So far, we have been focusing on various ways to get the raw results from RavenDB. What are the packages that match your queries, and whatever we can be really smart about it.

But let us say that we got the results that we wanted, this is still just half the work, because we can give the user additional information about those results. In particular, in this post I am going to talk about facets.

Facets are a way to provide easily understood context to a search, allowing the user to narrow down what he is looking for quickly. In our case, let us take a look what it takes to add facet supports to our NuGet console app. The first thing to do, of course, is to actually define the facets we want to work on. In this case, we care only for the Tags:

using (var session = store.OpenSession())
{
    session.Store(new FacetSetup
        {
            Id = "facets/PackagesTags",
            Facets =
            {
                new Facet
                    {
                        Name = "Tags",
                        MaxResults = 4,
                        Mode = FacetMode.Default,
                        TermSortMode = FacetTermSortMode.HitsDesc
                    }
            },
        });
    session.SaveChanges();
}

When doing facet search using this document, we will use the Tags field, using a value per each term found. We want to get the top 4, sorted by their hits.

And here is how we are actually doing the faceted query:

var facetResults = q.ToFacets("facets/PackagesTags");
foreach (var result in facetResults.Results)
{
    Console.WriteLine();
    Console.Write("{0}:\t", result.Key);
    foreach (var val in result.Value.Values)
    {
        Console.Write("{0} [{1:#,#}] | ", val.Range, val.Hits);
    }
    Console.WriteLine();
}

It is a one liner, with all of the rest of the code dedicated to just printing things out.

Finally, here are the results:

As you can see, searching for “dal”, we can narrow the searches for linq, orm, etc. Searching for events, we get reactive extensions, etc.

Using facets gives the user additional information about his search (including things like, am I close to what I want), discoverability over your dataset and additional tools to explore it.

All in all, I think that this is a pretty neat thing.