The Qddb Database Suite
In the simple case, Qddb uses a single set of tuple trees instead of flat tables. A tuple tree is simply the collection of prejoined rows from the relational tables in a one-many relationship. That is, each record represents some entity, and each entity can have an arbitrary number of values for some subset of the fields. More complex cases (such as many-many relationships) can be handled with a set of Qddb schemas.
We can use standard relational tables to model any Qddb schema. For example, our Properties schema requires four tables: one table for the address information and one table for each expandable field. The tables require a plethora of link information for the purpose of joining the rows.
It is the complexity of managing these tables that prevents normal users from building their own databases with standard relational tools. Even programmers avoid fields that can have multiple values because a separate table is required for each such field. With Qddb, users and programmers can allow multiple values for any field by simply appending an asterisk to the field's definition in the schema.
The relational model requires that field values be atomic. That is, when searching, field values cannot be broken into smaller searchable components. By default, Qddb disobeys this rule by breaking field values of type string at the separators. Most users do not care about the relational model, they simply want to do their work. Sometimes this work involves searching the database for a particular record. By relaxing this restriction, Qddb can search for words in textual fields such as comments or abstracts. For string fields where atomicity is important, you can specify an empty list of separators for that particular field.
Qddb uses several interesting storage techniques. If you look at the contents of the files in your database directory, you will see that all Qddb data and index files are stored as readable text. The Database file contains the field values for each record in the stable part of the database. Empty fields require no storage and are omitted from the Database file.
If you browse the Database file, you will also notice that each record is stored contiguously. Qddb's records are prejoined rows from the relational tables defined in the schema. When you perform a search, the matching tuple trees in the Database file are expanded into the equivalent relational rows for viewing purposes.
Qddb uses inverted indices for searching. When you perform a search, the criteria are quickly translated into the file offsets for the corresponding tuple trees. Each matching tuple tree can then be read from the disk with one disk read per tuple tree.
Since Qddb currently uses inverted indices for indexing, you should periodically reindex the database so Qddb can retain its speed. Only changes and additions are stored in a secondary location and can be reindexed on the fly. The qstall command is used for this purpose:
After stabilization, all changes, additions, and deletions are committed to the Database file.
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
- Non-Linux FOSS: Caffeine!
- Managing Linux Using Puppet
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Doing for User Space What We Did for Kernel Space
- Google's SwiftShader Released
- SuperTuxKart 0.9.2 Released
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