Overview

This document provides an introduction to the native C++ Xapian API. This API provides programmers with the ability to search through (potentially very large) bodies of data using probabilistic methods.

Note: The portion of the API currently documented here covers only the part of Xapian concerned with searching through existing databases, not that concerned with creating them.

It is probably a good idea to read the Introduction to Information Retrieval and the Introduction to Xapian before reading this document, or at least before attempting to use the API. You may also wish to read the QuickStart reference, for some simple worked examples of Xapian usage.

This document does not detail the exact calling conventions (parameters passed, return value, exceptions thrown, etc...) for each method in the API. For such documentation, you should refer to the automatically extracted documentation, which is generated from detailed comments in the source code, and should thus remain up-to-date and accurate. This documentation is generated using the Doxygen application. To save you having to generate this documentation yourself, we include the built version in our distributions, and also keep the latest version on our website.

Errors and exceptions

Error reporting is often relegated to the back of manuals such as this. However, it is extremely important to understand the errors which may be caused by the operations which you are trying to perform. This becomes particularly relevant when using a large system, with such possibilities as databases which are being updated while you search through them, and distributed enquiry systems.

Errors in Xapian are all reported by means of exceptions. All exceptions thrown by Xapian will be subclasses of OmError. Note that OmError is an abstract class; thus you must catch exceptions by reference rather than by value.

There are two flavours of error, derived from OmError:

Each of these flavours is further subdivided, such that any particular error condition can be trapped by catching the appropriate exception. If desired, a human readable explanation of the error can be retrieved by calling OmError::get_msg().

In addition, standard system errors may occur: these will be reported by throwing appropriate exceptions. Most notably, if the system runs out of memory, a std::bad_alloc() exception will be thrown.

Terminology

Databases

These may also occasionally be called Indexes. In Xapian (as opposed to a database package) a database consists of little more than indexed documents: this reflects the purpose of Xapian as an information retrieval system, rather than an information storage system.

The exact contents of a database depend on the type (see "Database Types" for more details of the database types currently provided).

Queries

The information to be searched for is specified by a Query. In Xapian, queries are made up of a structured boolean tree, upon which probabilistic weightings are imposed: when the search is performed, the documents returned are filtered according to the boolean structure, and weighted (and sorted) according to the probabilistic model of information retrieval.

Memory handling

The user of Xapian does not usually need to worry about how Xapian performs its memory allocation: Xapian objects can all be created and deleted as any other C++ objects. The convention is that whoever creates an object is ultimately responsible for deleting it. This becomes relevant when passing a pointer to data to Xapian: Xapian will not assume that such pointers remain valid across separate API calls, and it will be the callers responsibility to delete the object pointed to, as and when required.

The OmEnquire class

The OmEnquire() class is central to all searching operations. It provides an interface for

A typical enquiry session will consist of most of these operations, in various orders. The OmEnquire class presents as few restrictions as possible on the order in which operations should be performed. Although you must set the query before any operation which uses it, you can call any of the other methods in any order.

Many operations performed by the OmEnquire class are performed lazily (ie, just before their results are needed). This need not concern the user except to note that, as a result, errors may not be reported as soon as would otherwise be expected. In particular, errors regarding opening of the database may be reported when a query is performed (although they may not: you should catch exceptions in both situations).

Specifying a database

When creating an OmEnquire object, a database to search must be specified. Databases are specified by creating an OmDatabase object and calling OmDatabase::add_database() . This takes an OmSettings object as its only parameter, which contains a list of settings used to describe the database.

The main setting is the "backend" setting, which descibes the type of database to open. Which other settings are valid depend on which database type is selected.

Instead of setting the settings directly, the "auto" backend can be used to detect the settings automatically. This takes a single extra setting, "auto_dir" which points to the directory containing the database.

An extra feature available using the auto backend is "stub databases". If "auto_dir" points to a file rather than a directory, the file will be assumed to contain a set of settings (one per line, in the format "name=value"), which will be used to open the database. For example, a file might contain:

backend=remote
remote_type=tcp
remote_port=23876
remote_server=localhost

Note that these settings are case sensitive.

Database types

The current types understood by Xapian are:

quartz

This is the main database type, which should be used in almost all cases. The format allows progressive modifications, single-writer multiple-reader access to the database, and highly efficient and scalable access to data.

This takes several parameters.

da_flimsy

This is a proprietary, legacy format, holding a database in a non-updateable form (ie, the database can't be altered, it is built from an existing database). We support read-only access to this, and it is thus unlikely to be useful outside our company.

This takes one, two or three parameters. If one parameter is supplied, it represents the path to a directory containing the Record file in a file called "R", the Term file in a file called "T", and optionally the fast access key file in a file called "keyfile"

If two parameters are supplied, they represent the full paths to the Record and Term file, respectively. In this case, there is assumed to be no keyfile.

If three parameters are supplied, the first two are the full paths to the Record and Term files, respectively, and the third is the full path to the keyfile.

da_heavy This is a similar to da_flimsy, allowing access to the "heavy duty" variant for larger documents. This is the format produced by the "makeda" utility, and is thus useful while new file formats have not been created for the new development.
It takes the same parameters as da_flimsy.
db_flimsy

This is a proprietary, legacy format, holding a database in a dynamically updateable form (ie, the database can be altered while queries are being performed on it.) We support read-only access to this, and it is thus unlikely to be useful outside our company.

This takes one or two parameters. The first parameter is the full path to the DB file.

If a second parameter is supplied, it represents the full path to the fast access keyfile. If a second parameter is not supplied, the keyfile will be searched for at <first_parameter>_keyfile; if this doesn't exist, no keyfile will be used.

It takes one parameter, which is the full path to the DB file.

db_heavy This is a similar to db_flimsy, allowing access to the "heavy duty" variant for larger documents. We support read-only access to this, and it is thus unlikely to be useful outside our company.
It takes the same parameters as db_flimsy.
inmemory This type is a database held entirely in memory. It is really intended to be used for testing purposes only, but may occasionally prove useful for building up temporary small databases.
It takes no parameters at all, except for some undocumented parameters which cause special effects for testing.

Multiple databases

Xapian can search across several databases as easily as searching across a single one. Simply call OmDatabase::add_database() for each database that you wish to search through.

Other operations, such as setting the query, may be performed before or after this call. It is even possible to perform a query, add a further database, and then perform the query again to get the results with the extra database (although this isn't very likely to be useful in practice).

Specifying a query

Xapian implements both boolean and probabilistic searching. There are two obvious ways in which a pure boolean query can be combined with a pure probabilistic query:

There is in fact a subtle difference in these two approaches. In the first, the collection statistics for the probabilistic query will be determined by the document subset which is obtained by running the boolean query. In the second, the collection statistics for the probabilistic query are determined by the whole document collection. These differences can affect the final result.

Suppose for example the boolean query is being used to retrieve documents in English in a database containing English and French documents. A word like "grand", exists in both languages (with similar meanings), but is commoner in French than English. In the English subset it could therefore be expected to have a higher weight than it would get in the joint English and French databases.

In fact Xapian, as described below, goes for the second approach, which can be implemented very efficiently, despite the the fact that the first is more exact.

In reality, Xapian performs the combined boolean and probabilistic searches simultaneously. This allows various optimisations to be performed, such as giving up on calculating a boolean AND operation when the probabilistic weights that could result from further documents can have no effect on the result set. These optimisations have been found to give a two- or three-fold performance increase in certain cases. The performance is particularly good for queries containing many terms.

A query for a single term

All queries are represented by OmQuery() objects. The simplest possible (non-trivial) query is one which searches for a single term. This can be created as follows (where tname is the term to be searched for):

OmQuery query(tname);

A term in Xapian is represented simply by a string of binary characters. Usually, when searching text, these characters will be the word which the term represents, but during the information retrieval process Xapian attaches no specific meaning to the term.

This constructor actually takes a couple of extra parameters, which may be used to specify positional and frequency information for terms in the query:

OmQuery(const om_termname & tname_,
        om_termcount wqf_ = 1,
        om_termpos term_pos_ = 0)

The wqf (Within Query Frequency) is a measure of how common a term is in the query. This is particularly useful when generating a query from an existing document, but may also be used as a crude way of increasing the importance of a term in a query. Note that, if the intention is simply to ensure that a particular term is in the query results, you should use a boolean AND rather than set a high wqf.

The term_pos represents the position of the term in the query. This is used for phrase searching, passage retrieval, and other operations which require knowledge of the order of terms in the query (such as returning the set of matching terms in a given document in the same order as they occur in the query). If such operations are not required, the default value of 0 may be used.

Note that it may not make much sense to specify a wqf other than 1 when supplying a term position (unless you are trying to affect the weighting, as previously described).

Note also that the results of OmQuery(tname, 2) and OmQuery(OmQuery::OP_OR, OmQuery(tname), OmQuery(tname)) are exactly equivalent.

Compound queries

Out of single term queries, compound queries can be built up. A compound is made up from two sub-queries with a connecting operator, where each sub-query is a compound query or a single term query. This is done using the following constructor:

OmQuery(om_queryop op_,
        const OmQuery & left,
        const OmQuery & right)

The two most commonly used operators are OmQuery::OP_AND and OmQuery::OP_OR, which enable us to construct boolean queries made up from the usual AND and OR operations. But in addition to this, a probabilistic query in its simplest form, where we have a list of terms which give rise to weights that need to be added together, is also made up from a set of terms joined together with OmQuery::OP_OR.

The full set of available om_queryop operators is:
OmQuery::OP_AND Return documents returned by both subqueries.
OmQuery::OP_OR Return documents returned by either subquery.
OmQuery::OP_AND_NOT Return documents returned by the left subquery but not the right subquery.
OmQuery::OP_FILTER As OmQuery::OP_AND, but use only weights from left subquery.
OmQuery::OP_AND_MAYBE Return documents returned by the left subquery, but adding document weights from both subqueries.
OmQuery::OP_XOR Return documents returned by one subquery only.

Understanding queries

Each term, t, in the query has a weight, wQ(t), given by

                     (K' + 1) f't
             wQ(t) = ------------- w(t)
                      K'L' + f't

where f't is the wqf of t in the query, L' is the nql, or normalised query length, and K' is a constant. And the weight w(t) is given by,
                   (r + h) (N - R - n + r + h)
        w(t) = log ---------------------------, where h = 1/2
                    (R - r + h) (n - r + h)
See the Introduction to Information Retrieval for a full discussion. For any particular document, D, if t indexes D, there is a weight wD(t), which is the contribution, or partial score, of term t to the total score for document D, and it is given by,
                     (K + 1) ft
             wD(t) = ---------- wQ(t)
                      KL + ft

A query can be thought of as a tree structure. At each node is an om_queryop operator, and on the left and right branch are two other queries. At each leaf node is a term, t, transmitting documents and scores, D and wD(t), up the tree.

An OmQuery::OP_OR node transmits documents from both branches up the tree, summing the scores when a document is found in both the left and right branch. For example,

                           docs       1    8    12    16    17    18
                           scores    7.3  4.1   3.2  7.6   3.8   4.7 ...
                             |
                             |
                      OmQuery::OP_OR
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
An OmQuery::OP_AND node transmits only the documents found on both branches up the tree, again summing the scores,
                           docs       1   16
                           scores    7.3  7.6  ...
                             |
                             |
                      OmQuery::OP_AND
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
An OmQuery::OP_AND_NOT node transmits up the tree the documents on the left branch which are not on the right branch. The scores are taken from the left branch. For example, again summing the scores,
                           docs       12   17
                           scores    3.2  3.8 ...
                             |
                             |
                    OmQuery::OP_AND_NOT
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
An OmQuery::OP_MAYBE node transmits the documents up the tree from the left branch only, but adds in the score from the right branch for documents which occur on both branches. For example,
                           docs       1    12   16   17
                           scores    7.3  3.2  7.6  3.8 ...
                             |
                             |
                         OmQuery::OP_AND_MAYBE
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
OmQuery::OP_FILTER is like OmQuery::OP_AND, but weights are only transmitted from the left branch. For example,
                           docs       1   16
                           scores    3.1  3.1  ...
                             |
                             |
                    OmQuery::OP_FILTER
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
OmQuery::OP_XOR is like OmQuery::OP_OR, but documents on both left and right branches are not transmitted up the tree. For example,
                           docs       8    12    17    18
                           scores    4.1   3.2  3.8   4.7 ...
                             |
                             |
                      OmQuery::OP_XOR
                         /       \
                        /         \
                       /           \
                      /             \
   docs     1   12   16   17         1   8   16   18
   scores  3.1 3.2  3.1  3.8 ...    4.2 4.1 4.5  4.7 ...
OmQuery::OP_XOR is used internally, but we have not found a plausible use for it in query construction, so it will not be mentioned again.

A query can therefore be thought of as a process for generating an M set from the terms at the leaf nodes of the query. Each leaf node gives rise to a posting list of documents with scores. Each higher level node gives rise to a similar list, and the root node of the tree contains the final set of documents with scores (or weights), which are candidates for going into the M set. The M set contains the documents which get the highest weights, and they are held in the M set in weight order.

It is important to realise that within Xapian the structure of a query is optimised for best performance, and it undergoes various transformations as the query progresses. The precise way in which the query is built up is therefore of little importance.

Using queries

A plain probabilistic query is created by connecting terms together with OmQuery::OP_OR operators. For example,
    OmQuery query();   // undefined query; see next section

    query = OmQuery(OmQuery::OP_OR, query, OmQuery("regulation"));
    query = OmQuery(OmQuery::OP_OR, query, OmQuery("import"));
    query = OmQuery(OmQuery::OP_OR, query, OmQuery("export"));
    query = OmQuery(OmQuery::OP_OR, query, OmQuery("canned"));
    query = OmQuery(OmQuery::OP_OR, query, OmQuery("fish"));

This creates a probabilistic query with terms `regulation', `import', `export', `canned' and `fish'.

In fact this style of creation is so common that there is the shortcut construction:

    vector <om_termname> terms;
    terms.push_back("regulation");
    terms.push_back("import");
    terms.push_back("export");
    terms.push_back("canned");
    terms.push_back("fish");

    OmQuery query(OmQuery::OP_OR, terms.begin(), terms.end());
Suppose now we have this Boolean query,
    ('EEC' - 'France') and ('1989' or '1991' or '1992') and 'Corporate Law'
This could be built up as bquery like this,
    OmQuery bquery1(OmQuery::OP_AND_NOT, "EEC", "France");

    OmQuery bquery2("1989");
    bquery2 = OmQuery(OmQuery::OP_OR, bquery2, "1991");
    bquery2 = OmQuery(OmQuery::OP_OR, bquery2, "1992");

    OmQuery bquery3("Corporate Law");

    OmQuery bquery(OmQuery::OP_AND, bquery1, OmQuery(OmQuery::OP_AND(bquery2, bquery3)));
and this can be attached as a filter to query to run the probabilistic query with a Boolean filter,
    query = OmQuery(OmQuery::OP_FILTER, query, bquery);
This is the general technique for processing boolean queries, so to run a pure boolean query, attach it as a filter to an undefined query:
    bquery = OmQuery(OmQuery::OP_FILTER, OmQuery(), bquery);
    // bquery will now run as a pure boolean
A common requirement in search engine functionality is to run a probabilistic query where some terms are required to index all the retrieved documents (`+' terms), and others are required to index none of the retrieved documents (`-' terms). For example,
    regulation import export +canned +fish -japan
the corresponding query can be set up by,
    vector <om_termname> plus_terms;
    vector <om_termname> minus_terms;
    vector <om_termname> normal_terms;

    plus_terms.push_back("canned");
    plus_terms.push_back("fish");

    minus_terms.push_back("japan");

    normal_terms.push_back("regulation");
    normal_terms.push_back("import");
    normal_terms.push_back("export");

    OmQuery query(OmQuery::OP_AND_MAYBE,
                  OmQuery(OmQuery::OP_AND, plus_terms.begin(), plus_terms.end());
                  OmQuery(OmQuery::OP_OR, normal_terms.begin(), normal_terms.end()));

    query = OmQuery(OmQuery::OP_AND_NOT,
                    query,
                    OmQuery(OmQuery::OP_OR, minus_terms.begin(), minus_terms.end()));

Undefined queries

These are an added complication, although they make it possible to write much neater code, and to perform some extra types of query. (See "Specifying a pure boolean query").

Undefined queries are not empty queries, or queries which match nothing: rather, they should be thought of as placeholders. An undefined query is created by calling the default constructor for OmQuery(), and can then be used in many places in construction of a query.

Specifying a pure boolean query

Occasionally it may be desirable to perform a purely boolean query, and not to calculate weights for each document. This can be performed within Xapian as follows:

The boolquery will return a set of documents matching the criteria supplied: each document in the result set will have a weight of 0.

Retrieving the results of a query

The OmEnquire class does not require that a method be called in order to perform the query. Rather, you simply ask for the results of a query, and it will perform whatever calculations are necessary to provide the answer:

OmMSet OmEnquire::get_mset(om_doccount first,
                           om_doccount maxitems,
                           const OmRSet * omrset = 0,
                           const OmMatchOptions * moptions = 0,
                           const OmMatchDecider * mdecider = 0) const

When asking for the results, you must specify (in first) the first item in the result set to return, where the numbering starts at zero (so a value of zero corresponds to the first item returned being that with the highest score, and a value of 10 corresponds to the first 10 items being ignored, and the returned items starting at the eleventh).

You must also specify (in maxitems) the the maximum number of items to return. Unless there are not enough matching items, precisely this number of items will be returned. If maxitems is zero, no items will be returned, but the usual statistics (such as the maximum possible weight which a document could be assigned by the query) will be calculated. (See "The OmMSet" below).

The OmMSet

Query results are returned in an OmMSet object. The prime field in this is items, which is a list of OmMSetItem's comprising the selected part of the match results. This list is in descending sorted order of relevance (so the most relevant document is first in the list). Each OmMSet item contains a document id, and the weight calculated for this document.

An OmMSet also contains various information about the search result:
firstitem The index of the first item in the result which was put into the mset. (Corresponding to first in OmEnquire::get_mset())
max_attained The greatest weight which is attained in the full results of the search.
max_possible The maximum possible weight in the mset.
docs_considered The number of documents matching the query considered for the mset. This provides a lower bound on the number of documents in the database which have a weight greater than zero. Note that this value may change if the search is recalculated with different values for first or max_items.

See the automatically extracted documentation for more details of these fields.

The OmMSet also provides methods for converting the score calculated for a given document into a percentage value, suitable for displaying to a user. This may be done using the convert_to_percent() methods:

     int OmMSet::convert_to_percent(const OmMSetItem & item) const
     int OmMSet::convert_to_percent(om_weight wt) const
These methods return a value in the range 0 to 100, which will be 0 if and only if the item did not match the query at all.

Getting the document's data

Each document in the database has some data associated with it, represented by an OmDocument object. There are some arbitrary numeric keys (which are not yet available, and mainly useful in the match process) and an arbitrary lump of data. To get the OmDocument object, use OmEnquire::get_doc(). The returned OmDocument is fairly cheap to copy around.

This data can be used to store a summary of the document along with a URL, for example, or anything else the application developer would like.

The data can be retrieved with OmDocument::get_data() from the OmDocument object. This returns a C++ string with the data. It can include embedded nulls or other special characters.

Specifying a relevance set

Xapian supports the idea of relevance feedback: that is, of allowing the user to mark documents as being relevant to the search, and using this information to modify the search. This is supported by means of relevance sets, which are simply sets of document id's which are marked as relevant. These are held in OmRSet objects, one of which may optionally be supplied to Xapian in the omrset parameter when calling OmEnquire::get_mset().

Match options

There are various additional options which may be specified when performing the query. These are specified by passing an OmMatchOptions object. If no such object is passed, the default options will be used. The options are as follows.

collapse key Each document in a database may have a set of numbered keys. The contents of each key is a string of arbitrary length. The OmMatchOptions::set_collapse_key(om_keyno key_) method specifies a key number upon which to remove duplicates, and the OmMatchOptions::set_no_collapse() method specifies that no duplicate removal should be done. Only one duplicate removal key may be specified at any time, and the default is to perform no duplicate removal.
percentage cutoff It may occasionally be desirable to exclude any documents which have a weight less than a given percentage value. This may be done using OmMatchOptions::set_percentage_cutoff(). Note that documents rarely score 100 percent, so using set_percentage_cutoff(100) will be unlikely to return any documents.
sort direction Some weighting functions may frequently result in several documents being returned with the same weight. In this case, by default, the documents will be returned in ascending document id order. This can be changed by using OmMatchOptions::set_sort_forward() to set the sort direction. set_sort_forward(false) may be useful, for example, when it would be best to return the newest documents, and new documents are being added to the end of the database.

Match decision functors

Sometimes it may be useful to return only documents matching criteria which can't be easily represented by queries. This can be done using a match decision functor. To set such a condition, derive a class from OmMatchDecider and override the function operator, operator()(const OmDocument *doc). The operator can make a decision based on the document keys via OmDocument::get_key(om_keyno).

The functor will also have access to the document data stored in the database (via OmDocument::get_data()), but beware that, for most database backends, this is an expensive operation and is likely to slow down the search considerably.

Expand - Suggesting new terms for the query

Xapian also supports the idea of calculating terms to add to the query, based on the relevant documents supplied. A set of such terms, together with their weights, may be returned by:

OmESet OmEnquire::get_eset(om_termcount maxitems,
                           const OmRSet & omrset,
                           const OmExpandOptions * eoptions = 0,
                           const OmExpandDecider * edecider = 0) const

As for get_mset, up to maxitems expand terms will be returned, with fewer being returned if and only if no more terms could be found.

The expand terms are returned in sorted weight order in an OmESet item.

Expand options

Options to be used when performing the expand may be set using an OmExpandOptions.

Currently, there is only one such option:

use_query_terms(bool allow_query_terms_)
If allow_query_terms_ is true, terms which are already in the query will be returned by get_eset().
The default is false.

Expand decision functors

It is often useful to allow only certain classes of term to be returned in the expand set. For example, there may be special terms in the database with various prefixes, which should be removed from the expand set. This is accomplished by providing a decision functor. To do this, derive a class from OmExpandDecider and override the function operator, operator()(const om_termname &). The functor is called with each term before it is added to the set, and it may accept (by returning true) or reject (by returning false) the term as appropriate.

Thread safety

There's no pthread specific code in Xapian. If you want to use the same object concurrently from different threads, it's up to you to police access (with a mutex or in some other way) to ensure only one method is being executed at once. The reason for this is to avoid adding the overhead of locking and unlocking mutexes when they aren't required. It also makes the Xapian code easier to maintain, and simplifies building it.

For most applications, this is unlikely to be an issue - generally the calls to Xapian are likely to be from a single thread. And if they aren't, you can just create an entirely separate OmDatabase object in each thread - this is no different to accessing the same database from two different processes.

Examples

Extensively documented examples of simple usage of the Xapian API for creating databases and then for searching through them are given in the QuickStart tutorial.

Further examples of usage of Xapian are available in the xapian-examples package.