Posts Tagged “ef core”

Wednesday, December 1, 2021
  A Tour of myPrayerJournal v3: The Data Store

NOTE: This is the fourth post in a series; see the introduction for information on requirements and links to other posts in the series.

myPrayerJournal v1 used PostgreSQL with Entity Framework Core for its backing store (which had a stop on the v1 tour). v2 used RavenDB, and while I didn't write a tour of it, you can see the data access logic if you'd like. Let's take a look at the technology we used for v3.

About LiteDB

LiteDB is a single-file, in-process database, similar to SQLite. It uses a document model for its data store, storing Plain Old CLR Objects (POCOs) as Binary JSON (BSON) documents in its file. It supports cross-collection references, customizable mappings, different access modes, and transactions. It allows documents to be queried via LINQ syntax or via its own SQL-like language.

As I mentioned in the introduction, I picked it up for another project, and really enjoyed the experience. Its configuration could not be easier – the connection string is literally a path and file name – and it had good performance as well. The way it locks its database file, I can copy it while the application is up, which is great for backups. It was definitely a good choice for this project.

The Domain Model

When I converted from PostgreSQL to RavenDB, the data structure ended up with one document per request; the history log and notes were stored as F# lists (arrays in JSON) within that single document. RavenDB supports indexes which can hold calculated values, so I had made an index that had the latest request text, and the latest time an action was taken on a request. When v2 displayed any list of requests, I queried the index, and got the calculated fields for free.

The model for v3 is very similar.

/// Request is the identifying record for a prayer request
[<CLIMutable; NoComparison; NoEquality>]
type Request = {
  /// The ID of the request
  id           : RequestId
  /// The time this request was initially entered
  enteredOn    : Instant
  /// The ID of the user to whom this request belongs ("sub" from the JWT)
  userId       : UserId
  /// The time at which this request should reappear in the user's journal by manual user choice
  snoozedUntil : Instant
  /// The time at which this request should reappear in the user's journal by recurrence
  showAfter    : Instant
  /// The type of recurrence for this request
  recurType    : Recurrence
  /// How many of the recurrence intervals should occur between appearances in the journal
  recurCount   : int16
  /// The history entries for this request
  history      : History list
  /// The notes for this request
  notes        : Note list

A few notes would probably be good here:

  • The CLIMutable attribute allows this non-nullable record type to be null, and generates a zero-argument constructor that reflection-based processes can use to create an instance. Both of these are needed to interface with a C#-oriented data layer.
  • By default, F# creates comparison and equality implementations for record types. This type, though, is a simple data transfer object, so the NoEquality and NoComparison attributes prevent these from being generated.
  • Though not shown here, History has an “as-of” date/time, an action that was taken, and an optional request text field; Note has the same thing, minus the action but requiring the text field.

Customizing the POCO Mapping

If you look at the fields in the Request type above, you'll spot exactly one primitive data type (int16). Instant comes from NodaTime, but the remainder are custom types. These are POCOs, but not your typical POCOs; by tweaking the mappings, we can get a much more efficient BSON representation.

Discriminated Unions

F# supports discriminated unions (DUs), which can be used in different ways to construct a domain model in such a way that an invalid state cannot be represented (TL;DR - “make invalid states unrepresentable”). One way of doing this is via the single-case DU:

/// The identifier of a user (the "sub" part of the JWT)
type UserId =
  | UserId of string

Requests are associated with the user, via the sub field in the JWT received from Auth0. That field is a string; but, in the handler that retrieves this from the Authorization header, it is returned as UserId [sub-value]. In this way, that string cannot be confused with any other string (such as a note, or a prayer request).

Another way DUs can be used is to generate enum-like types, where each item is its own type:

/// How frequently a request should reappear after it is marked "Prayed"
type Recurrence =
  | Immediate
  | Hours
  | Days
  | Weeks

Here, these four values will refer to a recurrence, and it will take no others. This barely scratches the surface on DUs, but it should give you enough familiarity with them so that the rest of this makes sense.

For the F#-fluent - you may be asking “Why didn't he define this with Hours of int16, Days of int16, etc. instead of putting the number in Request separate from the type?” The answer is a combination of evolution – this is the way it worked in v1 – and convenience. I very well could have done it that way, and probably should at some point.

Converting These Types in myPrayerJournal v2

F# does an excellent job of transparently representing DUs, Option types, and others to F# code, while their underlying implementation is a CLR type; however, when they are serialized using traditional reflection-based serializers, the normally-transparent properties appear in the output. RavenDB (and Giraffe, when v1 was developed) uses JSON.NET for its serialization, so it was easy to write a converter for the UserId type:

/// JSON converter for user IDs
type UserIdJsonConverter () =
  inherit JsonConverter<UserId> ()
  override __.WriteJson(writer : JsonWriter, value : UserId, _ : JsonSerializer) =
    (UserId.toString >> writer.WriteValue) value
  override __.ReadJson(reader: JsonReader, _ : Type, _ : UserId, _ : bool, _ : JsonSerializer) =
    (string >> UserId) reader.Value

Without this converter, a property “x”, with a user ID value of “abc”, would be serialized as:

{ "x": { "Case": "UserId", "Value": "abc" } }

With this converter, though, the same structure would be:

{ "x": "abc" }

For a database where you are querying on a value, or a JSON-consuming front end web framework, the latter is definitely what you want.

Converting These Types in myPrayerJournal v3

With all of the above being said – LiteDB does not use JSON.NET; it uses its own custom BsonMapper class. This means that the conversions for these types would need to change. LiteDB does support creating mappings for custom types, though, so this task looked to be a simple conversion task. As I got into it, though, I realized that nearly every field I was using needed some type of conversion. So, rather than create converters for each different type, I created one for the document as a whole.

It was surprisingly straightforward, once I figured out the types! Here are the functions to convert the request type to its BSON equivalent, and back:

/// Map a request to its BSON representation
let requestToBson req : BsonValue =
  let doc = BsonDocument ()
  doc["_id"]          <- RequestId.toString
  doc["enteredOn"]    <- req.enteredOn.ToUnixTimeMilliseconds ()
  doc["userId"]       <- UserId.toString req.userId
  doc["snoozedUntil"] <- req.snoozedUntil.ToUnixTimeMilliseconds ()
  doc["showAfter"]    <- req.showAfter.ToUnixTimeMilliseconds ()
  doc["recurType"]    <- Recurrence.toString req.recurType
  doc["recurCount"]   <- BsonValue req.recurCount
  doc["history"]      <- BsonArray (req.history |> historyToBson |> Seq.ofList)
  doc["notes"]        <- BsonArray (req.notes   |> noteToBson    |> Seq.ofList)
  upcast doc
/// Map a BSON document to a request
let requestFromBson (doc : BsonValue) =
  { id           = RequestId.ofString doc["_id"].AsString
    enteredOn    = Instant.FromUnixTimeMilliseconds doc["enteredOn"].AsInt64
    userId       = UserId doc["userId"].AsString
    snoozedUntil = Instant.FromUnixTimeMilliseconds doc["snoozedUntil"].AsInt64
    showAfter    = Instant.FromUnixTimeMilliseconds doc["showAfter"].AsInt64
    recurType    = Recurrence.ofString doc["recurType"].AsString
    recurCount   = int16 doc["recurCount"].AsInt32
    history      = doc["history"].AsArray |> historyFromBson |> List.ofSeq
    notes        = doc["notes"].AsArray   |> noteFromBson    |> List.ofSeq

Each of these round-trips as the same value; line 6 (doc["userId"]) stores the string representation of the user ID, while line 19 (userId =) creates a strongly-typed UserId from the string stored in database.

The downside to this technique is that LINQ won't work; passing a UserId would look for the default serialized version, not the simplified string version. This is not a show-stopper, though, especially for such a small application as this. If I had wanted to use LINQ for queries, I would have written several type-specific converters instead.

Querying the Data

In v2, there were two different types; Request was what was stored in the database, and JournalRequest was the type that included the calculated fields included in the index. This conversion came into the application; ofRequestFull is a function that performs the calculations, and returns an item which has full history and notes, while ofRequestLite does the same thing without the history and notes lists.

With that knowledge, here is the function that retrieves the user's current journal:

/// Retrieve the user's current journal
let journalByUserId userId (db : LiteDatabase) = backgroundTask {
  let! jrnl = db.requests.Find (Query.EQ ("userId", UserId.toString userId)) |> toListAsync
    |> JournalRequest.ofRequestLite
    |> Seq.filter (fun it -> it.lastStatus <> Answered)
    |> Seq.sortBy (fun it -> it.asOf)
    |> List.ofSeq

Line 3 contains the LiteDB query; when it is done, jrnl has the type System.Collections.Generic.List<Request>. This “list” is different than an F# list; it is a concrete, doubly-linked list. F# lists are immutable, recursive item/tail pairs, so F# views the former as a form of sequence (as it extends IEnumerable<T>). Thus, the Seq module calls in the return statement are the appropriate ones to use. They execute lazily, so filters should appear as early as possible; this reduces the number of latter transformations that may need to occur.

Looking at this example, if we were to sort first, the entire sequence would need to be sorted. Then, when we filter out the requests that are answered, we would remove items from that sequence. With sorting last, we only have to address the full sequence once, and we are sorting a (theoretically) smaller number of items. Conversely, we do have to run the map on the original sequence, as lastStatus is one of the calculated fields in the object created by ofRequestLite. Sometimes you can filter early, sometimes you cannot.

(Is this micro-optimizing? Maybe; but, in my experience, taking a few minutes to think through collection pipeline ordering is a lot easier than trying to figure out why (or where) one starts to bog down. Following good design principles isn't premature optimization, IMO.)

Getting a Database Connection

The example in the previous section has a final parameter of (db: LiteDatabase). As Giraffe sits atop ASP.NET Core, myPrayerJournal uses the traditional dependency injection (DI) container. Here is how it is configured:

/// Configure dependency injection
let services (bldr : WebApplicationBuilder) =
  // ...
  let db = new LiteDatabase (bldr.Configuration.GetConnectionString "db")
  Data.Startup.ensureDb db
    // ...
    .AddSingleton<LiteDatabase> db
  |> ignore
  // ...

The connection string comes from appsettings.json. Data.Startup.ensureDb makes sure that requests are indexed by user ID, as that is the parameter by which request lists are queried; this also registers the converter functions discussed above. LiteDB has an option to open the file for shared access or exclusive access; this implementation opens it for exclusive access, so we can register that connection as a singleton. (LiteDB handles concurrent queries itself.)

Getting the database instance out of DI is, again, a standard Giraffe technique:

/// Get the LiteDB database
let db (ctx : HttpContext) = ctx.GetService<LiteDatabase> ()

This can be called in any request handler; here is the handler that displays the journal cards:

// GET /components/journal-items
let journalItems : HttpHandler =
  requiresAuthentication Error.notAuthorized
  >=> fun next ctx -> backgroundTask {
    let  now   = now ctx
    let! jrnl  = Data.journalByUserId (userId ctx) (db ctx)
    let  shown = jrnl |> List.filter (fun it -> now > it.snoozedUntil && now > it.showAfter)
    return! renderComponent [ Views.Journal.journalItems now shown ] next ctx

Making LiteDB Async

I found it curious that LiteDB's data access methods do not have async equivalents (ones that would return Task<T> instead of just T). My supposition is that this is a case of YAGNI. LiteDB maintains a log file, and makes writes to that first; then, when it's not busy, it synchronizes the log to the file it uses for its database. However, I wanted to control when that occurs, and the rest of the request/function pipelines are async, so I set about making async wrappers for the applicable function calls.

Here are the data retrieval functions:

/// Convert a sequence to a list asynchronously (used for LiteDB IO)
let toListAsync<'T> (q : 'T seq) =
  (q.ToList >> Task.FromResult) ()

/// Convert a sequence to a list asynchronously (used for LiteDB IO)
let firstAsync<'T> (q : 'T seq) =
  q.FirstOrDefault () |> Task.FromResult

/// Async wrapper around a request update
let doUpdate (db : LiteDatabase) (req : Request) =
  db.requests.Update req |> ignore

And, for the log synchronization, an extension method on LiteDatabase:

/// Extensions on the LiteDatabase class
type LiteDatabase with
  // ...
  /// Async version of the checkpoint command (flushes log)
  member this.saveChanges () =
    this.Checkpoint ()

None of these actually make the underlying library use async I/O; however, they do let the application's main thread yield until the I/O is done. Also, despite the saveChanges name, this is not required to save data into LiteDB; it is there once the insert or update is done (or, optionally, when the transaction is committed).

Final Thoughts

As I draft this, this paragraph is on line 280 of this post's source; the entire Data.fs file is 209 lines, including blank lines and comments. The above is a moderately long-winded explanation of what is nicely terse code. If I had used traditional C#-style POCOs, the code would likely have been shorter still. The backup of the LiteDB file is right at half the size of the equivalent RavenDB backup, so the POCO-to-BSON mapping paid off there. I'm quite pleased with the outcome of using LiteDB for this project.

Our final stop on the tour will wrap up with overall lessons learned on the project.

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Friday, August 31, 2018
  A Tour of myPrayerJournal: The Data Store


  • This is post 6 in a series; see the introduction for all of them, and the requirements for which this software was built.
  • Links that start with the text “mpj:” are links to the 1.0.0 tag (1.0 release) of myPrayerJournal, unless otherwise noted.

Up to this point in our tour, we've talked about data a good bit, but it has all been in the context of whatever else we were discussing. Let's dig into the data structure a bit, to see how our information is persisted and retrieved.

Conceptual Design

The initial thought was to create a document store with one document type, the request. The request would have an ID, the ID of the user who created it, and an array of updates/records. Through the initial phases of development, our preferred document database (RethinkDB) was going through a tough period, with their company shutting down; thankfully, they're now part of the Linux Foundation, so they're still around. RethinkDB supports calculated fields in documents, so the plan was to have a few of those to keep us from having to retrieve or search through the array of updates.

We also considered a similar design using PostgreSQL's native JSON support. While it does not natively support calculated fields, a creative set of indexes could also suffice. As we thought it through a little more, though, this seemed to be over-engineering; this isn't unstructured data, and PostgreSQL handles max-length character fields very well. (This is supposed to be a “minimalist” application, right?) A relational structure would fit our needs quite nicely.

The starting design, then, used 2 tables. request had an ID and a user ID; history had the request ID, an “as of” date, a status (created, updated, etc.), and the optional text associated with that update. Early in development, the journal view brought together the request/user IDs along with the latest history entry that affected the text of the request, as well as the last date/time an action had occurred on the request. When the notes capability was added, it got its own note table; its structure was similar to the history table, but with non-optional text and without a status. As snoozing and recurrence capabilities were added, those fields were added to the request table (and the journal view).

The final design uses 3 tables, 2 of which have a one-to-many relationship with the third; and 1 view, which provides the calculated fields we had originally planned for RethinkDB to calculate.

Database Changes (Migrations)

As we ended up using 3 different server environments over the course of this project, we ended up writing a DbContext class based on our existing structure. For the Node.js backend, we created a DDL file (mpj:ddl.js, v0.8.4+) that checked for the existence of each table and view, and also had the SQL to execute if the check failed. For the Go version (mpj:data.go, v0.9.6+), the EnsureDB function does a similar thing; looking at line 347, it is checking for a specific column in the request table, and running the ALTER TABLE statement to add it if it isn't there.

The only change that was required since the F#/Giraffe backend has been in place was the one to support request recurrence. Since we did not end up with a scaffolded EF Core initial migration/model, we simply wrote a SQL script to accomplish these changes (mpj:sql directory).1

The EF Core Model

EF Core uses the familiar DbContext class from prior versions of Entity Framework. myPrayerJournal does take advantage of a feature that just arrived in EF Core 2.1, though - the DbQuery type. DbSets are collections of entities that generally map to an underlying database table. They can be mapped to views, but unless it's an updateable view, updating those entities results in a runtime error; plus, since they can't be updated, there's no need for the change tracking mechanism to care about the entities returned. DbQuery addresses both these concerns, providing lightweight read-only access to data from views.

The DbContext class is defined in Data.fs (mpj:Data.fs), starting in line 189. It's relatively straightforward, though if you have only ever seen a C# model, it's a bit different. The combination of val mutable x : [type] and the [<DefaultValue>] attribute are the F# equivalent of C#'s [type] x; declaration, which creates a variable and initializes reference types to null. The EF Core runtime provides these instances to their setters (lines 203, 206, 209, and 212), and the application code uses them via the getters (a line earlier, each).

The OnModelCreating overridden method (line 214) is called when the runtime first creates its instance of the data model. Within this method, we call the .configureEF function of each of our database types. The name of this function isn't prescribed, and we could define the entire model without even referencing the data types of our entities; however, this technique gives us a “configure where it's defined” paradigm with each entity type. While the EF “Code First” model creates tables that don't need a lot of configuring, we must provide more information about the layout of the database tables since we're writing a DbContext to target an existing database.

Let's start out by taking a look at History.configureEF (line 50). Line 53 says that we're going to the table history. This seems to be a no-brainer, but EF Core would (by convention) be expecting a History table; since PostgreSQL uses a different syntax for case-sensitive names, these queries would look like SELECT ... FROM "History" ..., resulting in a nice “relation does not exist” error. Line 54 defines our compound key (requestId and asOf). Lines 55-57 define certain properties of the entity as required; if we try to store an entity where these fields are not set, the runtime will raise an exception before even trying to take it to the database. (F#'s non-nullability makes this a non-issue, but it still needs to be defined to match the database.) Line 58 may seem to do nothing, but what it does is make the text property immediately visible to the model builder; then, we can define an OptionConverter<string>2 for it, which will translate between null and string option (None = null, Some [x] = [x]). (Lines 60-61 are left over from when I was trying to figure out why line 62 was raising an exception, leading to the addition of line 58; they could safely be removed, and will be for a post-1.0 release.)

History is the most complex configuration, but let's take a peek at Request.configureEF (line 126) to see one more interesting technique. Lines 107-110 define the history and notes collections on the Request type; lines 138-145 define the one-to-many relationship (without a foreign key entity in the child types). Note the casts to IEnumerable<x> (lines 138 and 142) and obj (lines 140 and 144); while F# is good about inferring types in a lot of cases, these functions are two places it is not. We can use the :> operator for the cast, because these types are part of the inheritance chain. (The :?> operator is used for potentially unsafe casts.)

Finally, the attributes above each record type need a bit of explanation; each one has [<CLIMutable; NoComparison; NoEquality>]. The CLIMutable attribute creates a no-argument constructor for the record type, which the runtime can use to create instances of the type. (The side effect is that we may get null instances of what is expected to be a non-null type, but we'll look at dealing with that a bit later.) The NoComparison and NoEquality attributes keep F# from creating field-level equality and comparison methods on the types. While these are normally helpful, there is an edge case where they can raise NullReferenceExceptions, especially when used on null instances. As these record types are simply our data transfer objects (both from SQL and to JSON), we don't need the functionality anyway.

Reading and Writing Data

EF Core uses the “unit of work” pattern with its DbContext class. Each instance maintains knowledge of the entities it's loaded, and does change tracking against those entities, so it knows what commands to issue when .SaveChanges() (or .SaveChangesAsync()) is called. It doesn't do this for free, though, and while EF Core does this much more efficiently than Entity Framework proper, F# record types do not support mutation; if req is a Request instance, for example, { req with showAfter = 123456789L } returns a new Request instance.

This is the problem whose solution is enabled by lines 227-233 in Data.fs. We can manually register an instance of an entity as either added or modified, and when we call .SaveChanges(), the runtime will generate the SQL to update the data store accordingly. This also allows us to use .AsNoTracking() in our queries (lines 250, 258, 265, and 275), which means that the resultant entities will not be registered with the change tracker, saving that overhead. Notice that we don't specify that on line 243; since Journal is defined as a DbQuery instead of a DbSet, we get change-tracking-avoidance for free.

Generally speaking, the preferred method of writing queries against a DbContext instance is to define extension methods against it. These are static by default, and they enable the context to be as lightweight as possible, while extending it when necessary. However, since this context is so small, we've created 6 methods on the context that we use to obtain data.

If you've been reading along with the tour, we have already seen a few API handler functions (mpj:Handlers.fs) that use the data context. Line 137 has the handler for /api/journal, the endpoint to retrieve a user's active requests. It uses .JournalByUserId(), defined in Data.fs line 242, whose signature is string -> JournalRequest seq. (The latter is an F# alias for IEnumerable<JournalRequest>.) Back in the handler, we use db ctx to get the context (more on that below), then call the method; we're piping the output of userId ctx into it, so it gets its lone parameter from the pipe, then its output is piped to the asJson function we discussed as part of the API.

Line 192, the handler for /api/request/[id]/history, demonstrates both inserting and updating data. We attempt to retrieve the request by its ID and the user ID; if that fails, we return a 404. If it succeeds, though, we add a history entry (lines 201-207), and optionally update the showAfter field of the request based on its recurrence. Finally, the call on line 212 commits the changes for this particular instance. Since the .SaveChanges[Async]() methods return the number of records affected, we cannot use the do! operator for this; F# makes you explicitly ignore values you aren't either returning or assigning to a name. However, defining _ as a parameter or name demonstrates that we realize there is a value to be had, we just are not going to do anything with it.

We mentioned that CLIMutable record types could be null. Since record types cannot normally be null, we cannot code something like match [var] with null -> ...; it's a compiler syntax error. What we can do, though, is use the box operator. box “boxes” whatever value we have into an object container, where we can then check it against null. The function toOption in Data.fs on line 11 does this work for us; throughout the retrieval methods, we use it to return options for items that are either present or absent. This is why we could do the match statement in the /api/request/[id]/history handler against Some and None values.

Getting a DbContext

Since Giraffe sits atop ASP.NET Core, we use the same technique; we use the .AddDbContext() extension method on the IServiceCollection interface, and assign it when we set up the dependency injection container. In our case, it's in Program.fs (mpj:Program.fs) line 50, where we also direct it to use a PostgreSQL connection defined by the connection string “mpj”. (This comes from the unified configuration built from appsettings.json and appsettings.[Environment].json.) If we look back at Handlers.fs, lines 45-47, we see the definition of the db ctx call we used earlier. We're using the Giraffe-provided GetService<'T>() extension method to return this instance.


Our tour is nearing its end, but we still have a few stops to go. Next time, we'll look at how we generated documentation to tell people how to use this app.

1 Writing this post has shown me that I need to either create a SQL creation script for the repo, or create an EF Core initial migration/model, so the database ever has to be recreated from scratch. It's good to write about things after you do them!

2 This is also a package I wrote; it's available on NuGet, and I also wrote a post about what it does.

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Sunday, July 8, 2018
  F# Options with EF Core

UPDATE: The code described below is now available as a NuGet package.

The 2.1 release of Entity Framework Core brought the ability to do value conversions. This is implemented through an abstract class, ValueConverter, which you can implement to convert a data type. They also provided several built-in converters that you don't have to write, such as storing enums as strings. To use a value converter, you provide a new instance of it and attach it to a property in your model's OnModelCreating event.

F# provides an Option<'T> type as a way to represent a value that may or may not be present. There are many benefits to defining optional values as 'T option rather than checking for null; you can read all about it if you'd like.

As I was working on a project, I already used Option.ofObj to convert my possibly-null results from queries to options; at the field level, though, I was working with default values. Could I use this new feature to handle nullable columns as well? As it turns out, yes!

Here is the code for the value converter.

module Conversion =
  open Microsoft.FSharp.Linq.RuntimeHelpers
  open System
  open System.Linq.Expressions

  let toOption<'T> =
    <@ Func<'T, 'T option>(fun (x : 'T) -> match box x with null -> None | _ -> Some x) @>
    |> LeafExpressionConverter.QuotationToExpression
    |> unbox<Expression<Func<'T, 'T option>>>
  let fromOption<'T> =
    <@ Func<'T option, 'T>(fun (x : 'T option) -> match x with Some y -> y | None -> Unchecked.defaultof<'T>) @>
    |> LeafExpressionConverter.QuotationToExpression
    |> unbox<Expression<Func<'T option, 'T>>>

type OptionConverter<'T> () =
  inherit ValueConverter<'T option, 'T> (Conversion.fromOption, Conversion.toOption)

The Conversion module contains the functions that we'll need to provide in the ValueConverter constructor. (With the way class inheritance is coded in F#, and the way ValueConverter wants its expressions in its constructor, this is a necessary step. I would have liked to have seen a no-argument constructor and overridable properties as an option, but I'm not complaining; this is a really great feature.) Within those functions, we make use of code quotations, then convert the quotation expressions to Linq expressions.

One other note; in the toOption function, if we used Option.ofObj instead of box x, the code would not support value types. This means that things like an int option field wouldn't be supported.

Now that we have our option converter, let's hook it into our model. In my project, each entity type has a static configureEF function, and I call those from OnModelCreating. Here's an abridged version of one of my entity types:

  Member =
  { /// ...
    /// E-mail format
    format : string option
    /// ...
    /// ...
    static member configureEF (mb : ModelBuilder) =
      /// ... HasColumnName statements, etc.
      mb.Model.FindEntityType(typeof<Member>).FindProperty("format").SetValueConverter(OptionConverter<string> ())
      |> ignore

This line of code finds the type within the model, the property within the type, and provides the new instance of our option converter to it. In this entity, a None here indicates that the member uses the group's default e-mail format; Some would indicate that they've specified which format they prefer.

That's all there is to it! Define the converter once, and plug it in to all the optional fields; now we have nullable fields translated to options by EF Core. "Magic unicorn," indeed!

(Credits: Many thanks to Jiří Činčura for the excellent value conversion blog post and Tomas Petricek for his Stack Overflow answer on converting quotation expressions to Linq expressions.)

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