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RDBMS: All content tagged as RDBMS in NoSQL databases and polyglot persistence

A Different Approach to Data Modeling: Thinking of Data Service Requirements

Pedro Visintin:

We can distinguish several objects: Session, Cart, Item, User, Order, Product and Payment. Usually we use ActiveRecord to store all of them. But this time let’s think about it differently.

For sessions, we don’t need durable data at all — Redis can be a good option, and of course will be faster than any RDBMS. For Cart and Item,we will need high availability across different locations. Riak can fit well for this use case. For User Order Product and Payment, a relational database can fit well, focusing on Transactions and Reporting about our application.

This is a very good exercise for understanding the requirements for your data service layer. As much as I write about polyglot persistence, when architecting an application never leave aside or ignore the operational requirements for your service.

Original title and link: A Different Approach to Data Modeling: Thinking of Data Service Requirements (NoSQL database©myNoSQL)

via: http://news.dice.com/2012/04/06/nosql-and-sql/


Design Your Database Schema

Three paterns of making a relational database behave like a document database. Useful in the times there were no document databases around.

If we were to use a relational database we might end up with a single table with an ungodly amount of columns so that each event has all its specific columns available. But we will never use all columns for one event of course. Maybe try to re-use columns, and call them names like column1, column2 etc. Hmm… sounds like fun to maintain and develop against.

The other pattern would be to start creating a normalized schema with multiple tables – probably one per game, and one per even type etc. So then we end up with a complex schema that needs to be maintained and versioned. Inserts and selects will be spread across tables and for sure we need to change the schema when new games or events are introduced.

There is also a third pattern out there which is to store a binary blob in the database… lets not even talk about that one.

Original title and link: Design Your Database Schema (NoSQL database©myNoSQL)

via: http://www.cubeia.com/index.php/blog/archives/436


The NoSQL Hoopla … What Is NonsenSQL About It?

Dr. C. Mohan’s first post about NoSQL databases:

Having worked in the database field for more than 3 decades with a fair amount of impact on the research and commercial sides of this field (see bit.ly/cmohan), it pains me to see the casual way in which some designs have been done and some supposedly new ideas get proposed/implemented. Not enough efforts are being made to relate these proposals to what has been done in the past and benefit from the lessons learnt in the context of RDBMSs. Not everything needs to be done differently just because it is supposedly a very different world now! 

There are evolutionary and revolutionary products. And sometimes changing the perspective and starting from scratch is needed to validate or invalidate new or old time hypothesis. In the world of polyglot persistence there’s space for every solution that solves real problems. As perfect as one product could be it will not be able to address all the needs. The data storage space is not a zero-sum game. Winners don’t take it all.

Original title and link: The NoSQL Hoopla … What Is NonsenSQL About It? (NoSQL database©myNoSQL)

via: http://cmohan.tumblr.com/post/20141910210/the-nosql-hoopla-what-is-nonsensql-about-it


AlchemyDB: An Integrated GraphDB + RDBMS + KV Store + Document Store

I recently added a fairly feature rich Graph Database to AlchemyDB (called it LuaGraphDB) and it took roughly 10 days to prototype. I implemented the graph traversal logic in Lua (embedded in AlchemyDB) and used AlchemyDB’s RDBMS to index the data. The API for the GraphDB is modeled after the very advanced GraphDB Neo4j. Another recently added functionality in AlchemyDB, a column type that stores a Lua Table (called it LuaTable), led me to mix Lua-function-call-syntax into every part of SQL I could fit it into (effectively tacking on Document-Store functionality to AlchemyDB). Being able to call lua functions from any place in SQL and being able to call lua functions (that can call into the data-store) directly from the client, made building a GraphDB on top of AlchemyDB possible as a library, i.e. it didn’t require any new core functionality.

Two reasons for posting about it:

  1. relaxing contraints of the relational model can make a RDBMS partially adapt to other models (nb: this is just re-inforcing an old strategy used by)
  2. usually dismissed for the lack of portability in the RDBMS world, server side scripting support is an extremely powerful tool. Having processing close to data (i.e. data locality) is a well known advantage, but as shown in this post and in creating reliable queues with Redis Lua scripting it can open the doors to completely new features.

Original title and link: AlchemyDB: An Integrated GraphDB + RDBMS + KV Store + Document Store (NoSQL database©myNoSQL)

via: http://jaksprats.wordpress.com/2012/02/28/lightweight-oltp-data-platform/


MariaDB 5.5 Connection Thread Pool

MariaDB-5.5.21-beta is the first MariaDB release featuring the new thread pool. Oracle offers a commercial thread pool plugin for MySQL Enterprise, but now MariaDB brings a thread pool implementation to the community!

I’ve checked the timestamp of the post and knowledge base article three times. It is 2012.

Original title and link: MariaDB 5.5 Connection Thread Pool (NoSQL database©myNoSQL)


MongoDB or Relational Database: That Is the Question

Before we begin, I am going to ask a question of the reader, “do you have a data description document?” Something in writing (written down, not just in your head) that describes your data requirements and how you will be using your data. I know that some people are thinking that I want you to have a E-R diagram fully developed, that is not the case. I just want the reader to have some sense what data they will be working with, how is that data related (or not), what they need to do with the data, etc. If you know those things then you will be able to determine what issues are relevant to you and how important are they.

It’s impossible to use just a checklist to decide between a NoSQL database and relational database, but at least this post lists the main chapters you need to consider before making a decision. Keep in mind that the devil (read complexity) is in the details though.

Original title and link: MongoDB or Relational Database: That Is the Question (NoSQL database©myNoSQL)

via: http://chuckjohnson.wordpress.com/2012/03/19/to-sql-relational-or-not-to-sql-nosql-that-is-the-question/


Examples of Using MySQL in Interesting Ways

Maggie Nelson:

Here are a couple of examples of using MySQL in interesting (and it’s up to you whether unwise) ways:

  • MySQL as a graph database, like Twitter’s FlockDB.
  • MySQL as document store, like FriendFeed’s extremely custom schema design.
  • MySQL as a key/value store. This lets you play with NoSQL concepts using MySQL.

Such examples abound. In fact most of the companies known for their contributions or using NoSQL databases run some sort of interesting relational database deployment. Most of the time these examples are interpreted as clear proof that relational databases can solve any problem. Reality is different though: the engineers’ long time familiarity with relational databases allowed them to ingeniously overpass their limitations when lacking better alternatives. But with NoSQL databases getting more mature every day, less and less problems require acrobatic usages of relational databases.

Original title and link: Examples of Using MySQL in Interesting Ways (NoSQL database©myNoSQL)

via: http://phpadvent.org/2011/out-with-the-old-by-maggie-nelson


MongoDB Work Queues: Techniques to Easily Store and Process Complex Jobs

David Berube’s article debuts with a very good overview of the different approaches for creating and managing work queues:

There are many approaches to creating work queues. One option, though naive, is to use a relational database management system (RDBMS). This is simple to implement because many architectures already have a database system such as MySQL. However, performance is less than optimal compared with other approaches. The atomicity, consistency, isolation, and durability (ACID) compliance required for RDBMS is not necessary for this scenario and negatively impacts performance. A simpler system can perform better.

One system that has gained in popularity for this use is Redis. It’s a key-value data store, like the highly popular memcached, but with more features. For example, Redis has support for pushing and popping elements off lists in a highly scalable and efficient way. Resque, often used with Ruby on Rails, is a system built on top of Redis (see Resources for more details). However, Redis supports only simple primitives. You can’t insert complex objects into the lists, and it has relatively limited support for managing items in those lists.

Alternatively, many systems use a message broker such as Apache ActiveMQ or RabbitMQ. Although these systems are fast and scalable, they’re designed for simple messages. If you want to perform nontrivial reporting on your work queues or modify items in the queues, you are stuck because message brokers rarely offer those features. Fortunately, a powerful, scalable solution is available: MongoDB.

MongoDB allows you to create queues that contain complex nested data. Its locking semantics guarantee you won’t experience problems with concurrency, and its scalability ensures you can run large systems. Because MongoDB is a powerful relational database, you can also run robust reporting on your queue and prioritize by complex criteria. However, MongoDB is not a traditional RDBMS. For instance, it does not support Structured Query Language (SQL) queries.

MongoDB has many appealing features in addition to excellent performance for work queues, such as a flexible, schemaless approach. It supports nested data structures, meaning you can even store subdocuments. Because it is a more full-featured data store than Redis, it provides a richer set of management functions so you can easily view, query, update, and delete jobs on any arbitrary criteria.

Using MongoDB as a queueing system is in many regards as good and as wrong as using a relational database for this type of functionality. They completely lack the semantics and features required by both queues and pubsub. Redis (and obviously the dedicated MOMs) supports natively both queues and pubsub semantics.

So even if the article lists a couple of reasons why MongoDB could be used as a queuing system, consider this solution if and only if the only system you are allowed to run on your environment is MongoDB.

Original title and link: MongoDB Work Queues: Techniques to Easily Store and Process Complex Jobs (NoSQL database©myNoSQL)

via: http://www.ibm.com/developerworks/opensource/library/os-mongodb-work-queues/


The time for NoSQL is now

Andrew C. Oliver:

The transition to NoSQL databases will take time. We still don’t have TOAD, Crystal Reports, query language standardization and other essential tools needed for mass adoption. There will be missteps (i.e. I may need a different type of database for reporting than for my operational system), but I truly think this is one technology that isn’t just marketing.

This coming from someone that was happy to discover back in 1998 all the knobs in Oracle.

Original title and link: The time for NoSQL is now (NoSQL database©myNoSQL)

via: http://osintegrators.com/node/76


Doug Cutting About Hadoop, Its Adoption and Future, and Its Relationship With Relational Databases

Jaikumar Vijayan (Computerworld) interviews Doug Cutting:

Q: How would you describe Hadoop to a CIO or a CFO? Why should enterprises care about it?

A: At a really simple level, it lets you affordably save and process vastly more data than you could before. With more data and the ability to process it, companies can see more, they can learn more, they can do more. [With Hadoop] you can start to do all sorts of analyses that just weren’t practical before. You can start to look at patterns over years, over seasons, across demographics. You have enough data to fill in patterns and make predictions and decide, “How should we price things?” and “What should we be selling now?” and “How should we advertise?” It is not only about having data for longer periods, but also richer data about any given period.

The interview covers topics like why the interest in Hadoop, Hadoop adoption in the enterprise world and outside, limitations of relational database. It is a must read—if only they would have added some newlines here and there.

Original title and link: Doug Cutting About Hadoop, Its Adoption and Future, and Its Relationship With Relational Databases (NoSQL database©myNoSQL)

via: http://www.computerworld.com/s/article/9222758/The_Grill_Doug_Cutting


MySQL MEMORY as Poor Man’s Memcached Replacement

ServerFault Q&A:

Q: Copy MySQL to RAM as a poor man’s memcached replacement?

A: Use the the MEMORY storage engine on a read only slave to do your reads from, is exactly what you really want and a sane setup. Forget “dumping it to disk” (?!) or other strange things.

You can even put the slave as another instance on your existing server if you can’t afford to setup a dedicated slave, but properly tuning the MySQL parameters for mostly read workloads will bring a significant performance enhancement too!

Jiminy

Original title and link: MySQL MEMORY as Poor Man’s Memcached Replacement (NoSQL database©myNoSQL)


The Wonderful Wizard of Oz Through a Polyglot Persistence Glass

Adrian Giordani:

Relational databases are the Yellow Brick Road of managing large structured data globally. […] In the 1939 film of The Wizard of Oz, a red brick road is intertwined with the yellow one. Similarly, a new type of database might soon offer a different path: NoSQL, or Not-Only-SQL, first coined in 2008, is promising a faster and more scalable database architecture, at least for some cases.

But who’s the Tin Woodman?

Original title and link: The Wonderful Wizard of Oz Through a Polyglot Persistence Glass (NoSQL database©myNoSQL)

via: http://www.isgtw.org/feature/following-red-brick-road-data-management