At the Forge - Shoehorning Data into a Database
Relational databases are really great for storing and retrieving data, but sometimes, they aren't quite up to the task. Joe Celko, whose SQL for Smarties books are among my favorites, dedicated an entire volume to the issue of trees and hierarchies. These data structures might be common and useful in most programming languages, but they can be difficult to model as tables, particularly if you care about efficient use of the database. Things become even trickier if you're dealing with a number of related, but distinct, types of entities, such as different types of employees or different types of vehicles.
One way to solve this problem is not to use relational databases. Objects can be quite good at handling trees and arrays, as well as inheritance hierarchies. Furthermore, object databases do exist, and the Python-based Zope application framework has demonstrated that it's even possible to have object databases in production. Gemstone's demonstration of Ruby running on top of its Smalltalk VM, with its accompanying object database, means that Ruby programmers soon might have access to similar technology.
But, object databases still are far from the mainstream. Most Web developers have access to a relational database, and not much else. Is there anything that we can do for these people?
This month, we take a look at two different ways we can handle data that doesn't quite fit into a relational database. These techniques are quite different from one another, and they don't even come close to the full range of possibilities you can get with a relational database. But, they both work and are used in production environments—and if your data doesn't seem to fit into standard database paradigms, you might want to consider one of them.
Some data-modeling issues are typically even harder to deal with. For example, a classic introduction to the world of object-oriented programming describes a human resources department. The HR department tracks employees, all of whom have some common characteristics. But, some employees are programmers, some are secretaries, and some are managers—and each of the employee types has specific data that needs to be associated with them.
In an object-oriented world, it's easy to model this. You create an employee class, and then create multiple subclasses of programmer, secretary and manager. Subclassing creates an “is-a” relationship, such that a programmer is an employee. This means that programmers have all the attributes of an employee, but also have some additional characteristics that distinguish them from an ordinary employee. With these subclasses in place, we then can create an array (or any other data structure) of people in our company, knowing that although some are programmers and others are secretaries, they're all employees and can be treated as such.
Translating this idea to the world of relational databases can be a bit tricky. One solution is to use inheritance in your database tables. PostgreSQL has done this for years; thus, it's called an object-relational database by many users. You can do the following in PostgreSQL, for example:
CREATE TABLE Employees ( id SERIAL, first_name TEXT NOT NULL, last_name TEXT NOT NULL, email_address TEXT NOT NULL, PRIMARY KEY(id), UNIQUE(email_address) ); CREATE TABLE Programmers ( main_language TEXT NOT NULL ) INHERITS(Employees); CREATE TABLE Secretaries ( words_per_minute INTEGER NOT NULL ) INHERITS(Employees); INSERT INTO Employees (first_name, last_name, email_address) VALUES ('George', 'Washington', 'firstname.lastname@example.org'); INSERT INTO Programmers (first_name, last_name, email_address, main_language) VALUES ('Linus', 'Torvalds', 'email@example.com', 'C'); INSERT INTO Secretaries (first_name, last_name, email_address, words_per_minute) VALUES ('Condoleezza', 'Rice', 'firstname.lastname@example.org', 10);
If we ask for all employees in the system, we'll get all three of the people we have entered:
atf=# select * from employees; id | first_name | last_name | email_address ----+------------+------------+------------------------ 1 | George | Washington | email@example.com 2 | Linus | Torvalds | firstname.lastname@example.org 3 | Condoleezza| Rice | email@example.com (3 rows)
Of course, this query shows only the columns of the Employees table, which are common to that table and to those that inherit from it. If we want to find out how many words per minute someone types, we must address that query specifically to the Secretaries table:
atf=# select * from secretaries; id | first_name | last_name | email_address | words_per_minute ----+------------+-----------+----------------+------------------ 3 | Condoleezza| Rice | firstname.lastname@example.org | 10 (1 row)
Notice that the id column for all three tables, which was defined as SERIAL (that is, a nonrepeating incrementing integer), is unique across all three tables.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.View Now!
|The Firebird Project's Firebird Relational Database||Jul 29, 2016|
|Stunnel Security for Oracle||Jul 28, 2016|
|SUSE LLC's SUSE Manager||Jul 21, 2016|
|My +1 Sword of Productivity||Jul 20, 2016|
|Non-Linux FOSS: Caffeine!||Jul 19, 2016|
|Murat Yener and Onur Dundar's Expert Android Studio (Wrox)||Jul 18, 2016|
- The Firebird Project's Firebird Relational Database
- Stunnel Security for Oracle
- My +1 Sword of Productivity
- SUSE LLC's SUSE Manager
- Non-Linux FOSS: Caffeine!
- Managing Linux Using Puppet
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Parsing an RSS News Feed with a Bash Script
- Google's SwiftShader Released
- Doing for User Space What We Did for Kernel Space
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide