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Apologies if the title made no absolute sense. But, on the other hand, I would like to know if there is any programming model which would let us use Infinispan cache as a real datastore and not just a grid on top of an underlying rdbms.
I know Key-Value stores have real limitations but I couldn't stop thinking about the possibilities of an in-memory solution with all or a subset of RDBMS functionalities. For example: If I want to retrieve a particular set of Keys based on value>34.56%, just like how we would use a where clause in an sql stmt.
My doubt is not specific to infinispan but any IMKVS for that purpose. I know it's a shot in the dark considering the data structures and algorithms behind IMKVS specifications.
Any help or direction to resources which talk about these lines would be of great help.
I suggest you write down all the queries that you execute against SQL DB and check if these could be translated into KVS language.
In Infinispan you can index the values and execute queries for such filtering, but you can't do any table joins.
If you are in need for more powerful API, specifically using JPA, take a look at Hibernate OGM.
And while KVSs offer some level of reliability, in practice I wouldn't trust the documentation too much. You need to perform extensive testing of your system and check that you can retrieve the data even after various types of crashes and network failures (or that you can live with throwing the data away).
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I've been hearing things about NoSQL and that it may eventually become the replacement for SQL DB storage methods due to the fact that DB interaction is often a bottle neck for speed on the web.
So I just have a few questions:
What exactly is it?
How does it work?
Why would it be better than using a SQL Database? And how much better is it?
Is the technology too new to start implementing yet or is it worth taking a look into?
There is no such thing as NoSQL!
NoSQL is a buzzword.
For decades, when people were talking about databases, they meant relational databases. And when people were talking about relational databases, they meant those you control with Edgar F. Codd's Structured Query Language. Storing data in some other way? Madness! Anything else is just flatfiles.
But in the past few years, people started to question this dogma. People wondered if tables with rows and columns are really the only way to represent data. People started thinking and coding, and came up with many new concepts how data could be organized. And they started to create new database systems designed for these new ways of working with data.
The philosophies of all these databases were different. But one thing all these databases had in common, was that the Structured Query Language was no longer a good fit for using them. So each database replaced SQL with their own query languages. And so the term NoSQL was born, as a label for all database technologies which defy the classic relational database model.
So what do NoSQL databases have in common?
Actually, not much.
You often hear phrases like:
NoSQL is scalable!
NoSQL is for BigData!
NoSQL violates ACID!
NoSQL is a glorified key/value store!
Is that true? Well, some of these statements might be true for some databases commonly called NoSQL, but every single one is also false for at least one other. Actually, the only thing NoSQL databases have in common, is that they are databases which do not use SQL. That's it. The only thing that defines them is what sets them apart from each other.
So what sets NoSQL databases apart?
So we made clear that all those databases commonly referred to as NoSQL are too different to evaluate them together. Each of them needs to be evaluated separately to decide if they are a good fit to solve a specific problem. But where do we begin? Thankfully, NoSQL databases can be grouped into certain categories, which are suitable for different use-cases:
Document-oriented
Examples: MongoDB, CouchDB
Strengths: Heterogenous data, working object-oriented, agile development
Their advantage is that they do not require a consistent data structure. They are useful when your requirements and thus your database layout changes constantly, or when you are dealing with datasets which belong together but still look very differently. When you have a lot of tables with two columns called "key" and "value", then these might be worth looking into.
Graph databases
Examples: Neo4j, GiraffeDB.
Strengths: Data Mining
While most NoSQL databases abandon the concept of managing data relations, these databases embrace it even more than those so-called relational databases.
Their focus is at defining data by its relation to other data. When you have a lot of tables with primary keys which are the primary keys of two other tables (and maybe some data describing the relation between them), then these might be something for you.
Key-Value Stores
Examples: Redis, Cassandra, MemcacheDB
Strengths: Fast lookup of values by known keys
They are very simplistic, but that makes them fast and easy to use. When you have no need for stored procedures, constraints, triggers and all those advanced database features and you just want fast storage and retrieval of your data, then those are for you.
Unfortunately they assume that you know exactly what you are looking for. You need the profile of User157641? No problem, will only take microseconds. But what when you want the names of all users who are aged between 16 and 24, have "waffles" as their favorite food and logged in in the last 24 hours? Tough luck. When you don't have a definite and unique key for a specific result, you can't get it out of your K-V store that easily.
Is SQL obsolete?
Some NoSQL proponents claim that their favorite NoSQL database is the new way of doing things, and SQL is a thing of the past.
Are they right?
No, of course they aren't. While there are problems SQL isn't suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Especially because most database programmers were trained for decades to think of data in a relational way, and trying to press this mindset onto a new technology which wasn't made for it rarely ends well.
NoSQL databases aren't a replacement for SQL - they are an alternative.
Most software ecosystems around the different NoSQL databases aren't as mature yet. While there are advances, you still haven't got supplemental tools which are as mature and powerful as those available for popular SQL databases.
Also, there is much more know-how for SQL around. Generations of computer scientists have spent decades of their careers into research focusing on relational databases, and it shows: The literature written about SQL databases and relational data modelling, both practical and theoretical, could fill multiple libraries full of books. How to build a relational database for your data is a topic so well-researched it's hard to find a corner case where there isn't a generally accepted by-the-book best practice.
Most NoSQL databases, on the other hand, are still in their infancy. We are still figuring out the best way to use them.
What exactly is it?
On one hand, a specific system, but it has also become a generic word for a variety of new data storage backends that do not follow the relational DB model.
How does it work?
Each of the systems labelled with the generic name works differently, but the basic idea is to offer better scalability and performance by using DB models that don't support all the functionality of a generic RDBMS, but still enough functionality to be useful. In a way it's like MySQL, which at one time lacked support for transactions but, exactly because of that, managed to outperform other DB systems. If you could write your app in a way that didn't require transactions, it was great.
Why would it be better than using a SQL Database? And how much better is it?
It would be better when your site needs to scale so massively that the best RDBMS running on the best hardware you can afford and optimized as much as possible simply can't keep up with the load. How much better it is depends on the specific use case (lots of update activity combined with lots of joins is very hard on "traditional" RDBMSs) - could well be a factor of 1000 in extreme cases.
Is the technology too new to start implementing yet or is it worth taking a look into?
Depends mainly on what you're trying to achieve. It's certainly mature enough to use. But few applications really need to scale that massively. For most, a traditional RDBMS is sufficient. However, with internet usage becoming more ubiquitous all the time, it's quite likely that applications that do will become more common (though probably not dominant).
Since someone said that my previous post was off-topic, I'll try to compensate :-) NoSQL is not, and never was, intended to be a replacement for more mainstream SQL databases, but a couple of words are in order to get things in the right perspective.
At the very heart of the NoSQL philosophy lies the consideration that, possibly for commercial and portability reasons, SQL engines tend to disregard the tremendous power of the UNIX operating system and its derivatives.
With a filesystem-based database, you can take immediate advantage of the ever-increasing capabilities and power of the underlying operating system, which have been steadily increasing for many years now in accordance with Moore's law. With this approach, many operating-system commands become automatically also "database operators" (think of "ls" "sort", "find" and the other countless UNIX shell utilities).
With this in mind, and a bit of creativity, you can indeed devise a filesystem-based database that is able to overcome the limitations of many common SQL engines, at least for specific usage patterns, which is the whole point behind NoSQL's philosophy, the way I see it.
I run hundreds of web sites and they all use NoSQL to a greater or lesser extent. In fact, they do not host huge amounts of data, but even if some of them did I could probably think of a creative use of NoSQL and the filesystem to overcome any bottlenecks. Something that would likely be more difficult with traditional SQL "jails". I urge you to google for "unix", "manis" and "shaffer" to understand what I mean.
If I recall correctly, it refers to types of databases that don't necessarily follow the relational form. Document databases come to mind, databases without a specific structure, and which don't use SQL as a specific query language.
It's generally better suited to web applications that rely on performance of the database, and don't need more advanced features of Relation Database Engines. For example, a Key->Value store providing a simple query by id interface might be 10-100x faster than the corresponding SQL server implementation, with a lower developer maintenance cost.
One example is this paper for an OLTP Tuple Store, which sacrificed transactions for single threaded processing (no concurrency problem because no concurrency allowed), and kept all data in memory; achieving 10-100x better performance as compared to a similar RDBMS driven system. Basically, it's moving away from the 'One Size Fits All' view of SQL and database systems.
In practice, NoSQL is a database system which supports fast access to large binary objects (docs, jpgs etc) using a key based access strategy. This is a departure from the traditional SQL access which is only good enough for alphanumeric values. Not only the internal storage and access strategy but also the syntax and limitations on the display format restricts the traditional SQL. BLOB implementations of traditional relational databases too suffer from these restrictions.
Behind the scene it is an indirect admission of the failure of the SQL model to support any form of OLTP or support for new dataformats. "Support" means not just store but full access capabilities - programmatic and querywise using the standard model.
Relational enthusiasts were quick to modify the defnition of NoSQL from Not-SQL to Not-Only-SQL to keep SQL still in the picture! This is not good especially when we see that most Java programs today resort to ORM mapping of the underlying relational model. A new concept must have a clearcut definition. Else it will end up like SOA.
The basis of the NoSQL systems lies in the random key - value pair. But this is not new. Traditional database systems like IMS and IDMS did support hashed ramdom keys (without making use of any index) and they still do. In fact IDMS already has a keyword NONSQL where they support SQL access to their older network database which they termed as NONSQL.
It's like Jacuzzi: both a brand and a generic name. It's not just a specific technology, but rather a specific type of technology, in this case referring to large-scale (often sparse) "databases" like Google's BigTable or CouchDB.
NoSQL the actual program appears to be a relational database implemented in awk using flat files on the backend. Though they profess, "NoSQL essentially has no arbitrary limits, and can work where other products can't. For example there is no limit on data field size, the number of columns, or file size" , I don't think it is the large scale database of the future.
As Joel says, massively scalable databases like BigTable or HBase, are much more interesting. GQL is the query language associated with BigTable and App Engine. It's largely SQL tweaked to avoid features Google considers bottle-necks (like joins). However, I haven't heard this referred to as "NoSQL" before.
NoSQL is a database system which doesn't use string based SQL queries to fetch data.
Instead you build queries using an API they will provide, for example Amazon DynamoDB is a good example of a NoSQL database.
NoSQL databases are better for large applications where scalability is important.
Does NoSQL mean non-relational database?
Yes, NoSQL is different from RDBMS and OLAP. It uses looser consistency models than traditional relational databases.
Consistency models are used in distributed systems like distributed shared memory systems or distributed data store.
How it works internally?
NoSQL database systems are often highly optimized for retrieval and appending operations and often offer little functionality beyond record storage (e.g. key-value stores). The reduced run-time flexibility compared to full SQL systems is compensated by marked gains in scalability and performance for certain data models.
It can work on Structured and Unstructured Data. It uses Collections instead of Tables
How do you query such "database"?
Watch SQL vs NoSQL: Battle of the Backends; it explains it all.
in this post Stack Overflow Architecture i read about something called nosql, i didn't understand what it means, and i tried to search on google but seams that i can't get exactly whats it.
Can anyone explain what nosql means in simple words?
If you've ever worked with a database, you've probably worked with a relational database. Examples would be an Access database, SQL Server, or MySQL. When you think about tables in these kinds of databases, you generally think of a grid, like in Excel. You have to name each column of your database table, and you have to specify whether all the values in that column are integers, strings, etc. Finally, when you want to look up information in that table, you have to use a language called SQL.
A new trend is forming around non-relational databases, that is, databases that do not fall into a neat grid. You don't have to specify which things are integers and strings and booleans, etc. These types of databases are more flexible, but they don't use SQL, because they are not structured that way.
Put simply, that is why they are "NoSQL" databases.
The advantage of using a NoSQL database is that you don't have to know exactly what your data will look like ahead of time. Perhaps you have a Contacts table, but you don't know what kind of information you'll want to store about each contact. In a relational database, you need to make columns like "Name" and "Address". If you find out later on that you need a phone number, you have to add a column for that. There's no need for this kind of planning/structuring in a NoSQL database. There are also potential scaling advantages, but that is a bit controversial, so I won't make any claims there.
Disadvantages of NoSQL databases is really the lack of SQL. SQL is simple and ubiquitous. SQL allows you to slice and dice your data easier to get aggregate results, whereas it's a bit more complicated in NoSQL databases (you'll probably use things like MapReduce, for which there is a bit of a learning curve).
From the NoSQL Homepage
NoSQL is a fast, portable, relational database management system without arbitrary limits, (other than memory and processor speed) that runs under, and interacts with, the UNIX 1 Operating System. It uses the "Operator-Stream Paradigm" described in "Unix Review", March, 1991, page 24, entitled "A 4GL Language". There are a number of "operators" that each perform a unique function on the data. The "stream" is supplied by the UNIX Input/Output redirection mechanism. Therefore each operator processes some data and then passes it along to the next operator via the UNIX pipe function. This is very efficient as UNIX pipes are implemented in memory. NoSQL is compliant with the "Relational Model".
I would also see this answer on Stackoverflow.
Put simply, it means not using a relational database for data storage.
Here's a relevant article: http://www.computerworld.com/s/article/9135086/No_to_SQL_Anti_database_movement_gains_steam_
NoSql is the new database philosophy which talks about all the shortcomings of the relational database design, particularly the problems they have in scaling up for today's demanding web environments.
NoSql is quickly evolving into a movement with new tools, software and formats coming up as alternative to SQL.
RDBMS is as ubiquitous as OOP and while both of these design methodologies solve some problems wonderfully, they don't solve all.
So think of NoSql as the functional programmin of the database world.
Was this simple enough?
NoSQL is the idea that SQL-type databases don't satisfy the demands/requirements of a heavily-used database that requires transactions be reliable and failsafe (or close to it). This ties into the ideas of ACID and CAP, both things worth looking into but not something to lose sleep over unless you run a really popular site that is transaction-heavy (ie Amazon or Ebay). To get a great start on these subjects, I suggest:
http://www.eflorenzano.com/blog/post/my-thoughts-nosql/
and
http://www.julianbrowne.com/article/viewer/brewers-cap-theorem
Something everyone considering a "nosql" approach should consider:
(I shan't risk putting the image into this post as it contains a curse word, and I don't want offensive flags. So clicker beware -- there's an f-word in there. Only click if you have a sense of humor.)
http://browsertoolkit.com/fault-tolerance.png
Found this nice article about no-sql
and this as well:
NoSQL, Yes Search
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Apart from the google/bigtable scenario, when shouldn't you use a relational database? Why not, and what should you use? (did you learn 'the hard way'?)
In my experience, you shouldn't use a relational database when any one of these criteria are true:
your data is structured as a hierarchy or a graph (network) of arbitrary depth,
the typical access pattern emphasizes reading over writing, or
there’s no requirement for ad-hoc queries.
Deep hierarchies and graphs do not translate well to relational tables. Even with the assistance of proprietary extensions like Oracle's CONNECT BY, chasing down trees is a mighty pain using SQL.
Relational databases add a lot of overhead for simple read access. Transactional and referential integrity are powerful, but overkill for some applications. So for read-mostly applications, a file metaphor is good enough.
Finally, you simply don’t need a relational database with its full-blown query language if there are no unexpected queries anticipated. If there are no suits asking questions like "how many 5%-discounted blue widgets did we sell in on the east coast grouped by salesperson?", and there never will be, then you, sir, can live free of DB.
The relational database paradigm makes some assumptions about usage of data.
A relation consists of an unordered set of rows.
All rows in a relation have the same set of columns.
Each column has a fixed name and data type and semantic meaning on all rows.
Rows in a relation are identified by unique values in primary key column(s).
etc.
These assumptions support simplicity and structure, at the cost of some flexibility. Not all data management tasks fit into this kind of structure. Entities with complex attributes or variable attributes do not, for instance. If you need flexibility in areas where a relational database solution doesn't support it, you need to use a different kind of solution.
There are other solutions for managing data with different requirements. Semantic Web technology, for example, allows each entity to define its own attributes and to be self-describing, by treating metadata as attributes just like data. This is more flexible than the structure imposed by a relational database, but that flexibility comes with a cost of its own.
Overall, you should use the right tool for each job.
See also my other answer to "The Next-gen databases."
There are three main data models (C.J.Date, E.F.Codd) and I am adding a flat file to this:
flat file(s) (structure varies - from 'stupid' flat text to files conforming to grammars which coupled with clever tools do very clever things, think compilers and what they can do, narrow application in modelling new things)
hierarchical (trees, nested sets - examples: xml and other markup languages, registry, organizational charts, etc; anything can be modelled, but integrity rules are not easy to express and retrieval is hard to optimize automatically, some retrieval is fast and some is very slow )
network (networks, graphs - examples: navigational databases, hyperlinks, semantic web, again almost anything can be modelled but automatic optimizing of retrieval is a problem)
relational (first order predicate logic - example: relational databases, automatic optimization of retrieval)
Both hierarchical and network can be represented in relational and relational can be expressed in the other two.
The reason that relational is considered 'better' is the declarative nature and standardization on not only the data retrieval language but also on the data definition language, including the strong declarative data integrity, backed up with stable, scalable, multi-user management system.
Benefits come at a cost, which most projects find to be a good ratio for systems (multi application) that store long term data in a from that will be usable in foreseeable future.
If you are not building a system, but a single application, perhaps for a single user, and you are fairly certain that you will not want multiple applications using your data, nor multiple users, any time soon then you'll probably find faster approaches.
Also if you don't know what kind of data you want to store and how to model it then relational model strengths are wasted on it.
Or if you simply don't care about integrity of your data that much (which can be fine).
All data structures are optimized for a certain kind of use, only relational if properly modelled tries to represent the 'reality' in semantically unbiased way. People who had bad experience with relational databases usually don't realize that their experience would have been much worse with other types of data models. Horrible implementations are possible, and especially with relational databases, where it is relatively easy to build complex models, you could end up with quite a monster on your hands. Still I always feel better when I try to imagine the same monster in xml.
One example of how good relational model is, IMO, is ratio of complexity vs shortness of the questions that you will find that involve SQL.
I suggest you visit the High Scalability blog, which discusses this topic almost on a daily basis and has many articles about projects that chose distributed hashes, etc. over RDMBS.
The quick (but very incomplete answer) is that not all data translates well to tables in efficient ways. For example, if your data is essentially one big dictionary, there are probably much faster alternatives that plain old RDBMS. Having said that, it mostly a matter of performance, and if performance isn't a huge concern in a project, and stability, consistency and reliability, for example, are, then I don't see much point in delving into these technologies when RDBMS is a much more mature and well developed scheme, with support in all languages and platforms and a huge set of solutions to choose from.
Fifteen years ago I was working on a credit risk system (basically a big tree walking system). We were using Sybase on HPUX & solaris and performnce was killing us. We hired in consultants direct from Sybase who said it couldn't be done. Then we switched to an OO database (Object store in this case) and got a about a 100x performance increase (and the code was about 100x easier to write too)
But such situations are quite rare - a relational database is a good first choice.
When you schema varies a lot you will have a hard time with relational databases. This is where XML databases or key-value pair databases work best. or you could use IBM DB2 and have both relational data and XML data managed by a single database engine.
About 7-8 years ago I worked on a web site that grew in popularity beyond our initial expectations and it got us in trouble performance-wise. Since we were all relatively inexperienced in web based projects it posed a significant strain on us about what to do beyond usual database separation onto separate server, load balancing etc.
One day I've thought of something pretty simple. Since site was based on users, their profiles were stored in a database table the usual way someone would do it - user id, lots of info variables and stuff like that - which would show up as a users profile page which other users could look up. I've flushed all that data into a simple html file, already prepared as a users profile page and got a significant boost - basically a cache. I even made a system that when user edited their profile info, it would parse original html file, put it up for edit, and then flush out html back to the file system - got even more boost.
I made something simillar with messages users sent to each other. Basically wherever I could make a system bypass a database altogether, avoiding a INSERT or UPDATE, I got a significant boost. It may sound like a common sense, but it was an enlightening moment. It is not an avoidance of relational setup per se, but it is an avoidance of the database altogether - KISS.
Can you please point to alternative data storage tools and give good reasons to use them instead of good-old relational databases? In my opinion, most applications rarely use the full power of SQL--it would be interesting to see how to build an SQL-free application.
Plain text files in a filesystem
Very simple to create and edit
Easy for users to manipulate with simple tools (i.e. text editors, grep etc)
Efficient storage of binary documents
XML or JSON files on disk
As above, but with a bit more ability to validate the structure.
Spreadsheet / CSV file
Very easy model for business users to understand
Subversion (or similar disk based version control system)
Very good support for versioning of data
Berkeley DB (Basically, a disk based hashtable)
Very simple conceptually (just un-typed key/value)
Quite fast
No administration overhead
Supports transactions I believe
Amazon's Simple DB
Much like Berkeley DB I believe, but hosted
Google's App Engine Datastore
Hosted and highly scalable
Per document key-value storage (i.e. flexible data model)
CouchDB
Document focus
Simple storage of semi-structured / document based data
Native language collections (stored in memory or serialised on disk)
Very tight language integration
Custom (hand-written) storage engine
Potentially very high performance in required uses cases
I can't claim to know anything much about them, but you might also like to look into object database systems.
Matt Sheppard's answer is great (mod up), but I would take account these factors when thinking about a spindle:
Structure : does it obviously break into pieces, or are you making tradeoffs?
Usage : how will the data be analyzed/retrieved/grokked?
Lifetime : how long is the data useful?
Size : how much data is there?
One particular advantage of CSV files over RDBMSes is that they can be easy to condense and move around to practically any other machine. We do large data transfers, and everything's simple enough we just use one big CSV file, and easy to script using tools like rsync. To reduce repetition on big CSV files, you could use something like YAML. I'm not sure I'd store anything like JSON or XML, unless you had significant relationship requirements.
As far as not-mentioned alternatives, don't discount Hadoop, which is an open source implementation of MapReduce. This should work well if you have a TON of loosely structured data that needs to be analyzed, and you want to be in a scenario where you can just add 10 more machines to handle data processing.
For example, I started trying to analyze performance that was essentially all timing numbers of different functions logged across around 20 machines. After trying to stick everything in a RDBMS, I realized that I really don't need to query the data again once I've aggregated it. And, it's only useful in it's aggregated format to me. So, I keep the log files around, compressed, and then leave the aggregated data in a DB.
Note I'm more used to thinking with "big" sizes.
The filesystem's prety handy for storing binary data, which never works amazingly well in relational databases.
Try Prevayler:
http://www.prevayler.org/wiki/
Prevayler is alternative to RDBMS. In the site have more info.
If you don't need ACID, you probably don't need the overhead of an RDBMS. So, determine whether you need that first. Most of the non-RDBMS answers provided here do not provide ACID.
Custom (hand-written) storage engine / Potentially very high performance in required uses cases
http://www.hdfgroup.org/
If you have enormous data sets, instead of rolling your own, you might use HDF, the Hierarchical Data Format.
http://en.wikipedia.org/wiki/Hierarchical_Data_Format:
HDF supports several different data models, including multidimensional arrays, raster images, and tables.
It's also hierarchical like a file system, but the data is stored in one magic binary file.
HDF5 is a suite that makes possible the management of extremely large and complex data collections.
Think petabytes of NASA/JPL remote sensing data.
G'day,
One case that I can think of is when the data you are modelling cannot be easily represented in a relational database.
Once such example is the database used by mobile phone operators to monitor and control base stations for mobile telephone networks.
I almost all of these cases, an OO DB is used, either a commercial product or a self-rolled system that allows heirarchies of objects.
I've worked on a 3G monitoring application for a large company who will remain nameless, but whose logo is a red wine stain (-: , and they used such an OO DB to keep track of all the various attributes for individual cells within the network.
Interrogation of such DBs is done using proprietary techniques that are, usually, completely free from SQL.
HTH.
cheers,
Rob
Object databases are not relational databases. They can be really handy if you just want to stuff some objects in a database. They also support versioning and modify classes for objects that already exist in the database. db4o is the first one that comes to mind.
In some cases (financial market data and process control for example) you might need to use a real-time database rather than a RDBMS. See wiki link
There was a RAD tool called JADE written a few years ago that has a built-in OODBMS. Earlier incarnations of the DB engine also supported Digitalk Smalltalk. If you want to sample application building using a non-RDBMS paradigm this might be a start.
Other OODBMS products include Objectivity, GemStone (You will need to get VisualWorks Smalltalk to run the Smalltalk version but there is also a java version). There were also some open-source research projects in this space - EXODUS and its descendent SHORE come to mind.
Sadly, the concept seemed to die a death, probably due to the lack of a clearly visible standard and relatively poor ad-hoc query capability relative to SQL-based RDMBS systems.
An OODBMS is most suitable for applications with core data structures that are best represented as a graph of interconnected nodes. I used to say that the quintessential OODBMS application was a Multi-User Dungeon (MUD) where rooms would contain players' avatars and other objects.
You can go a long way just using files stored in the file system. RDBMSs are getting better at handling blobs, but this can be a natural way to handle image data and the like, particularly if the queries are simple (enumerating and selecting individual items.)
Other things that don't fit very well in a RDBMS are hierarchical data structures and I'm guessing geospatial data and 3D models aren't that easy to work with either.
Services like Amazon S3 provide simpler storage models (key->value) that don't support SQL. Scalability is the key there.
Excel files can be useful too, particularly if users need to be able to manipulate the data in a familiar environment and building a full application to do that isn't feasible.
There are a large number of ways to store data - even "relational databse" covers a range of alternatives from a simple library of code that manipulates a local file (or files) as if it were a relational database on a single user basis, through file based systems than can handle multiple-users to a generous selection of serious "server" based systems.
We use XML files a lot - you get well structured data, nice tools for querying same the ability to do edits if appropriate, something that's human readable and you don't then have to worry about the db engine working (or the workings of the db engine). This works well for stuff that's essentially read only (in our case more often than not generated from a db elsewhere) and also for single user systems where you can just load the data in and save it out as required - but you're creating opportunities for problems if you want multi-user editing - at least of a single file.
For us that's about it - we're either going to use something that will do SQL (MS offer a set of tools that run from a .DLL to do single user stuff all the way through to enterprise server and they all speak the same SQL (with limitations at the lower end)) or we're going to use XML as a format because (for us) the verbosity is seldom an issue.
We don't currently have to manipulate binary data in our apps so that question doesn't arise.
Murph
One might want to consider the use of an LDAP server in the place of a traditional SQL database if the application data is heavily key/value oriented and hierarchical in nature.
BTree files are often much faster than relational databases. SQLite contains within it a BTree library which is in the public domain (as in genuinely 'public domain', not using the term loosely).
Frankly though, if I wanted a multi-user system I would need a lot of persuading not to use a decent server relational database.
Full-text databases, which can be queried with proximity operators such as "within 10 words of," etc.
Relational databases are an ideal business tool for many purposes - easy enough to understand and design, fast enough, adequate even when they aren't designed and optimized by a genius who could "use the full power," etc.
But some business purposes require full-text indexing, which relational engines either don't provide or tack on as an afterthought. In particular, the legal and medical fields have large swaths of unstructured text to store and wade through.
Also:
* Embedded scenarios - Where usually it is required to use something smaller then a full fledged RDBMS. Db4o is an ODB that can be easily used in such case.
* Rapid or proof-of-concept development - where you wish to focus on the business and not worry about persistence layer
CAP theorem explains it succinctly. SQL mainly provides "Strong Consistency: all clients see the same view, even in presence of updates".
K.I.S.S: Keep It Small and Simple
I would offer RDBMS :)
If you do not wont to have troubles with set up/administration go for SQLite.
Built in RDBMS with full SQL support. It even allows you to store any type of data in any column.
Main advantage against for example log file: If you have huge one, how are you going to search in it? With SQL engine you just create index and speed up operation dramatically.
About full text search: SQLite has modules for full text search too..
Just enjoy nice standard interface to your data :)
One good reason not to use a relational database would be when you have a massive data set and want to do massively parallel and distributed processing on the data. The Google web index would be a perfect example of such a case.
Hadoop also has an implementation of the Google File System called the Hadoop Distributed File System.
I would strongly recommend Lua as an alternative to SQLite-kind of data storage.
Because:
The language was designed as a data description language to begin with
The syntax is human readable (XML is not)
One can compile Lua chunks to binary, for added performance
This is the "native language collection" option of the accepted answer. If you're using C/C++ as the application level, it is perfectly reasonable to throw in the Lua engine (100kB of binary) just for the sake of reading configs/data or writing them out.