WARNING

0.5 is not yet released and may contain bugs and regressions. However, it is shaping up nicely so I recommend you try it first. 0.4 remains available if you hit insurmountable problems; but please take time to file a ticket at github if you do.

Version Compatibility

Issue Tracking

Issue tracking at github.

Minimum System Requirements

Java 1.5 (or above) is required; the Sun version is recommended as it's regularly tested against.

Build couchdb-lucene

1. Install Maven 2.
2. checkout repository
3. type 'mvn'
4. configure couchdb (see below)

You will now have a zip file in the target/ directory. This contains all the couchdb-lucene code, dependencies, startup scripts and configuration files to run couchdb-lucene.

Configure CouchDB

[couchdb]
os_process_timeout=60000 ; increase the timeout from 5 seconds.
[external]
fti=/path/to/python /path/to/couchdb-lucene/tools/couchdb-external-hook.py
[httpd_db_handlers]
_fti = {couch_httpd_external, handle_external_req, <<"fti">>}

Hook options

Configure couchdb-lucene

couchdb-lucene runs in a single, standalone JVM. As such, you can choose to locate your couchdb-lucene server on a different machine to couchdb if you wish, or keep it on the same machine, it's your call.

Start couchdb-lucene

To start couchdb-lucene, run;

bin/run

To stop couchdb-lucene, simply kill the Java process.

Indexing Strategy

Document Indexing

You must supply a index function in order to enable couchdb-lucene as, by default, nothing will be indexed. To suppress a document from the index, return null. It's more typical to return a single Document object which contains everything you'd like to query and retrieve. You may also return an array of Document objects if you wish.

You may add any number of index views in any number of design documents. All searches will be constrained to documents emitted by the index functions.

Here's an complete example of a design document with couchdb-lucene features:

{
    "_id":"_design/a_design_document_with_any_name_you_like",
    "fulltext": {
        "by_subject": {
            "index":"function(doc) { var ret=new Document(); ret.add(doc.subject); return ret }"
        },
        "by_content": {
            "index":"function(doc) { var ret=new Document(); ret.add(doc.content); return ret }"
        }
    }
}

Here are some example URL's for the given design document;

https://localhost:5984/database/_fti/lucene/by_subject?q=hello
https://localhost:5984/database/_fti/lucene/by_content?q=hello

A fulltext object contains multiple index view declarations. An index view consists of;

The Defaults Object

The following indexing options can be defaulted;

The Analyzer Option

Lucene has numerous ways of converting free-form text into tokens, these classes are called Analyzer's. By default, the StandardAnalyzer is used which lower-cases all text, drops common English words ("the", "and", and so on), among other things. This processing might not always suit you, so you can choose from several others by setting the "analyzer" field to one of the following values;

• brazilian
• chinese
• cjk
• czech
• dutch
• english
• french
• german
• keyword
• perfield
• porter
• russian
• simple
• standard
• thai

The "perfield" option lets you use a different analyzer for different fields and is configured as follows;

"analyzer":"perfield:{field_name:\"analyzer_name\"}"

Unless overridden, any field name not specified will be handled by the standard analyzer. To change the default, use the special default field name;

"analyzer":"perfield:{default:\"keyword\"}"

The Document class

You may construct a new Document instance with;

var doc = new Document();

Data may be added to this document with the add method which takes an optional second object argument that can override any of the above default values.

// Add with all the defaults.
doc.add("value");
// Add a numeric field.
doc.add(35, {"type":"int"});
// Add a date field.
doc.add(new Date("2009-01-01"), {"type":"date"});
// Add a date field (object must be a Date object
// Add a subject field.
doc.add("this is the subject line.", {"field":"subject"});
// Add but ensure it's stored.
doc.add("value", {"store":"yes"});
// Add but don't analyze.
doc.add("don't analyze me", {"index":"not_analyzed"});
// Extract text from the named attachment and index it (but not store it).
doc.attachment("attachment name", {"field":"attachments"});

Example Transforms

Index Everything

function(doc) {
    var ret = new Document();
    function idx(obj) {
    for (var key in obj) {
        switch (typeof obj[key]) {
        case 'object':
        idx(obj[key]);
        break;
        case 'function':
        break;
        default:
        ret.add(obj[key]);
        break;
        }
    }
    };
    idx(doc);
    if (doc._attachments) {
    for (var i in doc._attachments) {
        ret.attachment("attachment", i);
    }
    }
    return ret;
}

Index Nothing

function(doc) {
  return null;
}

Index Select Fields

function(doc) {
  var result = new Document();
  result.add(doc.subject, {"field":"subject", "store":"yes"});
  result.add(doc.content, {"field":"subject"});
  result.add(new Date(), {"field":"indexed_at"});
  return result;
}

Index Attachments

function(doc) {
  var result = new Document();
  for(var a in doc._attachments) {
    result.attachment(a, {"field":"attachment"});
  }
  return result;
}

A More Complex Example

function(doc) {
    var mk = function(name, value, group) {
        var ret = new Document();
        ret.add(value, {"field": group, "store":"yes"});
        ret.add(group, {"field":"group", "store":"yes"});
        return ret;
    };
    if(doc.type != "reference") return null;
    var ret = new Array();
    for(var g in doc.groups) {
        ret.push(mk("library", doc.groups[g].library, g));
        ret.push(mk("method", doc.groups[g].method, g));
        ret.push(mk("target", doc.groups[g].target, g));
    }
    return ret;
}

Attachment Indexing

Couchdb-lucene uses Apache Tika to index attachments of the following types, assuming the correct content_type is set in couchdb;

Supported Formats

• Excel spreadsheets (application/vnd.ms-excel)
• HTML (text/html)
• Images (image/*)
• Java class files
• Java jar archives
• MP3 (audio/mp3)
• OpenDocument (application/vnd.oasis.opendocument.*)
• Outlook (application/vnd.ms-outlook)
• PDF (application/pdf)
• Plain text (text/plain)
• Powerpoint presentations (application/vnd.ms-powerpoint)
• RTF (application/rtf)
• Visio (application/vnd.visio)
• Word documents (application/msword)
• XML (application/xml)

Searching with couchdb-lucene

You can perform all types of queries using Lucene's default query syntax.

Numeric range queries

In addition to normal text-based range searches (using the "field:[lower TO upper]" syntax), couchdb-lucene all supports numeric range searches for the following types: int, long, float, double and date. The type is specified after the field name, as follows;

An example numeric range query for spatial searching.

?q=pizza AND lat<double>:[51.4707 TO 51.5224] AND long<double>:[-0.6622 TO -0.5775]

The following parameters can be passed for more sophisticated searches;

All parameters except 'q' are optional.

Special Fields

Dublin Core

All Dublin Core attributes are indexed and stored if detected in the attachment. Descriptions of the fields come from the Tika javadocs.

Examples

https://localhost:5984/dbname/_fti/design_doc/view_name?q=field_name:value
https://localhost:5984/dbname/_fti/design_doc/view_name?q=field_name:value&sort;=other_field
https://localhost:5984/dbname/_fti/design_doc/view_name?debug=true&sort;=billing_size&q;=body:document AND customer:[A TO C]

Search Results Format

The search result contains a number of fields at the top level, in addition to your search results.

The search results array

The search results arrays consists of zero, one or more objects with the following fields;

Here's an example of a JSON response without sorting;

{
  "q": "+content:enron",
  "skip": 0,
  "limit": 2,
  "total_rows": 176852,
  "search_duration": 518,
  "fetch_duration": 4,
  "rows":   [
        {
      "id": "hain-m-all_documents-257.",
      "score": 1.601625680923462
    },
        {
      "id": "hain-m-notes_inbox-257.",
      "score": 1.601625680923462
    }
  ]
}

And the same with sorting;

{
  "q": "+content:enron",
  "skip": 0,
  "limit": 3,
  "total_rows": 176852,
  "search_duration": 660,
  "fetch_duration": 4,
  "sort_order":   [
        {
      "field": "source",
      "reverse": false,
      "type": "string"
    },
        {
      "reverse": false,
      "type": "doc"
    }
  ],
  "rows":   [
        {
      "id": "shankman-j-inbox-105.",
      "score": 0.6131107211112976,
      "sort_order":       [
        "enron",
        6
      ]
    },
        {
      "id": "shankman-j-inbox-8.",
      "score": 0.7492915391921997,
      "sort_order":       [
        "enron",
        7
      ]
    },
        {
      "id": "shankman-j-inbox-30.",
      "score": 0.507369875907898,
      "sort_order":       [
        "enron",
        8
      ]
    }
  ]
}

Content-Type of response

The Content-Type of the response is negotiated via the Accept request header like CouchDB itself. If the Accept header includes "application/json" then that is also the Content-Type of the response. If not, "text/plain;charset=utf-8" is used.

Fetching information about the index

Calling couchdb-lucene without arguments returns a JSON object with information about the index.

https://127.0.0.1:5984/<db>/_fti/<ddoc>/<index

returns;

{"current":true,"disk_size":110674,"doc_count":397,"doc_del_count":0,
"fields":["default","number"],"last_modified":"1263066382000",
"optimized":true,"ref_count":2}

Index Maintenance

For optimal query speed you can optimize your indexes. This causes the index to be rewritten into a single segment.

curl -X POST https://localhost:5984/<db>/_fti/<ddoc>/<index>/_optimize

If you just want to expunge pending deletes, then call;

curl -X POST https://localhost:5984/<db>/_fti/<ddoc>/<index>/_expunge

If you recreate databases or frequently change your fulltext functions, you will probably have old indexes lying around on disk. To remove all of them, call;

curl -X POST https://localhost:5984/<db>/_fti/_cleanup