slow. Looking at the complexity of the book search, which includes filtering on things like average user votes, I can see that it produces some rather complex database access commands.

It turns out that the EF Core code for my book search performs badly, but there is plenty I can do about it. In fact, I have mapped out a whole section towards the end of my book where I show how I can improve the book search in a series stages, each getting more complex and taking more development time. They are:

  1. Can I improve the basic EF commands by rearranging or refining my EF code?
  2. Can I convert some or all of the EF code into direct SQL commands, calculated columns, store procedures etc.?
  3. Can I change the database structure, such as de-normalising the database, to improve the search performance?

Improving the book search will take quite a bit of time, but it’s worth it in this application. Quite honestly the other features aren’t worth the effort, as they are fast enough using standard EF Core commands.

Planning for possible performance tuning

While I totally agree with the idea that you shouldn’t try to performance tune too early, it is sensible to plan that you might have to performance tune. All the other approaches I have described, especially the encapsulation of the databases accesses, means that my database code is a) clearly isolated, and b) open for performance tuning.

So, my practice is to develop database accesses quickly with EF, but organise the code so that I encapsulate the database code cleanly. Then, if I have a performance problem then I can tune the database access code with minimal impact on other parts of your application.

Conclusion

When I came back to programming in 2009 some of my early code ended up with long methods which were intertwined with each other – they worked, but the code was hard to understand, debug and maintain. Since then I have taken the principles and practices from the software giants and worked out how to apply them real applications. With these I can write code that is robust, easy to test and easy to improve – and I write code much faster too.

However, for me, there is a gap between seeing a great idea and working out how it could help me. Some books I instantly loved, like Eric Evans “Domain-Driven Design” but it has still taken two or three projects before I had an implementation that works really well for me. I don’t think I’m unusual in this, as learning any new technique takes some time for it to become smooth and natural.

There are many great software thinkers out there and some great principles and practices. So the next time you think “there must be a better way of doing this” then go and have a look for other people’s thoughts. It’s a journey, but the reward is becoming a better developer.

Happy coding!

More information on my book, Entity Framework Core in Action

Quite a bit is going on around the book I am writing, called Entity Framework Core in Action, now that it is on early release. In this short article I want to point you to some resource you might like to look at, and also give you a bit more on the planned content of the book.

Resources available for the book

The following resources are available:

  • A slideshare presentation giving you an overview of what’s in the book.
    IMPORTANT: discount code in the slideshare presentation! See first slide.
  • The book page, with links to the first chapter, which is free.
  • The GitHub repo with all the code that goes with the book.
    NOTE: I use a branch-per-chapter, so you need to select a branch to see the code.
  • The live site, at http://efcoreinaction.com/ where you can see the example book selling site that I build in chapters 1 to 5, and improve in later chapters.

Note: You can buy an early-access version of my book. You will get the chapters as I write them, and the full book when the book is released. This means you have early access to the information and you get the final book with any updates I make to the chapters before it is releases.

Table of Content for the book

Here is the current table of contents for my book. It is quite possible that a couple of chapter in part 2 and 3 might get too big and be split into two.

Part 1:  Introduction to Entity Framework Core

By the end of part 1 you should be able to build a .NET application that uses a relational database, but how the database is organized is left to EF Core; for instance, EF Core’s default configuration will set the type and size of the database columns, which can be a bit wasteful on space. I also look at building and deploying a web application that uses EF Core to handle its database.

The chapters are:

  1. Introduction to Entity Framework Core
  2. Querying the database
  3. Updating the database: Create, Update & Delete
  4. Using EF Core in Business Logic
  5. Using EF Core in ASP.NET Core web applications

Part 2:  Entity Framework Core In Depth

Part 2 is more of a EF Core reference section. By the end of part 2 you will be able to create a database with the exact type and size of each column, or link to an existing database which has a specific schema. I go deeper into the commands in EF Core’s DbContext and look at different ways of migrating a database.

The chapters are:

  1. Modelling options for scalar properties
  2. Modelling options for relationships
  3. Going deeper into the DbContext
  4. Handling database migrations

Part 3: Using Entity Framework in real-world applications

Part 3 is all about improving your skills and making you a better developer, and debugger, of EF Core applications. I look at real-world applications of EF Core starting with a range of known patterns and practices that you can use with EF Core with chapters on Unit Testing EF Core applications, extending ER Core, and most importantly how to find and fix EF Core performance issues.

The chapters are:

  1. Patterns for using EF Core
  2. Unit Testing EF Core applications
  3. EF Core performance Tuning
  4. Extending EF Core

Appendices

A brief introduction to LINQ

Conclusion

A pretty short and sweet post, but I hope it helpful. My next post will be much bigger.

Happy coding!

 

Updating many to many relationships in Entity Framework Core

I wrote an article called Updating many to many relationships in entity framework back on 2014 which is still proving to be popular in 2017. To celebrate the release of my book Entity Framework Core in Action I am producing an updated version of that article, but for Entity Framework Core (EF Core).

All the information and the code comes from Chapter 2 of my book. In the book I use a book selling site, with books linked to authors via a many-to-many relationship. You can see the live example site and if you scroll down you will see books with multiple authors.

All the unit tests and classes I use in this article can be found in the Chapter03 branch of the Git repo associated with this book.

NOTE: If you are in a hurry I have added summaries for each section – just search for the word Summary to get the answers without needing to look at the code examples.

Creating a many-to-many relationship in EF Core

The example I am going to use is one taken from the book I am writing. It has a Book entity linked its Author(s) entities via a BookAuthor table. The database diagram below shows the three tables, with the foreign key links from the BookAuthor linking table to the other two tables.

A linking table like this has foreign keys (FK in the diagram) which link to the primary key(s) (PK in the disgarm) of each end of the relationship – in this case the BookId of the Book entity and the AuthorId of the Author entity. These foreign keys then form a composite primary key for the BookAuthor table.

EF6.x created this table for you when you defined a many-to-many relationship, but EF Core, which takes a leaner approach, doesn’t – you need to do it. Its not hard, so let me give you the code to define the BookAuthor entity class.

public class BookAuthor               
{
    public int BookId { get; set; }   
    public int AuthorId { get; set; } 
    public byte Order { get; set; }   

    //-----------------------------
    //Relationships

    public Book Book { get; set; }    
    public Author Author { get; set; }
}

NOTE: I have a property called Order because the order of the authors matter. If the order of items in your many-to-many list don’t matter then you can leave that out.

EF Core will find the relationships using its by convention rules because I have used names that it understands:

  • I used the same names, BookId and AuthorId, of the primary keys in the Book class and the Author class.
  • Because I used the classes Book and Author, which EF Core knows are part of the database, then it knows its a relationship.

Now, the one thing that EF Core can’t work out is what the primary key of the BookAuthor table because they don’t follow the normal convension. There are a number of ways to define the primary key(s) of an entity, but I have used EF Core’s Fluent API that I access via the OnModelCreating method inside my DbContext, as shown below:

public class EfCoreContext : DbContext
{
    public DbSet<Book> Books { get; set; }            
    public DbSet<Author> Authors { get; set; } 
    public DbSet<PriceOffer> PriceOffers { get; set; }       

    public EfCoreContext(                             
        DbContextOptions<EfCoreContext> options)      
        : base(options) {}

    protected override void
        OnModelCreating(ModelBuilder modelBuilder)    
    {                                                 
        modelBuilder.Entity<BookAuthor>()             
            .HasKey(x => new {x.BookId, x.AuthorId});
    }                                                 
}

You can see the .HasKey method towards the bottom of the code where I define the composite key consisting of the BookId and the AuthorId.

The easiest approach to setting up relationships is by using EF Core’s conventions, which is what I have done. But if you want to explicitly define the relationships you can, using the .HasOne/.HasMany Fluent API commands, as shown below.

Note: There are loads of configuration rules, which I cover in chapter 6 and 7 of my book, or you can look at the EF Core docs.

//NOTE: I only show the OnModelCreating part of the DbContext
protected override void
    OnModelCreating(ModelBuilder modelBuilder) 
{
    modelBuilder.Entity<BookAuthor>() 
       .HasKey(x => new {x.BookId, x.AuthorId});

    //If you name your foreign keys correctly, then you don't need this.
    modelBuilder.Entity<BookAuthor>() 
        .HasOne(pt => pt.Book)
        .WithMany(p => p.AuthorsLink)
        .HasForeignKey(pt => pt.BookId);

    modelBuilder.Entity<BookAuthor>() 
        .HasOne(pt => pt.Author) 
        .WithMany(t => t.BooksLink)
        .HasForeignKey(pt => pt.AuthorId);
}

NOTE: You can set the keys, but not the relationships, via attributes. The [Key] and [Order] attributes allow you to define the primary key, but I find the Fluent API is quite clean and easy to understand.

Summary – how to add a many-to-many relationship

To add a many-to-many relationship in EF Core you need to:

  1. Create a linking entity class that has the foreign keys(s) of the two entities you want to form a many-to-many link between. In my example these are BookId and AuthorId.
  2. It is efficient (but not necessary) to make these two foreign keys into the composite primary key. This will ensure that there is only ever one link between the two entities you want. It is also more efficient in terms of size of the database table.
    1. If you do want the two foreign keys to be the composite primary key you need to configure this in EF Core, either via Fluent API or by [Key]/[Order] attributes, because it cannot find them using its normal “by convention” method.
    2. If you use properties for the foreign key that don’t  have the same name as the primary key it links to, then you must manually configure the relationship. There are a number of ways to do that, but EF Core’s Fluent API is quick and fairly straightforward to understand.
  3. The linking class can, but don’t have to, have relationship links to the two entities it links. In my example my AuthorLinks class has a property Book of type Book, and a property Author of type Author.
  4. You most likely want a collection relationship to the linking table in one or both of the linked entities. In my example I have property called AuthorLinks with the type ICollection<BookAuthor> in my Book class, and a property called BooksLink of type ICollection<BookAuthor> in my Author class.

Adding a Book many-to-many Author link

Creating new many-to-many link is fairly easy in EF Core (but not as easy as EF6.x). The code below creates a new book with a new author.

Note: the variable context used at the bottom of the code below is an instance of the application’s DbContext, called EfCoreContext in this case (see its definition above).

var book = new Book
{
  Title = "Quantum Networking",
  Description = "faster-than-light data communications",
  PublishedOn = new DateTime(2057, 1, 1),
  Price = 220
};
var author = new Author { Name = "Future Person" };
book.AuthorsLink = new List<BookAuthor>
{
  new BookAuthor { 
    Author = author, 
    Book = book,
    Order = 0
  }
};

//Now add this book, with all its relationships, to the database
context.Books.Add( book);
context.SaveChanges();

Updating a many-to-many relationship

It turns out there are two scenarios under which you want to update a relationship. They are:

  1. Connected state: This is where the load of the data and the update is done in one go, that is within one instance of the application’s DbContext. You find this sort of update that happens in a console application, or inside your business logic (see this link on business logic and EF Core)
  2. Disconnected state: This is where the update is split into two halves: a) select the entities you want to change, b) and make the change. Each stage has a different instance of the application’s DbContext. This happens on web sites, where in the first stage the user picks what they want done and posts it back. The second stage then has to re-read the data and update it.

I am going to describe these two approaches separately, so you can go to the one that fits the application you are building.

1. Connected state update

In the connected state we read in the Book entity and update it immediately, i.e. within the same instance of the DbContext. For my Book/Author example I am going to read in the Book, with its linking entities, BookAuthor, as tracked entities. – that means that EF Core will take a copy of the entities we read in so that it can spot any changes when you call SaveChanges.

The listing below is one of my unit tests which adds the existing author “Martin Fowler” to the book called “Quantum Networking”, which currently has one author called “Future Person”. After the test has finished the book “Quantum Networking” has two authors, “Future Person” and “Martin Fowler”

NOTE: I am using a Sqlite, in-memory database, which I seed with four books with known titles and authors.

public void TestAddExtraAuthorOk()
{
    //SETUP
    var inMemDb = new SqliteInMemory();

    using (var context = inMemDb.GetContextWithSetup())
    {
        context.SeedDatabaseFourBooks();

        //ATTEMPT
        var book = context.Books                          
            .Include(p => p.AuthorsLink)                  
            .Single(p => p.Title == "Quantum Networking");

        var newAuthor = context.Authors                   
            .Single(p => p.Name == "Martin Fowler");

        book.AuthorsLink.Add(new BookAuthor
        {
            Book = book,
            Author = newAuthor,
            Order = (byte) book.AuthorsLink.Count
        });
        context.SaveChanges();

        //VERIFY
        var bookAgain = context.Books
            .Include(p => p.AuthorsLink)
            .Single(p => p.BookId == book.BookId);
        bookAgain.AuthorsLink.ShouldNotBeNull();
        bookAgain.AuthorsLink.Count.ShouldEqual(2);
        var authorsInOrder = bookAgain.AuthorsLink.OrderBy(p => p.Order).ToList();
        authorsInOrder.First().Author.Name.ShouldEqual("Future Person");
        authorsInOrder.Last().Author.Name.ShouldEqual("Martin Fowler");
    }
}

The most important parts of the code are lines 11 to 13. Here I load the Book entity using an Include method to load the AuthorLinks at the same time, as tracked entities. The AuthorLinks property holds a a collection of BookAuthor entities linking to the Author entities.

Note: The default way of loading data in EF Core is as tracked entities, that is, EF Core takes a copy of the loaded data so that it can detect if any changes have happened since they were loaded. You can turn off tracking by including the method .AsNoTracking to your query (useful in read-only queries, as it makes them a little faster).

In lines 15 to 16 I also load, again as a tracked entity, the Author entity who’s name is “Martin Fowler” as that the author I want to add to this book.

Then on lines 18 to 23 I create a new BookAuthor linking entry linking the Book entity I loaded to the Author entity of “Martin Fowler”.

When SaveChanges is called on line 24 it finds that the AuthorLinks property  of the book instance it loaded has changed. Because this is a relationship it looks at the entities and finds that there is a new BookAuthor entity, which it adds to the database, and a author which is already in the database so it doesn’t have to add it.

Summary – Connected state many-to-many update

To update a many-to-many relationship in the connected state:

  1. Load an entity at one end of the relationship (doesn’t matter which end) with the collection of links in the many-to-many linking table. In my example I loaded the Book entity I wanted to change along with its AuthorLinks property, which holds the collection of the linking entities in the BookAuthor table.
  2. Alter the linking table collection, either by adding or removing entries from the collection. In my example I added a new BookAuthor class to the AuthorLinks property collection.
    1. Note: if you want to replace the whole collection then you can simply assign a new list to the linking collection (there is an example of this in my unit tests – see test named TestChangeAuthorsNewListOk).

2. Disconnected state

The disconnected state happens when the initial read of the data and update of the data are done separately, i.e. they use different instances of the applications DbContext. This happens in a web application which has two stages:

  1. The first stage is where the user is presented with the book, its current author(s) and a list of possible authors to choose from. Once they have chosen the author to add then press a button which takes their choice back to the web application
  2. In the second stage the web application needs to re-read the data and execute the database update.

The unit test below does the same as the previous example, that is, it adds the existing author “Martin Fowler” to the book called “Quantum Networking”, which starts out with one author called “Future Person”. After the test has finished the book “Quantum Networking” has two authors, “Future Person” and “Martin Fowler”.

I simulate the disconnected state by having two instances of the application’s DbContext and passing the primary keys of the Book and the new Author to add via a small class called ChangeAuthorDto.

Note: This unit test uses two, separate instances of the DbContext to simulate the disconnected state. With the help of Daria (see comment below) I have found a way to use Sqlite in-memory with multiple instances of the DbContext. Daria pointed out that the in-memory part is held in the Sqlite connection, so I create one options (which uses the Sqlite connection) and used that to create two separate DbContext instances  – thanks Daria.

[Fact]
public void TestAddAuthorDisconnectedOk()
{
    //SETUP
    var options = SqliteInMemory.CreateOptions<EfCoreContext>();
    ChangeAuthorDto dto;
    using (var context = new EfCoreContext(options))
    {
        context.Database.EnsureCreated();
        context.SeedDatabaseFourBooks();
        var book = context.Books
            .Include(p => p.AuthorsLink)
            .Single(p => p.Title == "Quantum Networking");

        var newAuthor = context.Authors
            .Single(p => p.Name == "Martin Fowler");
        dto = new ChangeAuthorDto
        {
            BookId = book.BookId,
            NewAuthorId = newAuthor.AuthorId
        };
    }

    using (var context = new EfCoreContext(options))
    {
        //ATTEMPT
        var book = context.Books
            .Include(p => p.AuthorsLink)
            .Single(p => p.BookId == dto.BookId);
        var newAuthor = context.Authors.Find(dto.NewAuthorId);

        book.AuthorsLink.Add(new BookAuthor
        {
            Book = book,
            Author = newAuthor,
            Order = (byte)book.AuthorsLink.Count
        });
        context.SaveChanges();

        //VERIFY
        var bookAgain = context.Books
            .Include(p => p.AuthorsLink).ThenInclude(p => p.Author)
            .Single(p => p.BookId == dto.BookId);
        bookAgain.AuthorsLink.ShouldNotBeNull();
        bookAgain.AuthorsLink.Count.ShouldEqual(2);
        var authorsInOrder = bookAgain.AuthorsLink.OrderBy(p => p.Order).ToList();
        authorsInOrder.First().Author.Name.ShouldEqual("Future Person");
        authorsInOrder.Last().Author.Name.ShouldEqual("Martin Fowler");
    }
}

The important lines are 27 to 30, where I load in the Book, with its BookAuthor linking collection accessed via the Book’s AuthorLinks property, and I read in the Author that the user wants to add.

Having loaded that book I can use the same code I had in the connected state, that is, I  create a new BookAuthor linking entry linking the Book entity I loaded to the Author entity of “Martin Fowler” and add it to the existing AuthorLinks collection – see lines 32 to 37.

Summary – Disconnected state many-to-many update

To update a many-to-many relationship in the disconnected state:

  1. In the first stage you should return the primary key(s) of the two ends of the many-to-many relationships that you want to link/unlink. In my example I provide the BookId and the AuthorId I want to add to the book.
  2. In stage two you load one end of the relationship (doesn’t matter which end) with the collection of links the the linking table. In my example I loaded the Book entity using the BookId key along with its AuthorLinks property, which held the collection of the linking entities in the BookAuthor table.
  3. Now load the other end of the relationship. In my example the Author entity, using the AuthorId returned from the first stage.
  4. Alter the linking table collection, either by adding or removing entries from the collection. In my example I added a new BookAuthor class to the AuthorLinks property collection.
    1. Note: if you want to replace the whole collection then you can simply assign a new list to the linking collection with the new BookAuthor class.

2a. Another, quicker way to update a many-to-many relationships in the disconnected state

In chapter 3 of my book, Entity Framework Core in Action , I point out that you can often change relationships using the foreign keys, and it can me more efficent. This is especially true of many-to-many relationships because you can directly add/delete entries from the linking table, in this case BookAuthor.

By directly adding/deleting the linking table entries then it saves you having to load the Book entity with its AuthorLinks property and the Author you want to add. Coupled with the fact that the first stage is going to supply you with the primary keys anyway then it saves quite a few database queries.

The unit test below shows this in action. The first stage of the unit test hands back primary keys of the Book and the new Author to add via the ChangeAuthorDto class. The second stage, which uses a new instance of the DbContext, uses those primary keys to build a new BookAuthor linking entity to add to the database.

NOTE: The one downside of this approach for my Book/Author example is the setting the Order property, as it should be set to get the correct order of the authors. For this example know there is one author with an Order set to 0 so I simply set it to 1. In real application the first stage would have to define what order the authors should be shown in.

[Fact]
public void TestAddAuthorViaForeignKeyOk()
{
    //SETUP
    var options =
        this.NewMethodUniqueDatabaseSeeded4Books();

    ChangeAuthorDto dto;
    using (var context = new EfCoreContext(options))
    {
        var book = context.Books
            .Include(p => p.AuthorsLink)
            .Single(p => p.Title == "Quantum Networking");

        var newAuthor = context.Authors
            .Single(p => p.Name == "Martin Fowler");
        dto = new ChangeAuthorDto
        {
            BookId = book.BookId,
            NewAuthorId = newAuthor.AuthorId
        };
    }

    using (var context = new EfCoreContext(options))
    {
        //ATTEMPT
        context.Set<BookAuthor>().Add(new BookAuthor    
        {                                               
            BookId = dto.BookId,                        
            AuthorId = dto.NewAuthorId,                 
            Order = 1                                   
        });                                             
        context.SaveChanges();                          

        //VERIFY
        var bookAgain = context.Books
            .Include(p => p.AuthorsLink).ThenInclude(p => p.Author)
            .Single(p => p.BookId == dto.BookId);
        bookAgain.AuthorsLink.ShouldNotBeNull();
        bookAgain.AuthorsLink.Count.ShouldEqual(2);
        var authorsInOrder = bookAgain.AuthorsLink.OrderBy(p => p.Order).ToList();
        authorsInOrder.First().Author.Name.ShouldEqual("Future Person");
        authorsInOrder.Last().Author.Name.ShouldEqual("Martin Fowler");
    }
}

The important lines are 27 to 32, where I create a new BookAuthor class using the BookId and the AuthorId of the new author I want to add to the book. If you compare this to the last disconnected example you will see this avoids three database loads:

  1. The Book we wanted to update
  2. The Books AuthorLinks collection of BookAuthor entities
  3. And the Author that I wanted to add.

In this case I added an extra author. I could have removed an author by deleting the appropriate BookAuthor entry.

I do recommend this approach for many-to-many relationship updates because it is more efficient and still quite clear in its intent, i.e. its not complex so its obvious what is happening.

Summary – quicker way to update a many-to-many relationships in the disconnected state

For updates to many-to-many relationships in the disconnected state you can:

  1. In the first stage you should return the primary key(s) of the two ends of the many-to-many relationships that you want to link/unlink. In my example I provide the BookId and the NewAuthorId I wanted to add to the book.
  2. In the second stage you should add/delete entries from the linking table directly. For instance to add a new many-to-many relationship you add a new entry in the linking table. In my example I added a new BookAuthor link  with its BookId property set from the BookId returned by the first stage, and its AuthorId property set to the NewAuthorId value from the first stage.

Conclusion

Setting up and changing many-to-many links in EF Core is harder than in EF6.x. EF 6.x used the same approach as EF Core, but “hid” the linking table and the manipulation of that table from you. That certainly made it easier, but as the continued access to my original article on updating many-to-many in EF6.x shows, it can cause confusion sometimes.

At the moment EF Core makes you configure the linking table and update that linking table yourself. The EF Core roadmap says it will look at automating this in the future, but not for now. But I think there are some benefits in knowing what is going on in the database, and, as my last disconnected example shows, you can sometimes be more efficient that EF6.x is at updating many-to-many relationships.

I cover this an much more in my book Entity Framework Core in Action and you can access the unit test code you see here via the Chapter03 branch of the Git repo that goes with the book – see the test class Ch03_ManyToManyUpdate.

Happy coding!

Architecture of Business Layer working with Entity Framework (Core and v6) – revisited

I wrote an article a while ago called Architecture of Business Layer working with Entity Framework, which has been popular. I am now playing with Entity Framework (EF) Core and some sample code made me think – could I better isolate the EF part of my Business Logic? The answer is yes, and this article describes the new and improved approach, with a lot more examples.

Note: All the code in this article is using .NET Core and Entity Framework Core (EF Core). However, the code is easy to understand for anyone who has played with either flavour of EF, and the concepts work equally well with EF 6.x or EF Core.

UPDATE 2017: This article was a precursor to the book Entity Framework Core in Action that I am writing for Manning Publishing. Chapter 4 of the book covers business logic in detail.

The book also has a companion live site where the business logic in this article is used to provide an example checkout service. Have a go – no money needed:)

What is the Business Layer?

If you are new to idea of Business Logic, then I suggest you read the section near the top called ‘What is the Business Layer’ in my original article as it gives a good description. However, if you are in a hurry here is the short version.

Business Logic is what we call pieces of code that carry out operations that are specific to the job that the application is carrying out. For instance, on an e-commerce site like Amazon you would have Business Logic that handles a customer’s purchase: checking on availability and delivery, taking payments, sending an email confirmation etc. Business logic is definitely a step up on complexity over CRUD (Create, Read, Update and Delete) operations.

While Business Logic can be spread throughout an application and the database, it is accepted best practice to try and isolate the Business Logic into one place, which am calling the Business Layer.

Aside: While having all the business rules in the Business Layer is something we should always strive for I find that in practice some issues go outside the Business Layer for good reasons. For instance validation of data often flows up to the Presentation Layer so that the user gets early feedback. It may also flow down to the database, which checks data written to the database, to ensure database integrity.

Other business logic appears in the Presentation Layer, like not allowing users to buy unless they provide a credit card, but the status should be controlled by the Business Layer. Part of the skill is to decide whether what you are doing is justifiable or will come back and bite you later! I often get that wrong, but hopefully I learn from my mistakes.

The (improved) architecture of my Business Layer

Let me start with a diagram of the new, improved structure of the Business Logic within an application – in this case an ASP.NET web application but the same approach would work in many other application types.

BizLogic and BizDbAccess layer diagram - simplified

Next I will explain each of the main layers, with example code.

1. The DataLayer

The Data Layer is where the EF POCO (entity) classes are defined, along with the EF setup and DbContext. I am not going to describe how to create the EF entity classes or the setup of the DbContext as I will assume you already know about EF. For information on how to set up the database and EF entities see:

Note: I have shown the Data Layer as one assembly, but in bigger applications the EF entity classes are often in a different assembly to the EF setup code. In that case the BizLogic would only link to the EF entity classes.

2. The BizLogic Layer

I have written numerous applications, all of which has some quite complex Business Logic. Over the years I have tried different ways of organising these applications and I have come up with one key philosophy – that the “The Business Layer is King“, i.e. its design and needs drives everything else.

I am a big fan of Domain-Driven Design (DDD) and Eric Evans’ seminal book on DDD, Domain-Driven Design. Eric Evans’ book talks about how the business problem we are trying to solve, called the “Domain Model”, should be “the heart of the Software” (see Eric Evans book, page 4). Eric goes on to say “When the domain is complex, this is a difficult task, calling for the concentrated effort of talented ad skilled people”. Therefore, I try to make sure that the Business Logics data structures are defined by, and solely focused on, the business problem. How that data is stored and how that data is viewed are secondary issues.

Because of the way the EF works it is not sensible to combine the Business Logic with the actual EF POCO classes. Eric Evan’s recognises and discusses this problem in his book, see Eric Evans book, page 159. However, lots of people have tried to make EF more DDD-like.

NOTE: There is plenty of debate on how to apply DDD to EF. Personally I did try making the EF entity classes behave in a DDD way, but for me the amount of work for the gain in separation became too much (however, it is easier in EF Core though). In trying to go fully DDD with EF I felt I was losing the core tenant of Eric Evans book, which is that the business problem was the focus, not the technology. I recommend Jimmy Bogard comments near the end of this EF issue ‘The impedance mismatch between EF and domain models: what is Entity Framework’s answer?’, which I think are very helpful.

My rules for the Business Logic are:

a. The Business Logic data classes should not be affected by other higher layers

Business Logic can be hard to write. Therefore, I want to stay focused on business issue and not worry about how other layers above might need to reformat/adapt it. So, all data in or out of the Business Logic is either defined inside the BizLogic layer, or it is a Data Layer class. It is Service Layer’s job to act as an Adapter, i.e. Service Layer converts any data to/from what the Business Logic needs.

In new/modern databases the EF POCO (entity) classes should be a very good fit to the Business Logic needs, with a few extra properties needed to set up the database relationships, e.g. Foreign Keys. Therefore, the Business Logic can use the EF POCO (entity) classes directly without any adaption.

However, one of my reader, , pointed out that for for old/legacy databases the fit between what the database and what the Business Logic may not be a good match.

b. The Business Logic works on a set of in-memory data

I don’t want the Business Logic worrying about how to load and save data in the database. It is much simpler to design the Business Logic code to work on simple in-memory set of data. The changes described in this article improve this significantly over the previous article.

In the previous approach I used to have a specific part of the Business Logic, normally at the start, which loaded all of the data. However, the new approach has what I call a DbAccess class which handles all the reading and writing (see later for why this is better). The DbAccess is class is a Facade pattern.

Example Code

UPDATE: This article was a precursor to the book Entity Framework Core in Action that I am writing for Manning Publishing. There is a Git repo to go with the book, with the Chapter04 branch containing the code you see here. Chapter 4 of the book covers the whole area of business logic in detail.

Here is an example of Business Logic which is creating the first part of the order, which consists of an Order class instance with a number of LineItems. It is handed the customer’s Id, and list of LineItems which contain an entry for each BookId, followed by how many copies of that book the customer wants.

namespace BizLogic.Orders.Concrete
{
    public interface IPlaceOrderAction : IBizActionAsync<PlaceOrderInDto, Order> { }

    public class PlaceOrderAction : IPlaceOrderAction
    {
        private readonly IPlaceOrderDbAccess _dbAccess;
        private readonly List<string> _errors = new List<string>();

        public IImmutableList<string> Errors => _errors.ToImmutableList();

        public bool HasErrors => _errors.Any();

        public PlaceOrderAction(IPlaceOrderDbAccess dbAccess)
        {
            _dbAccess = dbAccess;
        }

        /// <summary>
        /// This validates the input and if OK creates an order 
        /// and calls the _dbAccess to add to orders
        /// </summary>
        /// <returns>returns an Order. Can be null if there are errors</return>;
        public async Task<Order> ActionAsync(PlaceOrderInDto dto)
        {
            if (!dto.TsAndCsAccepted)
            {
                _errors.Add("You must accept the T and Cs to place an order.");
                return null;
            }
            if (!dto.LineItems.Any())
            {
                _errors.Add("No items in your basket.");
                return null;
            }

            var booksDict = await 
                _dbAccess.FindBooksByIdsAsync(
                    dto.LineItems.Select(x => x.BookId))
                .ConfigureAwait(false);
            var order = new Order
            {
                CustomerName = dto.UserId,
                LineItems = FormLineItemsWithErrorChecking(
                     dto.LineItems, booksDict)
            };    

            _dbAccess.Add(order);

            return order;
        }

        private List<LineItem> FormLineItemsWithErrorChecking(
             IEnumerable<OrderLineItem> lineItems, 
             IDictionary<int,Book> booksDict)
        {
           // rest of the code left out…

A few things to point out about the above.

  1. We have created a generic IBizAction<Tin, Tout> interface that the PlaceOrderAction class has to implement. This is important as we use a generic BizRunner to run all the actions (described later in the Service Layer section). The IBizActionAsync interface ensures we have the Errors and HasErrors properties as well as the ActionAsync
  2. It uses the IPlaceOrderDbAccess provided via the constructor to a) find the books that were referred to in the order and b) to add the order to the EF db.Orders set.

NOTE: I also setup an interface, IPlaceOrderAction. I need this to get Dependency Injection (DI) to work. But I won’t be describing DI in this article. However, I really recommend the use of DI on any non-trivial application.

2. The BizDbAccess Layer (new)

The BizDbAccess layer contains a corresponding class for each BizLogic class that accesses the database. It is a very thin Facade over the EF calls. I should stress, I am not trying to hide the EF calls, or make a repository. I am just trying to Isolate the calls. Let me explain.

The problem with my previous approach, where the Business Logic called EF directly, wasn’t that it didn’t work, but that it was hard to remember where all the EF calls were. When it came to refactoring, especially when doing performance improvements, then I had to hunt around to check I had got every EF call. By simply moving all the EF calls into another class I have all of the EF commands in one place.

The main approach I will describe assumes that the database is of a new/modern design and there is a good fit between the data held in the database and the Business Logic requirements. The code below is a PlaceOrderDbAccess class, which goes with the PlaceOrderAction Business Logic described above:

public class PlaceOrderDbAccess : IPlaceOrderDbAccess
{
    private readonly EfCoreContext _db;

    public PlaceOrderDbAccess(EfCoreContext db)
    {
        _db = db;
    }

    /// <summary>
    /// This finds any books that fits the BookIds given to it
    /// </summary>
    /// <returns>A dictionary with the BookId as the key, and the Book as the value</returns>
    public async TaskIDictionary<int, Book>> FindBooksByIdsAsync(IEnumerable<int>; bookIds)
    {
        return await _db.Books.Where(x => bookIds.Contains(x.BookId))
            .ToDictionaryAsync(key => key.BookId)
            .ConfigureAwait(false);
    }

    public void Add(Order newOrder)
    {
        _db.Orders.Add(newOrder);
    }
}

Please note that I use a method called Add, to add the order to the EF Orders DbSet. As I said, I am not trying to hide what is going on, but just isolate the commands, so using the same terminology is helpful.

solution-view-bizlayer-bizdbaccessNOTE: To help with finding the relevant DbAccess I call the Business Logic class <name>Action and the DbAccess calls <name>DbAccess. They also have the same top-level directory name. See the attached picture of the solution directories on the right.

A secondary, but very powerful improvement, is that I could test my Business Logic without using an EF database. Because I provide an Interface then I can replace the DbAccess class with a Mock. Here is my Mock for PlaceOrderDbAccess.

public class MockPlaceOrderDbAccess : IPlaceOrderDbAccess
{
    public ImmutableList<Book> Books { get; private set; }

    public Order AddedOrder { get; private set; }
    public MockPlaceOrderDbAccess()
    {
        Books = CreateTestData.CreateBooks().ToImmutableList();
    }

    /// <summary>
    /// This finds any books that fits the BookIds given to it
    /// </summary>
    /// <param name="bookIds"></param>
    /// <returns>A dictionary with the BookId as the key, and the Book as the value</returns>
    public async Task<IDictionary<int, Book>> FindBooksByIdsAsync(
        IEnumerable<int> bookIds)
    {
        return Books.AsQueryable()
            .Where(x => bookIds.Contains(x.BookId))
            .ToDictionary(key => key.BookId);
    }

    public void Add(Order newOrder)
    {
        AddedOrder = newOrder;
    }
}

Being able to test all your Business Logic that way is very useful.

Handling old/legacy databases

One of my readers, , has been using a similar approach to the one describe in this article for some time. He pointed out in one of his comments that he finds this approach useful when the database tables are not a good fit for the Business Logic.

At that point, in addition to isolating the EF commands, the BizDbAccess class can carry out any adapting/ reformatting of the data between the database and the Business Logic.

For example I have come across database which does not mark its relationship keys and Foreign Keys. This stops EF from doing relational fixup, i.e. linking the various entities loaded. In that case you would add code to the DbAccess class to link the loaded entities so that the Business Logic can work on a linked, in-memory set of classes.

The Service Layer

As I said earlier in my applications the Service Layer is very important layer that links everything together. The Service Layer uses both the Command pattern and the Adapter pattern between the presentation layer and all the layers below.

Let me start by showing you one of what I call the BizRunner classes, which is the Command part of the code. This class contains the code to runs the PlaceOrderAction method above, which has a signature of taking data in and producing data out. It also needs data to be written to the database, hence the format/content of this BizRuner:

public class RunnerWriteDbAsync<TIn, TOut>
{
    private readonly IBizActionAsync<TIn, TOut> _actionClass;
    private readonly DbContext _db;

    public RunnerWriteDbAsync(IBizActionAsync<TIn, TOut> actionClass, DbContext db)
    {
        _db = db;
        _actionClass = actionClass;
    }

    public async Task<TOut> RunActionAsync(TIn dataIn)
    {
        var result = await _actionClass.ActionAsync(dataIn).ConfigureAwait(false);
        if (!_actionClass.HasErrors)
            await _db.SaveChangesAsync();

        return result;
    }
}

The RunnerWriteDbAsync BizRunner is a simple, but powerful class which, through the use of interfaces and generics can run different Business Logic.

One thing to note in this arrangement is that only the BizRunner’s RunActionAsync method should call the EF command SaveChangesAsync. That is important for a number of reasons:

  1. If any of the BizLogic/BizDbAccess methods called SaveChanges arbitrarily then you could get problems, with part of the data being saved while another part wasn’t. By having only the BizRunner calling SaveChanges it means that you know that all the Business Logic has finished.
  2. A subtler (and more advanced) reason is it allows us to chain together multiple Business Logic calls within a transaction (I describe this in the article ‘Architecture of Business Layer – Calling multiple business methods in one HTTP request’).
  3. The handling of errors is important, and you can see that the BizRunner will only call EF’s SaveChanges if there are no errors.
  4. EF6.x and EF Core differ on what happens when you call SaveChanges.
    1. In EF6.x it validates the data against any validation attributes, such as [MaxLength(20)], and against IValidatableObject if present. This means you will get a DbEntityValidationException exception if any validation error is found. The exception containing validation errors for every incorrect property or class that was new or updated.
    2. EF Core uses a lighter touch and does not validate the data, because in many cases the data has been already validated on input. Clearly that isn’t the case in business logic and you may want to add this feature back to EF Core by overriding SaveChanges and add validation – see this article for how to do that.

Note: The discarding of data by not calling ‘SaveChanges’ only works in situation where each call has its own DbContext. This is the case in a web application as each HTTP request gets a new DbContext. However, in Windows Applications etc. where the DbContext can be kept alive for longer you need to be careful about the lifetime/disposal of the DbContext.

Here is the code inside the Service Layer that uses the BizRunner to call PlaceOrderAction. You can see this acts as an Adapter pattern to the input and output of the Business Logic. The method:

  1. Gets current basket from a cookie and converts it into the format that the Business Logic needs.
  2. Calls the RunActionAction method from the PlaceOrderAction class.
  3. If RunActionAction method was successful it clears the basket, as the order now holds them.
  4. It extracts and returns the OrderId from the Order instance, as that is what the Presentation Layer needs.

The code is:

{
public class PlaceOrderService
{
    private readonly CheckoutCookie _checkoutCookie;
    private readonly EfCoreContext _db;

    public IImmutableList<string> Errors { get; private set; }

    public PlaceOrderService(IRequestCookieCollection cookiesIn, IResponseCookies cookiesOut, EfCoreContext db)
    {
        _db = db;
        _checkoutCookie = new CheckoutCookie(cookiesIn, cookiesOut);
    }

    /// <summary>
    /// This creates the order and, if successful clears the cookie
    /// </summary>
    /// <returns>Returns the OrderId, or zero if errors</returns>
    public async Task<int> PlaceOrderAsync(bool tsAndCsAccepted)
    {
        var checkoutService = new CheckoutCookieService(_checkoutCookie.GetValue());

        var action = new PlaceOrderAction(new PlaceOrderDbAccess(_db));
        var runner = new RunnerWriteDbAsync<PlaceOrderInDto, Order>(action, _db);

        var order = await runner.RunActionAsync(
            new PlaceOrderInDto(tsAndCsAccepted, 
            checkoutService.UserId, checkoutService.LineItems));
        Errors = action.Errors;

        if (action.HasErrors) return 0;

        //successful so clear the cookie line items
        checkoutService.ClearAllLineItems();
        _checkoutCookie.AddOrUpdateCookie(
             checkoutService.EncodeForCookie());

        return order.OrderId;
    }
}

You can see the PlaceOrderAction instance being created, along with its linked PlaceOrderDbAccess class. This would normally be done by Dependency Injection, but to make the example simpler to understand I have created it by hand.

You can see the adapter pattern in progress. On input the information is contained in a cookie, but the business layer shouldn’t have to deal with that. Therefore, the Service Layer method PlaceOrderAsync converts the cookie content into the form that the PlaceOrderAction wants.

Similarly, for the output of the PlaceOrderAction Business Logic, which returns an Order instance, but the presentation layer actually wants the OrderId, Again the PlaceOrderAsync method extracts the OrderId from the Order and returns that to the MVC Action.

Interestingly the pattern of returning a database class instance is quite typical, as the Business Logic won’t know what the entity’s key is, as it isn’t generated until the BizRunner in the Service Layer calls EF’s SaveChanges. It then becomes the job of the Service Layer to extract/adapt the parts that are needed by the Presentation Layer.

NOTE: The BizRunner and the code above works fine, but is simpler than what I use in practice. I have a private library called GenericActions, which has the same sort of features as my GenericServices library. This can identify the right signature of BizRunner to call by unpacking the Interface parts. GenericActions also uses the same ISuccessOrErrors<T> class as GenericServices, which returns both the status/errors and the class being returned. It also has the AutoMapper mapping built in to do any needed adapting.

Presentation layer

For completeness I have included the presentation layer, which in this case is an ASP.NET Core MVC page. If the order is successful it redirects to the confirmation page, otherwise it copies the errors into the ModelState and shows the basket again, with the error message(s).

public async Task<IActionResult> PlaceOrder(bool iAcceptTAndCs)
{
    var service = new PlaceOrderService(
        HttpContext.Request.Cookies, 
        HttpContext.Response.Cookies, _db);
    var orderId = await service.PlaceOrderAsync(iAcceptTAndCs);

    if (!service.Errors.Any())
        return RedirectToAction("ConfirmOrder", "Orders", 
             new { orderId});

    //Otherwise errors, so copy over and redisplay
    foreach (var error in service.Errors)
    {
        ModelState.AddModelError("", error);
    }
    var listService = new CheckoutListService(_db, 
        HttpContext.Request.Cookies);
    return View(listService.GetCheckoutList());
}

I try to keep my Controller Actions small and simple by putting as much logic as possible into the Service Layer or lower layers. I do this because it is hard to test Controller actions, but much easier to Unit Test the Service Layer or lower layers.

Quick aside – handling errors needs thought.

This article is already quite long, so I won’t dwell on this subject, but providing good error feedback to the user is not trivial. Many libraries use Exceptions to report errors – some of these errors are user friendly, like Validation Errors. However, many Exceptions, such as EF’s DbUpdateException, are very unfriendly and sometimes produce messages that relate to the inner working for the application, and hence should not be shown for security reasons.

In this article I have implemented a very simple error feedback mechanism. In practice you would need to implement a much more comprehensive approach. I suggest you look at my open-source library, GenericServices, with uses ISuccessOrErrors<T> to return a result with possible error messages.

Conclusion

Writing CRUD (Create, Read, Update and Delete) methods used to take a lot of (boring) development writing boiler-plate code. I solved that by writing a library called GenericServices, which has radically improves the speed of development (see this site CRUD-only site, which I wrote in about 10 days).

I now find I spend most of my development effort on a) the Business Logic and b) building responsive user-interfaces. Therefore, continuing to improve my approach to Business Logic writing is well worth the effort, which is what this article is all about. (See a different article on how I am improving my building responsive user-interfaces too).

I recently reviewed an e-commerce application I developed which used the older approach to writing Business Logic, i.e. EF called in the Business Logic. The Business Logic was quite complex, especially around pricing and delivery. My conclusion was that the Business Logic work fine, but it was sometimes difficult to find and understand the database accesses when refactoring and performance tuning the Business Logic.

I tried out the new approach described in this article by refactoring some of the Business Logic in this e-commerce application over to the new approach. Overall it did make a difference by bring all the EF access code into one clear group per Business Logic case.

In additions I wrote an example application using ASP.NET Core and EF Core with this new approach, again with good results (this is where the example code comes from in this article). Couple this with the other positive feature of being able to test the Business Logic with a Mock DbAccess class rather than accessing EF directly and it is clearly a good step forward.

The only down side to this new approach is that does need one more class than the previous approach. However, that class does provide a much better separation of concerns. Therefore, I think this new approach is a positive improvement to my previous approach to building Business Logic and is worth the effort extra effort of another class.

I hope this article gives you some ideas on how to better design and build complex Business Logic in your applications.

Flattening Entity Framework relationships with ExpressMapper

This article looks at how Microsoft’s Entity Framework (EF) handles extracting data from nested relationships, i.e. linked EF classes. This is a process known as flattening. I will show that EF produces efficient SQL commands for this type of access.

I then talk about making development of such accesses by using an Object-to-Object mapper and introduce the new Flattening feature in ExpressMapper, a relatively new mapper that is quite lean and quick. The last part is a detailed description, with lots of examples, of how to use flattening in ExpressMapper and what is is capable of.

Quick background on flattening

If you understand the issues around relational databases, foreign keys and flattening then skip this.

Well constructed relational databases try to minimise the duplication of data, e.g. only have one copy of the customer details. They do this by creating extra tables to hold each of these unique pieces of data and using ‘foreign keys’ if another piece of data needs to refer to it. Let me give you an example.

In the diagram below, which shows the EF classes, a product has a ‘Variant’, which is linked by the foreign key ‘VariantId’ and that Variant can have a number of size information, given by the ‘Size’, which has a foreign key ‘SizeId’.

Product, ProductVariant, Size

The benefit of splitting the data like this is we can change say the Color information on a specific  ProductVariant and it is automatically picked up by all the products that point to it. In software terms we would call this an application of the DRY (Don’t Repeat Yourself) principal.

The down side if if we want to show a list of products on screen its quite likely that we want to include the Color and Size information. This means we need to ‘pick out’ the Color column from the linked ‘ProductVariant’ table, and the Name column in the Variant’s linked ‘Size’ table. This is called flattening and happens a lot in real-life applications.

How Entity Framework does flattening

I’m going to use a very simple example to show how EF does flattening. The diagram below shows a simple Father->Son->Grandson nested relationship.

ExpressMapper-father-son-grandson

The EF command to get all of the properties from all of the linked records would be:

var flattened = dbContext.Fathers.Select(f => new { 
    f.MyInt,
    f.MyString, 
    SonMyInt = f.Son.MyInt,
    SonMyString = f.Son.MyString, 
    GrandsonMyInt = f.Son.Grandson.MyInt,
    GrandsonMyString = f.Son.Grandson.MyString});

The result is an anonymous class containing all the data from all the relationship.

The T-SQL EF produces for this access

It turns out that EF produces the very efficient T-SQL command for this sort of flattening. The T-SQL command that EF puts out is as follows:

SELECT 
    [Extent1].[MyInt] AS [MyInt], 
    [Extent2].[MyInt] AS [MyInt1], 
    [Extent2].[MyString] AS [MyString], 
    [Extent3].[MyInt] AS [MyInt2], 
    [Extent3].[MyString] AS [MyString1], 
    [Extent1].[MyString] AS [MyString2]
    FROM   [dbo].[Father] AS [Extent1]
    INNER JOIN [dbo].[Son] AS [Extent2] 
        ON [Extent1].[Son_Id] = [Extent2].[Id]
    LEFT OUTER JOIN [dbo].[Grandson] AS [Extent3] 
        ON [Extent2].[Grandson_Id] = [Extent3].[Id]

This is an optimal T-SQL command and here is execution plan that proves it.

ExpressMapper-father-son-grandson-sqlNOTE: Flattening is only useful for reading data, as any update to a column in a related table can only be done in EF by reading in the inner class, updating its data and calling EF’s .SaveChanges(). However reading data for display is very common and therefore we want it to be really efficient.

Using an Object-to-Object Mapper with EF

My experience has been that you spend a LOT of time writing EF .Select() statements like the one above in even a small application. They are repetitive and boring, which often leads to making mistakes. This caused me to build the open-source package GenericServices, which uses an object-to-object mapper  (currently AutoMapper, but l am planning to change to ExpressMapper).

The idea behind using an object-to-object mapper (referred to as mapper from now on) is that you create a class, or classes, which represent what you need from the database. These classes are often called DTOs (Data Transfer Objects), or in ASP.NET MVC style ViewModels – I will refer to them as DTOs in this article.

The role of the mapper is to work out how to map the EF classes to the DTO(s). The mapper does this based on a set of rules. Let’s consider one implementation of an object-to-object mapper that will work with EF – ExpressMapper.

Introducing ExpressMapper

As I said earlier I have used AutoMapper, but I have been tracking the development of a new mapper called ExpressMapper, which has some advantages. ExpressMapper is newer than AutoMapper and relies on building LINQ commands for all its mappings. The main advantage is that its setup is much faster than AutoMapper, and one or two other small things make it fit with EF more easily.

However while I wanted to start using ExpressMapper but it didn’t have the flattening feature. So, with the help of ExpressMapper’s author, Yuriy Anisimov, I added the flattening feature. This is now released in version 1.8.1 which you can find as a Nuget package here.

I will give you three examples of how ExpressMapper’s flattening works and then describe the differences between ExpressMapper and AutoMapper, which is used more widely because it has been around a long time.

1. Flattening nested references

1.a. Non-null nested references

In the case of flattening ExpressMapper and AutoMapper uses a concatenated name rule,  e.g. SonMyInt would match Son.MyInt and SonGrandsonMyString  would match Son.Grandson.MyString. So, looking at our previous example of Father->Son->Grandson if we created a class as shown below and then ran the code at the end then ExpressMapper would produce the same sort of result, and SQL that my hand-coded select would have done.

public class FlattenDto
{
   public int MyInt { get; set; }
   public string MyString { get; set; }

   public int SonMyInt { get; set; }
   public string SonMyString { get; set; }

   public int SonGrandsonMyInt { get; set; }
   public string SonGrandsonMyString { get; set; }
}

...
//code to set up the mapping 
ExpressMapper.Mapper.Register<Father, FlattenDto>().Flatten();
ExpressMapper.Mapper.Compile(CompilationTypes.Source);
//code to execute the EF command and get the data
var flattened = dbContext.Fathers.Project<Father, FlattenDto>().ToList(); 

The SQL code is the same as I showed on the hand-written code.

1.b. Nullable nested references

If either the Son or the Grandson can be null then these relationships are called an ‘optional relationship’ in EF, or One-to-ZeroOrOne references in SQL. ExpressMapper handles null references, so handling null references mapping is fairly straight forward. However you do need to think that some of the properties may be null if a nested relationship is null, otherwise EF will complain that it can put a null in it.

For example if we take our previous example and we make the GrandSon an optional relationship with the following EF configuration (see the HasOptional on line 7) then the Grandson property may be null.

public class SonConfiguration 
   : EntityTypeConfiguration<Son>
{
   public SonConfiguration()
   {
      ToTable("Son");
      HasKey(t => t.Id);
      HasOptional(t => t.Grandson);
   }
}

Having done that ExpressMapper will handle not having a grandson (or a son if you set that as optional too). However we do need to change the DTO, as now the the SonGrandsonMyInt can now be null (see the int? on line 13 of this class definition from the Unit Tests of ExpressMapper).

//code to set up the mapping 
ExpressMapper.Mapper
    .Register<Father, FlattenDtoWithNullable>().Flatten();
ExpressMapper.Mapper.Compile(CompilationTypes.Source);
//code to execute the EF command and get the data
var flattened = dbContext.Fathers
    .Project<Father,FlattenDtoWithNullable>().ToList(); 

The result of this will be that SonGrandsonMyInt and SonGrandsonMyString will both be null if no Grandson relationship exists, but will contain the Grandson’s MyInt and MyString if the relationship does not exist.

NOTE: AutoMapper does not have this feature by default – see later section of differences between ExpressMapper and AutoMapper.

2. Running Linq commands on collections

As well as accessing columns in one-to-one relationships there is another feature that EF and ExpressMapper’s flattening can provide for one-to-many relationships. That is it can convert a small but useful number of LINQ collection commands into SQL commands. The commands that EF supports are:

  • Any: This returns true if there is one or more rows in the collection.
  • Count: This returns an int of the number of rows in the collection.
  • LongCount: Same as Count, but returns a long (64 bit) type.

To use these you simply add the method name on the end of a EF POCO class properly that implements IEnumerable (but not a string). Let me give you a very simple example. The code below shows a EF POCO class called FatherSons which contains a collection called Sons. The DTO below it has a property called SonsCount, which ExpressMapper turns into the LINQ command Sons.Count().

public class FatherSons
{
   public int Id { get; set; }

   public int MyInt { get; set; }
   public string MyString { get; set; }

   public ICollection<Son> Sons { get; set; }
}
public class FlattenFatherSonsCountDto
{
   public int MyInt { get; set; }
   public string MyString { get; set; }

   public int SonsCount { get; set; }
}

...
//code to set up the mapping 
ExpressMapper.Mapper
    .Register<FatherSons, FlattenFatherSonsCountDto>().Flatten();
ExpressMapper.Mapper.Compile(CompilationTypes.Source);
//code to execute the EF command and get the data
var flattened = dbContext.Fathers
    .Project<FatherSons, FlattenFatherSonsCountDto>().ToList(); 

The resulting SQL is again very good (see below):

 
SELECT 
    [Extent1].[Id] AS [Id], 
    (SELECT 
        COUNT(1) AS [A1]
        FROM [dbo].[Son] AS [Extent2]
        WHERE [Extent1].[Id] = [Extent2].[FatherSons_Id]) AS [C1], 
    [Extent1].[MyInt] AS [MyInt], 
    [Extent1].[MyString] AS [MyString]
    FROM [dbo].[FatherSons] AS [Extent1]

Full list of ExpressMapper flattening features

  1. Flattening looks at properties inside classes, either EF relationships or EF Complex Types. For the DTO to match it must match on two counts:
    1. The Name must match: The DTO name should be  a concatenating the names without the dots, e.g. Variant.Size.Name is accessed by having a property called VariantSizeName in the DTO.
    2. The Type must match: The type of the DTO property must be either:
      1. The same type as the inner property, e.g. SonMyInt can be of type int.
      2. A nullable version of type of the inner property, e.g. SonGrandsonMyInt is of type Nullable<int>. This is useful when you have optional relationships (see 3 below).
      3. A class that has been registered with ExpressMapper as mapping between the inner property and the DTO class (see 2 below for a better explanation!)
  2. You can have nested DTOs within DTOs. For instance in our Father->Son->Grandson examples you can have a property called SonGrandson, which is a DTO class. The only rule is you must also register the SonGrandson DTO class as well as the outer DTO. There is an example of this in the ExpressMapper Unit Tests – see test on line 95 of FlattenEntityTests.cs and note the registering of the extra DTO class on line 27 of the same file.
  3. Flattening can handle optional relationships, i.e. null references to a class. In that case it will return null for all the properties you access in the nested class(es). See example 1.b above.
  4. Flattening can handle circular references, e.g. if class person has a property called Boss of type person then you can have a DTO property such as BossBossBoss to get the third boss up (or null if the hierarchy doesn’t go that high).
  5. Flattening also looks at collection properties, i.e. one-to-many relationships.  If the DTO contains a property with the name of the collection, plus an ending of Any, Count or LongCount then it adds the LINQ method of that name. The property must be of the right type, i.e. bool, int or long respectively otherwise you get an ExpressMapperException.

Configuring flattening in ExpressMapper

  • Flattening only happens if you add the method .Flatten() to the registration of the mapping.
  • If you use ExpressMapper’s .Ignore() or .Member() methods in registration with Flattening then your .Ignore() or .Member() calls will take over from flattening, i.e. Flattening will not touch those source properties. The order in which you apply .Flatten, .Ignore and .Member does not matter.
  • The comparison of names follows ExpressMapper configuration, which defaults is case insensitive. For instance in the Variant.Size.Name case the match would work with variantsizename or any other upper/lower sequence of the same letters.
    NOTE: this is useful when you are mapping to json in an API and you often want the first letter of the property to be lower case.
  • Flattening only works on the Source side, i.e. it does not ‘unflatten’ if you do a DTO->class mapping. I did not implement this because it just doesn’t work with EF, which needs you to link to tracked classes. It is also very difficult to get right in all cases, e.g. what do you do with a Count??
    NOTE: That is why you see the command ExpressMapper.Mapper.Compile(CompilationTypes.Source) in the examples. Because I only use the class->DTO direction then I don’t bother to compile the reverse direction.

Comparison with AutoMapper

Many people, including me, are already using AutoMapper, so it is useful to point out the differences.

  1. AutoMapper is a bit more specific on matching the concatenated names, and has some extra features for changing this. My implementation in ExpressMapper simply matches the next part of the string as it goes, plus the type. I think AutoMapper would not accept vAriantsiZenAme but ExpressMapper’s Flatten will.
  2. AutoMapper has other flattening features, like GetMethod. ExpressMapper’s Flattening does not support that as they don’t work with EF.
    NOTE:  The way to overcome this is either by specific .Member() set ups or more elegantly by using DelegateDecompiler. DelegateDecompiler allows you to add ‘computed’ properties which DelegateDecompiler can turn into LINQ code that EF can execute. I use DelegateDecompiler in my GenericServices package and it is very helpful. You should look it up!
  3. AutoMapper does not, by default, handle nullable nested references (see example 1.b above). This is a plus for ExpressMapper.

Conclusion

I wanted to get three main things across in this article:

  1. Entity Framework is creates very efficient SQL code when accessing nested One-to-One or One-to-ZeroOrOne relationships.
  2. Object-to-Object mappers can make your life a lot easier in writing EF POCO classes to DTO code.
  3. ExpressMapper now has a good Flattening feature.

When I have the time (!) I plan to update my GenericServices project to use ExpressMapper. It has some nice features and my very simple testing says its about eight times faster on setup, i.e. the registering of the mappings, than AutoMapper. However ExpressMapper is about the same speed as AutoMapper on the actual mapping of the data.

I should say that EF doesn’t do so well at producing efficient T-SQL commands if you use EF’s .Include() method to eager load properties that are collections. I would like to write about that later if I have time, as there are definitely some things to watch out for in cases like that.

Well done for getting to here. The article is a bit long but hopefully useful.

Happy coding!

Handling Entity Framework database migrations in production – part 4, release of EfSchemaCompare

Some months ago I wrote an article on Simple-Talk ‘Deploying an Entity Framework Database into Production‘, with a more detailed series on this blog site (see list below). I have finally found time to release the package I mentioned in the articles as an open-source (MIT) project and also on NuGet as EfSchemaCompare.EF6.

In this article I explain where EfSchemaCompare is useful and give three real-world users of the package in my own e-commerce application development. The whole series deals with the issues of creating/updating a database schema in a robust and testable way. The list of articles are:

Where would you find EfSchemaCompare useful

The main reason to use EfSchemaCompare is if you use EF with a database but also want to:

  1. Take over the creation, definition or migration of the database rather than having EF handle it.
  2. You want to build a EF model that works with an existing database.

If you work this way then you have a problem: you need to make sure any changes to the database are matched by changes to the EF classes/configuration, or vise-versa. For instance, if you added a foreign key relationship in your database, or a new column to a table then you need to make the correct changes to the EF classes/configuration.

The important thing to see is that, other than when using EF reverse engineering (see next paragraph), EF never looks at the schema of the database, even when it is doing migrations. It just assumes the database in the state that EF’s metadata model says it is. (For more on how this works under the hood then see Max Vasilyev’s interesting article about how EF migrations are calculated and stored).

So one way to overcome any mismatch between EF and SQL is to re-import the database via EF’s reverse engineer the database option. However I have found that to be a rather blunt instrument as it adds all the possible relationships and also has fixed names for properties.

However now EfSchemaCompare gives you a second option. EfSchemaCompare is designed to check that EF’s view of the database is in line with what SQL says the database schema is. You can use it in your Unit Tests, or in your deployment scripts. It returns a go/no-go response with useful error/warning messages to spot what is wrong. It can also help you work out how to set up SQL tables to handle some of the more complex EF table relationships, like Many-to-Many tables (see example 3 near the end).

Let me give you three examples of how I use EfSchemaCompare.

Examples of how I use EfSchemaCompare

I was building an e-commerce web site on Azure and I wasn’t comfortable that EF’s Code First Migrations approach was robust enough in my situation (read the first article for more details on why). I therefore swapped over to using DbUp, which is a script-based database deployment/update tool.

However it turns out matching a database change to the form that EF needs is quite complex! I therefore wrote EfSchemaCompare . I will detail three different ways I use EfSchemaCompare in my Unit Tests and deployment tests.

1. General checking in Unit Tests

The NUnit Test below is part of my standard tests and it simply checks that EF’s current model of the database matches the database my Unit Test has.

[Test]
public void CompareEfSqlTestLabelAppDbOk()
{
    using (var db = new MyDbContext())
    {
        var comparer = new CompareEfSql();
        var status = comparer.CompareEfWithDb(db);

        Assert.True(status.IsValid, status.GetErrors());
        Assert.False(status.HasWarnings, string.Join("\n", status.Warnings));
    }
}

The method it uses, CompareEfSql.CompareEfWithDb, is fairly quick, assuming the MyDbContext has been used in the Unit Tests, and finds 90% of the issues that can occur. It also gives error messages that talk about the EF classes rather than in SQL terms, so its easier for software developers to understand.

Note: The above version assumes the EF POCO classes are in the same assembly as the EF DbContext. There is another version if you have your EF POCO classes in a different  assembly to the EF DbContext (I do!). There is also another version if you have multiple DbContexts covering one database.

2. Detailed checking before I release to production

The NUnit Test below checks my production SQL database against the development database. The SQL to SQL checking is more thorough, but only checks things that EF cares about (see list of limitations).

[Test]
public void CompareLabelAppDbWithAzureTestLabelAppDbOk()
{
    using (var db = new MyDbContext())
    {
        var comparer = new CompareSqlSql();
        var status = comparer.CompareSqlToSql( "LabelAppDb", "AzureLabelAppDb");

        Assert.True(status.IsValid, status.GetErrors());
        Assert.False(status.HasWarnings, string.Join("\n", status.Warnings));
    }
}

The method it uses, CompareSqlSql.CompareSqlToSql which is very quick. It takes either a connection string name, or a full connection string. The error messages are all about SQL, so can be harder to interpret if your aren’t use to SQL, but its comprehensive.

Note that I use the more strict version where my Unit Test fails on warnings (see line 10 test).  Warning are things that should not affect EF, but are worth knowing about. You may not want to be so strict, but in my application I am. See the documentation on the difference between errors and warning.

3. When I am making complex changes to the database

Some changes are really easy, like adding a new column to a table. However when you change relationships it can be a challenge to get the SQL and EF classes in step. There is another command, CompareEfGeneratedSqlToSql, which is very useful.

This generates a brand new database using EF, with the name of the DbContext but with ‘.EfGenerated’ appended on the end. It then compares this with your current database using SQL-to-SQL testing.

[Test]
public void CompareLabelAppDbWithAzureTestLabelAppDbOk()
{
    using (var db = new MyDbContext())
    {
        var comparer = new CompareSqlSql();
        var status = comparer.CompareEfGeneratedSqlToSql(db, "LabelAppDb");

        Assert.True(status.IsValid, status.GetErrors());
        Assert.False(status.HasWarnings, string.Join("\n", status.Warnings));
    }
}

The method it uses, CompareSqlSql.CompareEfGeneratedSqlToSql, is quite slow because it gets EF to create a new database. However it does provide a way to a) compare exactly what EF would have done against what your SQL database looks like and b) you get a EF generated database from your current EF classes/configuration to inspect yourself.

Some things, like EF’s Many-to-Many relationships, require the SQL database to have a new table with a specific name and setup. Failing to follow the style that EF needs will mean EF won’t fill in the many-to-many links. Adding the right tables is often a case of seeing what EF does by inspecting the ‘.EfGenerated’ database and copying the SQL EF generated into your new database (or implementing your own many-to-many handling of course, which I cover in Part 2 of this article).

The other thing I noticed is that EF is somewhat forgiving if you make mistakes when you use EF’s Data Annotations or EF’s Fluent API to alter the way the database works. For instance if you configure a required to optional relationship but give it a non-nullable foreign key it will add its own hidden nullable foreign key. In fact I found about three errors like this in my 22-table application, all around relationships.

EfSchemaCompare will point out these discrepancies and you can fix them. Your minor configuration mistakes can be found mainly by the column names, which normally contain the name with a _ in the middle. I have also found places where the Cascade delete option was different, again through me misconfiguring a relationship.

Conclusion

Hopefully this article will give you a feel for why you might find the NuGet package EfSchemaCompare.EF6 useful, plus some tips on how to use it in your application. Its certainly a niche tool, but if you are using EF on a database that is not controlled by EF then its a life-saver. Do have a look at the project on GitHub for documentation and Unit Tests that might give you more ideas on how to use it.

Finally I wanted to thank and Hakon Thomas who field tested the first version. Their feedback has shaped the tool and hopefully made the documentation a bit clearer too.

Happy coding.


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