simple_search.rs

extern crate tantivy;
extern crate tempdir;

#[macro_use]
extern crate serde_json;

use std::path::Path;
use tempdir::TempDir;
use tantivy::Index;
use tantivy::schema::*;
use tantivy::collector::TopCollector;
use tantivy::query::QueryParser;

fn main() {

Let’s create a temporary directory for the sake of this example

    if let Ok(dir) = TempDir::new("tantivy_example_dir") {
        run_example(dir.path()).unwrap();
        dir.close().unwrap();
    }
}


fn run_example(index_path: &Path) -> tantivy::Result<()> {

Defining the schema

The Tantivy index requires a very strict schema. The schema declares which fields are in the index, and for each field, its type and “the way it should be indexed”.

first we need to define a schema …

    let mut schema_builder = SchemaBuilder::default();

Our first field is title. We want full-text search for it, and we also want to be able to retrieve the document after the search.

TEXT | STORED is some syntactic sugar to describe that.

TEXT means the field should be tokenized and indexed, along with its term frequency and term positions.

STORED means that the field will also be saved in a compressed, row-oriented key-value store. This store is useful to reconstruct the documents that were selected during the search phase.

    schema_builder.add_text_field("title", TEXT | STORED);

Our second field is body. We want full-text search for it, but we do not need to be able to be able to retrieve it for our application.

We can make our index lighter and by omitting STORED flag.

    schema_builder.add_text_field("body", TEXT);

    let schema = schema_builder.build();

Indexing documents

Let’s create a brand new index.

This will actually just save a meta.json with our schema in the directory.

    let index = Index::create(index_path, schema.clone())?;

To insert document we need an index writer. There must be only one writer at a time. This single IndexWriter is already multithreaded.

Here we use a buffer of 50MB per thread. Using a bigger heap for the indexer can increase its throughput.

    let mut index_writer = index.writer(50_000_000)?;

Let’s index our documents! We first need a handle on the title and the body field.

Create a document “manually”.

We can create a document manually, by setting the fields one by one in a Document object.

    let title = schema.get_field("title").unwrap();
    let body = schema.get_field("body").unwrap();

    let mut old_man_doc = Document::default();
    old_man_doc.add_text(title, "The Old Man and the Sea");
    old_man_doc.add_text(
        body,
        "He was an old man who fished alone in a skiff in the Gulf Stream and \
                          he had gone eighty-four days now without taking a fish.",
    );

… and add it to the IndexWriter.

    index_writer.add_document(old_man_doc);

Create a document directly from json.

Alternatively, we can use our schema to parse a document object directly from json. The document is a string, but we use the json macro from serde_json for the convenience of multi-line support.

    let json = json!({
       "title": "Of Mice and Men",
       "body": "A few miles south of Soledad, the Salinas River drops in close to the hillside \
                bank and runs deep and green. The water is warm too, for it has slipped twinkling \
                over the yellow sands in the sunlight before reaching the narrow pool. On one \
                side of the river the golden foothill slopes curve up to the strong and rocky \
                Gabilan Mountains, but on the valley side the water is lined with trees—willows \
                fresh and green with every spring, carrying in their lower leaf junctures the \
                debris of the winter’s flooding; and sycamores with mottled, white, recumbent \
                limbs and branches that arch over the pool"
    });
    let mice_and_men_doc = schema.parse_document(&json.to_string())?;

    index_writer.add_document(mice_and_men_doc);

Multi-valued field are allowed, they are expressed in JSON by an array. The following document has two titles.

    let json = json!({
       "title": ["Frankenstein", "The Modern Prometheus"],
       "body": "You will rejoice to hear that no disaster has accompanied the commencement of an \
                enterprise which you have regarded with such evil forebodings.  I arrived here \
                yesterday, and my first task is to assure my dear sister of my welfare and \
                increasing confidence in the success of my undertaking."
    });
    let frankenstein_doc = schema.parse_document(&json.to_string())?;

    index_writer.add_document(frankenstein_doc);

This is an example, so we will only index 3 documents here. You can check out tantivy’s tutorial to index the English wikipedia. Tantivy’s indexing is rather fast. Indexing 5 million articles of the English wikipedia takes around 4 minutes on my computer!

Committing

At this point our documents are not searchable.

We need to call .commit() explicitly to force the index_writer to finish processing the documents in the queue, flush the current index to the disk, and advertise the existence of new documents.

This call is blocking.

    index_writer.commit()?;

If .commit() returns correctly, then all of the documents that have been added are guaranteed to be persistently indexed.

In the scenario of a crash or a power failure, tantivy behaves as if has rolled back to its last commit.

Searching

Let’s search our index. Start by reloading searchers in the index. This should be done after every commit().

    index.load_searchers()?;

Afterwards create one (or more) searchers.

You should create a searcher every time you start a “search query”.

    let searcher = index.searcher();

The query parser can interpret human queries. Here, if the user does not specify which field they want to search, tantivy will search in both title and body.

    let mut query_parser = QueryParser::for_index(index, vec![title, body]);

QueryParser may fail if the query is not in the right format. For user facing applications, this can be a problem. A ticket has been opened regarding this problem.

    let query = query_parser.parse_query("sea whale")?;

A query defines a set of documents, as well as the way they should be scored.

A query created by the query parser is scored according to a metric called Tf-Idf, and will consider any document matching at least one of our terms.

Collectors

We are not interested in all of the documents but only in the top 10. Keeping track of our top 10 best documents is the role of the TopCollector.

    let mut top_collector = TopCollector::with_limit(10);

We can now perform our query.

    searcher.search(&*query, &mut top_collector)?;

Our top collector now contains the 10 most relevant doc ids…

    let doc_addresses = top_collector.docs();

The actual documents still need to be retrieved from Tantivy’s store.

Since the body field was not configured as stored, the document returned will only contain a title.

    for doc_address in doc_addresses {
        let retrieved_doc = searcher.doc(&doc_address)?;
        println!("{}", schema.to_json(&retrieved_doc));
    }

Wait for indexing and merging threads to shut down. Usually this isn’t needed, but in main we try to delete the temporary directory and that fails on Windows if the files are still open.

    index_writer.wait_merging_threads()?;

    Ok(())
}