This release further improves support for the just released beta of Sphinx 2.1 by allowing users to save JSON attributes to real-time indexes with the fluent API. The fluent API now also supports new query options and the creation of snippets within a query via the GetSnippets() extension method.

The native API has gotten support for new query flags and sub-selects introduced with Sphinx 2.1. A list of all changes is available in the version history.

Sphinx is a great full-text search engine with many amazing features, but there is one feature missing that would make it even better: the ability to directly index Word, Excel, PowerPoint, PDF files. How one can index these kinds of documents with Sphinx is something that is often asked in the Sphinx forum. Today I’d like to show you an easy way to extract text from these document types and store them in a Sphinx real-time index from your .NET application.

There are a bunch of tools and libraries out there that claim to be able to extract text from various document formats. As it is a pretty hard task to support many formats and extract text reliably, the quality of each library varies greatly. One tool that stands out is the Apache Tika™ toolkit. It is a Java library that

“detects and extracts metadata and structured text content from various documents using existing parser libraries.”

And it is really very good at it. Amongst others, it supports Microsoft Office, Open Document (ODF), and PDF files. But wait, I said Java library, didn’t I? “What am I supposed to do with a Java library in my .NET application?”, you might ask. Well, we’ll just convert it from Java to .NET using IKVM.NET. IKVM.NET is a .NET implementation of a Java Virtual Machine (JVM) which can be used as a drop-in replacement for Java. And it comes with a Java bytecode to CIL translator named ikvmc that we can use to build a .NET version of Apache Tika. In the next section, I’ll walk through the steps required to do this. At the end of this article you can download a complete sample application that uses Tika to extract text from some files and stores them in a Sphinx real-time index via SphinxConnector.NET.

Creating a .NET Version of Apache Tika

To create your own .NET version of Apacha Tika you need to:

1. Download IKVM.NET
2. Download the .jar file from the Tika project page (the current version at the time of writing is 1.3)
3. Extract IKVM.NET to a folder of your choice
4. Optional: Add the bin folder of IKVM.NET to your %PATH% variable
5. Execute the following command (add/change the paths to ikvmc.exe and tika-app-1.3.jar if needed):

ikvmc.exe -target:library -version:1.3.0.0 -out:Apache.Tika.dll tika-app-1.3.jar

Let’s walk through the command line parameters: With –target:library we tell ikvmc to convert the jar to a class library. This is needed because the jar file is also usable as a standalone console/gui application, i.e. contains a main() method, which by default would cause ikvmc to generate an exe file. Next, we specify the version for our output DLL because otherwise ikvmc would set the version to 0.0.0.0. Finally we specify the output file name via –out: and the path to the Tika jar file.

After hitting Enter, ikvmc starts to translate the Java library to .NET. It’ll output a lot of warning messages, but will eventually finish and produce a working DLL. Note that if you want to sign the output assembly you can do so by specifying a key file via the -keyfile: command line option.

Extracting Text from Documents

Now that we've created a .NET library of Tika, we can start extracting text from documents. I’ve created small wrapper that provides methods to perform the extraction. To build the wrapper DLL we need to add references to a couple of IKVM.NET libraries:

Note that you need to reference more of IKVM.NET’s DLL’s in an application that uses Tika, these are just the required files to compile the wrapper project.

The AutoTextExtractor class which handles the extraction of text from files and binary data (useful if your documents are stored in a DB) and the TextExtractionResult class are based on these by Kevin Miller:

public class AutoTextExtractor
{
  public TextExtractionResult Extract(string filePath,OutputType outputType = OutputType.Text)
  {
      return Extract(System.IO.File.ReadAllBytes(filePath), outputType);
  }

  public TextExtractionResult Extract(byte[] data, OutputType outputType = OutputType.Text)
  {
      var parser = new AutoDetectParser();
      var metadata = new Metadata();

      using (Writer outputWriter = new StringWriter())
      using (InputStream inputStream = TikaInputStream.get(data, metadata))
      {
          parser.parse(inputStream, GetTransformerHandler(outputWriter, outputType), 
              metadata, new ParseContext());
          
          return AssembleExtractionResult(outputWriter.toString(), metadata);
      }
  }

  private static TextExtractionResult AssembleExtractionResult(string text, Metadata metadata)
  {
      Dictionary<string, string> metaDataResult = metadata.names().
          ToDictionary(name => name, name => String.Join(", ", metadata.getValues(name)));

      string contentType = metaDataResult["Content-Type"];

      return new TextExtractionResult
      {
          Text = text,
          ContentType = contentType,
          Metadata = metaDataResult
      };
  }

  private TransformerHandler GetTransformerHandler(Writer outputWriter, OutputType outputType)
  {
      var factory = (SAXTransformerFactory)TransformerFactory.newInstance();
      TransformerHandler handler = factory.newTransformerHandler();
      handler.getTransformer().setOutputProperty(OutputKeys.METHOD, outputType.ToString());
      handler.setResult(new StreamResult(outputWriter));

      return handler;
  }
}

Here’s the TextExtractionResult class:

public class TextExtractionResult
{
    public string Text { get; set; }
    
    public string ContentType { get; set; }
    
    public IDictionary<string, string> Metadata { get; set; }
}

And the OutputType enumeration:

public enum OutputType
{
    Text,
    Html,
    Xml
}

Demo Application

I’ve created a small demo application that contains a DLL that wraps Tika with the help of the aforementioned classes, and a console application that demonstrates how to extract and store the contents of some files in a Sphinx real-time index with SphinxConnector.NET. The code that does the extraction is pretty simple:

private static SphinxDocumentModel[] GetDocuments()
{
    AutoTextExtractor textExtractor = new AutoTextExtractor();

    int id = 1;

    return (from filePath in Directory.EnumerateFiles(@"..\..\..\..\testfiles")
            select new SphinxDocumentModel
            {
                Id = id++,
                FileContent = textExtractor.Extract(filePath).Text,
                FilePath = Path.GetFullPath(filePath)
            }).ToArray();
}

I’ll omit the code that saves the documents to the index, as it is straightforward. Tip: if you are working with big files you might have to increase Sphinx’ max_packet_size setting.

The archive contains all required libraries so it’s pretty big (IKVM.NET and Tika alone take up about 50 MB in space).

Downloads:

[1] Zip-Package (22 MB)

[2] 7z-Package (18 MB)

An interesting article over at the MySQL Performance Blog was recently published about optimizing Sphinx queries that only filter by an attribute (i.e. do not contain a full-text query). I recommend reading the article first and then coming back here, but here’s a quick summary: a Sphinx query that only filters by an attribute may be relatively slow compared to an equivalent query in a regular DBMS. The reason for this is the fact that one cannot create indexes (as in B-tree indexes) for attributes in Sphinx as one would do in a DBMS. So to retrieve the results of such a query, Sphinx has to perform a full-scan of the index which is relatively costly depending on the size of the index.

The article describes a neat trick to get around this limitation: by adding a full-text indexed field for an attribute and querying that, one can achieve a greatly improved query time. In this post I’d like to demonstrate how this technique can be used with SphinxConnector.NET’s fluent API in conjunction with a real-time index.

The index in the articles example contains data about books, so I’ll be using that here as well. These documents have an integer attribute for a user id that we’d like to store as a full-text indexed field. Let’s take a look at what the document model should look like and which additional settings need to be applied.

To add the user id attribute to the full-text index it needs to be converted to a string. We’ll also add a prefix to each value to avoid it being included in the results of a “regular” full-text query. To do this, we add a string property to the document model that returns the converted and prefixed value:

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

    public int UserId { get; set; }

    public string Title { get; set; }

    public string UserIdKey
    {
        get { return "userkey_" + UserId; }
    }
}

As of Version 3.2, SphinxConnector.NET will automatically exclude any read-only property when selecting the results of a query, so no further setup is required here (it will of course still be inserted into the index during a save).

In previous versions of SphinxConnector.NET the UserIdKey property would have to be configured as follows:

fulltextStore.Conventions.IsFulltextFieldOnly = memberInfo => memberInfo.Name == "UserIdKey";

A query that uses the new attribute would then look this:

IList<CatalogItem> results = session.Query<CatalogItem>().
                                     Match("@UserIdKey userkey_42").
                                     ToList();

For the sake of completeness, here’s the corresponding Sphinx configuration:

index catalog
{
    type = rt
    path = catalog

    rt_field = title
    rt_field = useridkey
    rt_attr_string = title
    rt_attr_uint = userid    
}