Free implementations of sql software for linux

Fast MD5 Implementation in Java is a heavily optimized implementation of the MD5 hashing algorithm written in Java.
Fast MD5 Implementation in Java includes an optional native method for even greater speed improvements.
How Fast Is It?
Short answer:Much faster than any other Java implementation that I have tested and (surprisingly) even faster than the native, non-Java MD5 implementation on some systems.
Long answer:First of all, it is important to note that the term "fast" is used here in relative terms. The implementation of the MD5 message digest algorithm available on this page is written in Java and is fast compared with other implementations written in Java, both because it is heavily optimized by itself and because there is an optional native method that makes it even faster when the platform supports it. How it compares to a sensible implementation written in a language, such as C, that is compiled directly to machine code, is heavily dependent upon how good of a job the JIT compiler in your JVM does in compiling the code or whether you are able to use the optional native method.
Enhancements:
- Martin West contributed a bug fix and some code refactoring to make all targets work out of the box in the Ant build file. Previously, the "dist" target did not work if the "docs" directory was not present.

Dimedis::Sql is a SQL/DBI Interface for banking applications.

SYNOPSIS

use Dimedis::Sql;

my $sqlh = new Dimedis::Sql ( ... );
$sqlh->install ( ... );

my $seq_id = $sqlh->insert ( ... );
my $modified = $sqlh->update ( ... );
my $blob_sref = $sqlh->blob_read ( ... );

$sqlh->use_db ( ...)
my $db_prefix = $sqlh->db_prefix ( ...)

my $modified = $sqlh->do ( ... );
my $href = $sqlh->get ( ... );
my @row = $sqlh->get ( ... );

my ($from, $where) = $sqlh->outer_join ( ... );
my $cond = $sqlh->cmpi ( ... );
my $where = $sqlh->contains ( ... );

my $feature_href = $sqlh->get_features;

Chemistry::SQL is an access database functions module.

SYNOPSIS

use strict;
use Chemistry::SQL;
use Chemistry::Artificial::SQL;

my $db_name = $ARGV[0];
my $file = $ARGV[1];

my $db1 = Chemistry::SQL::new(db_host=>"127.0.0.1",db_user=>"root",db_port=>"3306",db_pwd=>"",
db_name=>$db_name,db_driver=>"mysql");
if ($db1->db_exist)
{ $db1->connect_db;
$db1->del_tables;
$db1->create_tables_mysql;
$db1->inscomp_from_file("$file");
}
else
{
$db1->create_db;
$db1->connect_db;
$db1->create_tables_mysql;
$db1->inscomp_from_file("$file");
}
# Reaction Insertion

my $qart = Chemistry::Artificial::SQL::new($db1);
my $qr =$qart->q_reaccion(C=CC=C.C=C>>C1=CCCCC1,smiles);
$db1->reactionsert($qr,"","0");

This package provides the necessary functions to interact with the database. The methods implemented in this module are oriented to give users control of the database without knowing how to use SQL queries.

libstratanetsh project provides source-code to a library (formerly called libnetshclient) for use in the development of applications which require direct communication (using the netsh protocol) with a Foxmoxie Networks STRATA-family router, or any STRATA protocol-capable implementation.
This library may be used to develop client or server implementations, and has also been used for purposes ranging from file transfer to voice applications.
Enhancements:
- TLS implementation optimized, updated API, server-operation support, bugfixes

Anemon Dhcp Servers goal is to create an implementation of the DHCP protocol under GPL and a lot of stuff around to facilitate network management.
The python sqlobject modules gives anemon the ability to use many database as backend (mysql, postgresql, mssql, sqllite, sybase, etc..)
The project is subdivised (at the moment) in 4 components :
- The Anemon Dhcp Server is a Python/SQL implementation of the DHCP server protocol
- The Anemon Web Center is a web management interface to Anemon Dhcp Server and MyDNS
- The Anemon Dhcp Client will be a Python implementation of the DHCP client protocol
- The Anemon Dhcp relay will be a Python implementation of the DHCP relay protocol

SQL::Statement::Embed can embed a SQL engine in a DBD or module.

SQL::Statement is designed to be easy to embed in other modules and to be especially easy to embed in DBI drivers. It provides a SQL Engine and the other module needs to then provide a data source and a storage mechanism. For example, the DBD::CSV module uses SQL::Statement as an embedded SQL engine by implementing a file-based data source and by using DBI as the user interface. Similarly DBD::Amazon uses SQL::Statement as its SQL engine, provides its own extensions to the supported SQL syntax, and uses on-the-fly searches of Amazon.com as its data source.

SQL::Statement is the basis for eight existing DBDs (DBI database drivers). If you have a new data source, you too can create a DBD without having to reinvent the SQL wheel. Its fun, its easy, become a DBD author today!

SQL::Statement can be also be embedded without DBI. Well explore that first since developing a DBD uses most of the same methods and techniques.

The role of SQL::Statement subclasses

SQL::Statement provides a SQL parsing and execution engine. It does not provide a data source or storage mechanism other than in-memory tables. The DBD::File module is a subclass of SQL::Statement that provides access to file-based storage mechanisms. Its quite possible to use things other than files as data souces, in which case we wouldnt use DBD::File, instead wed replace DBD::Files methods with our own. In the examples below, well use DBD::File, replacing only a few methods.

SQL::Statement provides SQL parsing and evaluation and DBD::File provides file-based storage. The only thing missing is a data source - what we actually want to store and query. As an example suppose we are going to create a subclass called Foo that will provide as a data source a simple file similar to a passwd file - one record per line, fields separated by colons, with only three fields "username, uid, gid".

Consider what needs to happen to perform a SELECT query on our Foo data:

* recieve a SQL string
* parse the SQL string into a request structure
* open the table(s) specified in the request
* define column names and postions for the table
* read rows from the table
* convert the rows from colon-separated format into perl arrays
* match the columns and rows against the requested selection criteria
* return requested rows and columns to the user

To perform operations like INSERT and DELETE, we also need to:

* convert rows from perl arrays into colon-separated format
* write rows
* delete rows

SQL::Statement takes care of all of the SQL parsing and evaluation. DBD::File takes care of file opening, reading, writing, and deleting. So the only things Foo is really responsible for are:

* define column names and postions for the table
* convert rows from colon-separated format into perl arrays
* convert rows from perl arrays into colon-separated format

In SQL::Statement subclasses these responsibilities are assigned to two objects, a ::Statement object is responsible for opening the table, defining the column names and positions, and for creating new ::Table objects. A ::Table object is responsible for reading, converting, writing, and deleting data.

BITE SQL Reports project consists of an advanced SQL reports for existing databases.
BITE is SQL reporting software that can be "plugged" into any existing database to add advanced reporting capabilities.
You can build reports and store them in the same database, view, print, save, and FTP these reports to pre-defined places, or distribute the "viewer" software so that others can run the reports that you build.
Enhancements:
- An initial Makefile has been added.
- gcc compile issues have been fixed.