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Searchable Documents? Yes You Can. Another Reason to Choose AsciiDoc

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Searchable Documents? Yes You Can. Another Reason to Choose AsciiDoc

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Elasticsearch is a flexible and powerful open source, distributed real-time search and analytics engine for the cloud based on  Apache Lucene which provides full text search capabilities. It is document oriented and schema free.

Asciidoctor is a pure Ruby processor for converting  AsciiDoc source files and strings into  HTML 5DocBook 4.5 and other formats. Apart of  Asciidoctor  Ruby part, there is an  Asciidoctor-java-integration project which let us call  Asciidoctor functions from  Java without noticing that  Ruby code is being executed.

In this post we are going to see how we can use  Elasticsearch over  AsciiDocdocuments to make them searchable by their header information or by their content.

Let's add required dependencies:

Lambdaj library is used to convert  AsciiDoc files to a json documents.
Now we can start an  Elasticsearch instance which in our case it is going to be an embedded instance.
node = nodeBuilder().local(true).node();

Next step is parse AsciiDoc document header, read its content and convert them into a json document.

An example of json document stored in Elasticsearch can be:

   "title":"Asciidoctor Maven plugin 0.1.2 released!",
         "author":"Jason Porter",
   "content":"= Asciidoctor Maven plugin 0.1.2 released!.....",

And for converting an AsciiDoc File to a json document we are going to useXContentBuilder class which is provided by ElasticsearchJava API to create jsondocuments programmatically.

package com.lordofthejars.asciidoctor;
import static org.elasticsearch.common.xcontent.XContentFactory.*;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.List;
import org.asciidoctor.Asciidoctor;
import org.asciidoctor.Author;
import org.asciidoctor.DocumentHeader;
import org.asciidoctor.internal.IOUtils;
import org.elasticsearch.common.xcontent.XContentBuilder;
import ch.lambdaj.function.convert.Converter;
public class AsciidoctorFileJsonConverter implements Converter<File, XContentBuilder> {
	private Asciidoctor asciidoctor;
	public AsciidoctorFileJsonConverter() {
		this.asciidoctor = Asciidoctor.Factory.create();
	public XContentBuilder convert(File asciidoctor) {
		DocumentHeader documentHeader = this.asciidoctor.readDocumentHeader(asciidoctor);
		XContentBuilder jsonContent = null;
		try {
			jsonContent = jsonBuilder()
				    .field("title", documentHeader.getDocumentTitle())
					Author mainAuthor = documentHeader.getAuthor();
								.field("author", mainAuthor.getFullName())
								.field("email", mainAuthor.getEmail())
					List<Author> authors = documentHeader.getAuthors();
					for (Author author : authors) {
								.field("author", author.getFullName())
								.field("email", author.getEmail())
				    		.field("version", documentHeader.getRevisionInfo().getNumber())
				    		.field("content", readContent(asciidoctor))
				    		.array("tags", parseTags((String)documentHeader.getAttributes().get("tags")))
		} catch (IOException e) {
			throw new IllegalArgumentException(e);
		return jsonContent;
	private String[] parseTags(String tags) {
		tags = tags.substring(1, tags.length()-1);
		return tags.split(", ");
	private String readContent(File content) throws FileNotFoundException {
		return IOUtils.readFull(new FileInputStream(content));
Basically we are building the  json document by calling  startObject methods to start a new object,  field method to add new fields, and  startArray to start an array. Then this builder will be used to render the equivalent object in  json format. Notice that we are using  readDocumentHeader method from  Asciidoctor class which returns header attributes from  AsciiDoc file without reading and rendering the whole document. And finally content field is set with all document content.
And now we are ready to start indexing documents. Note that  populateData method receives as parameter  a  Client object. This object is from  Elasticsearch  Java APIand represents a connection to  Elasticsearch database.
import static ch.lambdaj.Lambda.convert;
private void populateData(Client client) throws IOException {
		List<File> asciidoctorFiles = new ArrayList<File>() {{
			add(new File("target/test-classes/java_release.adoc"));
			add(new File("target/test-classes/maven_release.adoc"));
		List<XContentBuilder> jsonDocuments = convertAsciidoctorFilesToJson(asciidoctorFiles);
		for (int i=0; i < jsonDocuments.size(); i++) {
                                         "asciidoctor", Integer.toString(i)).setSource(jsonDocuments.get(i)).execute().actionGet();
                client.admin().indices().refresh(new RefreshRequest("docs")).actionGet();
private List<XContentBuilder> convertAsciidoctorFilesToJson(List<File> asciidoctorFiles) {
		return convert(asciidoctorFiles, new AsciidoctorFileJsonConverter());

It is important to note that the first part of the algorithm is converting all our AsciiDocfiles (in our case two) to XContentBuilder instances by using previous converter class and the method convert of Lambdaj project.

If you want you can take a look to both documents used in this example in https://github.com/asciidoctor/asciidoctor.github.com/blob/develop/news/asciidoctor-java-integration-0-1-3-released.adoc and https://github.com/asciidoctor/asciidoctor.github.com/blob/develop/news/asciidoctor-maven-plugin-0-1-2-released.adoc.

Next part is inserting documents inside one index. This is done by using prepareIndexmethod, which requires an index name (docs), an index type (asciidoctor), and the idof the document being inserted. Then we call setSource method which transforms theXContentBuilder object to json, and finally by calling execute().actionGet(), data is sent to database.

The final step is only required because we are using an embedded instance ofElasticsearch (in production this part should not be required), which refresh the indexes by calling refresh method.

After that point we can start querying Elasticsearch for retrieving information from our AsciiDoc documents.

Let's start with very simple example, which returns all documents inserted:

SearchResponse response = client.prepareSearch().execute().actionGet();

Next we are going to search for all documents that has been written by Alex Sotowhich in our case is one.

import static org.elasticsearch.index.query.QueryBuilders.matchQuery;
QueryBuilder matchQuery =  matchQuery("author", "Alex Soto");
QueryBuilder matchQuery =  matchQuery("author", "Alexander Soto");
Note that I am searching for field  author the string  Alex Soto, which returns only one. The other document is written by  Jason. But it is interesting to say that if you search for  Alexander Soto, the same document will be returned;  Elasticsearch is smart enough to know that  Alex and  Alexander are very similar names so it returns the document too.

More queries, how about finding documents written by someone who is called Alex, but not Soto.

import static org.elasticsearch.index.query.QueryBuilders.fieldQuery;
QueryBuilder matchQuery =  fieldQuery("author", "+Alex -Soto");
And of course no results are returned in this case. See that in this case we are using a field query instead of a  term query, and we use +, and - symbols to exclude and include words.

Also you can find all documents which contains the word released on title.

import static org.elasticsearch.index.query.QueryBuilders.matchQuery;
QueryBuilder matchQuery =  matchQuery("title", "released");

And finally let's find all documents that talks about 0.1.2 release, in this case only one document talks about it, the other one talks about 0.1.3.

QueryBuilder matchQuery =  matchQuery("content", "0.1.2");

Now we only have to send the query to Elasticsearch database, which is done by using prepareSearch method.

SearchResponse response = client.prepareSearch("docs")
SearchHits hits = response.getHits();
for (SearchHit searchHit : hits) {
Note that in this case we are printing the  AsciiDoc content through console, but you could use  asciidoctor.render(String content, Options options) method to render the content into required format.

So in this post we have seen how to index documents using  Elasticsearch, how to get some important information from  AsciiDoc files using  Asciidoctor-java-integration project, and finally how to execute some queries to inserted documents. Of course there are more kind of queries in  Elasticsearch, but the intend of this post wasn't to explore all possibilities of  Elasticsearch.
Also as corollary, note how important it is using  AsciiDoc format for writing your documents. Without much effort you can build a search engine for your documentation. On the other side, imagine all code that would be required to implement the same using any proprietary binary format like Microsoft Word . So we have shown another reason to use  AsciiDoc instead of other formats.

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Published at DZone with permission of Alex Soto, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.


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