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Closed 11 years ago.
I've found databases typically come in two flavors, your traditional row-oriented RDBMS or an object oriented database (OODBMS). However, in the mid 90s I remember, a new breed of databases showing up that were column oriented. Some of these were given the term 4GL, but I don't think it was a term that stuck.
What I'd like to know is the following:
What column oriented databases still exist?
What are the performance characteristics of these databases?
Are there any open source column oriented databases?
What platforms do they interoperate with (.NET, Java, etc)
What's been your general experience with them?
The two column oriented databases that I remember working with are FAME and KDB.
HBase is an open-source column-oriented database system modelled on Google's BigTable.
Infobright
It's a column oriented MySQL engine
You can use (almost) all MySQL api's/interfaces/tools but it's column oriented.
It's open-source and has a free version.
It's very good for warehousing. I had a 10Gig fact table in SQL server.
Infobright compressed it to 15MB.
Also check out Michael Stonebraker's C-store:
C-store (includes links to source code and research paper)
The paper contains an excellent viewpoint on column oriented databases, that should answer most of your questions.
To quote the paper,
"Most major DBMS vendors implement record-oriented
storage systems, where the attributes of a record (or tuple)
are placed contiguously in storage. With this row store
architecture, a single disk write suffices to push all of the
fields of a single record out to disk. Hence, high
performance writes are achieved, and we call a DBMS
with a row store architecture a write-optimized system.
In contrast, systems oriented toward ad-hoc querying
of large amounts of data should be read-optimized. Data
warehouses represent one class of read-optimized system,
in which periodically a bulk load of new data is
performed, followed by a relatively long period of ad-hoc
queries. Other read-mostly applications include customer
relationship management (CRM) systems, electronic
library card catalogs, and other ad-hoc inquiry systems. In
such environments, a column store architecture, in which
the values for each single column (or attribute) are stored
contiguously, should be more efficient. This efficiency
has been demonstrated in the warehouse marketplace by
products like Sybase IQ [FREN95, SYBA04], Addamark
[ADDA04], and KDB [KDB04]. In this paper, we discuss
the design of a column store called C-Store that includes a
number of novel features relative to existing systems."
Sybase IQ is one I have heard of.
InfiniDB was recently released open source (GPLv2) by Calpont. It supports most of the MySQL API and stores data in a column-oriented fashion, and is optimized for large-scale analytic processing.
Here's the different column oriented DBMS wiki has
Column-Oriented DBMS Implementations
Sybase IQ is column oriented . All columns are automatically indexed when you create a table and data is nicely compressed in the columns.
It's a nice OLAP database (...data warehouse) but I would not recommend it for any kind of transaction processing as it is designed for data warehouse operations.
As for performance characteristics, SELECTS are very fast for large volumes of data but INSERT / UPDATE / DELETEs are very slow compared to a standard OLTP DB such as Sybase ASE for example. Table locking is also very different to a OLTP database so expect exclusive table locks for write operations (INSERTS etc) when working in the MAIN data store.
Otherwise it supports T-SQL (Sybase version) and Watcom SQL.
Cheers,
Kevin
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Closed 10 years ago.
I'm gathering information for upcoming massive online game. I has my experience with MEGA MASSIVE farm-like games (millions of dau), and SQL databases was great solution. I also worked with massive online game where NoSQL db was used, and this particular db (Mongo) was not a best fit - bad when lot of connections and lot of concurrent writes going on.
I'm looking for facts, benchmarks, presentation about modern massive online games and technical details about their backend infrastructure, databases in particular.
For example I'm interested in:
Can it manage thousands of connection? May be some external tool can help (like pgbouncer for postgres).
Can it manage tens of thousands of concurrent read-writes?
What about disk space fragmentation? Can it be optimized without stopping database?
What about some smart replication? Can it tell that some data is missing from replica, when master fails? Can i safely propagate slave to master and know exactly what data is missing and act appropriately?
Can it fail gracefully? (like postgres for ex.)
Good reviews from using in production
Start with the premise that hard crashes are exceedingly rare, and when they occur
it won't be a tragedy of some information is lost.
Use of the database shouldn't be strongly coupled to the routine management of the
game. Routine events ought to be managed through more ephemeral storage. Some
secondary process should organize ephemeral events for eventual storage in a database.
At the extreme, you could imagine there being just one database read and one database
write per character per session.
Have you considered NoSQL ?
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.
In short, NoSQL database management systems are useful when working
with a huge quantity of data when the data's nature does not require a
relational model. The data can be structured, but NoSQL is used when
what really matters is the ability to store and retrieve great
quantities of data, not the relationships between the elements. Usage
examples might be to store millions of key–value pairs in one or a few
associative arrays or to store millions of data records. This
organization is particularly useful for statistical or real-time
analyses of growing lists of elements (such as Twitter posts or the
Internet server logs from a large group of users).
There are higher-level NoSQL solutions, for example CrouchDB, which has built-in replication support.
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Closed 10 years ago.
Recently I have encountered the concept of NoSQL and as far as I manage to comprehend is good for dealing with huge amount of data.
My question is, what is the limit were using NoSQL becomes worthwhile ? Is it only for companies which handle really huge amount of data like Google, Facebook etc. or it's worth the trouble to switching to it from a SQL database even for a smaller data amount .
I wonder what "concept of NoSQL" you mean, because it is an umbrella term for a wide field of different database technologies. The only thing they have in common is what sets them apart from each other: they are "not (only) SQL". They have widely different philosophies, use-cases and target groups.
Just to give you an overview, here are a few of the large factions of NoSQL databases.
There are document-based databases like MongoDB or CouchDB. 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.
There are graph databases like Neo4j or GiraffeDB. 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.
Then you have simple key-value stores like MemcacheDB, Cassandra or Google's BigTable. 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.
And these are just a few facets of the new database world.
But there is still one sector where relational databases excel, and that's when it comes to following the ACID principle. Most NoSQL databases don't fully guarantee all four of these:
Atomic transactions (chains of commands which are processed together, n-order and all-or-none)
Consistent database schema with constraints and triggers which ensure that garbage data can not exist in the database.
Isolation of transactions - transactions which are guaranteed to be unaffected by others which happen at the same time.
Durability - safety from data-loss even in case of a sudden system crash*
(* to be fair, most of the databases listed above are indeed pretty durable, especially those which are easy to set up as redundant fail-over clusters.
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I'm curious if anyone has implemented or even knows of any bitemporal databases built on NoSQL platforms (e.g., riak).
I don't know of any NoSQL datastore that are specifically designed to handle temporal data.
In order to put the valid and transaction time periods onto data in Riak you would need to either:
Wrap your documents/values with a structure that can hold metadata like:
{
meta:{
valid:["2001-11-08", "2001-11-09"],
transaction:["2011-01-29 10:27:00", "2011-01-29 10:28:00"]
}
payload:"This is the actual document/value I want to store!"
}
Create a "meta-document" for each document and use Riak Links to link them up.
I think this is a little bit cleaner but if you need to retrieve these times often then this method may be too slow.
If you want to retrieve documents by time then I don't think Riak (or any other key/value datastores that I know of) will be the right datastore to use. SQL or possibly some BigTable system may be your only good option.
I have written a small bitemporal, open source database layer based on Mongodb:
https://github.com/1123/bitemporaldb
When storing Scala or Java objects, the object is wrapped into a generic bitemporal object with bitemporal meta-information (valid time, transaction time). Subsequently it is serialized to json and stored as BSON in MongoDB.
It handles temporal and non-temporal updates to objects transparently. Search by bitemporal context is possible.
Document-oriented databases for bitemporal data are beneficial, since document oriented storage reduces the number of joins for data retrieval. Joins in a bitemporal context can be inefficient and hard to code by hand.
Feedback, contribution and feature-requests are very welcome.
To support a bitemporal (or temporal db model), you need acid transactions to perform the proper DML to update and insert records on two time dimensions (valid/effective time and transaction/system time). See for details on temporal modeling.
The popular NoSQL database like Cassandra, MongoDB, Couchbase, for example, don't have ACID support to perform the necessary record update/insert operations needed to support bitemporal record manipulation. With temporal and bitemporal databases records must never overlap and records must properly be terminated when superseded by succeeding valid/transaction time records.
MarkLogic NoSQL database claims support for bitemporal, but never tried it and is not open source. But you can roll your own solution by using ACID database that effectively functions as a valid/transaction time tracking journal and then use NoSQL for the actual data store. See high-level description of this approach here.
From Wikipedia:
"Bitemporal data is a concept used in a temporal database. It denotes both the valid time and transaction time of the data.
In a database table bitemporal data is often represented by four extra table-columns StartVT and EndVT, StartTT and EndTT. Each time interval is closed at its lower bound, and open at its upper bound."
So you can't just put these four values onto your data?
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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.
What exactly is NoSQL? Is it database systems that only work with {key:value} pairs?
As far as I know MemCache is one of such database systems, am I right?
What other popular NoSQL databases are there and where exactly are they useful?
Thanks, Boda Cydo.
I'm not agree with the answers I'm seeing, although it's true that NoSQL solutions tends to break the ACID rules, not all are created from that approach.
I think first you should define what is a SQL Solution and then you can put the "Not Only" in front of it, that will be more accurate definition of what is a NoSQL solution.
With this approach in mind:
SQL databases are a way to group all the data stores that are accessible using Structured Query Language as the main (and most of the time only) way to communicate with them, this means it requires that the database support the structures that are common to those systems like "tables", "columns", "rows", "relationships", etc.
Now, put the "Not Only" in front of the last sentence and you will get a definition of what means "NoSQL". NoSQL groups all the stores created as an attempt to solve problems which cannot fit into the table/column/rows structures or even in SQL Statements, in most of the cases these databases will not support relationships, they're abandoning the well known structures just because the problems have changed since their conception.
If you have a text file, and you create an API to store/retrieve/organize this information, then you have a NoSQL database in your hands.
All of these means that there are several solutions to store the information in a way that traditional SQL systems will not allow to achieve better performance, flexibility, etc etc. Every NoSQL provider tries to solve a different problem and that's why you wont be able to compare two different solutions, for example:
djondb is a document store created to be used as
NoSQL enterprise solution supporting transactions, consistency, etc.
but sacrifice performance of its counterparts.
MongoDB is a document store (similar to
djondb) which accomplish great performance but trades some of the
ACID properties to achieve this.
CouchDB is another document store which
solves the queries slightly different providing views to retrieve the
information without doing a full query every time.
...
As you may have noticed I only talked about the document stores, that's because I wanted to show you that 3 different document stores implementations have different approach, therefore you should keep in mind the golden rule of NoSQL stores "Use the right tool for the right job".
I'm the creator of djondb and I've been doing a lot of research even before trying to start my own NoSQL implementation, but this is a field where the concepts will keep changing the way we see the information storage.
From wikipedia:
NoSQL is an umbrella term for a loosely defined class of non-relational data stores that break with a long history of relational databases and ACID guarantees. Data stores that fall under this term may not require fixed table schemas, and usually avoid join operations. The term was first popularised in early 2009.
The motivation for such an architecture was high scalability, to support sites such as Facebook, advertising.com, etc...
To quickly get a handle on NoSQL systems, see this blog post I wrote: Visual Guide to NoSQL Systems. Essentially, NoSQL systems sacrifice either consistency or availability in favor of tolerance to network partitions.
What is NoSQL ?
NoSQL is the acronym for Not Only SQL. The basic qualities of NoSQL databases are schemaless, distributed and horizontally scalable on commodity hardware. The NoSQL databases offers variety of functions to solve various problems with variety of data types, where “blob” used to be the only data type in RDBMS to store unstructured data.
1 Dynamic Schema
NoSQL databases allows schema to be flexible. New columns can be added anytime. Rows may or may not have values for those columns and no strict enforcement of data types for columns. This flexibility is handy for developers, especially when they expect frequent changes during the course of product life cycle.
2 Variety of Data
NoSQL databases support any type of data. It supports structured, semi-structured and unstructured data to be stored. Its supports logs, images files, videos, graphs, jpegs, JSON, XML to be stored and operated as it is without any pre-processing. So it reduces the need for ETL (Extract – Transform – Load).
3 High Availability Cluster
NoSQL databases support distributed storage using commodity hardware. It also supports high availability by horizontal scalability. This features enables NoSQL databases get the benefit of elastic nature of the Cloud infrastructure services.
4 Open Source
NoSQL databases are open source software. The usage of software is free and most of them are free to use in commercial products. The open sources codebase can be modified to solve the business needs. There are minor variations in the open source software licenses, users must be aware of license agreements.
5 NoSQL – Not Only SQL
NoSQL databases not only depend SQL to retrieve data. They provide rich API interfaces to perform DML and CRUD operations. These are APIs are move developer friendly and supported in variety of programming languages.
Take a look at these:
http://en.wikipedia.org/wiki/Nosql#List_of_NoSQL_open_source_projects
and this:
http://www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
I used something called the Raima Data Manager more than a dozen years ago, that qualifies as NoSQL. It calls itself a "Set Oriented Database" Its not based on tables, and there is no query "language", just an C API for asking for subsets.
It's fast and easier to work with in C/C++ and SQL, there's no building up strings to pass to a query interpreter and the data comes back as an enumerable object rather than as an array. variable sized records are normal and don't waste space. I never saw the source code, but there were some hints at the interface that internally, the code used pointers a lot.
I'm not sure that the product I used is even sold anymore, but the company is still around.
MongoDB looks interesting, SourceForge is now using it.
I listened to a podcast with a team member. The idea with NoSQL isn't so much to replace SQL as it is to provide a solution for problems that aren't solved well with traditional RDBMS. As mentioned elsewhere, they are faster and scale better at the cost of reliability and atomicity (different solutions to different degrees). You wouldn't want to use one for a financial system, but a document based system would work great.
Here is a comprehensive list of NoSQL Databases: http://nosql-database.org/.
I'm glad that you have had success with RDM John! I work at Raima so it's great to hear feedback. For those looking for more information, here are a couple of resources:
Video Overview of RDM's General Architecture
Free Evaluation Download of RDM