Sql. Orm. Xanadu. Fairfax Underground Arrest/Ticket Search. Scalien. The Death of the Relational Database. The relational database is becoming increasingly less useful in a web 2.0 world. The reason for this is that, while the relational database model is great for storing information, it is horrible for storing knowledge. By knowledge I mean information that has value beyond the narrow current conception of the given application. I mean information that can have enduring value. In this context, one might say knowledge is information in non-disposable form. The reason the relational database doesn’t represent knowledge very well is that the relational database is only good at storing objects and relationships between them when one fully understands exactly what objects and what relationships will be managed upfront.
The way I usually describe the situation is to say that the relational database is brittle but strong. Storing the relationships between objects *in* the objects is a problem. This is bad. “Excuse me Mrs. Think about this. For example, imagine starting out with a contact list. Open Source - Scala Migrations. Scala Migrations is a library to manage upgrades and rollbacks to database schemas. Migrations allow a source control system to manage together the database schema and the code using the schema. It is designed to allow multiple developers working on a project with a database backend to design schema modifications independently, apply the migrations to their local database for debugging and when complete, check them into a source control system to manage as one manages normal source code. Other developers then check out the new migrations and apply them to their local database.
Finally, the migrations are used to migrate the production databases to the latest schema version. The package is based off Ruby on Rails Migrations and in fact shares the exact same schema_migrations table to manage the list of installed migrations. The Scala Migrations library is written in Scala and makes use of the clean Scala language to write easy to understand migrations, which are also written in Scala. Spreadsheets vs. Relational Databases: Bridging the Gap. For non-programmers, spreadsheets are usually the option of choice when it comes to keeping track of non-trivial amounts of structured data. This is seen in all kinds of settings ranging from the business world to public administration and academic research. Spreadsheets, however, can only capture one kind of data structure: separate tabular views of the data. This is a significant constraint for the user, who arguably thinks of the data, and needs to navigate it, in a more hierarchical manner (e.g.
“each student takes a number of courses, each which has a number of TAs”). 1) Strongly typed worksheets with “advisory” error checking. 2) Transparent many-to-many or one-to-many relationships between worksheets in a workbook (think foreign key relationships in database-speak). 3) Hierarchical presentation of relationships between worksheets in the workbook. (This project was done by Paul Grogan, Yod Watanaprakornkul, and me.) 10-80 times faster. The figure below shows the architecture of the new VectorWise engine. Theleft part shows the system architecture (“X100” execution engine andColumnBM buffer manager) and how it maps on the computer resources(CPU cache, RAM and disk). The right part shows a query in action, havingbeen decomposed into so-called relational operators (Aggregate, Project,Select and Scan) and execution primitives (such as summation –aggr_sum_flt_col).
A ground-breaking database kernel - is now being combined with the leading open source relational database from Ingres. The Ingres VectorWise project team has worked with Intel to evaluate database performance on the new Intel Xeon processor 5500 series based platform. To date, the results of the project have demonstrated dramatic cost and performance capabilities as evidenced by nearly 80 fold speed up on a query modelled after the Q1 query of TPC-H3 suite on the Intel Xeon processor. Episode 102: Relational Databases. Building Scalable Databases: Pros and Cons of Various Database S. Database sharding is the process of splitting up a database across multiple machines to improve the scalability of an application. The justification for database sharding is that after a certain scale point it is cheaper and more feasible to scale a site horizontally by adding more machines than to grow it vertically by adding beefier servers.
Why Shard or Partition your Database? Let's take Facebook.com as an example. In early 2004, the site was mostly used by Harvard students as a glorified online yearbook. You can imagine that the entire storage requirements and query load on the database could be handled by a single beefy server. Fast forward to 2008 where just the Facebook application related page views are about 14 billion a month (which translates to over 5,000 page views per second, each of which will require multiple backend queries to satisfy).
Besides query load with its attendant IOPs, CPU and memory cost there's also storage capacity to consider. Further Reading. How To Manage Hundreds of Thousands of Documents? Introducing Redis: a fast key-value database. Posted on Mar 11th, 2009 in Programming | 13 comments One of the many advantages of having remarkable friends is learning quite early on about their most ambitious and interesting projects. Today, I’m going to talk about Redis, one such project that my friend Salvatore “antirez” Sanfilippo started. Redis (REmote DIctionary Server) is a key-value database written in C.
It can be used like memcached, in front of a traditional database, or on its own thanks to the fact that the in-memory datasets are not volatile but instead persisted on disk. As such it’s also very similar to memcachedb, though unlike the latter, Redis provides you with the ability to define keys that are more than mere strings (as well as being able to handle multiple databases). At this early stage (beta 6), lists, sets and even basic master-slave replication are supported, but more features are in the works (including compression). No related posts. BASE: AN ACID ALTERNATIVE. Related Content Anatomy of a Solid-state Drive While the ubiquitous SSD shares many features with the hard-disk drive, under the surface they are completely different.
Browse this Topic: Queue on Reddit Dan Pritchett, Ebay Web applications have grown in popularity over the past decade. There are two strategies for scaling any application. Horizontal scaling offers more flexibility but is also considerably more complex. As figure 1 illustrates, both approaches to horizontal scaling can be applied at once. Functional Partitioning Functional partitioning is important for achieving high degrees of scalability.
Relying on database constraints to ensure consistency across functional groups creates a coupling of the schema to a database deployment strategy. Schemas that can scale to very high transaction volumes will place functionally distinct data on different database servers. CAP Theorem Consistency. Availability. Partition tolerance. ACID Solutions Atomicity. Consistency. Isolation. Durability. Different Foreign Keys for Different Tables. A foreign key can be used to implement table design patterns that span multiple tables. By choosing how a foreign key handles a DELETE attempt on the parent table, you can structure your table designs to follow two standard patterns. Welcome to the Database Programmer blog. This series of essays is for anybody who wants to learn about databases on their own terms.
There is a complete Table of Contents, as well as a summary of Table Design Patterns. A Simple Example of Two Foreign Keys Picture a basic shopping cart, with its two basic tables of CART and CART_LINES (or ORDERS and ORDER_LINES if you are more old-fashioned). CUSTOMERS | | /|\ CART Cart is child of customers | | /|\ CART_LINES Lines is child of Cart There are two foreign keys here. Table Types and Table Design Patterns In A Sane Approach To Choosing Primary Keys we saw that table design begins with identifying the basic kinds of tables: Reference and Small Master Tables, Large Master Tables, Transactions, and Cross-References. History Tables. A history table allows you to use one table to track changes in another table. While the basic idea is simple, a naive implementation will lead to bloat and will be difficult to query. A more sophisticated approach allows easier queries and can produce not just information about single rows, but can also support aggregrate company-wide queries.
This week in the Database Programmer Blog we return to table design patterns with an essay on history tables. The basic premise of this blog is that good coding skills do not lead magically to good database skills -- you can only make optimal use of a database by understanding it on its own terms. There is a new essay each Monday, and there is a Complete Table of Contents and a List of Table Design Patterns.
What to Put Into A History Table Naive approaches to history tables usually involve making a complete copy of the original (or new) row when something changes in the source table. Next we ask which columns we will definitely not need. Database Normalization and Table structures. Unfortunately, this is not quite correct. To give a quick summary, the normal forms are as follows: 1NF: Every row must be an identifiable relation. This means that, in a table, no row may be an exact duplicate of another row, and nor may the row be completely filled with NULL. 2NF All non-key attributes of the table must depend on the entire key.
This means that, when a table has a compound key, attributes which depend only on a subset of the key columns should be moved out of the table. For example, take the comment above which mentions a compound primary key of OrderID and LineNo; if the application wants to use invoice_bgcolor to alternate the background color of rows on an invoice, it should go outside of this table, because it depends on LineNo but not on OrderID 3NF You have 3NF pretty much correct above. BCNF Ummm.... what you have up above is actually pretty close to 2NF, but not BCNF. A database is normalized if it is in 5NF. Minimize Code, Maximize Data. Early in my career, I was fortunate to receive some programming lessons from one of the early pioneers in computer age, A.
Neil Pappalardo. While I am sure he would not remember me, I certainly remembered him, and he said one thing that I remember very much: Minimize Code, Maximize Data. This is the Database Programmer blog, for anybody who wants practical advice on database use. There are links to other essays at the bottom of this post. This blog has two tables of contents, the Topical Table of Contents and the list of Database Skills. The Best Kept Secret in Programming This week we are going to examine what I was told so many years ago: Minimize Code, Maximize Data Since then, and it was nearly 15 years ago, I have never once heard another programmer (except myself) express this very basic and simple idea.
First possibility: the guy was totally wrong. The second possibility seems much more likely to me: most programmers just don't think that way. The Example: Magazine Regulation Conclusion.