Filesystem Indexing with libferris
The libferris Project began in early 2001 in order to create a virtual filesystem operating as a shared library. Many tree-like structures are presented through a single filesystem interface by libferris. Operating in the user address space instead of in the kernel allows libferris to expose a large number of tree-like sources. These sources would be difficult to access from the Linux kernel. All filesystems are accessible through the root:// URI in libferris and include kernel file:// URLs, relational databases, XML files and databases, network-accessible resources like HTTP/FTP servers and other composite files like db4, tarballs and RDF, as well as standard kernel filesystems like ext3 and XFS.
Here's why you might choose libferris for filesystem indexing and querying:
Plugins can extract text that is trapped in files for full-text indexing.
Unified interface for all data sources used as input for indexing—for example, the following are all indexable with libferris: text inside SleepyCat dbxml files, inside tarballs and in individual messages in mbox files or relational databases.
Metadata trapped inside files can be indexed and searched for. For example, the ID3 tags for audio files can be indexed to provide search-by-artist functionality.
Identical basic add/query commands for all indexing plugins, so you can switch between indexing implementations fairly easily.
Combination searches for full-text and extended attributes for your filesystem. The ferris-search tool allows you to combine many searches into one result set.
Ability to search for files based on the metadata they once had.
Ability to search for files based on Supervised Machine Learning (SML) judgments—spam filtering for your filesystem. Unfortunately, covering SML is beyond the scope of this article.
The notion of files and directories is merged in libferris into a single abstraction. This allows things like tar archives to be mounted as a filesystem implicitly by libferris. In this case, the tarball is both a file and a directory at the same time.
The extended attribute (EA) interface presents data from many sources, including the kernel's listxattr(2) interface, RDF/bdb repositories and dynamically extracted values. An example of a dynamic EA is the width of an image. When an image's width EA is read, libferris uses a plugin to determine the width of the image file. Another example is the sample rate for audio files.
Table 1. EA Examples
|Name-extension||File's extension, such as tar|
|treeicon||The URL of an image that is appropriate for this file.|
|is-audio-object||The MIME major type audio.|
|is-source-object||This file's source code.|
|is-remote||This file remote to this machine.|
|language-human||Human language for this file.|
|a52-channels||Number of audio channels.|
|year||Year the album/single containing this track was released.|
More EAs and their descriptions are listed on the libferris Web site (see the on-line Resources).
This leads to two distinct types of indexes that libferris can create and query—full-text and EA. Full-text indexes allow you to find files based on the words that they contain. EA indexes allow you to find files based on the metadata for the file. The indexing structures needed to resolve queries against full text or EA are significantly different. For example, full-text indexes may store the list of documents containing each word (an inverted file) to resolve queries such as “find all documents containing the word libferris”. EA indexes need to be able to handle range queries such as “find all files modified last month”.
libferris uses plugins to handle the implementation of these indexes. For full-text indexes, you can use any or all of these: an internal format based on inverted files, Apache Lucene compiled with gcj, an ODBC supporting relational database, Xapian or the TSearch2 module in PostgreSQL. For EA indexes you can choose an internal design based on sorted inverted files, LDAP, Apache Lucene compiled with gcj, an ODBC supporting relational database or native PostgreSQL using some PGSQL. The recommended choices for general use are Xapian or TSearch2 for full text, and PostgreSQL or ODBC for EA indexes.
The PostgreSQL modules are similar to those of the ODBC, but they use PGSQL and other PostgreSQL-specific functionality. Using the PostgreSQL TSearch2 plugin for full text requires a template database to be set up on your PostgreSQL server. See the on-line Resources for details.
All indexes exist in their own directory in libferris. Default full-text and EA indexes are in ~/.ferris in full-text-index and ea-index, respectively. Indexes are created with either fcreate or gfcreate from the ferriscreate package. Like many tools in libferris, the gf prefixed tool does much the same thing as the f prefixed tool, but it offers a GTK+2 interface. The following sections describe creation, population and querying of both index types.
|Where's That Pesky Hidden Word?||Aug 28, 2015|
|A Project to Guarantee Better Security for Open-Source Projects||Aug 27, 2015|
|Concerning Containers' Connections: on Docker Networking||Aug 26, 2015|
|My Network Go-Bag||Aug 24, 2015|
|Doing Astronomy with Python||Aug 19, 2015|
|Build a “Virtual SuperComputer” with Process Virtualization||Aug 18, 2015|
- Concerning Containers' Connections: on Docker Networking
- Problems with Ubuntu's Software Center and How Canonical Plans to Fix Them
- A Project to Guarantee Better Security for Open-Source Projects
- Where's That Pesky Hidden Word?
- Firefox Security Exploit Targets Linux Users and Web Developers
- My Network Go-Bag
- Doing Astronomy with Python
- Three More Lessons
- Build a “Virtual SuperComputer” with Process Virtualization
- diff -u: What's New in Kernel Development