Free rdbms software for linux

DBD::RAM is a DBI driver for files and data structures.

SYNOPSIS

use DBI;
my $dbh = DBI->connect(DBI:RAM:,usr,pwd,{RaiseError=>1});
$dbh->func({
table_name => my_phrases,
col_names => id,phrase,
data_type => PIPE,
data_source => [ ],
}, import );
print $dbh->selectcol_arrayref(qq[
SELECT phrase FROM my_phrases WHERE id = 1
])->[0];
__END__
1 | Hello, New World
2 | Some other Phrase

This sample creates a database table from data, uses SQL to make a selection from the database and prints out the results. While this table is in-memory only and uses pipe "delimited" formating, many other options are available including local and remote file access and many different data formats.

DBD::RAM allows you to import almost any type of Perl data structure into an in-memory table and then use DBI and SQL to access and modify it. It also allows direct access to almost any kind of file, supporting SQL manipulation of the file without converting the file out of its native format.

The module allows you to prototype a database without having an rdbms system or other database engine and can operate either with or without creating or reading disk files. If you do use disk files, they may, in most cases, either be local files or any remote file accessible via HTTP or FTP.

Intelligent Multimedia Management System is an intelligent playlist framework that tracks your listening patterns and dynamically adapts to your taste. It is incredibly unobtrusive and easy to use as it requires no direct user interaction. Plugins are currently available for XMMS and BMP.
Main features:
- IMMS is easy to install. It is purely a plugin - no XMMS patch required.
- A very lightweight embedded SQL database is used, so theres no need to setup a RDBMS.
- IMMS is easy to use. Song rating is done completely transparently to the user. It does not get in your way.
- IMMS does a much better job of shuffling than most players. It keeps track of when a song was last played, and makes sure same songs are not repeated too often. It is even able to recognise different versions of the same song (eg. remixes) and treat them as one song!
- IMMS uses a variety of techniques to figure out which songs should be played together, and which should not. It studies your listening habits, as well as using acoustic properties of the songs themselves, such as BPM and frequency spectrum.
- IMMS is fair. Even unfavoured songs have a (small) chance of being played.
It should a relatively easy to write IMMS plugins for new players, provided they have a sufficiently robust plugins interface.
You will need things like get_current_song, get_playlist_length, get_playlist_entry, and ideally (but not necessarily) some mechanism for specifying the next song. XMMS/BMP plugins should give you a good idea of what needs to be done.

DBToy is a fuse-based filesystem for linux. DBToy that lets you browse the contents of a relational database through a set of directories and xml files.

You will see a directory for every schema in your db, each containing a directory for every table. In the "table" dirs there are a couple of files with the tables definition and data.

You will find instruction on how to compile and use the module and the daemon in the readme file contained in the source distribution.

Playing with dbtoyfs

1) Make sure your rdbms server is running and reachable from your linux box, modprobe fuse kernel module.

2) run "dbtoy -u username -p password mountpoint" (dbtoy with no args shows help)

3) walk through you filesystem.

Each schema has its directory, containing one subdirectory for each table. Inside the dirs there is one file with the description of the datatypes and another with the data.

Ex.:

mountpoint
|
+------schema1
| ....
+------schemaN
|
+-----table1
| .....
+-----tableM
|
+-----data
+-----types

You can "cat mountpoint/schemaN/tableM/types" to see a description of the table, or look at the data. At the moment XML is the only output format.

If you feel lucky can play with the EXPERIMENTAL query file feature: "cat data?col=val" where col is a valid column name and val is a feasible value for col (and string must be quoted!)

Tangram is a simple Perl module that can store pure objects in standard relational databases.

Tangram is an object-relational mapper. It is orthogonal, meaning that it does not require anything of the objects stored in it (other than the common convention that base objects be based upon HASHes; individual columns can be anything).

It consists of a schema language that can describe or be built around an object structure, or so as to closely match an existing SQL schema (with some limitations). This schema language is rich enough to express such common RDBMS features as links, foreign keys, and link tables.

It also consists of a relational database storage engine, which based on the schema structure, will make Perl structures persist in a relational (SQL compliant) database. References to other objects (or collections, as represented with foreign keys and link tables) may be loaded using on demand references that `lazily load data when it is needed.

As of Tangram 2.08, the schema need not describe every single object property, so that you can map only the columns you intend to query. The rest of the object is then stored in a column via a serialiser, like Data::Dumper, YAML or Storable. These structures themselves may contain arbitrary references to other objects in storage.

Tangram has soundly engineered transaction support, without sacrificing excellent data caching abilities. The general optimisation strategy of the code makes it most suited for OLTP (aka application servers) and other situations where it is better to select and update whole rows than to worry about which columns to retrieve/update or not retrieve/update.

Once your object are persistent, you can build query expressions to find them in terms of the schema language that you used to put them in. Therefore, the schema data structure does not describe a data structure, it describes a data access pattern.

If you are not picky about which accessor module to use, preferring to specify the schema once only, then you can use the seperately distributed Class::Tangram::Generator to make a set of classes from a Tangram schema structure.

If you are looking for a tool that implements SQL abstraction only, you have probably missed the point (of this module, anyway), and a well-supported module like Class::DBI, or an interactive SQL modeller like Alzabo will likely suit your needs better.

Tangram is beginning to include preliminary support for aggregation functions, and currently supports grouping, summing and counting. Joins must currently be in terms of integer primary key columns, to extend past this would require extra mapping types to be developed. Basic support for alternative join types is present, but in its infancy.

Tangram currently contains no support for database-side updates (ie, UPDATE foo SET bar = baz where frop = blarg), but support is planned.

There is no support for creating views based on existing classes to make new derived classes; you have to use your database SQL and create corresponding Tangram classes manually to do that.

Tangram has a web site at http://tangram.utsl.gen.nz/, currently sponsored by MarketView (New Zealand) Ltd.

Arguvision project is an enterprise class video surveillance system.
Arguvision is based on a distributed Linux or Unix cluster architecture, with up to 1000 cameras per single cluster with aggregation capability and instant access to recorded or live video.
Main features:
- Up to 1000 video channels per cluster - unlimited clusters
- 6 channels at 720x480 at 30 frames/sec per video grabber unit
- NTSC or PAL video input
- M-JPEG compression 1:15
- 1TB of storage for 10 days per camera at full resolution in high motion area
- Export video using AVI format , or single images using JPEG format
- Real-time view from any camera using client software
- Secure authenticated user access (SSL) based on user role
- Instant browse and archive search by date/time or alarm condition
- Alarm event notification by e-mail or pager
- Record on pre-programmed schedule, or on alarming condition
- Alarm events generated on motion, on video loss, or by activation of an external sensor
- NTP synchronization with time standards (like US Governments NIST)
- Data storage on internal/external network devices managed by SQL RDBMS
- RDBMS capacity monitoring
- Purge obsolete video data automatically

DBD::AnyData is a DBI access to XML, CSV and other formats.

SYNOPSIS

use DBI;
my $dbh = DBI->connect(dbi:AnyData(RaiseError=>1):);
$dbh->func( trains, CSV, /users/joe/cars.csv, ad_catalog);
$dbh->func( bikes, XML, [$xml_str], ad_import);
$dbh->func( cars, DBI, $mysql_dbh, ad_import);
#
# ... DBI/SQL methods to access/modify the tables cars,bikes,trains
#
print $dbh->func( cars, HTMLtable, ad_export);

or

use DBI;
my $dbh = DBI->connect(dbi:AnyData(RaiseError=>1):);
$dbh->func( Pipe, data.pipe, XML, data.xml, ad_convert);

or

(many combinations of a dozen other data formats, see below)

The DBD::AnyData module provides a DBI/SQL interface to data in many formats and from many sources.

Currently supported formats include general format flatfiles (CSV, Fixed Length, Tab or Pipe "delimited", etc.), specific formats (passwd files, web logs, etc.), a variety of other kinds of formats (XML, Mp3, HTML tables), and, for some operations, any DBI accessible database. The number of supported formats will continue to grow rapidly since there is an open API making it easy for any author to create additional format parsers which can be plugged in to AnyData.

Data in these various formats can come from local files, from remote files, or from perl data structures such as strings and arrays.

Regardless of the format or source of the data, it may be accessed and/or modified using all standard DBI methods and a subset of SQL syntax.

In addition to standard database access to files, the module also supports in-memory tables which allow you to create temporary views; to combine data from a number of sources; to quickly prototype database systems; and to display or save the data in any of the supported formats (e.g. to display data in a CSV file as an HTML table).

These in-memory tables can be created from any combination of DBI databases or files of any format. They may also be created from perl data structures which means its possible to quickly prototype a database system without any file access or rdbms backend.

Sourdough is a comprehensive web application framework for PHP5. Sourdough provides developers with real-world solutions for common system components such as User Management, Session Handling, User Authentication, Exception Handling and Logging, Template System as well as Form Building and Handling.
Sourdough project does also include a database abstraction layer with excellent support for the popular MySQL database as well as support for many other database systems like PostgreSQL, MSSQL and the new lightweight SQLite.
Sourdoughs extensive feature set can also simplify or eliminate many common, and often tedious, programming tasks.
Main features:
- Built for PHP5: new object model
- Fully object-oriented API
- Contains extensive unit test framework
- Truly independent. Database interactions with Sourdough are truly independent.
- Easy to use. All modules are accessed by a global factory. Dependencies are resolved by Sourdough itself.
Sourdough is a YAPF - Yet Another PHP Framework. Especially now, as interest on PHP 5 is growing, there are more and more frameworks beeing developed. Each of them provides us with nicer code, more functionality and higher abstraction. Still, there is no perfect framework.
Some frameworks better fit for this project, some better fit for another. And usually, designing a framework is just the consequence of a larger growing codebase of some independent projects. A framework then is usually based on existing code. No wonder there are some many frameworks around.
As far as it concerns PHP, I was pretty disappointed up to now. There was not a lot of clean code around. The only projects like PEAR, Horde or BlueShoes were huge and tricky to get into. The main goal of Sourdough is not to provide you with uncountable features and highest possible complexity. The main goal is to provide you with a easy-to-use light weighted tool for daily web applications.
The database abstraction layer that comes with Sourdough is somehow more flexible than others. It attempts to make your database applications completely portable without loosing the power of your RDBMS.
Sourdough does not intend to be the perfect PHP framework. It just wants to open your eyes if you havent found anything useful yet. Give it a try!
Enhancements:
- This release contains extended Captcha image verification and various additions.