Flat Table Projections

Marten has yet another projection recipe for writing event data to flat projections.

Let’s dive right into a sample usage of this. If you’re a software developer long enough and move around just a little bit, you’re going to get sucked into building a workflow for importing flat files of dubious quality from external partners or customers. I’m going to claim that event sourcing is a good fit for this problem domain for event sourcing (and also suggesting this pretty strongly at work). That being said, here’s what the event types might look like that are recording the progress of a file import:

public record ImportStarted(
    DateTimeOffset Started,
    string ActivityType,
    string CustomerId,
    int PlannedSteps);

public record ImportProgress(
    string StepName,
    int Records,
    int Invalids);

public record ImportFinished(DateTimeOffset Finished);

public record ImportFailed;

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At some point, we’re going to want to apply some metrics to the execution history to understand the average size of the incoming files, what times of the day have more or less traffic, and performance information broken down by file size, file type, and who knows what. This sounds to me like a perfect use case for SQL queries against a flat table.

Enter Marten flat table projection functionality. First off, let’s do this simply by writing some explicit SQL in a new projection that we can replay against the existing events when we’re ready. I’m going to use Marten’s EventProjection as a base class in this case:

public class ImportSqlProjection: EventProjection
{
    public ImportSqlProjection()
    {
        // Define the table structure here so that
        // Marten can manage this for us in its schema
        // management
        var table = new Table("import_history");
        table.AddColumn<Guid>("id").AsPrimaryKey();
        table.AddColumn<string>("activity_type").NotNull();
        table.AddColumn<DateTimeOffset>("started").NotNull();
        table.AddColumn<DateTimeOffset>("finished");

        SchemaObjects.Add(table);

        // Telling Marten to delete the table data as the
        // first step in rebuilding this projection
        Options.DeleteDataInTableOnTeardown(table.Identifier);
    }

    public void Project(IEvent<ImportStarted> e, IDocumentOperations ops)
    {
        ops.QueueSqlCommand("insert into import_history (id, activity_type, started) values (?, ?, ?)",
            e.StreamId, e.Data.ActivityType, e.Data.Started
        );
    }

    public void Project(IEvent<ImportFinished> e, IDocumentOperations ops)
    {
        ops.QueueSqlCommand("update import_history set finished = ? where id = ?",
            e.Data.Finished, e.StreamId
        );
    }

    public void Project(IEvent<ImportFailed> e, IDocumentOperations ops)
    {
        ops.QueueSqlCommand("delete from import_history where id = ?", e.StreamId);
    }
}

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A couple notes about the code above:

We’ve invested a huge amount of time in Marten and the related Weasel library building in robust schema management. The Table model I’m using up above comes from Weasel, and this allows a Marten application using this projection to manage the table creation in the underlying database for us. This new table would be part of all Marten’s built in schema management functionality.

The QueueSqlCommand() functionality gives you the ability to add raw SQL commands to be executed as part of a Marten unit of work transaction. It’s important to note that the QueueSqlCommand() method doesn’t execute inline, rather it adds the SQL you enqueue to be executed in a batch query when you eventually call the holding IDocumentSession.SaveChangesAsync(). I can’t stress this enough, it has consistently been a big performance gain in Marten to batch up queries to the database server and reduce the number of network round trips.

The Project() methods are a naming convention with Marten’s EventProjection. The first argument is always assumed to be the event type. In this case though, it’s legal to use Marten’s IEvent<T> envelope type to allow you access to event metadata like timestamps, version information, and the containing stream identity.

Now, let’s use Marten’s brand FlatTableProjection recipe to do a little more advanced version of the earlier projection:

public class FlatImportProjection: FlatTableProjection
{
    // I'm telling Marten to use the same database schema as the events from
    // the Marten configuration in this application
    public FlatImportProjection() : base("import_history", SchemaNameSource.EventSchema)
    {
        // We need to explicitly add a primary key
        Table.AddColumn<Guid>("id").AsPrimaryKey();

        TeardownDataOnRebuild = true;

        Project<ImportStarted>(map =>
        {
            // Set values in the table from the event
            map.Map(x => x.ActivityType);
            map.Map(x => x.CustomerId);
            map.Map(x => x.PlannedSteps, "total_steps")
                .DefaultValue(0);

            map.Map(x => x.Started);

            // Initial values
            map.SetValue("status", "started");
            map.SetValue("step_number", 0);
            map.SetValue("records", 0);
        });

        Project<ImportProgress>(map =>
        {
            // Add 1 to this column when this event is encountered
            map.Increment("step_number");

            // Update a running sum of records progressed
            // by the number of records on this event
            map.Increment(x => x.Records);

            map.SetValue("status", "working");
        });

        Project<ImportFinished>(map =>
        {
            map.Map(x => x.Finished);
            map.SetValue("status", "completed");
        });

        // Just gonna delete the record of any failures
        Delete<ImportFailed>();

    }
}

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A couple notes on this version of the code:

• FlatFileProjection is adding columns to its table based on the designated column mappings. You can happily customize the FlatFileProjection.Table object to add indexes, constraints, or defaults.
• Marten is able to apply schema migrations and manage the table from the FlatFileProjection as long as it’s registered with Marten.
• When you call Map(x => x.ActivityType), Marten is by default mapping that to a snake_cased derivation of the member name for the column, so “activity_type”. You can explicitly map the column name yourself.
• The call to Map(expression) chains a fluent builder for the table column if you want to further customize the table column with default values or constraints like the NotNull().
• In this case, I’m building a database row per event stream. The FlatTableProjection can also map to arbitrary members of each event type.
• The Project<T>(lambda) configuration leads to a runtime, code generation of a Postgresql upsert command so as to not be completely dependent upon events being captured in the exact right order. I think this will be more robust in real life usage than the first, more explicit version.

The FlatTableProjection in its first incarnation is not yet able to use event metadata.