Simple search example

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AnalyzerBasics.java BooleanQueryANDInternals.java BooleanQueryIntro.java BottomUpIndexReader.java BytesRefHashExample.java CombinedFieldQueryExample.java DirectoryFileContents.java DocValuesSearchExample.java FunctionQuerySearchExample.java KnnSearchExample.java PointTreeRangeQuery.java PrimitivesRef.java SearchWithTermsEnum.java SimpleSearch.java TextVectorSearchExample.java VisualizePointTree.java

This will walk through a very simple search example with Lucene.

The example has the following steps:

Create documents. Documents are iterable collections of IndexableField.
Open an IndexWriter over a filesystem directory (FSDirectory).
Write the documents using the IndexWriter.
Open an IndexReader from the IndexWriter, then open an IndexSearcher around that IndexReader.
Construct a TermQuery to search for the word fox in the text field.
Use the IndexSearcher to find the documents that match the TermQuery.
Iterate through the returned documents to output their score, ID, and stored text value.

package example.basic;

import org.apache.lucene.document.Field;
import org.apache.lucene.document.TextField;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.index.IndexableField;
import org.apache.lucene.index.StoredFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.FSDirectory;

import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.List;

public class SimpleSearch {

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Creating Documents

We will create a list of documents, where each document has a single field, text.

We use the TextField type to indicate that it’s a “full text” field, to be split into individual tokens during indexing.

In order to retrieve the original field value in our search results, we indicate that we want the field value stored using Field.Store.YES.

    private static List<List<IndexableField>> createDocuments() {
        List<String> texts = List.of(
                "The quick fox jumped over the lazy, brown dog",
                "Lorem ipsum, dolor sit amet",
                "She left the web, she left the loom, she made three paces through the room",
                "The sly fox sneaks past the oblivious dog"
        );
        List<List<IndexableField>> docs = new ArrayList<>();
        for (String text : texts) {
            List<IndexableField> doc = new ArrayList<>();
            doc.add(new TextField("text", text, Field.Store.YES));
            docs.add(doc);
        }
        return docs;
    }

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The example code

    public static void main(String[] args) throws IOException {

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We start by creating a temporary directory, wherever your JVM specifies its default java.io.tmpdir. This will hold the index files.
```
        Path tmpDir = Files.createTempDirectory(SimpleSearch.class.getSimpleName());
```
§

We “open” the file system directory as a Lucene Directory, then open an IndexWriter able to write to that directory.

Lucene places and locks a write.lock file in the directory to make sure that other processes are not able to write to the directory while we hold the lock (as long as those other processes try to obtain the lock).
```
        try (Directory directory = FSDirectory.open(tmpDir);
             IndexWriter writer = new IndexWriter(directory, new IndexWriterConfig())) {
```
§

Use our createDocuments helper function to create the documents and write them to the index. Since they are all written at once without calling writer.flush() in between, they get written contiguously in a single segment. We’ll cover segments in another lesson.
```
            for (List<IndexableField> doc : createDocuments()) {
                writer.addDocument(doc);
            }
```
§

Open an IndexReader based on the writer. This causes writer to flush pending documents, so they can be read. Note that reader has a view of the index at this point in time. If we were to write more documents with writer, they would not be visible to reader.
```
            try (IndexReader reader = DirectoryReader.open(writer)) {
```
§

An IndexReader is able to read the underlying structure of the index, but high-level searching requires an IndexSearcher. We’ll explore the low-level IndexReader operations in a later lesson.
```
                IndexSearcher searcher = new IndexSearcher(reader);
```
§

A TermQuery is one of the simpler query types supported by Lucene. It is able to search for a single “term” (usually a word, but not necessarily) in a field. In this case, we’re going to search for the term fox in the text field.
```
                TermQuery termQuery = new TermQuery(new Term("text", "fox"));
```
§

We ask searcher to run our termQuery and return the top 10 documents. There are only 2 documents in our index with the term fox, so it will only return those 2.
```
                TopDocs topDocs = searcher.search(termQuery, 10);
```
§

If our query had matched more than 10 documents, then topDocs would contain the top 10 documents, while topDocs.totalHits would have the total number of matching documents (or a lower bound on the total number of matching documents, if more than 1000 documents match).
```
                System.out.println("Query " + termQuery + " matched " + topDocs.totalHits + " documents:");
```
§

The topDocs contains a list of ScoreDoc, which just have scores and Lucene-generated doc IDs. Since these doc IDs are likely meaningless to us as users, we ask the reader for a StoredFields instance able to retrieve stored field values based on the doc IDs.
```
                StoredFields storedFields = reader.storedFields();
                for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
```

Using the doc’s ID, we load a Document, and load the text field (the only field).

                    String storedText = storedFields.document(scoreDoc.doc).get("text");

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Each document has a BM25 score based on their relevance to the query. See https://en.wikipedia.org/wiki/Okapi_BM25 for details on the BM25 formula.

The parts of the formula relevant to this example are:
1. A search term occurring more frequently in a document makes the score go up.
2. More total terms in the field makes the score go down.
In this case both of our matching documents contain the word fox once, so they’re tied there. The last document is shorter than the first document, so the last document gets a higher score.
```
                    System.out.println(scoreDoc.score + " - " + scoreDoc.doc + " - " + storedText);
```
§
```
                }
            }
```
§
```
        } finally {
```
§

Clean up

Before we finish the program, we delete each of the files in the directory.

            for (String indexFile : FSDirectory.listAll(tmpDir)) {
                Files.deleteIfExists(tmpDir.resolve(indexFile));
            }

Then we delete the directory itself.

            Files.deleteIfExists(tmpDir);
        }
    }
}

Creating Documents

The example code

Clean up